1
0:00:00 --> 0:00:10
So hello everybody, welcome to today's meeting of Medical Doctors for COVID Ethics International.

2
0:00:10 --> 0:00:15
This group was founded by Dr. Stephen Frost over three years ago with a desire to pursue

3
0:00:15 --> 0:00:19
truth, ethics, justice, freedom and health.

4
0:00:19 --> 0:00:22
Stephen has stood up against government and power over the years and has been a whistleblower

5
0:00:22 --> 0:00:23
and activist.

6
0:00:23 --> 0:00:25
His medical specialty is radiology.

7
0:00:25 --> 0:00:28
I'm Charles Covess, the moderator of this group.

8
0:00:28 --> 0:00:30
I'm Australasian passion provocateur.

9
0:00:30 --> 0:00:33
We have plenty of passionate people in these meetings.

10
0:00:33 --> 0:00:38
I practiced law for 20 years before changing career 31 years ago.

11
0:00:38 --> 0:00:44
And over the last 13 years, I've helped parents and lawyers to strategize remedies for vaccine

12
0:00:44 --> 0:00:49
damage and damage from bad medical advice.

13
0:00:49 --> 0:00:54
The numbers now show clearly that it is bad medical advice that is the number one cause

14
0:00:54 --> 0:00:57
of death in the USA.

15
0:00:57 --> 0:01:01
I don't know what it is in Australia where I'm located.

16
0:01:01 --> 0:01:04
I'm also the CEO of an industrial hemp company.

17
0:01:04 --> 0:01:10
We comprise lots of professions here and we're from all around the world.

18
0:01:10 --> 0:01:14
If this is your first time here, welcome and feel free to introduce yourself on the chat

19
0:01:14 --> 0:01:16
and where you're from.

20
0:01:16 --> 0:01:18
Many of us thought that vaccines were okay.

21
0:01:18 --> 0:01:24
Now many of us proudly say, yes, we are passionate anti-vaxxers and I number myself among those,

22
0:01:24 --> 0:01:28
even though my five children are vaccinated.

23
0:01:28 --> 0:01:33
The number to remember for Australia, UK, America, very similar.

24
0:01:33 --> 0:01:37
When I was a kid, I only got three or four vaccines in the first four or five years of

25
0:01:37 --> 0:01:40
life in Australia.

26
0:01:40 --> 0:01:43
We have a no jab, no pay, no play principle.

27
0:01:43 --> 0:01:50
If you don't give the jab to your children, vaccines to your children, then you give no

28
0:01:50 --> 0:02:00
government benefits and they can't play in kindergarten.

29
0:02:00 --> 0:02:07
The number of vaccines that a child has to have injected into them in Australia is 43

30
0:02:07 --> 0:02:10
in the first four years of life.

31
0:02:10 --> 0:02:11
43.

32
0:02:11 --> 0:02:16
No safety and efficacy tests have been done on that mad regime.

33
0:02:16 --> 0:02:23
Those 43 antigens are made up of 15 different vaccines pumped into children with amazing

34
0:02:23 --> 0:02:27
frequency.

35
0:02:27 --> 0:02:30
If you publish a newsletter or a podcast or you have a radio or TV show or you've written

36
0:02:30 --> 0:02:34
a book, put the links into the chat so we can follow you, promote you and find you.

37
0:02:34 --> 0:02:36
I'm on TNT radio.

38
0:02:36 --> 0:02:40
TNT radio is a place where there's no political correctness.

39
0:02:40 --> 0:02:42
There's no censorship.

40
0:02:42 --> 0:02:45
There's no wokeness.

41
0:02:45 --> 0:02:49
Most of us understand we're in the middle of World War III and that the medical science

42
0:02:49 --> 0:02:53
battle is only one of 12 battle fronts of this latest World War.

43
0:02:53 --> 0:02:55
There's no time to be tired.

44
0:02:55 --> 0:02:59
We are some four years into what I consider will be a seven year war.

45
0:02:59 --> 0:03:01
We've got three more years to go, at least.

46
0:03:01 --> 0:03:04
Get ready for the fight.

47
0:03:04 --> 0:03:10
Most of us understand the development of science and science is never settled.

48
0:03:10 --> 0:03:13
This meeting runs for two and a half hours after which for those with the time, Tom Rodman

49
0:03:13 --> 0:03:15
runs a video telegram meeting.

50
0:03:15 --> 0:03:18
Tom puts the links into the chat if you're able to join.

51
0:03:18 --> 0:03:22
We will listen to our guest presenter today, Andrew Paquette, for as long as Andrew wishes

52
0:03:22 --> 0:03:23
to speak.

53
0:03:23 --> 0:03:25
And then we have Q&A.

54
0:03:25 --> 0:03:30
Stephen Frost, by long established tradition, asks the first questions for 15 minutes.

55
0:03:30 --> 0:03:34
This is a free speech environment with appropriate moderating.

56
0:03:34 --> 0:03:39
Please understand that free speech is free speech except for ad hominem attacks.

57
0:03:39 --> 0:03:41
No attacking people.

58
0:03:41 --> 0:03:47
If you attack people and you're participating, you run a big risk of being removed from the

59
0:03:47 --> 0:03:48
meeting.

60
0:03:48 --> 0:03:51
If you're offended by anything, be offended.

61
0:03:51 --> 0:03:53
We are lovingly not interested.

62
0:03:53 --> 0:03:58
We reject the offense industry that requires nobody to say anything that may offend another.

63
0:03:58 --> 0:04:01
We come with an attitude and perspective of love, not fear.

64
0:04:01 --> 0:04:02
Fear is the opposite of love.

65
0:04:02 --> 0:04:03
Fear squashes you.

66
0:04:03 --> 0:04:06
Love, on the other hand, expands you.

67
0:04:06 --> 0:04:07
Fear imprisons you.

68
0:04:07 --> 0:04:09
We're all about freedom here.

69
0:04:09 --> 0:04:15
So you come to these meetings to be reminded to not have your life driven by fear.

70
0:04:15 --> 0:04:17
These twice weekly meetings are not just talk fests.

71
0:04:17 --> 0:04:23
An extraordinary range of activities and actions and initiatives have been generated from linkages

72
0:04:23 --> 0:04:26
made by attendees in these meetings.

73
0:04:26 --> 0:04:30
If you have a solution or a product or links or resources that will help people put the

74
0:04:30 --> 0:04:36
details into the chat, the meeting is recorded and is uploaded onto the Rumble channel.

75
0:04:36 --> 0:04:44
And now a quick reminder about Reiner Fulmick in the German jail in a corrupt German legal

76
0:04:44 --> 0:04:45
process.

77
0:04:45 --> 0:04:52
Celia Farber, one of our presenters in her Substruct this morning, shared the news of

78
0:04:52 --> 0:04:58
how corrupt the German legal system is in the case of a 14-year-old girl who was raped

79
0:04:58 --> 0:05:00
by nine men.

80
0:05:00 --> 0:05:02
It's extraordinary.

81
0:05:02 --> 0:05:08
And Reiner Fulmick is in the hands of that same corrupt German system.

82
0:05:08 --> 0:05:09
And we need to speak out about it.

83
0:05:09 --> 0:05:15
I speak about it at the start of each of these meetings and in my TNT radio program.

84
0:05:15 --> 0:05:17
All of you spread the word.

85
0:05:17 --> 0:05:23
We need to put pressure on the German government, just like ongoing pressure was put on the

86
0:05:23 --> 0:05:28
US government, UK government, Australian government about the case of Julian Assange.

87
0:05:28 --> 0:05:33
And it's worth noting at the start of this meeting that Julian Assange is now no longer

88
0:05:33 --> 0:05:36
in prison in the UK.

89
0:05:36 --> 0:05:38
He did a plea deal.

90
0:05:38 --> 0:05:40
So now welcome to our guest presenter, Andrew Paquette.

91
0:05:40 --> 0:05:44
And we thank you, Andrew, for giving us your time, wisdom and insights.

92
0:05:44 --> 0:05:49
His detailed CV is on the show notes, but we'll ask Jerome Corsi if you would kindly

93
0:05:49 --> 0:05:52
introduce Andrew before Andrew presents to us.

94
0:05:52 --> 0:05:56
And thank you, Stephen Frost, again, for creating this group and for organizing Andrew to be

95
0:05:56 --> 0:05:57
with us today.

96
0:05:57 --> 0:05:58
Over to you, Jerome.

97
0:05:59 --> 0:06:01
Well, thank you very much, Charles.

98
0:06:02 --> 0:06:05
It's a real pleasure and honor to bring Andrew onto this forum.

99
0:06:06 --> 0:06:13
Andrew, I just got word today that the article I've written on Andrew's work identifying

100
0:06:13 --> 0:06:18
an algorithm in the New York State Voters Database will be published in American Thinker.

101
0:06:19 --> 0:06:23
So very next week, we'll probably have an article on the research you're going to preview

102
0:06:23 --> 0:06:27
today. Now, Andrew's got a very interesting background.

103
0:06:27 --> 0:06:34
He has a PhD from King's College London, education, and his dissertation was on

104
0:06:34 --> 0:06:42
spirit, spatial visualization, visualization and professional competence, the development

105
0:06:42 --> 0:06:44
of proficiency among digital artists.

106
0:06:46 --> 0:06:51
Andrew's specialty is really computer art and computer games.

107
0:06:52 --> 0:06:55
Very advanced in terms of his digital capabilities.

108
0:06:56 --> 0:07:03
And he's got a wide background, including working in comic books with comic book companies

109
0:07:03 --> 0:07:05
like Marvel and DC, Harris Comics.

110
0:07:06 --> 0:07:12
He also does produce cartoon training manuals for the U.S.

111
0:07:12 --> 0:07:14
Army. He's currently doing that right now.

112
0:07:15 --> 0:07:20
And he's had a series of professional appointments and publications.

113
0:07:20 --> 0:07:27
His dissertation was published by Springer Verlong in London, a very prestigious publishing

114
0:07:27 --> 0:07:34
house, and he's had several of his publications picked up by Springer Verlong, also an

115
0:07:34 --> 0:07:40
introduction to computer graphics, his second edition in 2013.

116
0:07:41 --> 0:07:48
He's published in a number of scientific journals, including his most recent article on the

117
0:07:48 --> 0:07:53
New York algorithm and the voter rolls, which we're going to study today.

118
0:07:53 --> 0:08:00
He's going to present to us today, which was published in 2023 in the Journal of

119
0:08:00 --> 0:08:04
Information Warfare, volume 22, number two.

120
0:08:04 --> 0:08:11
So he has had a wide range of experience, has a is not his background is not cryptography.

121
0:08:12 --> 0:08:17
But like many of the people who have done extremely well in cryptography, he has very

122
0:08:17 --> 0:08:23
advanced technical skills that have to do with visualization, the ability to do

123
0:08:23 --> 0:08:26
computer graphics at a very, very advanced mathematical level.

124
0:08:27 --> 0:08:33
He's obviously been accomplished with not only in his profession, but also in his

125
0:08:33 --> 0:08:40
publications. And he's come upon, I think, one of the fundamental understandings of why

126
0:08:40 --> 0:08:46
the voting records, certainly in the United States and very probably around the world,

127
0:08:47 --> 0:08:50
are intentionally subject to fraud.

128
0:08:50 --> 0:08:56
So I think this is an extremely important presentation and one that can be internationally

129
0:08:56 --> 0:08:58
game changing in its implications.

130
0:08:59 --> 0:09:01
And I have some conflicts today.

131
0:09:01 --> 0:09:03
I have to leave the meeting at 3.30.

132
0:09:03 --> 0:09:07
I'll be back at 4.30 to participate in the questions and answers.

133
0:09:07 --> 0:09:14
Andrew has done a very, very effective deck of slides and he's going to go through it.

134
0:09:14 --> 0:09:18
So with that introduction, it's a real honor and pleasure to introduce Andrew and Andrew.

135
0:09:19 --> 0:09:24
The show is yours now for as long as you care to do the presentation, followed by Q&A.

136
0:09:25 --> 0:09:27
Andrew. OK, thank you very much.

137
0:09:28 --> 0:09:32
Now, just so everyone knows, I do have a low bandwidth problem today, so it's possible

138
0:09:32 --> 0:09:35
that my mic will drop out every once in a while.

139
0:09:36 --> 0:09:43
For that reason, I'm going to stop my camera and I'm going to share my screen with you.

140
0:09:43 --> 0:09:48
And while I do that, I also want to say something has something to do with your mission here,

141
0:09:49 --> 0:09:52
because in some of my earliest presentations,

142
0:09:53 --> 0:09:57
I wound up being in contact with the Children's Health Defense Fund,

143
0:09:58 --> 0:10:00
John F. Kennedy, Robert F.

144
0:10:00 --> 0:10:02
Kennedy, Jr.'s group.

145
0:10:02 --> 0:10:06
And I had a conversation with their attorney and

146
0:10:06 --> 0:10:09
forget the title of the other woman I spoke with.

147
0:10:09 --> 0:10:14
And they said to me that they felt that my discoveries in the

148
0:10:15 --> 0:10:20
New York state's voter rolls concerned them a great deal because the voter rolls

149
0:10:21 --> 0:10:25
controlled the elections and the elections control the politicians who were making

150
0:10:25 --> 0:10:28
policy that was contrary to their interests.

151
0:10:29 --> 0:10:34
So it was it's kind of interesting to, again, be speaking to people who have the

152
0:10:34 --> 0:10:37
anti vaccination stance because it's all related.

153
0:10:37 --> 0:10:40
OK, so can you see my screen?

154
0:10:40 --> 0:10:45
Yep. OK, so let me move this off to the side.

155
0:10:45 --> 0:10:50
All right. So this image, this 3D graphic that I threw together,

156
0:10:51 --> 0:10:53
just so you guys have an idea of what we're talking about.

157
0:10:54 --> 0:10:57
The way I look at the voter rolls is kind of like you're walking along the road and you run

158
0:10:57 --> 0:10:59
across a steering column lying on the road.

159
0:11:00 --> 0:11:02
You know, it's design. You don't see a car.

160
0:11:02 --> 0:11:05
You don't know what it's used for. You don't know why it's there.

161
0:11:05 --> 0:11:09
That's what the algorithms in the voter rolls are like.

162
0:11:09 --> 0:11:13
They pose a number of questions with no obvious answers.

163
0:11:13 --> 0:11:18
However, one thing is obvious, and that is it's designed and it does have a purpose.

164
0:11:18 --> 0:11:21
What that purpose is, is something that we're going to explore.

165
0:11:22 --> 0:11:27
So let me go to the next slide and let's start off with what voter rolls are for.

166
0:11:28 --> 0:11:30
OK, so it's pretty simple.

167
0:11:30 --> 0:11:33
We want to know that all the voters are qualified.

168
0:11:33 --> 0:11:36
So what that means is before you can be registered, you have to present

169
0:11:36 --> 0:11:41
evidence that you're a qualified voter, meaning you're 18 years or older,

170
0:11:42 --> 0:11:47
meaning that you live in the jurisdiction you're registering in and you are a U.S. citizen.

171
0:11:47 --> 0:11:51
OK, there are a couple of other procedural requirements, but those are the main ones.

172
0:11:51 --> 0:11:54
You're 18 years old or older, you're a U.S. citizen and you live where you say you live.

173
0:11:55 --> 0:11:58
So to prove that, what you have to do is you have to bring proof of identity,

174
0:11:58 --> 0:12:01
proof of address and proof of citizenship.

175
0:12:01 --> 0:12:04
And sometimes the proof of identity is the equivalent of proof of citizenship.

176
0:12:05 --> 0:12:10
But that's why we have the voter roll is to is to prove these things.

177
0:12:10 --> 0:12:15
It also helps with fraud prevention because you can do post-election audit

178
0:12:15 --> 0:12:19
by comparing the number of people who voted or who are recorded as having voted

179
0:12:19 --> 0:12:22
in the voter rolls with the number of ballots that are cast.

180
0:12:22 --> 0:12:24
If there's not a match, you know you have a problem.

181
0:12:25 --> 0:12:29
They also help with election management because they give a list of names

182
0:12:29 --> 0:12:35
and addresses of people that can be contacted if necessary for administrative reasons.

183
0:12:36 --> 0:12:39
However, for these things to work, they have to be accurate, current and transparent.

184
0:12:39 --> 0:12:44
And these are all very important items because unfortunately in America anyway,

185
0:12:44 --> 0:12:47
they fail on well, at least two of these.

186
0:12:48 --> 0:12:52
The New York voter rolls that I've looked at, as well as rolls in other states

187
0:12:52 --> 0:12:56
that I've looked at, are demonstrably inaccurate and not in small numbers

188
0:12:56 --> 0:12:58
and very, very large numbers.

189
0:12:58 --> 0:13:03
As far as currency is concerned, for the most part, they appear to be current.

190
0:13:03 --> 0:13:08
However, if I don't know if it counts as current, if what you're looking at is a bunch

191
0:13:08 --> 0:13:11
of records that are illegally present in the rolls.

192
0:13:11 --> 0:13:15
So if you have something that was illegally deposited yesterday,

193
0:13:16 --> 0:13:19
you could say that's current, and yet it's also illegal at the same time.

194
0:13:20 --> 0:13:25
The transparency is interesting because this creates a problem.

195
0:13:25 --> 0:13:29
So if somebody wants to do something nefarious in the voter rolls,

196
0:13:30 --> 0:13:32
they have to account for the fact that the voter rolls are public.

197
0:13:32 --> 0:13:37
Therefore, whatever it is that they do has to somehow be concealed from public disclosure,

198
0:13:38 --> 0:13:41
even though the records by law must be publicly disclosed.

199
0:13:42 --> 0:13:47
And this is where the difference between cryptography and steganography comes in.

200
0:13:47 --> 0:13:51
Now, on the off chance, some of the people on this call aren't aware of the difference.

201
0:13:51 --> 0:13:57
Cryptography, the result of cryptography is a cipher that is readily recognized.

202
0:14:01 --> 0:14:04
We'll be patient while he comes back.

203
0:14:07 --> 0:14:09
Low bandwidth, I'm sure.

204
0:14:09 --> 0:14:13
Yep. He will come back as a cipher.

205
0:14:13 --> 0:14:15
So if you look at, for instance, any, can you hear me?

206
0:14:16 --> 0:14:20
We know we missed a piece there where you were just talking about

207
0:14:20 --> 0:14:22
transparency, if you'll repeat that.

208
0:14:23 --> 0:14:27
OK, thank you. So the thing is about transparency.

209
0:14:28 --> 0:14:32
Anyone who wants to do something nefarious with the voter rolls has a problem there.

210
0:14:33 --> 0:14:38
And the reason is that if they do it, it will be publicly disclosed

211
0:14:38 --> 0:14:42
because the law requires that the voter rolls be publicly disclosed.

212
0:14:42 --> 0:14:47
So whatever they do has to somehow be something they can hide.

213
0:14:47 --> 0:14:49
They have to have a way to make it covert.

214
0:14:49 --> 0:14:53
And this is where the difference between cryptography and steganography comes into play.

215
0:14:54 --> 0:14:56
Now, I'm assuming that maybe a couple of people don't know the difference

216
0:14:56 --> 0:14:58
between cryptography and steganography.

217
0:14:58 --> 0:15:00
So I'll just say it this way.

218
0:15:00 --> 0:15:04
Steganography is where you hide the fact that there is a cipher.

219
0:15:04 --> 0:15:07
OK, so you don't even know there's something to decode.

220
0:15:07 --> 0:15:11
And cryptography is where you you in cipher information,

221
0:15:11 --> 0:15:13
but you do it in such a way that it's very clear that it's been encoded.

222
0:15:14 --> 0:15:18
So if you look at something from World War Two, where they use the Enigma machines

223
0:15:18 --> 0:15:23
to encode German transmissions, it's obvious that they're encoded when you look at it.

224
0:15:23 --> 0:15:27
It's it's it's nonsense, but it has structure and that's readily apparent.

225
0:15:27 --> 0:15:31
However, steganography, like, for instance, if you watch most modern movies,

226
0:15:31 --> 0:15:35
they have embedded in the images copyright information.

227
0:15:35 --> 0:15:39
So while you're watching the movie, you don't see this and you're not aware that it's even there.

228
0:15:40 --> 0:15:44
So what you'd have to do with the voter rolls, because they're transparent,

229
0:15:44 --> 0:15:48
is you would have to conceal what you've done somehow in a way

230
0:15:48 --> 0:15:52
that doesn't interfere with the fact that the information is publicly disclosed.

231
0:15:52 --> 0:15:55
And that's the direction we're going to head here in this talk.

232
0:15:55 --> 0:15:57
So let's go to the next slide.

233
0:15:57 --> 0:16:00
So we have a couple of laws here that are going to be violated

234
0:16:01 --> 0:16:04
when people fool around with the with the voter rolls. OK.

235
0:16:05 --> 0:16:10
But chief among them is that the states must maintain accurate

236
0:16:10 --> 0:16:11
and current voter registration rules.

237
0:16:11 --> 0:16:16
Now, when other people talk about laws in relation to the voter rolls,

238
0:16:16 --> 0:16:21
they normally don't talk about this particular provision as being all that important.

239
0:16:21 --> 0:16:25
They talk about the importance of voters themselves

240
0:16:25 --> 0:16:28
not going in and individually making a false registration.

241
0:16:28 --> 0:16:33
The idea of the election boards doing something at a systematic

242
0:16:33 --> 0:16:37
or a systemic level is ordinarily not contemplated.

243
0:16:37 --> 0:16:40
But this here deals with the systemic level. OK.

244
0:16:40 --> 0:16:44
So if the states aren't maintaining accurate and current voter registration rules,

245
0:16:44 --> 0:16:48
they're violating the National Voter Registration Act.

246
0:16:49 --> 0:16:51
And let's see.

247
0:16:51 --> 0:16:55
And next, we're going to go to the Help America Vote Act, also known as HAVA.

248
0:16:55 --> 0:16:59
In this case, they're requiring that there's a unique identifier for each voter.

249
0:16:59 --> 0:17:03
This is important because this has been violated quite a lot of times in New York,

250
0:17:04 --> 0:17:08
which means that the state board of elections is actually in violation

251
0:17:08 --> 0:17:10
of this law many times over.

252
0:17:10 --> 0:17:12
I'll get to the numbers in just a minute.

253
0:17:13 --> 0:17:18
So here we have the eligibility issue, and eligibility is quite important.

254
0:17:18 --> 0:17:22
So what they do is they say that the voter registration

255
0:17:22 --> 0:17:25
application form is literally an affidavit.

256
0:17:25 --> 0:17:29
And this is why it must be hand signed by the voter who's registering.

257
0:17:30 --> 0:17:34
This is extremely important because if you have fake records in the rolls,

258
0:17:34 --> 0:17:37
as I believe there are,

259
0:17:38 --> 0:17:41
where would the paperwork be for those fake records

260
0:17:41 --> 0:17:43
and who would have signed for those fake records?

261
0:17:43 --> 0:17:46
Now, I happen to have found about two million records

262
0:17:47 --> 0:17:51
while I was engaged in work with a group known as New York Citizens Audit.

263
0:17:52 --> 0:17:55
And these records are are basically fake.

264
0:17:55 --> 0:17:56
They're illegal records.

265
0:17:56 --> 0:18:02
So, for instance, there's a person who has 25 different state ID numbers,

266
0:18:02 --> 0:18:07
which allows 25 absentee ballots to be sent to him every single election.

267
0:18:07 --> 0:18:12
Which allows him to vote in 25 different jurisdictions simultaneously,

268
0:18:12 --> 0:18:15
which grossly violates the law.

269
0:18:15 --> 0:18:17
So the thing is, I was wondering

270
0:18:18 --> 0:18:23
how many of those are going to have a signature, right?

271
0:18:23 --> 0:18:27
Do they even have documents that support all of these excess records?

272
0:18:27 --> 0:18:31
We've got two million records, and it seemed to me that the test for

273
0:18:31 --> 0:18:33
whether this was an innocent mistake

274
0:18:34 --> 0:18:38
or it was something that was intentionally illegal

275
0:18:38 --> 0:18:40
is going to be what the paperwork looks like.

276
0:18:40 --> 0:18:43
So I asked. So here is an example of that.

277
0:18:43 --> 0:18:46
Now, I've masked off a lot of the data, so you're not going to be able to tell

278
0:18:46 --> 0:18:49
who this is. But what this is,

279
0:18:50 --> 0:18:52
is a voter information report for two different records.

280
0:18:53 --> 0:18:56
Now, if you look at the county ID here on the left,

281
0:18:56 --> 0:19:00
you see it ends in 072 and you see right next to it on the right,

282
0:19:00 --> 0:19:02
there's another one that ends in 074.

283
0:19:02 --> 0:19:04
So these are different county IDs.

284
0:19:04 --> 0:19:07
Now, that's actually OK if the person moves from one county to another.

285
0:19:08 --> 0:19:11
But in this case, if you look at the residential address zip code, it's identical.

286
0:19:11 --> 0:19:15
So and I happen to know that the full address is exactly identical.

287
0:19:15 --> 0:19:20
So this is actually already illegal at the county ID level, but it gets worse.

288
0:19:20 --> 0:19:25
If you look at the state ID, it ends in 1837 on one side

289
0:19:25 --> 0:19:27
and on the other, it ends in 8056.

290
0:19:27 --> 0:19:29
So these are different state IDs.

291
0:19:29 --> 0:19:33
And that allows for multiple mail in ballots to be mailed to this person.

292
0:19:33 --> 0:19:36
And by the way, this person has three records, not two.

293
0:19:36 --> 0:19:39
I just felt it was easier to see two than to look at all three.

294
0:19:40 --> 0:19:43
So we have two different records for the same person with the same birthdate,

295
0:19:43 --> 0:19:45
with the same address.

296
0:19:45 --> 0:19:49
If you look at the registration dates, February 7th, 2020 and February 8th, 2020.

297
0:19:49 --> 0:19:50
So these are a day apart.

298
0:19:50 --> 0:19:53
And I can tell you that the third record, I forget if it's the seventh

299
0:19:53 --> 0:19:56
or the eighth, but it's on one of these same two days.

300
0:19:57 --> 0:20:02
So somehow this person got three records within about 25 hours of each other

301
0:20:03 --> 0:20:05
with different ID numbers, three different ID numbers.

302
0:20:06 --> 0:20:09
And if you look at the signature and this is where it gets very interesting

303
0:20:10 --> 0:20:12
and I've blocked off half the signature so you don't see the last name.

304
0:20:12 --> 0:20:14
But this is identical.

305
0:20:14 --> 0:20:16
Every pixel is identical.

306
0:20:16 --> 0:20:19
So if you look, there's like this very tiny dot right near

307
0:20:19 --> 0:20:21
where my cursor is on the right.

308
0:20:21 --> 0:20:24
And if I go over to the left, you see that same dot is right there.

309
0:20:24 --> 0:20:28
And if you look at the end of the R here, those dots are all exactly the same.

310
0:20:28 --> 0:20:29
If I.

311
0:20:34 --> 0:20:37
We've lost him again, we'll wait.

312
0:20:48 --> 0:20:50
Charles, could this been going on in Australia?

313
0:20:50 --> 0:20:53
No, that's quite quite different.

314
0:20:54 --> 0:20:56
You're back. Oh, sorry.

315
0:20:56 --> 0:20:59
Yeah, but there might be a variation on it, which you.

316
0:21:00 --> 0:21:05
No, it's it's a it's quite a different system, Steven, but it's a great.

317
0:21:05 --> 0:21:09
It's a the question is how how much beyond fraud is ours?

318
0:21:09 --> 0:21:12
What the AEC, the Australian Electoral Commission.

319
0:21:16 --> 0:21:17
Is Andrew back?

320
0:21:18 --> 0:21:19
Is Andrew back?

321
0:21:20 --> 0:21:24
No, he hasn't gone, but we can't hear him going.

322
0:21:24 --> 0:21:27
He's got his bandwidth problems.

323
0:21:29 --> 0:21:31
He's picked the wrong band, Steven, he's picked a con.

324
0:21:31 --> 0:21:33
And there you are, Andrew.

325
0:21:35 --> 0:21:37
No, I can't hear you.

326
0:21:43 --> 0:21:47
He's picked a symphony orchestra, Steven, when a when a quartet would have done.

327
0:21:47 --> 0:21:48
And.

328
0:21:52 --> 0:21:55
Or actually a big just tell me when you can hear me.

329
0:21:55 --> 0:21:56
I will will do it.

330
0:21:56 --> 0:21:59
There's a text he puts me in the chance just.

331
0:22:00 --> 0:22:03
When we get him, we'll tell you, but we can't hear you yet, Andrew.

332
0:22:18 --> 0:22:21
We would tell you when we can hear you, Andrew, but we can't hear you.

333
0:22:28 --> 0:22:31
Steven, I love it when computer experts have computer problems.

334
0:22:33 --> 0:22:35
Makes me feel less incompetent.

335
0:22:40 --> 0:22:43
Yeah, I must admit, it's a bandwidth problem.

336
0:22:43 --> 0:22:44
You know that, Andrew.

337
0:22:47 --> 0:22:48
So.

338
0:22:50 --> 0:22:52
Andrew's keeping us informed in the chat, everybody.

339
0:22:55 --> 0:22:58
This is a bandwidth problem started 10 days ago.

340
0:23:00 --> 0:23:01
Well.

341
0:23:03 --> 0:23:06
Started 10 days ago wasn't actually a bandwidth.

342
0:23:06 --> 0:23:09
There wasn't a problem previous to that, no.

343
0:23:10 --> 0:23:14
Yeah, he says, I'm sorry if I was paranoid, I'd say it was a CIA.

344
0:23:15 --> 0:23:16
Uh.

345
0:23:26 --> 0:23:27
It was.

346
0:23:27 --> 0:23:28
It was perfect.

347
0:23:28 --> 0:23:31
In dozens of previous zoom meetings, says Andrew.

348
0:23:34 --> 0:23:35
Well.

349
0:23:35 --> 0:23:38
And as soon as Andrew meets Jerome low bandwidth.

350
0:23:38 --> 0:23:39
Haha.

351
0:23:39 --> 0:23:40
They are, of course,

352
0:23:40 --> 0:23:42
they're going to meet each other on a regular basis.

353
0:23:42 --> 0:23:44
Aha, there you are.

354
0:23:44 --> 0:23:45
Corsi, you're very powerful.

355
0:23:45 --> 0:23:46
He's gone too.

356
0:23:46 --> 0:23:47
He's had to go to his meeting.

357
0:23:50 --> 0:23:52
Try dialing in, Andrew.

358
0:23:52 --> 0:23:56
I can move the slides forward.

359
0:23:56 --> 0:24:01
So prior to 10 days ago, he had no problem with his bandwidth.

360
0:24:01 --> 0:24:02
Is that right?

361
0:24:02 --> 0:24:05
Correct.

362
0:24:05 --> 0:24:05
Correct.

363
0:24:05 --> 0:24:10
And I've got the slide deck so I can show the slide deck.

364
0:24:10 --> 0:24:12
Oh, you need the number.

365
0:24:12 --> 0:24:16
Tom, can you put the number in the chat, please?

366
0:24:16 --> 0:24:18
Tom Rodman.

367
0:24:18 --> 0:24:19
That Andrew calls in.

368
0:24:19 --> 0:24:22
Andrew's in New York, I think.

369
0:24:22 --> 0:24:23
Well, where are you, Andrew?

370
0:24:23 --> 0:24:25
What state?

371
0:24:25 --> 0:24:28
I think New York.

372
0:24:28 --> 0:24:30
Well, Tom has put the New York.

373
0:24:30 --> 0:24:31
Yep.

374
0:24:31 --> 0:24:34
Tom, put the New York number in if you can dig it out

375
0:24:34 --> 0:24:35
of the list on the invite.

376
0:24:35 --> 0:24:37
Here it is, Andrew.

377
0:24:57 --> 0:25:00
Stop your sharing.

378
0:25:00 --> 0:25:03
And then I will share the slides.

379
0:25:13 --> 0:25:16
OK, Andrew, I've got the slides there.

380
0:25:16 --> 0:25:23
And I can go through here.

381
0:25:23 --> 0:25:25
Andrew's calling in on his phone.

382
0:25:53 --> 0:25:54
Sorry.

383
0:25:54 --> 0:25:55
Da da da da.

384
0:25:55 --> 0:25:58
What's the meeting ID, Tom?

385
0:25:58 --> 0:25:59
Andrew needs the meeting ID.

386
0:26:05 --> 0:26:09
Meeting ID, I suppose, passcode.

387
0:26:09 --> 0:26:12
Yeah, we're sending it to you, Andrew.

388
0:26:14 --> 0:26:17
OK, Ulrike has put it in there now.

389
0:26:17 --> 0:26:19
OK, I'm going to go through it.

390
0:26:20 --> 0:26:23
OK, Ulrike has put it in there.

391
0:26:23 --> 0:26:26
9352118786.

392
0:26:29 --> 0:26:32
Say it again, Charles.

393
0:26:32 --> 0:26:34
It's on the chat.

394
0:26:44 --> 0:26:47
OK, what's the password?

395
0:26:47 --> 0:26:49
Passcode 211230.

396
0:26:49 --> 0:26:50
Thanks, Tom.

397
0:26:50 --> 0:26:52
211230.

398
0:27:00 --> 0:27:03
We'll edit all this out in the recording, everybody.

399
0:27:05 --> 0:27:08
So now in the chat, you've got the code, Tom,

400
0:27:08 --> 0:27:10
sorry, Andrew, and the passcode.

401
0:27:17 --> 0:27:20
Not for the first time we've had problems like this, Charles,

402
0:27:20 --> 0:27:23
but there's always this plausible deniability,

403
0:27:23 --> 0:27:25
which they always build in.

404
0:27:25 --> 0:27:28
You're never quite sure.

405
0:27:28 --> 0:27:31
Yeah, Andrew can hear us through the process.

406
0:27:31 --> 0:27:34
Yeah, that's true, Stephen.

407
0:27:34 --> 0:27:39
We can't hear you yet, Andrew, on your phone for some reason.

408
0:27:39 --> 0:27:41
We can't hear you.

409
0:27:41 --> 0:27:43
We can't hear you.

410
0:27:44 --> 0:27:47
We can't hear you yet, Andrew, on your phone for some reason.

411
0:27:56 --> 0:27:58
That's strange, Tom.

412
0:27:58 --> 0:28:01
Why would Andrew...

413
0:28:01 --> 0:28:03
Yeah, exactly.

414
0:28:03 --> 0:28:06
Do you see a new person joining on a phone?

415
0:28:08 --> 0:28:10
That would be him?

416
0:28:10 --> 0:28:12
No, we can't.

417
0:28:12 --> 0:28:14
We would be hearing him.

418
0:28:14 --> 0:28:19
I mean, he'd have to come in as a separate entry,

419
0:28:19 --> 0:28:24
and then we could look and see if his phone was muted or not.

420
0:28:26 --> 0:28:29
Well, Andrew's phone is the phone number there,

421
0:28:29 --> 0:28:32
845-303-0624, that looks muted.

422
0:28:33 --> 0:28:38
So, just say he needs to unmute himself, you mean, Charles?

423
0:28:38 --> 0:28:40
Yeah, but he doesn't know how.

424
0:28:40 --> 0:28:43
He doesn't know how to unmute.

425
0:28:43 --> 0:28:45
Oh, sorry.

426
0:28:45 --> 0:28:47
Star 6.

427
0:28:47 --> 0:28:50
Star 6, Andrew?

428
0:28:52 --> 0:28:53
OK, can you hear me now?

429
0:28:53 --> 0:28:55
Yep, can hear you.

430
0:28:55 --> 0:28:57
Excellent, Tom.

431
0:28:57 --> 0:28:59
For crying out loud.

432
0:28:59 --> 0:29:01
What a pain.

433
0:29:01 --> 0:29:03
OK, sorry, guys.

434
0:29:03 --> 0:29:05
Can I share my screen again?

435
0:29:05 --> 0:29:07
Or you're sharing yours so we can see this.

436
0:29:07 --> 0:29:09
All right, fine.

437
0:29:09 --> 0:29:12
OK, so the main thing that's important about this particular

438
0:29:12 --> 0:29:18
slide is that we have several laws that govern how the database

439
0:29:18 --> 0:29:20
is handled, OK?

440
0:29:20 --> 0:29:24
And it says that if it's not accurate, it's not current,

441
0:29:24 --> 0:29:27
then it violates these two laws.

442
0:29:28 --> 0:29:31
And this is very important because they definitely have been

443
0:29:31 --> 0:29:33
violated.

444
0:29:33 --> 0:29:35
So let's go to the next slide.

445
0:29:37 --> 0:29:39
I'm assuming you have, yeah, there we go.

446
0:29:39 --> 0:29:41
OK, we already talked about this.

447
0:29:41 --> 0:29:43
Let's get to that signature slide.

448
0:29:43 --> 0:29:45
So that's the next one.

449
0:29:47 --> 0:29:49
And there we go.

450
0:29:49 --> 0:29:52
OK, so what's important about this, the requests went through a

451
0:29:52 --> 0:29:54
group called New York Citizens Audit.

452
0:29:54 --> 0:29:56
And I think it was somewhere in the neighborhood of about 10

453
0:29:56 --> 0:29:58
counties reacted with a group of records.

454
0:29:58 --> 0:30:02
Now, we didn't ask for all of the records because we anticipated

455
0:30:02 --> 0:30:06
they would balk at sending hundreds of thousands of these

456
0:30:06 --> 0:30:08
signature files.

457
0:30:08 --> 0:30:12
So what we did was I selected what I consider to be a

458
0:30:12 --> 0:30:15
strategically friendly county, which is the one we got these

459
0:30:15 --> 0:30:19
two signatures from, and then gave a very, made a very limited

460
0:30:19 --> 0:30:21
request.

461
0:30:21 --> 0:30:24
So I only asked for a very limited number of signatures.

462
0:30:25 --> 0:30:29
So I only asked for records that were clones that were within

463
0:30:29 --> 0:30:32
the same county and were registered within a 10 day

464
0:30:32 --> 0:30:34
period.

465
0:30:34 --> 0:30:37
So they sent us all of those files and all of them were like

466
0:30:37 --> 0:30:39
this, every single one of them.

467
0:30:39 --> 0:30:42
So then we, after that, we brought in the request to other

468
0:30:42 --> 0:30:44
counties.

469
0:30:44 --> 0:30:46
There are 62 counties in New York State.

470
0:30:46 --> 0:30:49
And we received records from about 20 of those counties,

471
0:30:49 --> 0:30:52
notably the worst offenders, like, for instance, New York City

472
0:30:52 --> 0:30:55
where they have, I think it's the neighborhood of about a

473
0:30:55 --> 0:30:58
million of these types of cloned records, they didn't respond.

474
0:30:58 --> 0:31:01
So we never got any records from New York City.

475
0:31:01 --> 0:31:05
Mainly the places we got the records from were the more

476
0:31:05 --> 0:31:07
honest counties.

477
0:31:07 --> 0:31:09
But all of them looked like this.

478
0:31:09 --> 0:31:12
Every single one that I have seen, they have these

479
0:31:12 --> 0:31:14
photographically identical signatures.

480
0:31:14 --> 0:31:16
And that's whether there's one clone or two clones or three

481
0:31:16 --> 0:31:18
clones.

482
0:31:18 --> 0:31:20
They're all like this.

483
0:31:21 --> 0:31:24
So I showed them the state police special investigations

484
0:31:24 --> 0:31:26
unit.

485
0:31:26 --> 0:31:28
I showed them to district attorney, which I don't know if

486
0:31:28 --> 0:31:30
the equivalent is in Australia and England, but it's the type

487
0:31:30 --> 0:31:32
of prosecutor.

488
0:31:32 --> 0:31:34
I showed them to sheriff.

489
0:31:34 --> 0:31:36
They all accept.

490
0:31:36 --> 0:31:38
These are forgeries.

491
0:31:38 --> 0:31:40
They are illegal.

492
0:31:40 --> 0:31:42
And they need to be investigated.

493
0:31:42 --> 0:31:44
Interestingly enough, at the state police, the guy who was

494
0:31:44 --> 0:31:46
in charge of that unit who told me this was very important and

495
0:31:46 --> 0:31:49
has to go further, sent it to the FBI, and then he got

496
0:31:49 --> 0:31:51
arrested.

497
0:31:51 --> 0:31:53
So I showed them to the police.

498
0:31:53 --> 0:31:55
And they all accepted.

499
0:31:55 --> 0:31:57
So I showed them to the next slide, please.

500
0:31:57 --> 0:31:59
Okay.

501
0:31:59 --> 0:32:01
So as far as errors go, there are errors in just about every

502
0:32:01 --> 0:32:03
field I checked.

503
0:32:03 --> 0:32:05
So we've got names that are spelled wrong, date of birth,

504
0:32:05 --> 0:32:07
registration dates that are wrong, addresses, voter history

505
0:32:07 --> 0:32:09
status, that is, say, whether they're active, inactive or

506
0:32:09 --> 0:32:11
purged.

507
0:32:11 --> 0:32:13
And then, of course, there's the presence of illegal records.

508
0:32:13 --> 0:32:15
And many of the illegal records have some of the types of errors

509
0:32:15 --> 0:32:17
that I mentioned above.

510
0:32:18 --> 0:32:20
And there's a lot of other errors in addition to these that

511
0:32:20 --> 0:32:22
I'm not mentioning.

512
0:32:22 --> 0:32:24
In addition to the fact that we see this information, which

513
0:32:24 --> 0:32:26
should be immutable, changing between versions of the database.

514
0:32:26 --> 0:32:28
So we've got different versions of the database where people's

515
0:32:28 --> 0:32:30
date of birth is altered.

516
0:32:30 --> 0:32:32
Their registration dates are changed.

517
0:32:32 --> 0:32:34
Their addresses are changed in ways that make it apparent it

518
0:32:34 --> 0:32:36
has nothing to do with an actual address change.

519
0:32:36 --> 0:32:38
And the voter histories are changing as well.

520
0:32:38 --> 0:32:40
So there are a lot of very strange things going on in the

521
0:32:40 --> 0:32:42
database that make it appear that they're not actually

522
0:32:42 --> 0:32:44
registered.

523
0:32:44 --> 0:32:46
And so there's a lot of very strange things going on in the

524
0:32:46 --> 0:32:48
database that make it quite clearly inaccurate.

525
0:32:48 --> 0:32:50
And the thing about that that's quite fascinating is that what

526
0:32:50 --> 0:32:52
this means for the most part is they can't be using any normal

527
0:32:52 --> 0:32:54
form of data validation.

528
0:32:54 --> 0:32:56
Data validation is something that you encounter when you go

529
0:32:56 --> 0:32:58
to a kiosk on the web, for instance, to buy something.

530
0:32:58 --> 0:33:00
So if you want to rent a movie or buy something at the Amazon

531
0:33:00 --> 0:33:02
.com or something, you can go to Amazon.com and click on the

532
0:33:02 --> 0:33:04
link at the top of the screen.

533
0:33:04 --> 0:33:06
So you're going to need to get in.

534
0:33:06 --> 0:33:08
So the data validation is something that you can encounter

535
0:33:08 --> 0:33:10
when you go to a kiosk on the web, for instance, to buy

536
0:33:10 --> 0:33:12
something.

537
0:33:12 --> 0:33:14
So if you want to rent a movie or buy something at Amazon.com

538
0:33:14 --> 0:33:19
Amazon.com or something, you have to enter your name, your address, your credit card information,

539
0:33:20 --> 0:33:25
as well as information regarding using OneAbide, etc. But if you enter data that's invalid,

540
0:33:25 --> 0:33:31
like for instance, if you type in a postcode that doesn't match where you live or the address that

541
0:33:31 --> 0:33:37
you provide, or you give a phone number that's non-functional for some reason, it will not allow

542
0:33:37 --> 0:33:41
the application to be processed. So it'll give you an error message. It'll tell you that the

543
0:33:41 --> 0:33:45
information is invalid and it asks you to re-enter the data. If something like that was

544
0:33:45 --> 0:33:51
implemented in New York's photo rolls, none of the problems I found would be there. It would be able

545
0:33:51 --> 0:33:56
to catch absolutely every single one of them. For instance, these cloned records like I've just been

546
0:33:56 --> 0:34:02
talking about here, the problem with those is there is an existing record. So this is like,

547
0:34:02 --> 0:34:09
I had gone to the Board of Elections, registered to vote, and then come back later and tried to do

548
0:34:09 --> 0:34:15
it again. So when I come back on that second occasion, what their system should do is pull

549
0:34:15 --> 0:34:19
my name and date of birth and address to see if there's an existing registration that matches

550
0:34:19 --> 0:34:25
that information. And if there is, then it should not allow that registration application to be

551
0:34:25 --> 0:34:29
processed. And this, by the way, this exact circumstance is contemplated in the law,

552
0:34:29 --> 0:34:37
is described in the law, which says that the County Board of Elections must check for existing

553
0:34:37 --> 0:34:43
registrations before processing new applications. That alone would have prevented the majority of

554
0:34:43 --> 0:34:50
the clones that I found having been created. Now there are clones that could have been created

555
0:34:50 --> 0:34:54
anyway. For instance, when the name is spelled slightly wrong or the date of birth is a little

556
0:34:54 --> 0:34:59
different because then that data doesn't match. However, the law also specifies that even if

557
0:35:01 --> 0:35:06
it looks like it's a good registration where nothing matches, they still have to take it

558
0:35:06 --> 0:35:10
another step by checking what's called the social security number and the driver's license number.

559
0:35:10 --> 0:35:17
And only then are they allowed to process it. Now the thing is that at that level, all of these

560
0:35:17 --> 0:35:21
state registrations would have been captured, which means that at a systemic level, the Board

561
0:35:21 --> 0:35:26
of Elections are not performing this task. If they were performing this task, the data I'm seeing in

562
0:35:26 --> 0:35:33
the voter rolls would be impossible. It could never have been created. And this affects almost 10%,

563
0:35:33 --> 0:35:37
actually, I think a little bit more than 10% of all the records, which is quite significant when

564
0:35:37 --> 0:35:42
it comes to elections that are decided by, in some cases, a few dozen votes. Okay, let's go to the

565
0:35:42 --> 0:35:50
next slide, please. All right. So one of the more interesting artifacts that I've found in the

566
0:35:52 --> 0:35:59
voter rolls are discrepant voter histories. So what this is, is when we have the County

567
0:35:59 --> 0:36:03
Board of Elections, we have the State Board of Elections. So the counties are the local

568
0:36:05 --> 0:36:11
boards where the data is essentially collected and organized and then shipped to the State Board

569
0:36:11 --> 0:36:18
of Elections and then they collect it. Now, under the law in New York, the only entity that has the

570
0:36:18 --> 0:36:24
authority to create, alter, change, or delete records is the County Board of Elections. And

571
0:36:24 --> 0:36:29
the reason is because they're the source of all the data. So if the state was deleting records

572
0:36:29 --> 0:36:36
or changing records, there's no infrastructure to allow that data to go back to the counties to

573
0:36:36 --> 0:36:41
correct their records. So if something like that needs to be done, the county has to do it and then

574
0:36:41 --> 0:36:47
it should go to the state in the corrected form so that the state database is always synchronized

575
0:36:47 --> 0:36:53
with whatever the most current version of the county database is. So what we see is, and I'm

576
0:36:53 --> 0:36:56
just going to use New York City as an example because they're large and they have the largest

577
0:36:56 --> 0:37:05
number of errors, a discrepancy of about 254,000 votes that occur in the county voter histories

578
0:37:05 --> 0:37:10
but do not appear in the state voter histories for the same people. So if you look at a voter

579
0:37:10 --> 0:37:16
ID number, let's just say voter ID number one, and that represents John Doe. So and it says he

580
0:37:16 --> 0:37:22
voted in the 2020 election. But then you go to the State Board of Elections voter rolls and you look

581
0:37:22 --> 0:37:27
up voter number one and it says it's John Doe, it does not say he voted in 2020. So this information

582
0:37:27 --> 0:37:32
from the county rolls is not being transmitted to the state or the information after being

583
0:37:32 --> 0:37:37
transmitted to the state is being deleted by the state, which would be illegal. So that's a problem.

584
0:37:38 --> 0:37:45
Now the number of votes involved is huge, 254,000 votes. Now you're looking at this slide and you're

585
0:37:45 --> 0:37:50
thinking I don't see the number 254,000 up there anyway, anywhere rather. The reason you don't see

586
0:37:50 --> 0:37:56
that number there is because they've done something very, very strange. I call this the bus problem.

587
0:37:56 --> 0:38:00
Okay, now for those of you who aren't familiar with the way New York counties are organized,

588
0:38:02 --> 0:38:09
what we generically call New York City is actually five counties. It's Bronx, Queens, Queens,

589
0:38:09 --> 0:38:15
New York, and Richmond counties. But they're treated as if they're one county as far as the

590
0:38:15 --> 0:38:20
Board of Elections is concerned. So each of those counties has a discrepancy of about 50,000. It's

591
0:38:20 --> 0:38:28
a little less in some and a little more in others, but the total is 254,000 votes. Okay, so what they've

592
0:38:28 --> 0:38:35
done is they set it up in such a way that if you compare the voter ID numbers, you can see that it's

593
0:38:35 --> 0:38:43
missing the 250,000 votes. But if you look at the number of votes recorded by the county and compare

594
0:38:43 --> 0:38:47
it to the state, it comes very close. So that's what we're seeing here in the top. Okay, so on the

595
0:38:47 --> 0:38:53
top row on the right, you see the certified count of votes. This is what's going into the state

596
0:38:53 --> 0:39:01
certification process for certifying the vote. So 788,262 votes were certified as it happens in

597
0:39:01 --> 0:39:08
count. If you look at the precinct count, so this is where the count originated, you see the count is

598
0:39:08 --> 0:39:12
slightly higher than that. It's not enough to make a huge difference, but it is different. That all

599
0:39:12 --> 0:39:17
by itself is problematic because this means that somewhere between the precinct and the

600
0:39:18 --> 0:39:24
state level, about a thousand votes went missing. Okay, actually it's more than a thousand votes.

601
0:39:24 --> 0:39:29
It's like it's more like 16 or 17 hundred votes. Anyway, so that tells you there's a problem,

602
0:39:29 --> 0:39:34
but it's not a huge problem. But now you look at the state voter roll count of voters who voted,

603
0:39:34 --> 0:39:39
and that's a significant difference. That's 737,986 is the difference from 50,000 or so.

604
0:39:40 --> 0:39:45
And then you look at the county record and it says 739,885, which is really close. That's just,

605
0:39:45 --> 0:39:49
it looks like it's 1899 is the difference between those two. So it's not much.

606
0:39:50 --> 0:39:57
But here's the problem. If you compare ID numbers, the difference is actually, and this is by the way,

607
0:39:57 --> 0:40:02
this is for one county. This looks like it's Queen County. So the difference for Queen County is

608
0:40:02 --> 0:40:09
actually 55,000 based on ID numbers. But when you look at the actual number of votes,

609
0:40:10 --> 0:40:15
how does that happen? This is what I call the bus problem. So the bus problem, imagine this,

610
0:40:15 --> 0:40:20
you have a bus that has 20 passengers, it crashes and eight passengers are killed.

611
0:40:21 --> 0:40:27
However, when the number of survivors are counted, there are 19 survivors. So how do you take eight

612
0:40:27 --> 0:40:35
people out of a bus that has 20 people and end up with 19 people? The answer to that is that more

613
0:40:35 --> 0:40:41
people got on the bus after those eight people were killed. So that's what they're doing here

614
0:40:41 --> 0:40:49
in the voter rolls. They have effectively taken votes away from one set of ID numbers, but they've

615
0:40:49 --> 0:40:56
added them back in with another set of ID numbers. Now, why they would do this is a little mysterious

616
0:40:56 --> 0:41:01
to me, except for the fact that they're adding and subtracting the votes from different counties. So

617
0:41:01 --> 0:41:08
it looks like they're shifting the numbers so that they can reach the number of certified votes

618
0:41:08 --> 0:41:14
that they say were certified, despite the fact that those votes are coming from different counties,

619
0:41:14 --> 0:41:22
which obviously I would think invalidates the purpose. So let's go on to, and by the way,

620
0:41:22 --> 0:41:26
if I was unclear about that, please ask questions. I always found this particularly

621
0:41:26 --> 0:41:30
interesting that they did one of the trickier things I've ever seen. And it took me some time to

622
0:41:31 --> 0:41:37
get my head wrapped around it. But that's what the left-hand image is meant to show you. So if you

623
0:41:37 --> 0:41:45
look at this, we've got 10 records and on one side you have four missing from the state and you've

624
0:41:45 --> 0:41:51
got on the other side of two missing from the county, but they're making it up with votes from

625
0:41:51 --> 0:41:58
the other side. So what I found when I looked at these is that in every case, the missing vote

626
0:41:59 --> 0:42:06
was connected to a cloned record. So if John Doe has two records, record one and two, and at the

627
0:42:06 --> 0:42:13
county level it says he voted with ID number one, at the state level it says he voted with ID number

628
0:42:13 --> 0:42:19
two, but not with number one. And meanwhile, I have no record of a county registration for this

629
0:42:19 --> 0:42:26
person for ID number two. I only have the state record of that. So this is quite an interesting

630
0:42:26 --> 0:42:30
problem that definitely needs to be investigated. Now, by the way, the reason I'm showing you this

631
0:42:30 --> 0:42:36
stuff before I get into the other, because I want you to be aware of and comfortable with the idea

632
0:42:36 --> 0:42:44
that there are problems worthy of investigation sitting in the... Can you hear me? I can't hear

633
0:42:44 --> 0:42:50
anyone. Yeah, yeah. Hello? Yeah, we can hear you. Okay. Okay. Okay. So you've heard me talking about

634
0:42:51 --> 0:42:58
about the bus problem, right? Now, Andrew, now we've got, you need to, we'll get feedback because

635
0:42:58 --> 0:43:05
we're also getting your microphone working. So if you're on your computer, so reduce the input on

636
0:43:05 --> 0:43:14
your computer. So how about now? Yeah, can you hear me now? Yep. Okay, good. So I'm going to stick

637
0:43:14 --> 0:43:19
with the phone because I don't know if the mic's going to go out on me again. Okay. So anyway, so

638
0:43:19 --> 0:43:24
this is interesting, but the reason I'm telling you about it is because I want you to know that

639
0:43:24 --> 0:43:29
there are problems in the voter rolls because this gets to why the algorithm is needed. So let's go

640
0:43:29 --> 0:43:38
to the next slide, please. All right. So purged records are kind of interesting because the way

641
0:43:38 --> 0:43:43
you purge a record is you have a dropdown menu that has the words active, inactive, and purged on

642
0:43:44 --> 0:43:51
or in the more recent version of the database, AI or Q. All you have to do to purge a record is

643
0:43:52 --> 0:43:57
bring up that dropdown menu and select purge. And if you want to make that record active again,

644
0:43:57 --> 0:44:04
you just select active and it's done. So leaving the record in the database as a purge record is

645
0:44:04 --> 0:44:09
not a particularly effective safety measure because it can always be changed back to active.

646
0:44:09 --> 0:44:15
And then after it's been used to count a fake vote, it can then be purged again,

647
0:44:16 --> 0:44:21
and no one will know it was ever used that way. We have two million records that actually lack

648
0:44:21 --> 0:44:27
a purge date, which means that you can't retrospectively know whether a vote was

649
0:44:29 --> 0:44:36
legal or not. Because if your purge date is before your record is having voted, then that

650
0:44:36 --> 0:44:41
vote is invalid. But if it's after, then it's valid. So not having a purge date means it's

651
0:44:41 --> 0:44:49
impossible to assess or to audit this particular, to audit votes that are associated with records

652
0:44:49 --> 0:44:55
that have purged without a purge date. So that's very important. And interestingly, there also are

653
0:44:55 --> 0:45:01
about two million clones. And one thing about the clones that's interesting is that about a half

654
0:45:01 --> 0:45:06
million of those are actually not in the voter rolls, but I know they were in the voter rolls

655
0:45:06 --> 0:45:11
and I know who they belong to originally because of the algorithm I discovered. If it wasn't for

656
0:45:11 --> 0:45:15
the algorithm, I would never know that those numbers had ever been assigned to anyone and

657
0:45:15 --> 0:45:20
I certainly wouldn't have a way to figure out who they originally belong to. But I actually can do

658
0:45:20 --> 0:45:27
that now. So let's go to the next slide. Okay, so let me just show you what a clone looks like.

659
0:45:28 --> 0:45:34
I'm going to show you a little bit more about this now against the algorithm. So if you look here on

660
0:45:34 --> 0:45:39
the left, and by the way, I've blocked out the names and address and so on information, but this

661
0:45:39 --> 0:45:44
is the same person. It's identical names, identical birth dates, and identical addresses. So if you

662
0:45:44 --> 0:45:50
look at what's called the short ID, that's the state ID, you see that it's almost consecutive.

663
0:45:50 --> 0:45:56
So the first two are consecutive and then there's a run that looks like six numbers that are

664
0:45:56 --> 0:46:00
consecutive and then another run of numbers that are consecutive. And you notice the registration

665
0:46:00 --> 0:46:04
dates all the same. And oh, actually I left the date of birth in there so you can see that's all

666
0:46:04 --> 0:46:10
the same too. But this is all the same person and this is what a clone looks like. We do have people

667
0:46:10 --> 0:46:18
who have like 18 records or 21 or 25. Most clones are actually just two records or three records,

668
0:46:19 --> 0:46:25
but there are I think a few hundred that look like this. So now that you know what this looks

669
0:46:25 --> 0:46:29
like, let's look at how many there are and where they appear. This is interesting. So go to the

670
0:46:29 --> 0:46:37
next slide please. Okay, so if you look at this by year, you see that the percentage of clones

671
0:46:37 --> 0:46:46
to new registration goes up rapidly year over year. In fact, prior to about 1990, there were

672
0:46:46 --> 0:46:52
in most years zero clones. So you would look at 1950s if you wanted to, all the way up to 1990s.

673
0:46:52 --> 0:46:56
I recall that there were a couple that might have had like two clones or three clones,

674
0:46:56 --> 0:47:03
maybe even as many as five, but nothing really significant. But in 1990, you get 514 clones,

675
0:47:03 --> 0:47:09
which you know that doesn't seem too bad in the context of 6,000 registrations, but it's

676
0:47:09 --> 0:47:15
certainly not good compared to having no clones in prior years. And then if you look at that number,

677
0:47:16 --> 0:47:24
it keeps on going up. Okay, now election years, it goes up even more. So 1992, 1996, you see bumps

678
0:47:24 --> 0:47:29
for every four years, it goes up by a lot more. But if you look down at the bottom, take a look

679
0:47:29 --> 0:47:37
at 2020, now you're getting 178,755 clones. That is really significant. That's actually almost as

680
0:47:37 --> 0:47:43
many total registrations as we had in 1990. And yet in this case, that's all clones.

681
0:47:46 --> 0:47:56
Okay, can you hear me? Hello? Yep. Okay. Yeah. So this is now a lot of clones. And if you look at

682
0:47:56 --> 0:48:06
the percentages, right, you're going from 0.25% in 1990, all the way up to 17.36% in 2022. And if

683
0:48:06 --> 0:48:11
you look at the percentages from on the, you know, just on the bottom, just look at those year over

684
0:48:11 --> 0:48:20
year, it consistently increases every single year. Okay. So from my perspective, what I'm seeing here

685
0:48:20 --> 0:48:25
is I'm seeing the value of electronic voter registration. In other words, it's not very

686
0:48:25 --> 0:48:31
good because it seems to be making a problem that didn't exist and making it worse every single year.

687
0:48:32 --> 0:48:39
So this is not a good thing. All right, let's go to the next slide, please. All right. Now,

688
0:48:39 --> 0:48:46
what I was wondering when I found all these records was how on earth would you make use of them? Okay.

689
0:48:48 --> 0:48:52
The thing is, when I was going through the records, there's 21 million records in New York, right?

690
0:48:53 --> 0:48:59
It's very difficult to find anything in that database just because there are so many records.

691
0:48:59 --> 0:49:02
You could be looking right at something that's problematic and not know it because the thing

692
0:49:02 --> 0:49:08
that makes it problematic is another record that's 2 million numbers away. So the way they're mixed in

693
0:49:09 --> 0:49:16
makes it very, very hard to find what you want if you're a bad guy. So if what you've done is you've

694
0:49:16 --> 0:49:23
created essentially random fake registrations and scattered them in the voter roll, in order to use

695
0:49:23 --> 0:49:30
them, you would have to have a way to find them again. Okay. And this problem or this idea bothered

696
0:49:30 --> 0:49:35
me a lot because as soon as I found these, I was thinking if there's no way to find these again,

697
0:49:36 --> 0:49:43
then this has to be some kind of an accident. Okay. So it's going to be some day that this is

698
0:49:43 --> 0:49:47
legitimate somehow. It's an error. It shouldn't be there. It's illegal. So it needs to be fixed,

699
0:49:48 --> 0:49:51
but it's not intentional. At least that's the way I was thinking at the time.

700
0:49:52 --> 0:49:59
So I wanted to keep my eyes open for something that would tag the record. Now, I was literally

701
0:49:59 --> 0:50:03
expecting a tag of some kind. So I was thinking that they would alter somehow

702
0:50:03 --> 0:50:09
one of the data fields in a consistent way that would allow me to detect cloned records.

703
0:50:09 --> 0:50:15
And now I figured I probably didn't find all of the cloned records because obviously I don't know

704
0:50:15 --> 0:50:21
what they know. So the likelihood is that I found a lot of them and I probably have a few false

705
0:50:21 --> 0:50:25
positives in there. I wouldn't know which ones are which without talking to the people involved, but

706
0:50:26 --> 0:50:34
involved. But my guess was I had probably found 75 to 80% of all of the bad records. So it seemed

707
0:50:34 --> 0:50:39
to me I could use the bad records as a way to find the tagging mechanism. Now, when I looked at

708
0:50:39 --> 0:50:46
the data field and asked myself, so which of these data fields is the most logical to use as a tagging

709
0:50:46 --> 0:50:53
field, I settled on the ID numbers. And the reason is because the ID numbers are unique, or at least

710
0:50:53 --> 0:50:57
they're supposed to be unique. The names are. You can have a number of John Smiths, for instance, or

711
0:50:57 --> 0:51:03
my name, Andrew Paquette. I know of several Andrew Paquettes in America and a couple in Canada.

712
0:51:04 --> 0:51:08
One of the Americans, by the way, is apparently a career criminal. A friend of mine in California

713
0:51:08 --> 0:51:11
sends me newspaper articles about this guy every single time he steals a car.

714
0:51:13 --> 0:51:17
So you can have the same name. You can also have the same birthday. You could even have the same

715
0:51:17 --> 0:51:23
name and birth date, or the same address. Every time you add another layer, the complexity becomes

716
0:51:23 --> 0:51:29
a little bit less likely to happen, or to happen in volume. But it's possible. But the ID numbers

717
0:51:29 --> 0:51:35
should be completely unique. So I decided to look at the ID numbers. And the first problem is that

718
0:51:35 --> 0:51:42
the state ID number, which is the more important one, is eight digits for everyone. And as far as

719
0:51:42 --> 0:51:48
I could tell, they appeared to be completely randomized. What the programmers were looking at

720
0:51:48 --> 0:51:56
is, as you can probably see, it's very difficult to find. The reason was that, from his point of view,

721
0:51:56 --> 0:52:02
if you did put a tag in there, somebody was bound to find it. And so that would not work.

722
0:52:03 --> 0:52:10
So it turns out there was something there. They did find it, but it wasn't a tag. It was something

723
0:52:10 --> 0:52:14
far more clever than that. So can you please go to the next slide?

724
0:52:17 --> 0:52:24
So I'm actually moving along with you. Okay. So actually, I already talked about this in the

725
0:52:24 --> 0:52:31
sentence. So let me go farther. Okay. So what I found was algorithms that associated the

726
0:52:31 --> 0:52:38
county ID number with the state ID number by using a very specific counter, as in the

727
0:52:39 --> 0:52:46
algorithm that causes numbers to be associated in a specific and reversible way. So that association

728
0:52:46 --> 0:52:51
is something that can be extracted for the purposes of identifying special records. But because

729
0:52:51 --> 0:52:56
they're not actually altering the numbers, no one using the database would have any idea that this

730
0:52:56 --> 0:53:02
association was made. It's kind of like making a bunch of secret narratives. So it's like,

731
0:53:02 --> 0:53:10
kind of takes the database for all the voters in Australia, let's just say, and assigns each of the

732
0:53:10 --> 0:53:16
women to a certain man as a marriage, right? It records this secretly and doesn't tell them

733
0:53:16 --> 0:53:21
that in their database, these people are married. But the way they do it is something that they can

734
0:53:21 --> 0:53:26
reverse. So they can always know who's married to who based on their database and that information

735
0:53:27 --> 0:53:31
is something that they can use to identify those people uniquely. So they did something like that

736
0:53:32 --> 0:53:38
in New York's federal and there are four algorithms. So I've made a chart here or an

737
0:53:38 --> 0:53:44
illustration just to illustrate what they look like because they hit them very well by

738
0:53:46 --> 0:53:53
dividing the state space into partition. So if you look at the way this is laid out, the county ID

739
0:53:53 --> 0:54:01
are on the x-axis and the state IDs are on the y-axis. Okay. And we have four patterns.

740
0:54:01 --> 0:54:08
We've got the button shingle metronome and spiral. Now I call these spiral metronome section

741
0:54:08 --> 0:54:13
in range and the rest of it is out of range. And the reason I describe it that way is because the

742
0:54:13 --> 0:54:21
in range numbers are ordered. They have a very specific, highly disciplined, mathematically

743
0:54:21 --> 0:54:27
precise order to them and they're deterministic and reversible. The out of range territory has a

744
0:54:27 --> 0:54:34
pseudo random quality that may be deterministic, but I, if it is, I haven't figured it out how yet.

745
0:54:35 --> 0:54:39
I can see them and I can see that there are different patterns, but I haven't been able to

746
0:54:39 --> 0:54:43
solve those algorithms yet just because they have the pseudo random quality here.

747
0:54:43 --> 0:54:49
But the point is by mixing the four algorithms this way and in such a way that only people who

748
0:54:49 --> 0:54:52
understand the algorithms would be able to identify numbers that's belonging to,

749
0:54:53 --> 0:54:59
say, the shingle versus the tartan or the metronome, is that it actually creates the equivalent of

750
0:55:00 --> 0:55:05
flat so that you would not be able to see that there are any algorithms. So for instance, if I

751
0:55:05 --> 0:55:12
asked for all of the ID numbers for Clinton County, New York, it would include numbers that use

752
0:55:12 --> 0:55:16
all of these algorithms. And since they're all mixed together in the county, I would have no way

753
0:55:16 --> 0:55:21
to detect any one of these algorithms because they're only visible if I see all of the counties

754
0:55:21 --> 0:55:29
together. So this partitioning of the number space was the first problem I had to deal with.

755
0:55:29 --> 0:55:33
But once I did that, and let's go to the next slide so I can show you what it looks like.

756
0:55:37 --> 0:55:38
Are we changing slides?

757
0:55:38 --> 0:55:40
Seconds, Andrew.

758
0:55:42 --> 0:55:49
Yeah, can you change the slide, please, to the next one? Thank you. Okay, so now we're looking at

759
0:55:49 --> 0:55:54
what the spiral subject partitions look like. So you may recall in the previous slide I told you

760
0:55:54 --> 0:56:00
there are four algorithms. The primary algorithm that I've been dealing with is the spiral.

761
0:56:02 --> 0:56:07
And if you look at the image on the right, that shows how the counties are broken down. So what

762
0:56:07 --> 0:56:12
it does is it gives a certain range of numbers to each of these counties. I'm using a three character

763
0:56:13 --> 0:56:19
set of letters to identify each one. So it looks like it's in any county, Wyoming County,

764
0:56:20 --> 0:56:24
County of New York, etc. And on the left, you see the actual number ranges that are assigned

765
0:56:24 --> 0:56:31
to those counties. Now, they had another layer of optimization added here. New York State has

766
0:56:31 --> 0:56:37
assigned what's called a county code to each county. And this code is based on an alphabetized

767
0:56:37 --> 0:56:41
list of the county. So Albany County, which is the highest in the alphabetized list,

768
0:56:41 --> 0:56:46
is county number one. And Allegheny, which is the next in the alphabetized list is county two.

769
0:56:46 --> 0:56:53
Yates, which is last, is county number 62. But if you put the numbers assigned to these counties

770
0:56:53 --> 0:56:59
in SVO ID order, which you see here in the looks like the fifth and the sixth columns,

771
0:56:59 --> 0:57:04
it scrambles the county code. So if you look at the county codes on the left, they're in

772
0:57:05 --> 0:57:09
basically a random order. I don't believe it's really random, but I haven't figured out what the

773
0:57:09 --> 0:57:15
order is. So I'm just saying it's scrambled for the time being. But that, by the way,

774
0:57:15 --> 0:57:20
prevented me from seeing these ranges for, I think it took me about a week to see that this

775
0:57:20 --> 0:57:25
was going on because I was expecting that Allegheny would follow Albany and Bronx would follow

776
0:57:25 --> 0:57:31
Allegheny and so on. So I was looking for numbers that weren't there because they had scrambled this

777
0:57:31 --> 0:57:36
list. So let's take a look at what the spiral actually looks like in the database. Please go

778
0:57:36 --> 0:57:46
to the next slide. Okay. So what we have here on the left is a list of numbers that have been

779
0:57:47 --> 0:57:54
sorted by looks like the county ID number. Okay. So if you look at the county ID numbers,

780
0:57:54 --> 0:57:58
although they're not consecutive, they are sequential. Okay. And this is because they're

781
0:57:58 --> 0:58:04
missing county IDs. But if you look at the gap between the state ID numbers, you notice there are

782
0:58:04 --> 0:58:10
a bunch of 11 and then there's 12. Now the way this looks, and I've done a lot of research on this,

783
0:58:10 --> 0:58:17
so off the top of my head, I kind of tend to forget which view it is where they're every 10th row and

784
0:58:17 --> 0:58:21
which view is every 11th row. I think in this case we're looking at every 10th row. But basically

785
0:58:21 --> 0:58:29
what's going on here is they have scrambled these numbers in such a way that they're assigning to a

786
0:58:29 --> 0:58:36
consecutive list of CID numbers, a list of SBO ID numbers that are consistently 11 units apart. And

787
0:58:36 --> 0:58:43
then on the 11th number is going to be 12 units apart. And every 100th, it's going to be 111 and

788
0:58:43 --> 0:58:49
every 1000th between 1100 and 1100 and every 10,000th of the 11,000 111, et cetera. So that's

789
0:58:49 --> 0:58:57
what these rep units come in. Now the reason every 10th record has a rep unit plus one is because

790
0:58:57 --> 0:59:04
that's making space for a rep unit from a higher order. So in this case it would be for a rep unit

791
0:59:04 --> 0:59:12
in the 111s for instance. And when you get to the higher order rep unit, you start seeing 12

792
0:59:12 --> 0:59:17
in the middle of the block of 10, but they always appear in exactly the same position. So if it's 100,

793
0:59:17 --> 0:59:22
it appears in the 9th position and if it's 1000, it appears in the 4th position and if it's 10,000,

794
0:59:22 --> 0:59:29
it appears in the 3rd position. And if it's 100,000, you get a 100 and a 10,000 in the same block

795
0:59:30 --> 0:59:37
or something like that. Now what this looks like if you look at the CID numbers is the image on the

796
0:59:37 --> 0:59:45
right. And this is why I call it a spiral. So what they've done is they divide the numbers into

797
0:59:45 --> 0:59:52
powers of 10. So in this particular case, 11 corresponds to literally the number 10. But

798
0:59:52 --> 1:00:00
111 corresponds to 100 and 111 corresponds to 1000, etc. So if you look at the CID numbers that are

799
1:00:00 --> 1:00:07
assigned to each of those columns, you see that they make a spiral pattern. So the lowest number

800
1:00:07 --> 1:00:12
is zero and then the next highest number is 10,000. You notice, look at the little arrow here,

801
1:00:12 --> 1:00:21
you go 7,377 to 7,387 and it goes down to 7,203 and then you go to the next row, which is 1000.

802
1:00:21 --> 1:00:26
And now all of a sudden, the next number isn't at the top anymore, it's at the bottom. There's a

803
1:00:26 --> 1:00:30
reason for this, but I'm going to skip that detail because there's a lot of women can talk like that.

804
1:00:32 --> 1:00:39
The numbers continue consecutively until 7,383 and then that cycles back up to the top of the row

805
1:00:39 --> 1:00:43
and then goes consecutively until it hits this number. And then it continues doing that,

806
1:00:43 --> 1:00:49
making essentially a spiral pattern in the number. So that's one calling the spiral.

807
1:00:51 --> 1:00:51
Next slide.

808
1:00:55 --> 1:01:01
And there we go. Okay. And they do another thing. This differs county by county. So not every county

809
1:01:01 --> 1:01:06
does this. And I'm actually showing you two transformations. Some counties do one, some

810
1:01:06 --> 1:01:13
counties do the other, some counties do both, and some counties do neither. And there are actually

811
1:01:13 --> 1:01:20
a couple of counties that are... Okay, Albert. Although I'm not the moderator, so it's up to him.

812
1:01:22 --> 1:01:26
And some counties actually do transformations that are unique to those counties. I'm not going

813
1:01:26 --> 1:01:31
to show those because they're not as generic as these two. So this is a CID transformation. So

814
1:01:31 --> 1:01:36
what they've done here is they've taken the CID numbers, which if you look at, let's see, it's

815
1:01:36 --> 1:01:42
count one, two, three, four. Okay. And you look at them, they don't appear to be in order. But if

816
1:01:42 --> 1:01:50
you look at the short ID, those are in consecutive order. Okay. So if you sort by short ID, you derive

817
1:01:50 --> 1:01:57
what the county sort order is. Okay. So let's just look at this 149777. Okay. That's the third item

818
1:01:57 --> 1:02:03
down, right? Right under it is 14978 and then 149783. Right? So you've got a six-digit number

819
1:02:03 --> 1:02:08
followed by a five-digit followed by a six-digit. Okay. So these are going up and down by an order

820
1:02:08 --> 1:02:14
of magnitude. Right? So those don't appear to be related. But then, wait a minute, let's look here

821
1:02:14 --> 1:02:23
at this 1055 to highlight it. And then you go down 10 and 11, you get a 105500. So these numbers are

822
1:02:23 --> 1:02:30
actually very closely related. Look at this, the 1498 and then 14980 and then 149807. What they've

823
1:02:30 --> 1:02:39
done is they've decimalized these numbers. Okay. So I've gone ahead and I decimalized the numbers.

824
1:02:39 --> 1:02:44
You look at them sorted by decimalization and you see now these are in perfect order because by adding

825
1:02:44 --> 1:02:49
a decimal point to the left of each of these, which by the way is an improper way to decimalize

826
1:02:49 --> 1:02:54
these numbers. Okay. Because it actually changes the values of the numbers. For instance, a 10,

827
1:02:54 --> 1:02:58
a thousand and a million all have the same value when they're decimalized. They're all going to be

828
1:02:58 --> 1:03:07
0.1. And by doing that, a million will actually sort before two. And two is obviously a smaller

829
1:03:07 --> 1:03:12
number, but when it's decimalized, it's the other way around. So what they do is they decimalize

830
1:03:12 --> 1:03:17
the CID numbers and then they sort them that way. And then they apply those numbers or map them

831
1:03:17 --> 1:03:22
through the SBO ID numbers after they've gone through the spiral algorithm. So in that way,

832
1:03:22 --> 1:03:28
they're actually obfuscating the numbers at both of those levels. Now on the right, what we see are

833
1:03:28 --> 1:03:33
a bunch of numbers from New York County. And what they did is they used an alpha transformation. So

834
1:03:33 --> 1:03:38
their CID numbers have some of them anyway, have an alpha component. When I say some of them anyway,

835
1:03:38 --> 1:03:45
I want to say that it's about 30 or 40% of all of their numbers have an alpha character. Okay.

836
1:03:46 --> 1:03:51
So if you go ahead and sort these by state ID number, which you see here, these are all

837
1:03:51 --> 1:03:57
consecutive numbers here in this middle row. And then you look at the CID numbers, you'll see

838
1:03:57 --> 1:04:03
that the alpha that starts the number changes every time. Just like you see these numbers being

839
1:04:03 --> 1:04:09
interlaced with the string that starts with 1055 on the other side. Okay. So it's the same kind

840
1:04:09 --> 1:04:14
of transformation, but it's being done using letters. Now, the thing that makes this so tricky

841
1:04:14 --> 1:04:20
is that not all of their numbers have an alpha character in them. Okay. So with that, now if they

842
1:04:20 --> 1:04:24
did, it would be a totally different story because then what would happen is you go ahead and sort

843
1:04:24 --> 1:04:29
these and it would sort all the A's and then all the B's and then all the C's separately from each

844
1:04:29 --> 1:04:35
other. Right. But because they're mixed, what happens is you have these alpha numeric

845
1:04:37 --> 1:04:43
numbers that are sort of kind of in the middle of everything else. There is absolutely no way

846
1:04:43 --> 1:04:49
with any tool I am familiar with to do this. You can't reproduce this. It's impossible. The

847
1:04:49 --> 1:04:55
only way to get this is to write code that specifically adds these alpha characters to

848
1:04:55 --> 1:05:00
these numbers and puts them in this order. It has to have been done intentionally like that.

849
1:05:01 --> 1:05:06
And there's literally no way to reproduce this order other than, well, okay, there is a way. It's

850
1:05:06 --> 1:05:11
by sorting by short ID, but you can't do it by sorting the CID numbers. You have to sort the

851
1:05:11 --> 1:05:15
state ID numbers. And the only reason that works is because of the algorithm they use to assign

852
1:05:15 --> 1:05:22
these things. So in my opinion, this was very tedious. Now, again, in some cases, what they did

853
1:05:22 --> 1:05:30
is they did both of these things. So you have both the CID number being decimalized and you have an

854
1:05:30 --> 1:05:35
alpha characteristic in addition to that. And let's look at something else that you might find

855
1:05:35 --> 1:05:39
interesting. I don't know if any of you guys noticed this while I was bothering on, but the

856
1:05:39 --> 1:05:44
registration dates, take a look at those. These are all January 1st. Now, I don't know about you, but

857
1:05:44 --> 1:05:48
in America, January 1st is the day when most federal buildings are closed.

858
1:05:48 --> 1:05:55
Now, I have had a very poor explanation for this by actually, it wasn't given to me directly,

859
1:05:55 --> 1:05:58
but it was something I heard them say as an explanation in a state board of elections,

860
1:05:58 --> 1:06:04
and they made it public. But they explained this as young drivers who, when they got the driver's

861
1:06:04 --> 1:06:09
license, they became what's called pre-registered. So they're not able to vote legally, but their

862
1:06:09 --> 1:06:14
information center system is automatically activated on their birthday. So if they happen

863
1:06:14 --> 1:06:19
to have been born on January 1st, then it would look like they've been registered on January 1st.

864
1:06:19 --> 1:06:26
However, this applied to about somewhat less than 5,000 people who actually had a birthday

865
1:06:26 --> 1:06:32
of January 1st. But there were almost a million people who had a January 1st registration date

866
1:06:32 --> 1:06:37
in the database. So this only applied to about 0.05% of the full database. This is not a very

867
1:06:37 --> 1:06:41
good explanation for where I come from. Okay, so let's go to the next slide.

868
1:06:42 --> 1:06:49
Yep. Okay, I hope you guys continue when I take a drink of water so they can help.

869
1:06:52 --> 1:06:56
So what they do is they're stacking the deck, and then they use the shift type, or essentially,

870
1:06:56 --> 1:07:05
to do what they're doing here. So this block of numbers that you see on the left is my solution

871
1:07:05 --> 1:07:11
to the algorithm, and it works. So this is how they do it. If you have this, and you have the

872
1:07:11 --> 1:07:16
formulas that I put in each one of those cells, you would be able to predict how they're going

873
1:07:16 --> 1:07:22
to map all the numbers to each other. Okay? So what they do is they start with the minimum and

874
1:07:22 --> 1:07:28
maximum values for the range for that county. Now, if you recall, and I'm not going to make you go

875
1:07:28 --> 1:07:33
back to it because it'll be complicated directing me that way, but I gave you the list that showed

876
1:07:33 --> 1:07:40
the ranges. SBO ID number X to SBO ID number Y is, for instance, Allegheny County. Okay? So they had

877
1:07:40 --> 1:07:47
to hard code what those ranges were going to be. Okay? And then what they had to do is they had to

878
1:07:47 --> 1:07:53
calculate how many numbers were in that range. Okay? So that calculation is pretty easy. It's

879
1:07:53 --> 1:07:58
maximum minus minimum plus one because it's inclusive. These are actual things, numbers.

880
1:07:59 --> 1:08:06
And then what they had to do is they assign one to the minimum value, which is going in a zero

881
1:08:06 --> 1:08:11
column. Now that that number is assigned, it's no longer available. So the range has to be adjusted.

882
1:08:11 --> 1:08:19
So it goes from range 969351 to 969350. Okay? Now what they do is they go to the highest rep unit.

883
1:08:19 --> 1:08:23
Notice they go from the minimum to the maximum. Okay? So in this particular example, the highest

884
1:08:23 --> 1:08:30
rep unit is 111,111, which goes in the 100,000 power column. Okay? So now what they have to do

885
1:08:30 --> 1:08:37
is see how many times that rep unit fits in this adjusted range, which in this case is eight. So

886
1:08:37 --> 1:08:42
now they subtract that eight from this and they get this new adjusted range. And they keep on

887
1:08:42 --> 1:08:47
doing that all the way down until they've assigned all the numbers to one of these columns. Okay?

888
1:08:48 --> 1:08:53
And then what they do once they've assigned these numbers is they shift the numbers. Okay? But the

889
1:08:53 --> 1:08:57
first step, I'm going to show you the shift in just a second, is stacking the deck. And the way

890
1:08:57 --> 1:09:01
they stack the deck is by making these columns. So in this case, one, two, three, four, five,

891
1:09:01 --> 1:09:06
six, seven columns. So this is like a seven player game of poker. Okay? Where each one of these

892
1:09:06 --> 1:09:12
columns represents a player. So what they're doing is they're placing the equivalent of cards,

893
1:09:12 --> 1:09:18
that is to say group of numbers, that are going to each one of these players. And so all the

894
1:09:18 --> 1:09:25
cards that are 111,111 numbers apart are going to go to the player in the 100,000 position. And in

895
1:09:25 --> 1:09:31
the same way, all the numbers that are 10 apart are going to go, or rather 11 apart, are going to

896
1:09:31 --> 1:09:36
the player in the 10 position. Okay? So that's stacking the deck. But they actually make it a

897
1:09:36 --> 1:09:41
little bit more complicated in the way they interlace these numbers. So we just go to the next

898
1:09:41 --> 1:09:47
slide. Okay. Oh, wait. There it is. All right. So this is the Caesar cipher at the top. It's

899
1:09:47 --> 1:09:53
known as the shift cipher, which is one of the easiest forms of cipher you could use. And pretty

900
1:09:53 --> 1:09:59
much everybody learns this on day one of cipher school, or at least that's my guess, considering

901
1:09:59 --> 1:10:07
what I've read on the internet. So what they do is they have the full alphabet, A through Z.

902
1:10:07 --> 1:10:11
They shift it to three characters. Now, of course, you can shift it to any number of characters, but

903
1:10:11 --> 1:10:16
the traditional Caesar cipher, you shift it to three. And this pushes the X, Y, Z out of place.

904
1:10:16 --> 1:10:22
So you put those at the beginning, and now you map them. So the A is now equal to X, B is equal to Y,

905
1:10:22 --> 1:10:27
C to Z, et cetera, all the way down. So that's what a shift cipher is. So what they do with all

906
1:10:27 --> 1:10:34
those columns is, you see here that, now in this case, I'm using a group of numbers that doesn't

907
1:10:34 --> 1:10:39
have enough numbers to go to 100,000. So it starts with 10,000 here. Okay. So what they do is they

908
1:10:39 --> 1:10:48
shift all of the numbers by three quarters of a rep unit, which is, in the case of 10,000,

909
1:10:52 --> 1:11:01
I'm blanking on it for a minute there, 8,333. And then 1,833, and then 838, and then one. Okay.

910
1:11:01 --> 1:11:06
But they've also got something else hard-coded into the system, and that is that the last number

911
1:11:07 --> 1:11:16
in each of these rows has to be in rank order a three-quarter rep unit. So if you go back to the,

912
1:11:16 --> 1:11:23
let's go back to the previous question for a second. Okay. So now I have to go back there.

913
1:11:23 --> 1:11:28
All right. So if you look at each one of these columns, if you look all the way to the top row

914
1:11:28 --> 1:11:34
where it says min, okay, so for the 100,000 columns, that number is the minimum number plus,

915
1:11:36 --> 1:11:43
in this case, I think it's 83,333, and then the 10,000 columns plus 8,333, et cetera. Okay.

916
1:11:44 --> 1:11:48
But this is important. If you go down, you see this AID, you see the AID all in caps, you see

917
1:11:48 --> 1:11:53
a first and then high and then low and then last, right? Okay. So that is related to what I was just

918
1:11:53 --> 1:11:59
showing you. The AID is the algorithm ID because the algorithm actually creates an ID in order to

919
1:11:59 --> 1:12:03
do the sorting process. And the reason I know that is because they've hard-coded it that the last

920
1:12:03 --> 1:12:11
number in each of these columns has to be one of these three-quarter rep units. Okay. So, and that

921
1:12:11 --> 1:12:21
is after it hits nine. Okay. So in this case, the 100,000 row or column rather is not cut because

922
1:12:21 --> 1:12:28
there aren't enough numbers, but once you hit the 10,000 column, they are. So this is cut at 83.

923
1:12:28 --> 1:12:33
This means the 83rd number. So you count from zero, one, and then you count eight more numbers here.

924
1:12:33 --> 1:12:38
It's nine. And then the number after nine, notice, is 10. That's eight cut low. And then you go down

925
1:12:38 --> 1:12:43
to the bottom and that's 83. And then it cycles back up to the top where the 84th number is the

926
1:12:43 --> 1:12:47
first number in this column, goes down to 96. And from there, you see the spiral. Now it goes to 97,

927
1:12:47 --> 1:12:54
which is the low cut on the next column, it goes to 833, et cetera. Okay. And notice how these

928
1:12:55 --> 1:12:59
three-quarter rep units keep going up all the way across. So that's the shift part of the Cypher.

929
1:12:59 --> 1:13:07
So let's go to the next slide, please. Is this one missing ID numbers with spiral or this one?

930
1:13:07 --> 1:13:13
Yep. Okay. Nope. Nope. Nope. There we go. Okay. So now I hope you kind of understand how this works.

931
1:13:13 --> 1:13:16
It's actually got a few details I'm not talking about again, because

932
1:13:18 --> 1:13:23
the details I'm giving you are complicated enough. I don't want to make it worse. So in

933
1:13:23 --> 1:13:27
this particular case, I'm able to do something because of my knowledge of the spiral that I

934
1:13:27 --> 1:13:34
should not be able to do. Okay. I can tell based on your ID number, what numbers have been deleted

935
1:13:34 --> 1:13:40
from the database and who they originally belonged to. Okay. Now, if I was somebody working for the

936
1:13:40 --> 1:13:44
county or the state, and I wanted to know that information, but I didn't know anything about

937
1:13:44 --> 1:13:49
the algorithm, it would be impossible to do. And if I tried to use the method that I'm using,

938
1:13:49 --> 1:13:53
again, without knowledge of the algorithm, it would also be impossible. And the reason is because the

939
1:13:53 --> 1:13:59
spiral shingles, metronome and tartan patterns are all intermingled. So I wouldn't be able to

940
1:14:00 --> 1:14:06
recreate the original sort order of the numbers that's required to find the missing CID numbers,

941
1:14:06 --> 1:14:12
because I have discovered that all of the missing records are cloned. Okay. So what I can do is

942
1:14:12 --> 1:14:23
this. So I go ahead and I work out the solution to a county's records, right? And I'm doing that

943
1:14:23 --> 1:14:27
using the method I showed you before. What that does is it gives me an opportunity to calculate

944
1:14:27 --> 1:14:31
what the numbers should be. And then I compare them to the numbers that are actually there.

945
1:14:31 --> 1:14:35
And that tells me when I'm missing numbers. Now, you could say, and you'd be right,

946
1:14:35 --> 1:14:39
well, you could find out which ones are missing as long as you know the range for that county,

947
1:14:39 --> 1:14:43
because all the numbers are assigned, right? Well, I would say to you, no, I don't know that all the

948
1:14:43 --> 1:14:47
numbers are assigned at that level. All I know is that there are those numbers are assigned to that

949
1:14:47 --> 1:14:51
county, not whether they were assigned. What tells me they were assigned is the ID number.

950
1:14:52 --> 1:14:57
So once I've got this arranged, what I've done is I've gone ahead and I've created these black bars,

951
1:14:57 --> 1:15:03
highlighted columns where I have calculated that there should be a state ID, but there isn't one.

952
1:15:03 --> 1:15:09
Okay. But I do notice that when I look at the actual state ID, the numbering continues

953
1:15:09 --> 1:15:14
in the way I predict based on my projections, right? So then what I do is I take a look at what

954
1:15:14 --> 1:15:20
the CID number was supposed to be. Okay. So in this case, there's no SBO ID number, but I see that

955
1:15:20 --> 1:15:25
the difference between the two CID numbers that bracket it is this number in the middle. So in

956
1:15:25 --> 1:15:32
this case, it's 1,086, it jumps to 1,088. So obviously it's missing 1,087. So I looked up

957
1:15:32 --> 1:15:38
the ID number 1,087 for that county and I find who is associated with that number. And at that

958
1:15:38 --> 1:15:43
point, the person on the other side can say, well, that's not a deleted number. What it is,

959
1:15:43 --> 1:15:47
is it's a number that's been assigned to a different person, just as illegal.

960
1:15:49 --> 1:15:55
But the thing is I also have the benefit of having seen multiple databases and I know for sure that

961
1:15:56 --> 1:16:01
these, what they've done here is they've taken another SBO ID number, actually the missing SBO

962
1:16:01 --> 1:16:07
ID number that is associated with this CID number and another SBO ID number. And they've gone ahead

963
1:16:07 --> 1:16:14
and deleted this SBO ID number leaving the other one. Okay. And that tells me that these are all

964
1:16:14 --> 1:16:19
clones. So this could be their way of covering up the former existence of clones. But the problem

965
1:16:19 --> 1:16:23
is once they do this, they're deleting the evidence of whatever crime might've been committed with

966
1:16:23 --> 1:16:28
that ID number. Now, of course, this is assuming crime was committed, but I think it's

967
1:16:28 --> 1:16:35
plausible at the very least. So for instance, if this missing record was used to vote illegally,

968
1:16:35 --> 1:16:39
okay. And then they delete the number, but they leave the rest of the record now associated with

969
1:16:39 --> 1:16:44
a different number. And that new record doesn't reflect the voter history that is now missing.

970
1:16:45 --> 1:16:48
Then you would have lost all your evidence. Yes. Did you want to say something?

971
1:16:49 --> 1:16:50
No, keep going.

972
1:16:50 --> 1:16:55
Okay. I'll just say that was just me. All right. So this is kind of interesting that I can use my

973
1:16:55 --> 1:17:02
knowledge of the algorithm to learn something about people in the database that should be

974
1:17:02 --> 1:17:06
unknowable based on their number alone. Okay. So let's go to the next slide, please.

975
1:17:10 --> 1:17:19
Okay. So this is what the AID looks like and how it's organized. I really want to explain all this.

976
1:17:19 --> 1:17:25
Okay. So if you look at the AID number, they have all these like critical points that are important.

977
1:17:25 --> 1:17:30
Right? So you've got your number one, number eight, and 75. Each one of these corresponds to

978
1:17:30 --> 1:17:37
a distance to the minimum SBO ID number. That's quite interesting. So the first AID is one unit

979
1:17:37 --> 1:17:42
away from the minimum number. And then you've got the eighth one is nine away and 10 is 11,

980
1:17:42 --> 1:17:47
et cetera, et cetera. And actually I could see a mistake here. The 75 is actually 83 apart, not 82.

981
1:17:48 --> 1:17:52
There was a missing record when I made this. So I wasn't aware of that. But anyway, the point is

982
1:17:53 --> 1:18:00
that the AID is hard coded into the algorithm. The spiral is proven to exist. There is absolutely

983
1:18:00 --> 1:18:09
no chance whatsoever that this could accidentally find its way into the state voter rolls for 59

984
1:18:09 --> 1:18:15
counties. And by the way, I say 59, not 62 because three counties use a different algorithm, which

985
1:18:15 --> 1:18:23
we'll get to in a little bit. But the point is here that these can be used for tracking data.

986
1:18:25 --> 1:18:32
Is that AID is effectively a third ID number. And that ID number is not going to be known to

987
1:18:32 --> 1:18:36
anybody at the county or the state level who doesn't know anything about the algorithm. And

988
1:18:36 --> 1:18:41
everyone I've talked to at those levels has no idea what the algorithm is even there. And one guy

989
1:18:41 --> 1:18:47
actually has expressed skepticism to which I just replied, look, you'll find it. And by the way,

990
1:18:47 --> 1:18:51
just in case you guys are curious, I shared my findings with other researchers, at least two of

991
1:18:51 --> 1:18:56
whom have independently verified this with their own data. So I asked them, I said, look, I'm not

992
1:18:56 --> 1:19:03
going to send you the version of the voter rolls that was obtained for my research. You get your own

993
1:19:03 --> 1:19:08
copy. Okay. So a lady in North Carolina or South Carolina, I forget which, North or South, it was

994
1:19:08 --> 1:19:13
one of those. She requested the voter rolls directly from the state and got them and was able

995
1:19:13 --> 1:19:20
to reproduce exactly my findings in Schenectady County. And another researcher in Florida did the

996
1:19:20 --> 1:19:24
same thing. So we're talking multiple different versions of the voter rolls all show this.

997
1:19:26 --> 1:19:33
So it's there. Now, the thing is, this does not tag each record with the fact that it's cloned. I

998
1:19:33 --> 1:19:40
have been completely unable to find a clear and convincing link between having a specific number

999
1:19:40 --> 1:19:47
and being a counterfeit record. However, it does provide a very clear and convincing new ID number

1000
1:19:47 --> 1:19:53
and that ID number can be used to access all the records. So what better way to track records,

1001
1:19:53 --> 1:20:00
to do it completely covertly than by giving all of the records, good and bad, a new ID number that

1002
1:20:00 --> 1:20:04
nobody knows is there. Okay. So then what you can do is you can have your unauthorized copy of the

1003
1:20:04 --> 1:20:11
database and you can go ahead and use these new ID numbers to track everything. And that way,

1004
1:20:11 --> 1:20:15
you know, which are good and bad and the information, which is good and bad is never present on the

1005
1:20:15 --> 1:20:26
publicly available system. Google is actually done. Is that I can't prove that for sure unless I have

1006
1:20:26 --> 1:20:30
access to that other database, presuming it exists. And if you're all meeting my defense

1007
1:20:30 --> 1:20:37
guys, you probably have a couple of times on that. So if you would please go to the next slide.

1008
1:20:41 --> 1:20:47
Okay. So by the way, pardon the frequent sips of water. I went swimming every time my body

1009
1:20:47 --> 1:20:53
is inundated with chlorine. I need to drink a lot of water. Okay. So now what we're looking at is

1010
1:20:53 --> 1:20:59
one of the side effects of the shingle algorithm. The shingle algorithm is, I would say, probably

1011
1:20:59 --> 1:21:06
the most suspicious of the four algorithms. Now, if you look at the image or the scatter plot on

1012
1:21:06 --> 1:21:14
the left, you're looking at the out of range active record. Okay. The SPO ID number is on the

1013
1:21:14 --> 1:21:19
Y axis. The CID number is on the X axis. This is how to normally do it. So if I don't say anything,

1014
1:21:19 --> 1:21:24
just assume that's the case. So what you should see here is when you look at the active records,

1015
1:21:24 --> 1:21:28
all of the numbers are vertically aligned. If you look at the next image on the right,

1016
1:21:29 --> 1:21:35
and this is just purged records, and this looks like almost 2.5 million records versus almost

1017
1:21:35 --> 1:21:42
6 million on the other side, this is only purged records. So what you see here are the same columnar

1018
1:21:42 --> 1:21:48
structure that you see in the active records, but you also see a bunch of horizontal lines.

1019
1:21:48 --> 1:21:54
Those horizontal lines are the shingle algorithm. Okay. Now, this is a very poor representation of

1020
1:21:54 --> 1:21:59
it because we're looking at, as I said, about 2.5 million records represented by, what, a couple

1021
1:21:59 --> 1:22:04
hundred dots. I'll show you the structure in another slide in a minute. But what's going on

1022
1:22:04 --> 1:22:10
is that this particular algorithm creates very specific shapes that I can use to extract the

1023
1:22:10 --> 1:22:16
numbers so I know that they belong to the shingle as opposed to the tartan, which is these more

1024
1:22:16 --> 1:22:23
vertical lines. Okay. But the point I wanted to make with this particular slide is that the

1025
1:22:25 --> 1:22:32
numbers tell me that they're purged. Okay. So all of these horizontal records are 100% purged,

1026
1:22:33 --> 1:22:41
and they are also 100% of the shingle algorithm, which means that whoever assigned those numbers

1027
1:22:41 --> 1:22:46
using the shingle algorithm knew they were purged at the time they created the numbers.

1028
1:22:46 --> 1:22:50
Why would you do that? Because a purged record is ineligible to vote and must be removed from the

1029
1:22:50 --> 1:22:58
database after it's been there for two years, and all these records are quite old. There's no legal

1030
1:22:58 --> 1:23:02
reason or legitimate reason that I've been able to come up with for doing this. However, there is

1031
1:23:02 --> 1:23:07
a spectacular reason for doing this if you have nefarious intent, and that is that these become

1032
1:23:07 --> 1:23:14
basically a shelf of usable registration numbers for when you want them. And then what you can do

1033
1:23:14 --> 1:23:19
is you can go ahead and make them active, use them to vote, and then go ahead and deactivate

1034
1:23:19 --> 1:23:24
them after the election certified, and then they go back to looking like they're not doing anything,

1035
1:23:24 --> 1:23:31
but in fact they actually have been used. And coincidentally, a lot of these, and I say a lot

1036
1:23:31 --> 1:23:36
because I don't remember the exact number, but it's significant, are also numbers that have no

1037
1:23:36 --> 1:23:41
purge date. It's significant enough that I would say there's a causal relationship there somehow.

1038
1:23:41 --> 1:23:45
So this is kind of interesting. So let's look at what the structure of the shingle is. So let's

1039
1:23:45 --> 1:23:54
go to the next slide please. Okay, great. So on the left we're seeing what a normal sequential

1040
1:23:54 --> 1:23:58
assignment of numbers would look like, right? So if the numbers on the x-axis are CID numbers,

1041
1:23:58 --> 1:24:03
and then the vertical axis are SPID numbers, and they're assigned on a first come first serve basis,

1042
1:24:03 --> 1:24:08
you're going to get a graph that looks just like this. It's going to be a 45 degree angle ascending

1043
1:24:08 --> 1:24:15
line, okay? This is because the numbers are going up in both directions, right? So you get a request

1044
1:24:15 --> 1:24:20
for a voter registration number, and you get the next available number. That's the line you get.

1045
1:24:20 --> 1:24:22
Now you might have some gaps in the line, because let's just say you're

1046
1:24:24 --> 1:24:31
Stoke-Harris County, and Brooklyn or Kings County goes ahead and sends in a bunch of new registrations

1047
1:24:31 --> 1:24:35
in the middle of them dealing with your request. What's going to happen is if you look at only

1048
1:24:35 --> 1:24:40
Stoke-Harris records, you're going to see a gap, because those numbers would have been assigned to

1049
1:24:40 --> 1:24:43
Kings County, and then you go back to your numbers. But it doesn't really matter that there's a gap.

1050
1:24:43 --> 1:24:47
What matters is that they are still going to continue to ascend. They're never going to go

1051
1:24:47 --> 1:24:53
backwards in time, okay? But the actual shingle algorithm does what we see in these upper two

1052
1:24:53 --> 1:24:59
images on the right. It creates this zigzag pattern. Now the interesting thing, and then it just keeps

1053
1:24:59 --> 1:25:03
on overlapping itself. Now the thing that's interesting about this is that the numbers go

1054
1:25:03 --> 1:25:10
up and down, okay? So if you look at the first number, right? That's already higher

1055
1:25:11 --> 1:25:16
than later numbers in the sense that you see this number one, and you see 11, and it's a 21.

1056
1:25:18 --> 1:25:26
The 11 has a lower SBOID number than the one, okay? So as far as the county is concerned,

1057
1:25:26 --> 1:25:30
they're actually counting backwards here, and they do this over and over again with large

1058
1:25:30 --> 1:25:36
volumes of numbers. And if you look at the blue chart on the bottom right, okay? What you see is

1059
1:25:36 --> 1:25:42
what the shingle algorithm looks like and the Torton algorithm together. So these sort of

1060
1:25:42 --> 1:25:50
scattered numbers you see on the left, they're very sparsely arranged, and then the kind of block

1061
1:25:51 --> 1:25:55
of a column on the right, those are actually part of the same pattern. It's just that the pattern

1062
1:25:55 --> 1:26:00
on the left is less well developed. But in between, you have the shingle. And if you zoom in on the

1063
1:26:00 --> 1:26:05
shingle, it looks like these zigzag patterns that I just showed you. But the pattern made by the

1064
1:26:05 --> 1:26:13
Torton is more like a bunch of arcs. It's defined by three to five dots. So we have here two different

1065
1:26:13 --> 1:26:19
algorithms that were, by the way, assigned at the same time, okay? So that's what the shingle looks

1066
1:26:19 --> 1:26:25
like. So let's go to the next one. This is a metronome. Okay, so the way the metronome works

1067
1:26:25 --> 1:26:32
is it assigns the first number at one extreme of the CID range and then shoots all the way over

1068
1:26:32 --> 1:26:37
to the other extreme of the CID range and then keeps going back and forth, dropping numbers as

1069
1:26:37 --> 1:26:43
it goes in a semi-random pattern. Now, by the time it's done, what you get is a scatter plot like

1070
1:26:43 --> 1:26:50
this one you see on the right, which is almost all black, okay? It appears to be the equivalent of

1071
1:26:50 --> 1:26:57
a 2D computer graphics flood fill program. So this is, by the way, the first time in my life

1072
1:26:57 --> 1:27:00
I ever saw a scatter plot that looked like a flood fill, but that's exactly what this does.

1073
1:27:02 --> 1:27:06
And the interesting thing about the metronome is that it's assigned to counting 15, 30, and 60. So

1074
1:27:06 --> 1:27:14
that's 15 times 2 is 30 times 2 is 60. I don't know what the significance of that is, but based

1075
1:27:14 --> 1:27:20
on everything else I found, I do believe it is significant somehow. But the point again in this

1076
1:27:20 --> 1:27:30
case is that we have multiple algorithms operating in the same space, ostensibly for the same

1077
1:27:30 --> 1:27:36
purposes and also assigned at approximately the same time. So let's go to the next slide, please.

1078
1:27:38 --> 1:27:43
Okay, so this is the Tartan algorithm. Now, you might notice this is red and blue. The red dots

1079
1:27:43 --> 1:27:47
are the suspicious records and the blue dots are the records that, as far as we could tell,

1080
1:27:47 --> 1:27:55
were good. So this should tell you there are a lot of suspicious records. And in fact, the largest

1081
1:27:55 --> 1:28:01
number of cloned records are found in the Tartan region. So this is where you're going to find a

1082
1:28:01 --> 1:28:07
person who's got 18 records assigned on the same day with different ID numbers. So the fact that

1083
1:28:07 --> 1:28:13
these algorithms are present in the roles is a concern to me and I think should be a concern to

1084
1:28:13 --> 1:28:18
any authority who are responsible for maintaining these databases. I mean, clearly on the basis of

1085
1:28:18 --> 1:28:24
the presence of the cloned or counterfeit records and the presence of the forged signatures, or if

1086
1:28:24 --> 1:28:28
you want to put it another way, mechanically duplicated signatures, that all by itself

1087
1:28:28 --> 1:28:34
recommends a pretty serious investigation. But the algorithms are a different order of problem

1088
1:28:34 --> 1:28:42
because the algorithms allow external manipulation of the records themselves in a way that is almost

1089
1:28:42 --> 1:28:48
impossible to discover. And actually, I would say that without access to somebody else's computer

1090
1:28:48 --> 1:28:54
who's responsible for this, it would be undiscoverable what they're doing. All I can see

1091
1:28:54 --> 1:28:59
by deciphering the algorithms is I can see that something is happening. And I can kind of get an

1092
1:28:59 --> 1:29:04
idea of what's going on and what it's related to, but to say exactly what they've done is really

1093
1:29:05 --> 1:29:10
hard to do. So let's go to the next slide, please. Next slide.

1094
1:29:14 --> 1:29:19
Are you still there, whoever you are? Yes, I'm still here. Can you see that? I'm showing.

1095
1:29:20 --> 1:29:26
Can you advance? Can you advance the slide, please? Well, I'm advancing. Why were these

1096
1:29:26 --> 1:29:33
algorithms used is what I've got on the screen now. Okay, I should see on my screen, I'm seeing the

1097
1:29:33 --> 1:29:41
tartan. I should see other states. Good. Now other states is on the screen. Yeah, there we go.

1098
1:29:41 --> 1:29:48
Okay, so in Hawaii, what they do, they have a 32 digit UUID, which is essentially unbreakable. A

1099
1:29:48 --> 1:29:53
friend of mine in Pennsylvania discovered that they had tagged about 10% of their records,

1100
1:29:53 --> 1:29:58
interesting number, with a 12 digit algebraic sequence. So what they did is they took the last

1101
1:29:58 --> 1:30:03
12 digits of a little over 100,000 records. There's only a million registered voters in Hawaii.

1102
1:30:04 --> 1:30:10
And they replaced those 12 digits with the same 12 digit sequence on each of those records. So

1103
1:30:10 --> 1:30:14
what happens is if you're somebody who's working at the county clerk's office in Hawaii,

1104
1:30:15 --> 1:30:18
and you run across one of these numbers, it's going to look like all the rest of the numbers.

1105
1:30:18 --> 1:30:24
It looks completely consistent. You would have to have maybe 100 or 200 of these numbers right in

1106
1:30:24 --> 1:30:28
front of you. And you'd have to be scrutinizing those numbers very carefully in order to see

1107
1:30:29 --> 1:30:33
what they had done. It's easier to see when you put the numbers really close to each other, but

1108
1:30:33 --> 1:30:37
that's the thing is that what they did counts on the fact that they're going to be separated

1109
1:30:37 --> 1:30:41
because they're in different counties and they've got different last names and so on. So it's very

1110
1:30:41 --> 1:30:47
unlikely that anyone working their database is actually going to see this 12 digit tag that they

1111
1:30:47 --> 1:30:53
added to the end of those records. Now in Hawaii, they didn't give as much information in their

1112
1:30:53 --> 1:30:58
voter rolls as I have in New York. So I was unable to ascertain whether they were clones or not,

1113
1:30:58 --> 1:31:02
because they didn't have birth date information. But those records are suspicious and worthy of

1114
1:31:02 --> 1:31:08
investigation because that tag can be used literally to tell the difference between that

1115
1:31:08 --> 1:31:13
and good records. In New Jersey, what they did, they actually did use the cipher algorithm.

1116
1:31:14 --> 1:31:20
And this one I thought was more devious than the one in New York, but I figured it out a little

1117
1:31:20 --> 1:31:26
faster. But what they did is they have an 11 digit number and they broke it into three components

1118
1:31:26 --> 1:31:30
where they took the first component and put it in the middle and they took the second component

1119
1:31:30 --> 1:31:37
and put it at the end. And then they converted the middle component into a hex value and then

1120
1:31:38 --> 1:31:42
they turned the entire number into a hex value and then that's how you got the

1121
1:31:43 --> 1:31:49
number that it really was. So that was very complicated and I think I actually misstated

1122
1:31:49 --> 1:31:54
part of that. But that's the idea is that they're scrambling the position of the numbers and

1123
1:31:54 --> 1:32:01
they're using hexadecimal values in place of what they actually had. And then Ohio and North

1124
1:32:01 --> 1:32:06
Carolina, what you're looking at right now is a scatter plot of Mahoning County. Actually,

1125
1:32:06 --> 1:32:13
it's a partial scatter plot that shows a pattern that is very similar to the Tartan in some respect.

1126
1:32:14 --> 1:32:21
And North Carolina has something else that's also similar to New York's Tartan pattern. But

1127
1:32:21 --> 1:32:25
interestingly enough, in other counties in Ohio, they don't use this algorithm. They have

1128
1:32:25 --> 1:32:29
something else that looks exactly like that normally ascending line that I showed you earlier.

1129
1:32:30 --> 1:32:34
But this particular county is one of their larger counties where it would be easier to

1130
1:32:35 --> 1:32:39
fraud because you have that many more voters that you could mix the take those in with.

1131
1:32:40 --> 1:32:41
So let's go to the next slide, please.

1132
1:32:44 --> 1:32:47
Yep. Why were these algorithms used?

1133
1:32:47 --> 1:32:53
Next slide. Yeah, yeah, I'm not seeing it on the screen, but that's the slide I want.

1134
1:32:54 --> 1:32:57
That's up there. I presume others can see that.

1135
1:32:59 --> 1:33:00
I see other states.

1136
1:33:01 --> 1:33:06
I've moved from other states. There was other states and now come to why were these used.

1137
1:33:06 --> 1:33:08
Yeah, that's true.

1138
1:33:08 --> 1:33:08
Can you click?

1139
1:33:11 --> 1:33:14
Who's right? Am I? Are you? There you go. Now I see it. Okay.

1140
1:33:14 --> 1:33:19
I think it's your bandwidth problem, Andrew.

1141
1:33:21 --> 1:33:26
Oh, okay. So the thing is, is that there are no privacy concerns whatsoever. Now, normally when

1142
1:33:26 --> 1:33:34
you mask numbers or you encrypt them, you do it because for security or privacy. And in both cases,

1143
1:33:34 --> 1:33:37
what you're doing is you're either changing the numbers to something else. Like I've got a friend

1144
1:33:37 --> 1:33:44
who works for an insurance company and every time they send data out, they mask the ID numbers. And

1145
1:33:44 --> 1:33:47
he calls it masking, but based on what he said, they're actually encrypting them. So they're

1146
1:33:47 --> 1:33:53
turning them to a different number. But in this case, we're looking at public records that by law

1147
1:33:53 --> 1:33:59
must be made available to the public for examination and review. So there's no privacy concern. Also,

1148
1:33:59 --> 1:34:04
the type of manipulation that I'm seeing doesn't change any of the data at all. Therefore, there's

1149
1:34:04 --> 1:34:10
no security impact. So that is to say that the ID number that I see in the role associated with

1150
1:34:10 --> 1:34:17
any given person is the ID number that's associated with that person. So they don't actually gain

1151
1:34:17 --> 1:34:22
anything security wise by doing this. And the same thing goes for privacy. And it doesn't improve

1152
1:34:23 --> 1:34:27
efficiency. And the reason it doesn't, I'm thinking of the spiral right now, is because

1153
1:34:28 --> 1:34:38
the specific nature of the algorithm has a concentration of effect in the, you know,

1154
1:34:38 --> 1:34:43
less than 10% of the records. So if you want efficient search, usually what you're going to

1155
1:34:43 --> 1:34:47
do is you're going to divide the records in half and then divide those in half. So basically,

1156
1:34:47 --> 1:34:53
you make a decision tree where is it inside A or B. If A, then divide again. And then when you divide

1157
1:34:53 --> 1:34:56
that, it's like, is it in the A side or the B side? And you keep on going until you find it.

1158
1:34:56 --> 1:35:00
But when you divide it this way, where you're breaking it up based on powers of 10,

1159
1:35:01 --> 1:35:06
you're going to have to divide many more times to find the record you want because you're dividing

1160
1:35:06 --> 1:35:13
10 to 90 or 1 to 90 or 1 to 900 or something like that. So you're actually retarding the

1161
1:35:13 --> 1:35:19
speed of use. And on top of that, you're destroying normal relationships that might help people who

1162
1:35:19 --> 1:35:23
are using the database understand what they're looking at. So for instance, a very simple example

1163
1:35:23 --> 1:35:31
is if the registration date corresponds somehow to the number, people could look at a number and

1164
1:35:31 --> 1:35:35
say, oh yeah, that number is from the range of numbers that were assigned in the 1960s,

1165
1:35:35 --> 1:35:40
the 1980s. And they would be able to derive knowledge of the records just by looking at

1166
1:35:40 --> 1:35:45
the number. They could actually answer questions and save them time searching. So what they've

1167
1:35:45 --> 1:35:48
done by introducing these algorithms, it doesn't help the privacy, it doesn't help security,

1168
1:35:48 --> 1:35:52
and it makes it less efficient. So none of these things are good. And on top of that,

1169
1:35:52 --> 1:35:57
it actually adds data because the algorithm creates another ID. So this is another thing to

1170
1:35:57 --> 1:36:01
keep track of, multiplied by 21 million records, which is also a very bad thing.

1171
1:36:02 --> 1:36:08
So I'm wondering why were they used? Now you may think, well, it's pretty obvious,

1172
1:36:08 --> 1:36:12
they're using it for fraud. Well, I like to say it this way. I like to say, I don't know why they're

1173
1:36:12 --> 1:36:16
using it. I know it certainly can be used for fraud, but I haven't been able to prove that they

1174
1:36:16 --> 1:36:23
are using it for fraud. Nevertheless, I think there is very, very strong evidence here to justify,

1175
1:36:23 --> 1:36:29
at the very least of position, it's probable that these were invented and implemented for the

1176
1:36:30 --> 1:36:35
purpose of fraud. But at the very least that an investigation should take place to find out,

1177
1:36:35 --> 1:36:41
for sure, one way or the other. I do think it is completely fair to say that it is not reasonable

1178
1:36:41 --> 1:36:47
to assume that they're legitimate. Okay, so let's go to the last slide, which is just me asking if

1179
1:36:47 --> 1:36:51
there are any questions. And that's great. I'll stop the sharing now, Andrew.

1180
1:36:52 --> 1:36:57
That's fine. I'll put on my camera. Oh, my camera's already on. There you are.

1181
1:36:57 --> 1:37:03
It looks like it's not pointing at me. Wow, it's all stretched or something. All right. Well,

1182
1:37:03 --> 1:37:08
let's answer the questions then. So I'm available for that.

1183
1:37:08 --> 1:37:18
Very good. Well, traditionally, Steven asks questions. And Steven, show us your screen.

1184
1:37:18 --> 1:37:22
Steven, show us your screen. Yeah, sorry. Yeah, I'm here.

1185
1:37:22 --> 1:37:27
Show us your face. Yeah, sure. Yes.

1186
1:37:30 --> 1:37:33
Hello, Andrew. Hello.

1187
1:37:34 --> 1:37:41
Thank you for speaking to us. So first thing, I was very interested, well, I was interested to see

1188
1:37:41 --> 1:37:45
that you got your PhD, at least at King's College London. Is that right?

1189
1:37:45 --> 1:37:52
Yes. So did you? Can you hear me? Yes.

1190
1:37:52 --> 1:37:59
Your first degree was in what, Andrew? Oh, I don't have one. I'm one of those

1191
1:37:59 --> 1:38:04
special people who just skipped that. I left High School when I was 14.

1192
1:38:05 --> 1:38:10
Oh, my PhD is in education. I studied the development of expertise in

1193
1:38:10 --> 1:38:17
CG art. And the primary reason was I wanted to contest the presumption that a certain

1194
1:38:17 --> 1:38:22
amount of time is required to develop experience or expertise rather, because I had observed

1195
1:38:23 --> 1:38:27
many people at a young age or with very little experience becoming experts rather

1196
1:38:27 --> 1:38:34
rapidly. So I wanted to study how that process occurs. And I found that it happens when people

1197
1:38:34 --> 1:38:39
encounter keynotes and then understand them. And that can happen basically at any age with any

1198
1:38:39 --> 1:38:43
level of experience. When they encounter what did you say, Andrew?

1199
1:38:45 --> 1:38:50
I'm sorry, what did you say? When they encounter what? I didn't catch, was it keynotes you said?

1200
1:38:52 --> 1:39:01
A keynode, N-O-D-E. So for instance, in computer graphics, a keynode is understanding topological

1201
1:39:01 --> 1:39:09
relationships between geometric elements. Many CG artists do not ever learn how to do that.

1202
1:39:09 --> 1:39:13
But the ones who do develop this topological awareness is what I call it,

1203
1:39:14 --> 1:39:17
are the ones who tend to excel and become experts.

1204
1:39:18 --> 1:39:24
Let's see. So as much of an interest, how did you come into contact with King's College London?

1205
1:39:25 --> 1:39:26
You were born in America, were you?

1206
1:39:26 --> 1:39:36
Well, yeah, I was teaching at a school in the Netherlands, the Enhanteve,

1207
1:39:36 --> 1:39:43
which was a university of tourism. But then they created a game development program and hired me

1208
1:39:43 --> 1:39:50
to be their first director of the visual arts program. So I created the visual arts program

1209
1:39:50 --> 1:40:00
there. Now at the time, I had gone to college in the United States and I was studying at art school,

1210
1:40:01 --> 1:40:05
but without a degree. And that's just because I started getting hired and I was working.

1211
1:40:06 --> 1:40:12
When I was in the Netherlands, they said that was a problem. I needed to have a degree. So

1212
1:40:13 --> 1:40:19
I wound up writing a couple of books on computer graphics, which they used as my thesis to grant

1213
1:40:19 --> 1:40:28
me a degree. But then they wanted to get hired. So no, no, this was the school I was teaching at.

1214
1:40:29 --> 1:40:33
This is the school I was teaching at. So then they wanted me to get a higher degree

1215
1:40:33 --> 1:40:40
and I started applying for programs in the Netherlands. And I was hoping to go directly

1216
1:40:40 --> 1:40:47
into a PhD program, skipping a master's because by that time I had years of professional experience.

1217
1:40:47 --> 1:40:50
I'd written a couple of books on computer graphics and I was considered an expert by people in the

1218
1:40:50 --> 1:40:57
field. And eventually I discovered that none of the Dutch schools wanted to accept somebody who

1219
1:40:57 --> 1:41:04
spoke English and not Dutch. So I decided to try English colleges instead and I got accepted at

1220
1:41:04 --> 1:41:12
several. It was King's, Northampton and I believe it was Essex. And which was the last one?

1221
1:41:14 --> 1:41:19
I think it was Essex. I remember they were like in the southwest of the UK. I forget exactly where.

1222
1:41:19 --> 1:41:21
I see. Okay.

1223
1:41:24 --> 1:41:30
But King's was my preference and I'd already received acceptances from the other two. So when

1224
1:41:30 --> 1:41:35
I went for my interview at King's, I told them I have to answer the other guys tomorrow. So if you

1225
1:41:35 --> 1:41:40
make me an offer, I'll accept it because you're my preference. But if I don't hear from you soon,

1226
1:41:40 --> 1:41:45
I'm going to have to go with one of the other ones. So they accepted the proposal and I went

1227
1:41:45 --> 1:41:50
from there. But that was a lot of work. It was much more than I expected. I assumed that because

1228
1:41:51 --> 1:41:55
I was already an expert in the field and had written a couple of books that it would be easy.

1229
1:41:55 --> 1:41:59
But it turns out a research degree at King's College is something they don't play games with.

1230
1:42:00 --> 1:42:02
Is it something about the end?

1231
1:42:08 --> 1:42:12
It's not something they play games with at King's College.

1232
1:42:13 --> 1:42:14
I see.

1233
1:42:17 --> 1:42:17
Sorry.

1234
1:42:17 --> 1:42:23
I looked at some pieces written by other people at other colleges and I realized when I read them

1235
1:42:23 --> 1:42:28
that they would have not been passed at King's College. They would have failed.

1236
1:42:29 --> 1:42:29
No.

1237
1:42:29 --> 1:42:31
In fact, actually I would have failed them.

1238
1:42:34 --> 1:42:36
You know what? I'm having a very hard time hearing you.

1239
1:42:37 --> 1:42:40
Sorry. Hearing me at all or?

1240
1:42:40 --> 1:42:46
I'm hearing you but there's considerable loss of audio. So it's like fractions of your words.

1241
1:42:47 --> 1:42:53
I'm sorry. So anyway, with regard to that, Andrew, I'll try and speak clearly. Does that help?

1242
1:42:56 --> 1:42:57
So far, yes. Continue.

1243
1:42:58 --> 1:43:02
So those three places you mentioned, the three universities you mentioned,

1244
1:43:02 --> 1:43:08
the three universities you mentioned, King's College London is world class. The other two

1245
1:43:08 --> 1:43:14
aren't. So well, Essex is known for one or two things and Northampton may be full. But it's not,

1246
1:43:14 --> 1:43:18
you know, they're not in the same classes. King's College London. So that's what...

1247
1:43:18 --> 1:43:18
No, they're not.

1248
1:43:21 --> 1:43:25
Yeah. And the funny thing is I wasn't aware of the rankings of these schools when I applied.

1249
1:43:26 --> 1:43:31
And I didn't really care because as far as I was concerned, I was satisfying a requirement for my

1250
1:43:31 --> 1:43:38
job because my boss told me that due to a new law passed in or regulation passed by the Ministry

1251
1:43:38 --> 1:43:45
of Education in the Netherlands, I actually had to get a PhD to retain tenure. So I had tenure there.

1252
1:43:46 --> 1:43:52
And so I was going to go with whatever school accepted me. But then my boss told me, you know,

1253
1:43:52 --> 1:43:56
King's is actually better than those other schools. So you should go to them. So that's what I did.

1254
1:43:57 --> 1:44:02
Excellent. And fair play to King's for taking you when you hadn't got a primary degree. They took a

1255
1:44:02 --> 1:44:07
chance in their, you know, in their world, they'd be taking a chance, but they backed the right horse,

1256
1:44:07 --> 1:44:14
it seems. So what you did in your PhD at King's, when do you think... Could you have done... You

1257
1:44:14 --> 1:44:20
talked about very young people being very skilled at the kind of things that you're skilled in.

1258
1:44:22 --> 1:44:26
You know, when you look back to that, could you have done that when you were much younger

1259
1:44:26 --> 1:44:29
or I don't know how old you were at King's College, but I'm just interested.

1260
1:44:31 --> 1:44:40
I think I started at King's when I was in my late 40s. I'm 59 now. I might have even been 50.

1261
1:44:40 --> 1:44:46
But in any event, yeah, I think I could have done it when I was younger. And that's the thing is

1262
1:44:46 --> 1:44:51
that these key nodes, you have to be introduced to them. And this is something I'd seen in my

1263
1:44:51 --> 1:44:57
own career, where I've actually had several careers, and I've learned multiple disciplines,

1264
1:44:57 --> 1:45:05
and I've discovered that the way to quickly achieve my goal is to first find someone who knows the

1265
1:45:05 --> 1:45:11
answers to the questions that I have to have answered. So I look for a mentor, and then I

1266
1:45:11 --> 1:45:18
start asking questions and doing work and getting feedback until I figure it out. And then once I

1267
1:45:18 --> 1:45:23
have that, then I'm essentially an expert from that point forward. So when I got into comic books,

1268
1:45:23 --> 1:45:28
for instance, the key node was that comic books is not about drawing. Comic books is about

1269
1:45:28 --> 1:45:35
storytelling and knowing what to draw on the page. So I happened to run into a comic book writer in

1270
1:45:35 --> 1:45:40
Hollywood who told me his frustration with artists who think it's all about drawing and how it's all

1271
1:45:40 --> 1:45:44
about figuring out the camera angles and what to put in front of the camera. And as soon as I got

1272
1:45:44 --> 1:45:50
that figured out, I started working in the business. It's like that for pretty much everything.

1273
1:45:52 --> 1:45:59
Interesting. Yeah. So, and then you met Jerome Corsi or Dr. Corsi a couple of weeks ago,

1274
1:45:59 --> 1:46:03
did you? And that's when your problems began, as far as being censored was concerned or not.

1275
1:46:05 --> 1:46:11
Well, as far as my bandwidth is concerned, yes. I had never had low bandwidth problems. And then

1276
1:46:11 --> 1:46:17
all of a sudden, right around the time I met Jerome, now, of course, I'm not sure when it

1277
1:46:17 --> 1:46:23
happened because there was a gap of some months between the conversation with him on Zoom and

1278
1:46:24 --> 1:46:29
prior conversations on Zoom with other people. But I had never had a low bandwidth problem

1279
1:46:29 --> 1:46:34
until the conversations with him. And ever since then, every conversation I've had with anybody

1280
1:46:34 --> 1:46:39
has had. I'm sure, Stephen, it was monitored and they're suppressing him. They don't want,

1281
1:46:40 --> 1:46:45
they don't want what Andrew knows out and they're probably not happy that I'm working with them.

1282
1:46:46 --> 1:46:50
I understand that. Yeah. Well, that's what I was suggesting. But so, and that you only met each

1283
1:46:50 --> 1:46:54
other two weeks ago, is that right? It's a little bit more than that.

1284
1:46:56 --> 1:47:01
It's very, within the last month, in June, we met, we met in June, I can't remember the exact time,

1285
1:47:01 --> 1:47:13
but in June we met. And I got past Stephen's work, someone, the law firm in Akron, that Stephen,

1286
1:47:14 --> 1:47:22
this whole group, there's a group involved called United Sovereign Americans. And that's

1287
1:47:22 --> 1:47:29
the two women, a woman named Marley and a guy named Harry who runs that. And they brought Andrew in

1288
1:47:29 --> 1:47:34
and they had problems with Andrew. Marley is a particularly difficult person. It seems like to

1289
1:47:34 --> 1:47:42
me that Marley and Andrew did not get along. And Thomas Connors from the Mendelssohn firm sent me

1290
1:47:44 --> 1:47:49
Andrew's paper and he said, would you please figure out for me if this guy knows what he's

1291
1:47:49 --> 1:47:55
talking about? Well, I have a background, a mathematical background is very extensive and

1292
1:47:55 --> 1:48:02
I have a background in cryptography. And so I started into the paper that he wrote and published

1293
1:48:02 --> 1:48:10
in that journal. And I quickly understood what he was saying. And I really got into it very deeply.

1294
1:48:11 --> 1:48:14
And then I've called Andrew, we had a couple of conversations about it.

1295
1:48:15 --> 1:48:20
And Andrew demonstrated some of it for me. And I became absolutely convinced that he was right.

1296
1:48:21 --> 1:48:29
And so I communicated this to the Mendelssohn law firm. And actually, we've had kind of a conflict

1297
1:48:30 --> 1:48:36
with the United Sovereigns of America because they are going in a different direction.

1298
1:48:37 --> 1:48:43
And I decided with Andrew that I want to do the same thing I did with HSBC Bank. In other words,

1299
1:48:43 --> 1:48:50
I want to write a series of articles. First one was accepted by American thinker today.

1300
1:48:51 --> 1:48:55
And it will be published the next few days. And then we're going to write a series of articles

1301
1:48:55 --> 1:49:03
in which we present to the American people saying, why are there algorithms in the voter database?

1302
1:49:03 --> 1:49:11
And I believe one of my skills is being able to make an argument that is maybe difficult,

1303
1:49:12 --> 1:49:19
simple enough for the average person to understand. And so here, you know, the one of the

1304
1:49:19 --> 1:49:26
two or three lines I've been working on is that Andrew did a very good job today. I'm sure

1305
1:49:27 --> 1:49:30
having looked at the deck, I unfortunately couldn't hear the whole presentation. I'll

1306
1:49:30 --> 1:49:38
review it later. But I'm saying that there just should not be this number kind of sequence

1307
1:49:38 --> 1:49:45
imposed upon a database. And the number, the schemes involved here are National Security

1308
1:49:45 --> 1:49:52
Agency quality encryption schemes. As he says, they're not really specifically encryption schemes.

1309
1:49:52 --> 1:50:00
They're much more this denography. But you can't tell that they're there until you kind of an expert

1310
1:50:00 --> 1:50:04
way to detect them and figure it out. And Andrew figured it out. And when he figured it out, he must

1311
1:50:04 --> 1:50:09
have spent, he must have spent enormous time doing it. Probably spent a lot of sleepless nights,

1312
1:50:09 --> 1:50:13
got paid probably nothing to do it. But he conquered it and mastered it.

1313
1:50:16 --> 1:50:21
I figured I was and he got it, but he figured it out. And so then I'm saying to what next thing

1314
1:50:21 --> 1:50:29
I'm going to say to the American public is that and to world public, we have databases for credit

1315
1:50:29 --> 1:50:39
cards for banks, for mortgage companies that are remarkably validated databases and work so that if

1316
1:50:39 --> 1:50:46
you miss a month paying your credit card bill, you're going to get called and qualified to talk to.

1317
1:50:47 --> 1:50:51
If you miss it for a sequence of months, like six months, they'll send it to collection and

1318
1:50:51 --> 1:50:56
collection is going to hound you until you pay something. These are very effective systems.

1319
1:50:56 --> 1:51:02
But yet the voting systems in the United States, and I think worldwide, are intentionally

1320
1:51:03 --> 1:51:10
not validated, are intentionally full of errors, are intentionally full of records that are a mess

1321
1:51:12 --> 1:51:20
because they are all subject and they are designed for fraud. And the voting systems that we're

1322
1:51:20 --> 1:51:27
allowing to go are like mail-in ballots are inherently schemes that are subject to fraud.

1323
1:51:28 --> 1:51:35
And so therefore, we have created data systems for voting that are dysfunctional

1324
1:51:35 --> 1:51:38
because we don't intend to conduct fair elections.

1325
1:51:40 --> 1:51:46
But we're not going to say that this election was false or that election was false or that Trump won

1326
1:51:46 --> 1:51:52
2020 because then we're getting into problems of saying that I think the genius of what

1327
1:51:52 --> 1:51:59
Andrew found is that not only can you vote false votes, but you can certify them

1328
1:52:00 --> 1:52:06
so that a false vote requests a mail-in ballot. That mail-in ballot is printed with that number

1329
1:52:06 --> 1:52:12
and voted. When you look at it, that vote was requested and it voted so it's certified.

1330
1:52:12 --> 1:52:17
You don't know that it was false because you can't get that deeply into the record. All you know is

1331
1:52:18 --> 1:52:25
it matched. And what bothered me in 2020 was you could obviously see all the mail-in ballots

1332
1:52:25 --> 1:52:29
coming in and being counted in the middle of the night, but yet these elections were certified.

1333
1:52:29 --> 1:52:33
And I couldn't figure out how they were getting certified. And Andrew showed me they were getting

1334
1:52:33 --> 1:52:40
certified because the false records were identified in the deck where whoever was the card shark

1335
1:52:41 --> 1:52:46
knew where the marked cards were and could vote the marked cards in the number they needed at

1336
1:52:46 --> 1:52:52
any time they needed them. And so therefore it explained to me how mail-in voting fraud could be

1337
1:52:52 --> 1:52:59
done and certified. Okay, but again, we're going to just raise these questions because it's much

1338
1:52:59 --> 1:53:05
more powerful to say to the American people, like with HSBC, why are they running all these millions

1339
1:53:05 --> 1:53:08
of dollars through these accounts and closing them and that account had your social security

1340
1:53:08 --> 1:53:13
number and your name. You were formerly a customer of the bank. You closed your account. You didn't

1341
1:53:13 --> 1:53:20
know this. Why is that happening? Raising these questions is enough to make the people mad about

1342
1:53:20 --> 1:53:26
the system. And eventually some of the regulators have to pay attention because you've called into

1343
1:53:26 --> 1:53:30
question and undermine the integrity of the entire voting system throughout the United States and

1344
1:53:30 --> 1:53:39
maybe throughout the world. And that's my goal. And Andrew can validate it. And it's very similar.

1345
1:53:39 --> 1:53:45
John Cruz had the data from HSBC. He had the data. Andrew's got the data. And I'm going to

1346
1:53:45 --> 1:53:51
communicate the data with Andrew, like I did with John Cruz. And pretty soon we'll have regulators

1347
1:53:51 --> 1:53:57
at the Department of Homeland Security or others. I'm sure we already have them interested. And

1348
1:53:57 --> 1:54:02
they're already concerned about what we're going to do. So you're talking about, you've got new

1349
1:54:03 --> 1:54:09
evidence against HSBC or is this the evidence? No, I'm just using that as an allergy. I did it

1350
1:54:09 --> 1:54:15
then. I'm going to repeat that today. It's my MO, my modus operandi to get this communicated.

1351
1:54:15 --> 1:54:21
And when I found Andrew, I found the guy with whom I can do it. Just like when I found John Cruz,

1352
1:54:21 --> 1:54:26
I found the guy with whom I could do it. I knew they were laundering money. You can't run

1353
1:54:27 --> 1:54:33
a drug organization without a bank and the security agencies, the intelligence agencies,

1354
1:54:33 --> 1:54:39
and the finance treasuries run all the bank systems. And so they're in on it.

1355
1:54:39 --> 1:54:47
And I knew that I've known that studying into other materials going back into the opium trade

1356
1:54:48 --> 1:54:58
in the 1800s in Great Britain was running them in China. And HSBC was originally part of the opium

1357
1:54:58 --> 1:55:05
trades and they were doing it fraudulently then. So they were still involved. And I know that the

1358
1:55:05 --> 1:55:11
intelligence agencies are involved in the voting and they know this is going on. They don't want

1359
1:55:11 --> 1:55:18
it exposed. And again, rather than going public and saying, I can prove that, we're just going

1360
1:55:18 --> 1:55:23
to ask a lot of questions. Why would this be? Why would this be? Why could you use this to do this?

1361
1:55:24 --> 1:55:35
So how did the questions turn into HSBC being fined in the US in 2012? I think it was

1362
1:55:35 --> 1:55:41
$1.9 billion for money laundering, which you exposed together with Cruz.

1363
1:55:46 --> 1:55:50
And now, of course, they're asking their customers. They're saying that they have to

1364
1:55:50 --> 1:55:54
know what the customers are spending their money on. And I know this is happening.

1365
1:55:55 --> 1:56:02
The banks are the criminals, typically. The people who commit the kind of fraud that they are doing

1366
1:56:02 --> 1:56:09
with all these rules and regulations that bank officers, if somebody takes out more than $10,000

1367
1:56:09 --> 1:56:15
in cash, you've got to file a report. Those are just control measures. Those are just totalitarian

1368
1:56:15 --> 1:56:22
measures. Those aren't the people committing real money laundering in banks. The governments

1369
1:56:22 --> 1:56:27
are the criminals. The governments are doing the money laundering. So you begin to get to realize

1370
1:56:27 --> 1:56:33
that we're fighting in the war in Ukraine. The CIA was involved with the Bidens, the money laundering

1371
1:56:33 --> 1:56:38
in Ukraine. What we want in Ukraine is we want to preserve the corruption because they've got the

1372
1:56:38 --> 1:56:43
best money laundering scheme I've ever seen. They run things through Cyprus and they get rid of

1373
1:56:43 --> 1:56:48
trillions of dollars that disappear. And so you don't want to lose that asset.

1374
1:56:50 --> 1:56:54
So we fight the war. Keep that money laundering asset going for the intelligence agencies.

1375
1:56:55 --> 1:57:00
And so one of my themes in life is to expose to people just like Jack Kennedy being killed by

1376
1:57:01 --> 1:57:07
the deep state that we are living in a Truman show that the intelligence agencies are running

1377
1:57:07 --> 1:57:12
and they're evil. The voting is another example. So we're not going to hold.

1378
1:57:13 --> 1:57:17
The people who are going to win elections are the people that CIA want to win the elections.

1379
1:57:18 --> 1:57:23
Donald Trump was an exception and that squeaked by. They weren't prepared for that one. They

1380
1:57:23 --> 1:57:32
thought they had him beat. But the point was that we now have enough. Why do they pay $1.9 billion?

1381
1:57:32 --> 1:57:38
dollars. Even Aldous Huxley said this. I've written about this in the book on the

1382
1:57:38 --> 1:57:43
Anti-Globalist Alliance. These totalitarian schemes these governments build, and they're

1383
1:57:43 --> 1:57:52
very close to having this transhuman totalitarian system in place, can only be beaten when people

1384
1:57:52 --> 1:57:59
refuse to participate. That's the only tactic you can use to beat them. We're not sending our kids

1385
1:57:59 --> 1:58:07
to a public school about transgenderism. And so once the people say we're done with public schools,

1386
1:58:08 --> 1:58:16
public schools collapse. We're done with watching network news. We're done with

1387
1:58:16 --> 1:58:23
buying Budweiser beer. We're done with watching the BBC. We're done with buying Budweiser beer

1388
1:58:23 --> 1:58:28
of a transgender is going to be the guy who's the spokesperson. We're not going to do it anymore.

1389
1:58:29 --> 1:58:36
And we're done with going to a pediatric department in a hospital in the NHS in the UK,

1390
1:58:36 --> 1:58:42
which flies the gay flag in the welcoming area for the children.

1391
1:58:42 --> 1:58:49
That's right. And so then the governments have got to do something. And they blew the whistle on HB8.

1392
1:58:49 --> 1:58:55
Nobody went to jail, but they did pay the fine. And it's still going on. But it's another

1393
1:58:56 --> 1:58:59
brick in the wall to remove, to expose how they operate.

1394
1:59:00 --> 1:59:04
Now, I don't want to dominate the conversation because there's others with questions. I

1395
1:59:04 --> 1:59:07
want others to answer the questions. But this is-

1396
1:59:07 --> 1:59:14
Can I just ask Andrew, Jerome, what is the importance, Andrew, in your mind? What could

1397
1:59:14 --> 1:59:21
be the importance of what you've found? So I'm not saying what have you proved, because maybe

1398
1:59:21 --> 1:59:24
you haven't proved anything, but that doesn't mean to say that you're not correct.

1399
1:59:26 --> 1:59:28
I just wonder- Yeah, as far as I'm concerned,

1400
1:59:30 --> 1:59:36
what I can show and what I think is important is that the voter rolls are being manipulated

1401
1:59:36 --> 1:59:40
covertly in ways that even the people who are responsible for managing the voter rolls don't

1402
1:59:40 --> 1:59:48
understand. And that has to be problematic for voters for whom the voter rolls are meant to be

1403
1:59:48 --> 1:59:56
a safeguard against fraud. So what I am seeing here is a way to hide what's happening. So anytime

1404
1:59:56 --> 2:00:01
you have something that obfuscates, which should be transparency, you have what should be a

1405
2:00:02 --> 2:00:07
untrustworthy system. And it should be treated as such, basically discarded for that reason,

1406
2:00:07 --> 2:00:15
just as I discarded CNN when I discovered how biased they were. So if nothing else,

1407
2:00:15 --> 2:00:24
I would like to think that these findings lead to the discarding and possible regeneration of

1408
2:00:24 --> 2:00:28
more honest systems. Discarding what we have and rebuilding it.

1409
2:00:30 --> 2:00:39
Doesn't this point to a wider problem of super elites, if you like, who can be hired to

1410
2:00:39 --> 2:00:47
to manipulate systems on behalf of corrupt individuals who understand the potential of

1411
2:00:47 --> 2:00:54
the system? But the point is that you can't regulate them. So Andrew, the point is you can't

1412
2:00:54 --> 2:01:00
regulate them, it seems. Mere human beings can't regulate this stuff because it's so complicated.

1413
2:01:00 --> 2:01:06
So if you've got someone acting nefariously in a very complicated world, it's extremely difficult

1414
2:01:06 --> 2:01:14
to nail them down. Except by thinking in big, big steps, if you understand me. So

1415
2:01:14 --> 2:01:23
you're never going to get there trying to prove it on your own. But you could actually prove it

1416
2:01:23 --> 2:01:30
by default, if you like, by hypothesizing correctly. And Jerome is very good at that. And I'm kind of

1417
2:01:30 --> 2:01:35
reasonably good. So do you understand what I'm getting at?

1418
2:01:36 --> 2:01:45
Yeah, I think I do. The thing is, these computer systems are so complicated that anybody

1419
2:01:46 --> 2:01:53
who is nefarious, who gets involved, the potential for wrongdoing is so great that actually

1420
2:01:54 --> 2:01:57
nobody can detect it. So therefore we need to get rid of the damn computer systems.

1421
2:01:57 --> 2:02:03
So it's very convenient to have the computer systems. Yeah, exactly. So people need to understand.

1422
2:02:03 --> 2:02:10
Yeah, I agree completely. Well, the problem with computers is they multiply the possibilities of

1423
2:02:11 --> 2:02:16
fraud. You know, the funny thing about this, I'll just tell you an insight I had when I was something

1424
2:02:16 --> 2:02:21
like seven or eight years old and I first learned about election. I was thinking, given the number

1425
2:02:21 --> 2:02:26
of people in America, and given the number of people who have to manage those elections,

1426
2:02:26 --> 2:02:30
it is almost certain that there are tremendous opportunities to create fraud there.

1427
2:02:32 --> 2:02:38
And I have never thought that we should trust our elections just on that basis alone, because

1428
2:02:39 --> 2:02:47
as far as I could tell, the numbers alone argue against a trustworthy system, because the more

1429
2:02:47 --> 2:02:51
moving parts there are, the more opportunities there are to get in the cracks and gum it up somehow.

1430
2:02:52 --> 2:02:58
But also the wider potential for things that are very complicated, Andrew, which most people can't

1431
2:02:58 --> 2:03:05
really understand, you know, that gives potential for, as I was saying, multiplying the possibilities

1432
2:03:05 --> 2:03:10
for fraud. And for that reason, and for also, you know, if computers take over our lives,

1433
2:03:10 --> 2:03:18
like mobile phones and social media, so people, young people in particular, spending loads and

1434
2:03:18 --> 2:03:22
loads of time on the, and they're taking, their humanity has been taken away from them

1435
2:03:23 --> 2:03:29
by the thing that they actually love and are addicted to in the true sense of addiction.

1436
2:03:29 --> 2:03:34
And any other addiction would be regarded, you know, as dangerous, but not the mobile

1437
2:03:34 --> 2:03:40
phones and not the social media, and because everybody else is doing it, you know. And so

1438
2:03:40 --> 2:03:44
people need to draw the right conclusions, I think, and they're not thinking of the wider

1439
2:03:45 --> 2:03:50
implications of these computers, you know, and, oh, but people say, they're so wonderful,

1440
2:03:50 --> 2:03:54
you can find out this and you can find out that, it's so convenient, you know. Yeah,

1441
2:03:54 --> 2:03:58
but if it's going to lead to the destruction of human species, then it's not worth it, is it?

1442
2:03:58 --> 2:04:05
And we need to really think about this. Yeah, I agree with all of that. And on a philosophical

1443
2:04:05 --> 2:04:10
level, I'll tell you one of the things I discovered when I moved to Europe that kind of shocked me,

1444
2:04:11 --> 2:04:16
and I think is analogous to what we're seeing here, and that is that when I moved to the

1445
2:04:16 --> 2:04:22
Netherlands, they have a completely different way of planning their town. So as a result,

1446
2:04:22 --> 2:04:28
I could ride a bicycle to work and to get groceries and to literally do anything I wanted to do

1447
2:04:29 --> 2:04:33
from my house. And so I didn't own a car for 12 years and didn't really need one. A couple

1448
2:04:33 --> 2:04:40
times I rented one, but that was very, you know, far between. But the thing is that if I tried to

1449
2:04:40 --> 2:04:45
live the same way in America, there are only a couple of towns in America where that would even

1450
2:04:45 --> 2:04:53
be possible. For the most part, the infrastructure doesn't exist to, for instance, do farming the

1451
2:04:53 --> 2:04:56
way they did there to gather the vegetables and fruits and bring them to the people in the

1452
2:04:56 --> 2:05:02
marketplace and then to buy them and eat them and find their way into restaurants and so on. But

1453
2:05:02 --> 2:05:08
so the lack of infrastructure problem that I see right now that's analogous to our situation with

1454
2:05:08 --> 2:05:14
elections is we don't have an alternative way of thinking about elections. At this moment,

1455
2:05:14 --> 2:05:18
we are acculturated to believe that we have to have computers because they're so complex,

1456
2:05:18 --> 2:05:22
because there's so many people involved and it's just impossible to do it any other way.

1457
2:05:23 --> 2:05:29
When France, by the way, proved us wrong on that assumption just recently by counting all their

1458
2:05:29 --> 2:05:33
votes overnight and doing it on paper and by hand, and actually every other country does the same

1459
2:05:33 --> 2:05:43
thing. Our culture right now is designed around computers. If all of our computers failed

1460
2:05:43 --> 2:05:49
simultaneously, we'd have a lot of power plants and other types of industries that would immediately

1461
2:05:49 --> 2:05:54
stop functioning until they removed the computers from their systems, which all by itself I consider

1462
2:05:54 --> 2:06:01
to be an incredibly risky thing to do to make yourself dependent like that. But that's exactly

1463
2:06:01 --> 2:06:07
what our society has done. So we are actually right now in essentially the same situation that

1464
2:06:07 --> 2:06:12
a heroin addict finds themselves in where not taking the heroin actually causes physical

1465
2:06:13 --> 2:06:20
symptoms that are very unpleasant. So for that reason, it's very hard for them to get over

1466
2:06:20 --> 2:06:26
that addiction. And in this case, I'll just give an example from my own life. I'm watching,

1467
2:06:26 --> 2:06:32
I was a subscriber to Netflix and I was watching the programming become increasingly vulgar.

1468
2:06:32 --> 2:06:37
The profanity became very common. Pretty much every show was full of it, then every character

1469
2:06:37 --> 2:06:42
was full of it, and then the subject matter was obviously very biased in a lot of ways, et cetera.

1470
2:06:42 --> 2:06:47
And eventually I opted out and canceled my subscription and I went to Disney, which I thought

1471
2:06:47 --> 2:06:52
would be safer. And then Disney started doing the same thing. And then I canceled that and went to

1472
2:06:52 --> 2:06:56
Amazon where I could at least look at old movies and TV shows. And then I noticed all of the bias

1473
2:06:56 --> 2:07:00
in the old ones. I was really surprised to see how many were pushing the gay agenda, for instance,

1474
2:07:00 --> 2:07:07
as far back as the 1960s. It's really kind of a shock to see that. And I realized that for other

1475
2:07:07 --> 2:07:13
people who maybe feel a little less strongly than I do, it's really hard to let go of those kinds of

1476
2:07:13 --> 2:07:18
things. That those are the things that keep their minds in these very narrow tunnels of

1477
2:07:19 --> 2:07:24
that don't give them any room to see the wider world outside of what they're getting.

1478
2:07:24 --> 2:07:30
Absolutely. So I would use different words, Andrew. I think that human beings are a huge

1479
2:07:30 --> 2:07:34
disappointment to me over the past four and a half years because it's made me realize that

1480
2:07:34 --> 2:07:38
they have a predilection, a very dangerous predilection for dangerous cults.

1481
2:07:41 --> 2:07:46
Anyway, yeah, well, you actually I'll tell you something else that happened when I moved to the

1482
2:07:46 --> 2:07:54
Netherlands. At that time in 2006, there was an issue going on in Israel with a soldier that had

1483
2:07:54 --> 2:08:01
been kidnapped by Hezbollah. And they had been named Galeid Shalit, I think was his name. And

1484
2:08:01 --> 2:08:06
they demanded his return for I think it was something like a year or two, it was some long

1485
2:08:06 --> 2:08:12
time. And the other side refused to do it. So Israel gave them a warning, if you don't do it

1486
2:08:12 --> 2:08:17
by such and such a date, which I think was a 30 day warning, we're going to attack you. And they

1487
2:08:17 --> 2:08:20
didn't do it. And then they gave them a little bit more time. And they still didn't give him the guy.

1488
2:08:21 --> 2:08:28
So they went in and started blowing the heck out of Lebanon. And so it was right then that I moved

1489
2:08:28 --> 2:08:35
to the Netherlands, and I was watching CNN, and saw a program on this subject. And they had some

1490
2:08:35 --> 2:08:40
sort of press representative for the Israeli government and another one for the Israeli

1491
2:08:41 --> 2:08:47
government and another one for the Palestinian organization. They gave the Israeli guy, I think

1492
2:08:47 --> 2:08:53
it was, I timed this out with my, with a not a stopwatch, but my computer, I think it was like 35

1493
2:08:53 --> 2:08:59
or 40 seconds, during which he was basically attacked. They were saying, how dare you attack

1494
2:08:59 --> 2:09:04
a sovereign nation? And why did you do this horrible thing? And why are you committing murder

1495
2:09:04 --> 2:09:07
against all these innocent civilians? And the Israeli guy was like, what are you talking about?

1496
2:09:07 --> 2:09:12
That's not what's going on at all. And then they cut to the other guy from the Palestinian side and

1497
2:09:12 --> 2:09:17
gave him 12 minutes. And they were asking him all these softball questions like, how does it feel to

1498
2:09:17 --> 2:09:24
be the victim of this horrible tyranny imposed on you by the Israeli people? And so anyway,

1499
2:09:24 --> 2:09:30
I'm watching this and I was thinking, oh my gosh, I can't trust the media at all. I had never seen

1500
2:09:30 --> 2:09:36
CNN like that. And in Europe, it was actually more biased than it is here, if you can believe that.

1501
2:09:38 --> 2:09:44
But that's how it was. And it was really shocking. The thing is, though, that that revelation that I

1502
2:09:44 --> 2:09:51
had is something that I wouldn't have had if I hadn't moved to the Netherlands. And today,

1503
2:09:51 --> 2:09:55
I probably wouldn't have had it at all, because today America and the Netherlands are probably

1504
2:09:55 --> 2:10:00
the same as far as CNN is concerned. So it's tough. This has to happen on an individual basis.

1505
2:10:00 --> 2:10:05
So Andrew, I want to let the others ask a question. One last question. Do you think it's possible

1506
2:10:05 --> 2:10:12
that the present worship of computers by so many human beings and computers and mobile phones and

1507
2:10:13 --> 2:10:16
social media and all the rest of it, you know, this kind of non-human world,

1508
2:10:17 --> 2:10:21
do you think it's possible that the worship of computers in the largest

1509
2:10:21 --> 2:10:24
is going to bring us down, the human species down?

1510
2:10:26 --> 2:10:31
I think it's possible. I think it's possible, especially now that we have drones and AI.

1511
2:10:31 --> 2:10:37
Sure. Drones actually are, I think, one of the scariest inventions I've ever

1512
2:10:37 --> 2:10:41
heard of. And, you know, when they were first created, I remember one of my colleagues at work

1513
2:10:41 --> 2:10:47
coming in with a little helicopter, a drone helicopter. And at the time, I was thinking

1514
2:10:47 --> 2:10:55
it was cool. But then I saw drone robot dogs being deployed in, I want to say Iraq, some place in the

1515
2:10:55 --> 2:11:02
Middle East. And they were shooting at targets that they'd identified as hostile. But as I watched

1516
2:11:02 --> 2:11:07
those things, it looked like little robot dogs running around in a street crowded with civilians.

1517
2:11:07 --> 2:11:12
I was thinking, oh my gosh, this is incredibly dangerous because now there's no risk of loss of

1518
2:11:12 --> 2:11:20
life to the people running these robots and tremendous risk to the other side. Just very

1519
2:11:20 --> 2:11:24
frightening. Yeah, I agree with you. Yeah, I think it could bring down humanity.

1520
2:11:25 --> 2:11:32
So essentially, and you could say in the wider sense, he could say in the way human beings

1521
2:11:32 --> 2:11:39
are in danger from other human beings, hubris and lack of humility.

1522
2:11:42 --> 2:11:46
I would look at it just a slightly different way. As far as I'm concerned, the computers may be a

1523
2:11:46 --> 2:11:52
tool, but the real problem, and this might sound a little cuckoo to you guys, but as far as I'm

1524
2:11:52 --> 2:12:01
concerned, is atheism. Because I'm looking at people turning away from God and moral principles, and

1525
2:12:01 --> 2:12:07
that all by itself led to everything else. That's my opinion. And I started out as an atheist,

1526
2:12:07 --> 2:12:13
by the way, just so you know. Sure, there's no fear of accountability from God in the future.

1527
2:12:13 --> 2:12:20
Yeah, well, and also the preposterous arrogance of thinking that since God doesn't exist from

1528
2:12:20 --> 2:12:27
their point of view, that we are God, and we can make it. Exactly. Yes, well put. Very brilliant.

1529
2:12:27 --> 2:12:32
Thank you, Andrew, so much for coming on and for your brilliant insights, especially that last one.

1530
2:12:33 --> 2:12:39
Thank you so much. So Albert, I don't know where Charles is. He's in, it's early in Australia, so

1531
2:12:39 --> 2:12:47
Albert is from VAERS, and I think he might need you, Andrew. Okay. Hi, Andrew. My name is Albert

1532
2:12:47 --> 2:12:55
Benavides. I'm the creator of VAERSAware.com. My background is just medical billing for multiple

1533
2:12:55 --> 2:13:04
decades, and at one point I was a HMO claims auditor, and I think I was a very, very, very

1534
2:13:04 --> 2:13:11
you know, at one point I was a HMO claims auditor, and I think a lot of what I had to do

1535
2:13:12 --> 2:13:25
auditing for fraud in medical systems had a lot of similarities of what you just presented today.

1536
2:13:26 --> 2:13:35
And like one of the things when you presented your record, like the record that said,

1537
2:13:38 --> 2:13:46
you know, the status was duplicate or active, and I think you used a word, you're using a word clone,

1538
2:13:46 --> 2:13:52
like when I see this clone record in my head, I'm thinking, I'm correlating that. I'm like, man,

1539
2:13:52 --> 2:13:58
in medical billing we would call that a duplicate. Like you made reference, you know, like around

1540
2:13:58 --> 2:14:05
here, you know, doctors offices, the duplicate record is the bane of their existence. It's like

1541
2:14:05 --> 2:14:13
there's everybody has like 50 Maria Hernandez's, you know, so these duplicates. Is that what you

1542
2:14:13 --> 2:14:17
meant when you're, when you refer to these clone records? They're in effect kind of like, no,

1543
2:14:17 --> 2:14:25
I'm not. And no, and I specifically use the word clone because I'm aware of what a duplicate is,

1544
2:14:25 --> 2:14:31
and a clone is not a duplicate. Okay. And this is also because the nomenclature in New York

1545
2:14:31 --> 2:14:37
only allows for the one term, which means it encompasses both types of records, which doesn't

1546
2:14:37 --> 2:14:43
make any sense because they can't be distinguished. It's like calling us humans, but not distinguishing

1547
2:14:43 --> 2:14:46
by sex means we can't differentiate between men and women. And in this case, it means you can't

1548
2:14:46 --> 2:14:54
differentiate between legal and illegal. So in New York, a duplicate record will have different

1549
2:14:54 --> 2:14:59
county ID numbers, but the same state ID numbers. And this is allowed because the, you can move from

1550
2:14:59 --> 2:15:07
one county to another, provided of course, that the old county ID number is made inactive by

1551
2:15:07 --> 2:15:13
purging it. But the thing is, is that because you have the same state ID that goes with you wherever

1552
2:15:13 --> 2:15:18
you go, you can vote and other people can vote with confidence that people aren't getting an

1553
2:15:18 --> 2:15:23
opportunity to vote more than one. But a clone is where they take all of your identity information

1554
2:15:23 --> 2:15:28
and create another record that is exactly the same as the original one, except for one thing.

1555
2:15:28 --> 2:15:34
They give it another ID number. And so as far as the system is concerned, it's another human being,

1556
2:15:34 --> 2:15:39
which means it's another valid record to which they can send a absentee mail in ballot. And one

1557
2:15:39 --> 2:15:44
thing I didn't mention in my presentation, but I'll just mention right now is one thing I know is

1558
2:15:44 --> 2:15:48
in one particular case, and by the way, I didn't check too many of these because they're very

1559
2:15:48 --> 2:15:53
difficult to check. This is the kind of thing where other people have to get in their cars and

1560
2:15:53 --> 2:15:58
drive someplace and ask questions to find the answers. But one guy who had 25 registrations,

1561
2:15:58 --> 2:16:06
he had marked his voter registration document so that he was asking to have absentee ballots sent

1562
2:16:06 --> 2:16:13
to him, but they were being sent to a rental mailbox in a shopping center. So he had 25 IDs

1563
2:16:13 --> 2:16:21
in 25 counties, but he was getting 25 ballots, mail in ballots sent to a mailbox in another county.

1564
2:16:23 --> 2:16:28
That kind of thing can't be done with cloned records. I'm sorry with duplicate records,

1565
2:16:28 --> 2:16:29
but it can be done with cloned records. Does that make sense?

1566
2:16:30 --> 2:16:42
Yeah, absolutely. I was wondering if these election records and transactions, and as it's

1567
2:16:42 --> 2:16:50
flying through space from one database, from one computer to another computer, are they covered

1568
2:16:50 --> 2:16:53
under the HIPAA covered transactions?

1569
2:16:53 --> 2:16:59
You know, it's interesting you asked that. That was something I wanted to explore with

1570
2:17:01 --> 2:17:06
Marley at United Sovereign Americans, but that just got dropped through the cracks at some point.

1571
2:17:08 --> 2:17:12
But it seems to me that HIPAA's... What was that?

1572
2:17:12 --> 2:17:14
Oh, go ahead. Go ahead. I'm sorry. I interrupted.

1573
2:17:15 --> 2:17:21
Yeah, it seemed to me that the HIPAA requirements would have prevented the kind of problems that

1574
2:17:21 --> 2:17:26
we're seeing. And having actually committed these errors or having these errors committed

1575
2:17:26 --> 2:17:32
to the voter rolls would be considered a violation of HIPAA. But the thing is, HIPAA has to do with

1576
2:17:32 --> 2:17:37
patient privacy more than anything else. And in this case, the records are all public,

1577
2:17:37 --> 2:17:42
so it wouldn't necessarily apply to them. However, the standards of HIPAA, it seems to me,

1578
2:17:42 --> 2:17:49
are normal standards for database management. And on that level, they become relevant to the New

1579
2:17:49 --> 2:17:57
York voter rolls. Make sense? Yeah. One of the P's in the word in the acronym HIPAA is portability.

1580
2:17:57 --> 2:18:04
And that's kind of where I had my niche working for, you know, in my medical... I could say

1581
2:18:04 --> 2:18:12
medical billing, but I actually, for a good part, I worked at a laboratory for a good 10 years. And

1582
2:18:12 --> 2:18:21
I learned a lot of my electronic data interchange chops during that time. And, you know, with this

1583
2:18:21 --> 2:18:28
health information, you know, we're receiving reimbursements. So it crosses over and it

1584
2:18:28 --> 2:18:35
overlaps with a lot of the financial data, you know? So that's why this standard called ANSI

1585
2:18:35 --> 2:18:44
5010, with the acronym for ANSI, is American National Standards Institute, 5010 language,

1586
2:18:44 --> 2:18:55
ASC X-12 format. It's just the structure of how data is moving through the system and like the

1587
2:18:55 --> 2:19:03
rules. So then we're talking about these loops and segments and headers and trailers and packets of

1588
2:19:03 --> 2:19:09
information traveling, you know, on the information highway, like even kind of like when we get into

1589
2:19:09 --> 2:19:15
our own cars and we drive down the highway and we have a license plate on the front of our car and

1590
2:19:15 --> 2:19:23
in the back of our car, we now become that packet of information on the highway with a header and a

1591
2:19:23 --> 2:19:32
trailer. And a lot of that has to do with, you know, this encryption thing to keep things private

1592
2:19:32 --> 2:19:40
or, you know, stuff like that. So I put a link in there in the comments up above and it was called

1593
2:19:40 --> 2:19:48
the Washington Publishing Institute or something like that. And it's all the ANSI codes. And I was

1594
2:19:48 --> 2:19:54
wondering if you ever dove into that or familiar with that or you already know about that. But

1595
2:19:54 --> 2:19:59
those were like, gives you all the codes like the rules. I'll have to look to see. It sounds like

1596
2:19:59 --> 2:20:05
something I did look at, but I have looked into so many things now that it's kind of hard to know for

1597
2:20:05 --> 2:20:11
sure. One thing I will tell you, and I'm not going to say too much on this because I've got a

1598
2:20:12 --> 2:20:16
a friend that I have been consulting ever since I first discovered the algorithm

1599
2:20:16 --> 2:20:22
because I wanted his insights due to his interactions with very large databases because

1600
2:20:22 --> 2:20:28
he works at a huge company and a senior physician. And I wanted to compare what I was finding with

1601
2:20:28 --> 2:20:34
normal for him, but he likes privacy. So I don't want to go too far with this. But the,

1602
2:20:35 --> 2:20:42
but he his industry works with the government and he often has to deal with internal fraud

1603
2:20:42 --> 2:20:49
investigators. And the way it works is people on his team will flag something or he'll have an

1604
2:20:49 --> 2:20:54
idea for how to find something to flag and he'll ask his team to look for it. And once they've

1605
2:20:54 --> 2:20:59
found the flags, they'll go ahead and send it to their fraud team. And then they investigate.

1606
2:20:59 --> 2:21:07
He's developed tools for improving the security of their product because they have a very large

1607
2:21:09 --> 2:21:12
fraud problem where they can lose as much as several hundred million dollars

1608
2:21:12 --> 2:21:15
in a year or two fraud. So policing that is very important to them.

1609
2:21:17 --> 2:21:22
What he was telling me is that even some of the more minor things that I've discovered

1610
2:21:23 --> 2:21:29
would have been a major red alarm situation at his company that it would, they would drop

1611
2:21:29 --> 2:21:35
everything to fix it and find out exactly what happened, who did it, why it happened, et cetera,

1612
2:21:35 --> 2:21:40
and fire everybody responsible and then get it dealt with. But when it came down to the

1613
2:21:40 --> 2:21:45
the algorithms and some of the, and the port signatures and so on, he said at that point,

1614
2:21:45 --> 2:21:49
there are lawyers would be involved and they'd be sending people to jail. And he said there's

1615
2:21:49 --> 2:21:52
absolutely no question in his mind whatsoever exactly what the outcome would be.

1616
2:21:53 --> 2:21:59
Given his level at the company, he would be in a position to actually make that happen. So

1617
2:22:00 --> 2:22:05
that's my impression. But he's the one who mentioned HIPAA to me the first time. And I

1618
2:22:05 --> 2:22:09
started looking into it and realized that standard actually looks like something that would be

1619
2:22:09 --> 2:22:15
applicable here. But as far as the ANSI standard that you're talking about, I just clicked on your

1620
2:22:15 --> 2:22:21
link. And I think I'm not going to be able to concentrate on reading this while I'm trying

1621
2:22:21 --> 2:22:25
to answer questions. So I'm going to have to defer and answer later. I'm sorry.

1622
2:22:25 --> 2:22:37
No, later. Keep my number. We got the same passions, I think. And I'm glad to help you

1623
2:22:37 --> 2:22:44
any way I can for anything I know. But what makes me slightly unique in this little group here is

1624
2:22:45 --> 2:22:54
I'm an HMO claims auditor, medical data guy by profession, looking for fraud. So it's kind of

1625
2:22:54 --> 2:23:05
similar. But I was really, as technical as your presentation was, it was fascinating to me because

1626
2:23:05 --> 2:23:11
it's like my bells and whistles go off in my head. And I can't explain why. But it's just like,

1627
2:23:11 --> 2:23:17
yeah, yeah, I get that. Your algorithms, your spiral and your jingle, I was like, yeah, yeah,

1628
2:23:19 --> 2:23:27
but anyways, thank you. Well, I've got no problem. And thank you. And you too. Yeah, my impression

1629
2:23:27 --> 2:23:32
is that there are a lot of similarities between the type of findings I've made in New York's

1630
2:23:32 --> 2:23:38
voter rolls and what I've seen of fraud in the healthcare industry. So for instance, when you

1631
2:23:38 --> 2:23:47
have a cartel by a street and then go ahead and populate one half of it with a bunch of paid for

1632
2:23:47 --> 2:23:52
doctors and the other side with a bunch of homeless shelters that you encourage homeless people to go

1633
2:23:52 --> 2:23:56
into, and then you give them prescriptions from the doctors on the other side of the street for

1634
2:23:56 --> 2:24:01
drugs. And then you go ahead and build the insurance company for the drugs that you're actually not

1635
2:24:01 --> 2:24:04
delivering. Instead, you're giving them these addictive things, which is why they live on the

1636
2:24:04 --> 2:24:09
other side of the street. You have to create all these identities and you wind up doing essentially

1637
2:24:09 --> 2:24:15
what you did in the voter rolls here. That sounds familiar to you? Yeah, we had the same kind of

1638
2:24:15 --> 2:24:21
frauds even in lab, you know, and maybe these buzzwords might ring a bell to you, but we had,

1639
2:24:21 --> 2:24:29
you know, requisition forms. And then we had accession numbers. So when the request was made,

1640
2:24:29 --> 2:24:34
and then the accession got into our system. And, you know, at the fourth largest lab in the country,

1641
2:24:35 --> 2:24:38
there's, you know, there's like a hundred draw stations all over the nation,

1642
2:24:40 --> 2:24:46
people trying to enter in a record at all at once at the same time. So like, like down to the femto

1643
2:24:46 --> 2:24:52
second, the system would, would issue that internally an accession number. And a part of my

1644
2:24:53 --> 2:24:58
keeping trying to keep track of all these things, it's like, you have to figure out, you could kind

1645
2:24:58 --> 2:25:03
of figure out based on the timestamps with the accession numbers and the requisition numbers,

1646
2:25:04 --> 2:25:11
you know, when, because we realized like, hey, you know, call back to this draw station in Los

1647
2:25:11 --> 2:25:18
Angeles, we think that somebody is snatching like big gaps of, of, of recs of requisition,

1648
2:25:18 --> 2:25:25
like blank ones, because you're requesting new ones way too fast. And it's not matching

1649
2:25:25 --> 2:25:30
the volume of accession numbers that you're generating. So the, so we found a scam and we

1650
2:25:30 --> 2:25:35
found a scam in there, you know, like, oh yeah, one of our employees was stealing a whole bunch.

1651
2:25:38 --> 2:25:40
You just remind me of something that I didn't say. And I want to mention

1652
2:25:41 --> 2:25:48
is that to date, many of the, the, much of the research that's been conducted into the election

1653
2:25:49 --> 2:25:56
has concentrated on things that the other side can, to an extent, plausibly deny as

1654
2:25:56 --> 2:26:03
innocent or accidental somehow, or a glitch that's, you know, not anyone's fault. What the algorithm

1655
2:26:03 --> 2:26:09
does is show that it's all intentional. And that's something that's really hard to get around. You,

1656
2:26:09 --> 2:26:16
you just can't say that this alien algorithm that doesn't belong in the voter roll database

1657
2:26:16 --> 2:26:23
has no legitimate purpose for being there, could have possibly been a unintended artifact of some

1658
2:26:23 --> 2:26:29
other process. And that I think is actually a very important point. Oh, and I see a hand. Am I supposed

1659
2:26:29 --> 2:26:32
to, no, I'm not picking, the moderator's picking the wrong answers. Okay, go ahead.

1660
2:26:33 --> 2:26:37
Right on, Andrew. Thank you so much. Yep, no problem.

1661
2:26:39 --> 2:26:45
So that's really important point you made just then, Andrew. Next question from Diana Marie

1662
2:26:46 --> 2:26:56
Henry. Well, Diana. Thank you very much. I really, I'm horrified, of course,

1663
2:26:57 --> 2:27:05
but I think that, you know, I understand there's, there's great potential here for informing the

1664
2:27:05 --> 2:27:13
public, especially with the help of Jerome Corsi. But I wonder if you've also considered

1665
2:27:13 --> 2:27:20
Judicial Watch as a place to contact. They've had a lot of success, of course,

1666
2:27:21 --> 2:27:27
clean up their voter rolls. Yeah, let me just, let me just tell you something about my personality.

1667
2:27:28 --> 2:27:34
My nature, I don't go out and try to contact people. They contact me and I'm generally willing

1668
2:27:34 --> 2:27:40
to talk to them, but I usually don't like going out and networking, especially when I have to make

1669
2:27:40 --> 2:27:46
a living, which I'm not doing very well right now. So that's, that's my primary focus. However,

1670
2:27:46 --> 2:27:51
when I was working with New York Citizens Audit, I was interested in Judicial Watch

1671
2:27:51 --> 2:27:58
and I did talk about it with Marlee Hornick. And she did contact them. And as I remember the response,

1672
2:27:59 --> 2:28:05
and at the risk of being diplomatic, rather undiplomatic, actually, I think I remember Marlee

1673
2:28:05 --> 2:28:11
saying that Tom Fitton wouldn't take her calls for quite a while. And then finally, when he did,

1674
2:28:11 --> 2:28:18
he basically blew her off. But one thing I will say about Judicial Watch that is kind of interesting

1675
2:28:18 --> 2:28:25
is they came out with a lawsuit against New York City for having voter registrations that

1676
2:28:25 --> 2:28:31
needed to be removed. And they settled that case when New York City's Board of Elections

1677
2:28:31 --> 2:28:37
agreed to remove the records. So I saw the settlement agreement, which is published

1678
2:28:38 --> 2:28:45
on Judicial Watch's site, and they gave a number of records that were removed as of a certain date.

1679
2:28:45 --> 2:28:51
And I realized when I looked at that, that I had a voter roll database before and after that date.

1680
2:28:51 --> 2:28:57
So I could tell if they were actually removed or not. I suspected they hadn't been. And I checked

1681
2:28:57 --> 2:29:04
and I found, in fact, that they hadn't been. And that information, I tried to get to Tom Fitton.

1682
2:29:04 --> 2:29:08
I don't know if he ever got it, but I wrote an article about it for Red Voice Media.

1683
2:29:10 --> 2:29:15
And I talked about it quite a bit. It's time. And I think I may even have mentioned it. I'm

1684
2:29:15 --> 2:29:22
not totally sure about this in my Caesar Cipher article as well. But he never seemed to react to

1685
2:29:22 --> 2:29:28
that. But my impression was that although some records were purged, they were either

1686
2:29:29 --> 2:29:34
instantly replaced with more clones or they never deleted the records they said they deleted.

1687
2:29:36 --> 2:29:41
And yet, Fitton was claiming that as a group victory. So I don't really count it as a victory

1688
2:29:41 --> 2:29:45
when the people on the other side don't do what they say they did. And when by saying it, they

1689
2:29:45 --> 2:29:49
are committing a fraud against the court, which that's my opinion of what happened. I suppose

1690
2:29:49 --> 2:29:52
it's possible that there's some data out there that would make me change my mind. But at the

1691
2:29:52 --> 2:29:58
moment, that's my opinion. So that's my answer about Tom Fitton. Sorry. Otherwise, I like them.

1692
2:29:58 --> 2:30:04
I think they're great. It wasn't necessarily about Tom Fitton. I sent you some information

1693
2:30:04 --> 2:30:10
about their investigative reporter with whom I was attached. I was not in touch with Tom Fitton.

1694
2:30:11 --> 2:30:18
But I have double checked on Judicial Watch's agreement and then

1695
2:30:19 --> 2:30:26
they were, I guess, maybe deceived or didn't follow up. Anyway, thank you.

1696
2:30:26 --> 2:30:32
Yeah, I think they were deceived and then they either didn't. And I think they didn't follow up.

1697
2:30:32 --> 2:30:36
Why they didn't follow up. Maybe they felt it wasn't worth it because they got part of what they

1698
2:30:36 --> 2:30:47
wanted. I have no idea why. But I always thought that the fact that the settlement agreement appears

1699
2:30:47 --> 2:30:55
to be false and false by the county board of elections in New York City was much more

1700
2:30:55 --> 2:31:00
interesting than the lawsuit itself. And I've always been disappointed that that particular

1701
2:31:00 --> 2:31:10
story never went anywhere. Thank you. Thank you. No problem. Thank you.

1702
2:31:10 --> 2:31:15
So one outrage replaced succeeded by another. Amazing.

1703
2:31:19 --> 2:31:22
Oh, yes. Yes. By the way, Stephen, I do have a hard time hearing you. So

1704
2:31:24 --> 2:31:27
until you were into your fourth or fifth word, I hadn't heard a thing.

1705
2:31:28 --> 2:31:33
I don't understand that. No. Yeah. Okay. Sorry about that. So Peter Underwood is next.

1706
2:31:33 --> 2:31:35
That's okay. He's in South Africa.

1707
2:31:36 --> 2:31:41
Thank you. Yeah, I'm in South Africa. Hello, Andrew. Thank you so much for a wonderful

1708
2:31:41 --> 2:31:49
presentation. I'd really like to ask you, well, I get the impression on these meetings and everything

1709
2:31:49 --> 2:31:55
else I do. Researching all this area. I've written a book too about the global financial system,

1710
2:31:56 --> 2:32:06
which was sparked by the Cyprus bank robbery in 2013. I'm getting the impression, I think,

1711
2:32:06 --> 2:32:12
that Jerome said it struck a chord with me because it's dear to my heart. I got the

1712
2:32:12 --> 2:32:17
impression that we don't think we're going to win against this, that these guys own basically the

1713
2:32:17 --> 2:32:27
world or certainly the Western world. And he made the point that the best way or the best defense

1714
2:32:28 --> 2:32:36
is to not play the game, not comply and move away. Get out of the system. How do you feel about that?

1715
2:32:38 --> 2:32:45
I tend to agree, but I also feel that God has a role to play in this and that against God,

1716
2:32:46 --> 2:32:53
nothing can withstand God's will. So if God decides to help us out, then it really doesn't

1717
2:32:53 --> 2:32:58
matter what's going on on the other side, we're going to win. And on the other hand, I also feel

1718
2:32:58 --> 2:33:03
the same way. If he wants to punish us somehow and let us all run this course, then that's going to

1719
2:33:03 --> 2:33:09
happen no matter what we do. So we can only, but the thing is we can only behave righteously and

1720
2:33:09 --> 2:33:20
hope for the best as far as I'm concerned. I agree, Andrew. I really agree. Ephesians 6

1721
2:33:20 --> 2:33:28
tells us to take up the armor of God and I'm with you 100% with that regard.

1722
2:33:29 --> 2:33:38
We did face total extinction during the flood, so there's no guarantee either way, I suppose.

1723
2:33:39 --> 2:33:43
So I'd just like to ask you. Go on.

1724
2:33:45 --> 2:33:50
Oh, I'm sorry. There's some kind of a delay on this, which is making it difficult to talk

1725
2:33:50 --> 2:33:56
far over you. It's not your fault, it's technology. But as far as I'm concerned,

1726
2:33:57 --> 2:34:01
the fact that this is all ultimately in God's hands does not mean we sit around waiting

1727
2:34:02 --> 2:34:09
for somebody else to do something because we all have a positive duty to always be righteous

1728
2:34:09 --> 2:34:16
in everything we do and obedience to God. And so what that means to me is I can't let go of this.

1729
2:34:17 --> 2:34:23
I have to let people know what I have found because to do otherwise is to allow them to be harmed

1730
2:34:24 --> 2:34:29
by whatever is going on here. And that's not something that I feel I can ethically allow.

1731
2:34:31 --> 2:34:38
And I believe that with me and you and Jerome and Stephen and other people and Diana and all

1732
2:34:38 --> 2:34:44
the people on this call and elsewhere, eventually there will be that critical mass that's needed

1733
2:34:45 --> 2:34:53
to give God basically the tools that are necessary to fix this or to right this boat,

1734
2:34:54 --> 2:35:00
which ultimately I believe is going to happen. But I actually don't even care if it's going to

1735
2:35:00 --> 2:35:07
happen or not because either way it's up to me to always behave righteously. And so I'm just going

1736
2:35:07 --> 2:35:11
to take that and let somebody else worry about the rest because that's not my responsibility.

1737
2:35:11 --> 2:35:17
Does that make sense? Yeah, that's wonderful, Andrew. Thank you. I applaud that and share your

1738
2:35:17 --> 2:35:24
views. And articulating what you've just said, Andrew, terrifies the people we're up against,

1739
2:35:24 --> 2:35:30
absolutely terrifies them to hear people like you talking like that. But I respect what you

1740
2:35:30 --> 2:35:40
say and I feel the same way. Jim is the next question. He's a doctor and a radiologist.

1741
2:35:41 --> 2:35:48
And I really applaud your work. I'm not sure if you know about Leah Hoops and Greg Stenstrom

1742
2:35:48 --> 2:35:54
in Pennsylvania. They wrote a book called The Parallel Election. And in that book,

1743
2:35:54 --> 2:36:00
I have seen the book. Okay, I'd like to put you in touch with them because they need to just have

1744
2:36:00 --> 2:36:09
this information in addition to what they already have. Your algorithms are fascinating. And it's

1745
2:36:09 --> 2:36:18
very important to identify how the what I would call the demonic presence that is that maybe within

1746
2:36:18 --> 2:36:26
our intelligence agencies, the Department of Defense that have taken over. And we need to

1747
2:36:26 --> 2:36:32
align with the truth. I'm very I'm grateful for what you've told everybody because what we you

1748
2:36:32 --> 2:36:39
know, we're this battle is bringing the truth to this earth. And and not just keeping it to ourselves,

1749
2:36:39 --> 2:36:44
but bringing it to the opposition, including the intelligence networks and saying, Hey, please

1750
2:36:44 --> 2:36:50
start telling the truth. Please stop interfering and rigging the elections. And please go back to

1751
2:36:50 --> 2:36:55
telling the truth and giving this world back to God that rather than letting the demons take over.

1752
2:36:56 --> 2:37:02
And you know, I'm very glad to hear you say all of that. I'm sorry for talking over you just there,

1753
2:37:02 --> 2:37:09
but you hesitated. But my feeling is exactly that. And that's what I pray for when I go to bed at

1754
2:37:09 --> 2:37:16
night. I pray for truth for people for God to return truth to the world for truth to emerge,

1755
2:37:16 --> 2:37:21
because that is absolutely central to all of the problems that we're seeing.

1756
2:37:21 --> 2:37:26
And the thing about the demonic thing is business. I completely agree with you about that. And the

1757
2:37:26 --> 2:37:32
thing about that, that really, I find kind of shocking is I would have had a very hard time

1758
2:37:32 --> 2:37:37
agreeing with your that statement a few years ago, very, very hard. But as I look at all the

1759
2:37:37 --> 2:37:43
stuff that's going on around the world, not just in America, and I'm seeing how even the people who

1760
2:37:43 --> 2:37:47
are doing these things don't benefit from them. I'm trying to figure out who benefits. There's

1761
2:37:47 --> 2:37:58
basically only one one beneficiary. And that's just a second. No, it's sorry, it was not intended.

1762
2:37:58 --> 2:38:06
Good time. So here's the deal. I talked to a couple different times, we're working on stuff together.

1763
2:38:08 --> 2:38:14
So Craig Campbell, can you believe me? Sorry. There you go. Yes. And that's very important.

1764
2:38:16 --> 2:38:21
I can go into exactly why I think the Department of Defense has made a deal with perhaps actual

1765
2:38:21 --> 2:38:29
demons. And because there's very few reasons that the Department of Defense would be allowed to kill

1766
2:38:29 --> 2:38:35
off the people of the United States, allowed to genetically and people on this call know that I

1767
2:38:35 --> 2:38:40
speak about the genetic specificity of the SARS-CoV-2 spike protein, but be able to kill

1768
2:38:40 --> 2:38:47
off people in a genetically specific way. And what allows this to happen? And that is a really

1769
2:38:47 --> 2:38:51
complicated, really strange situation that I can talk to you about a little bit offline, maybe.

1770
2:38:51 --> 2:38:56
But the point being that we really need to bring the truth to this earth at this time,

1771
2:38:56 --> 2:39:01
because if we don't do it very soon in the next weeks or months, it looks like they are setting

1772
2:39:01 --> 2:39:08
us up for a catastrophe that is outlined somewhat in a movie produced by the Obamas called

1773
2:39:11 --> 2:39:18
Leave the World Behind, which has everything in it from, there's a lot of these incidents that

1774
2:39:18 --> 2:39:23
are going on, including cyber warfare. And the cyber warfare that you're dealing with right now

1775
2:39:23 --> 2:39:30
is really specific, not just to the NSA and the CIA, but also to a group called Unit 8200.

1776
2:39:31 --> 2:39:37
And are you familiar with Unit 8200? I'm not. Unit 8200 is...

1777
2:39:37 --> 2:39:44
The stuff you're talking about is making me think that if you really want the hairs on the end of

1778
2:39:44 --> 2:39:49
your neck to stand up, you need to look at my other sub-stacks that I don't advertise, which

1779
2:39:49 --> 2:39:55
has to do with my dream research, because I had some very interesting stuff up there too. And Jerome

1780
2:39:55 --> 2:39:58
has seen some of that, so if he's still on the line, he could comment.

1781
2:39:59 --> 2:40:06
Okay, I can contact him and try to get the information. So, right. But the issue is...

1782
2:40:07 --> 2:40:11
Again? I'm just waiting for you to finish. Go ahead.

1783
2:40:12 --> 2:40:17
Okay. So the issue is that we really need to... The cyber intelligence stuff is very,

1784
2:40:17 --> 2:40:21
very important, because if we don't stop the intelligence agencies, who seem to have been

1785
2:40:21 --> 2:40:26
not only rigging our elections, but infiltrating everything from our hospital networks to the

1786
2:40:26 --> 2:40:32
military to the township governments, then we are going to have a serious problem on our hands,

1787
2:40:32 --> 2:40:41
because... And they are going to false flag other countries like Russia, Iran, Iran, China.

1788
2:40:43 --> 2:40:49
And they're going to blame all these other places when the cyber powers are the United States and

1789
2:40:49 --> 2:40:56
Israel. And we know that. Yeah, well, I'm looking at this as an existential global threat.

1790
2:40:57 --> 2:41:02
Well, the existential global threat is the United States intelligence agencies, along with Six Eyes,

1791
2:41:02 --> 2:41:09
the six intelligence agencies of the countries that are being depopulated the most. United States,

1792
2:41:11 --> 2:41:16
Australia, New Zealand, Canada, United Kingdom, and the Six Eyes, Israel. And we really need to

1793
2:41:17 --> 2:41:21
come to grips with the fact that there are good and bad in all these countries. And we need to

1794
2:41:21 --> 2:41:30
bring the good people back to God and stop God's bad son or the fallen angels from allowing everybody

1795
2:41:30 --> 2:41:37
to be deceived and stop people for doing evil stuff, even though they're getting paid for it.

1796
2:41:38 --> 2:41:44
And that's really the crux of it. Yeah. Yeah, I agree. Actually, I think that that is the heart

1797
2:41:44 --> 2:41:49
of everything. If we bring people back to God, everything, all these problems go away.

1798
2:41:49 --> 2:41:52
Every single one. But we have to bring the yes, but we have to bring the truth to the people.

1799
2:41:52 --> 2:41:58
And we have to actually bring the evidence to the opposition. Any any evidence. And we were

1800
2:41:58 --> 2:42:07
talking with Kirk Moore, who has a case where the HHSOIG and the AUSA are suing him for not

1801
2:42:07 --> 2:42:12
giving the vaccines or allegedly not giving the vaccines or whatever. But we know that these

1802
2:42:13 --> 2:42:18
vaccines or virus, the SARS-CoV-2 spike protein is a bioterror weapon. And they, the intelligence

1803
2:42:18 --> 2:42:24
agencies aren't telling us that they developed it or they knew about it. They knew it was the

1804
2:42:24 --> 2:42:29
intelligence agencies know it's carcinogenic. I mean, how can they mandate that we vaccinate

1805
2:42:29 --> 2:42:38
people with a carcinogen? And if they're obligated to... Actually, I'm sorry. One thing that bugs me

1806
2:42:38 --> 2:42:43
about the vaccine that I don't hear talked about too much. It is talked about, but it's just not

1807
2:42:43 --> 2:42:49
as dramatic, I guess, as some of the other things is how it causes miscarriages and sterilizes its

1808
2:42:49 --> 2:42:56
victims. Because there's this larger agenda where every single front seems to be designed to

1809
2:42:57 --> 2:43:01
prevent people from having kids or to kill the ones that we do.

1810
2:43:01 --> 2:43:09
Yes, but if you know that Albert Borla, the CEO of Pfizer, is not a physician, he's a veterinarian.

1811
2:43:10 --> 2:43:17
All right. He's a veterinarian. And his specialty is two things. Number one, his specialty is how

1812
2:43:17 --> 2:43:24
melatonin protects the fertility of ram sperm. So consider taking melatonin at night. And number two,

1813
2:43:25 --> 2:43:33
his specialty is immunocastration, castrating animals with a series of vaccines.

1814
2:43:35 --> 2:43:41
Why is a guy whose specialty is castrating animals with a series of vaccines the head of Pfizer?

1815
2:43:41 --> 2:43:45
My gosh. And we know that if you get SARS-CoV-2, right?

1816
2:43:49 --> 2:43:50
And if you get...

1817
2:43:50 --> 2:43:51
That is interesting. I agree.

1818
2:43:51 --> 2:43:55
Well, I'm glad you like it. I'm glad you find this interesting. And I don't want to talk too

1819
2:43:55 --> 2:44:01
much longer because I've exceeded my time. But if that's the guy, if that's Albert Borla's specialty,

1820
2:44:03 --> 2:44:12
why don't we ask him how exactly his specialty has to do with Pfizer and why he is not giving

1821
2:44:12 --> 2:44:18
us the treatments and apologizing to the American people and to the people of the world for castrating

1822
2:44:18 --> 2:44:24
us in a genetically specific manner that may not affect him or the people who commissioned this

1823
2:44:24 --> 2:44:30
spike protein. And that's the real secret. You can't have a spike protein that castrates

1824
2:44:30 --> 2:44:34
everybody. You have to protect yourself and your children or the people who invented it have to

1825
2:44:34 --> 2:44:40
protect themselves and their children. Who did it? And nobody will tell, including the intelligence

1826
2:44:40 --> 2:44:45
agencies, will not say who invented the SARS-CoV-2 spike protein and on which supercomputer. And that

1827
2:44:45 --> 2:44:51
brings back to you, it's the supercomputers that are developing these things, the antidotes,

1828
2:44:51 --> 2:44:56
and we're not being given the antidotes. So I look forward to your solutions on how we prevent the

1829
2:44:56 --> 2:45:04
next one, because this 2024 election is going to be really horrific. And how do we stop? And as far

1830
2:45:04 --> 2:45:08
as I can see, it took us four years to figure out all this nonsense. We have to stop it from

1831
2:45:08 --> 2:45:12
happening this next round. How are we going to do that? How are we going to stop?

1832
2:45:12 --> 2:45:14
I think it's going to come down to non-compliance.

1833
2:45:15 --> 2:45:22
And here's the easy way. We have to vote, but the issue is they've got three-way election now,

1834
2:45:22 --> 2:45:30
which is so easy to throw. They've got instead a Democrat, a Kennedy, can't run as a Democrat.

1835
2:45:30 --> 2:45:34
I mean, my gosh, that's ridiculous. How are we going to stop a three-way election from being thrown?

1836
2:45:35 --> 2:45:36
Thank you.

1837
2:45:36 --> 2:45:45
So thanks, Joe. So I wanted to just say, so to your point, Andrew, about getting people back to God.

1838
2:45:48 --> 2:45:52
And you mentioned the other problem that, you know, when these people do not

1839
2:45:53 --> 2:45:58
have a God, then they create their own God. And the gods for many of these human beings

1840
2:45:59 --> 2:46:06
seem to be themselves. Harari being a good example. But so one thing that was interesting

1841
2:46:06 --> 2:46:11
about three days ago, I saw an email just happened to see one email which had a subject and

1842
2:46:12 --> 2:46:20
the 10 commandments are going to be taught in Louisiana schools again, which raised the question

1843
2:46:20 --> 2:46:28
in my mind, how in the world did Louisiana ever get rid of that requirement? It's a deeply religious

1844
2:46:28 --> 2:46:35
state, as I understand it. Yeah, that's my impression too. I can't answer that question

1845
2:46:35 --> 2:46:41
because I don't know. I grew up in California and I actually do remember when there was prayer

1846
2:46:41 --> 2:46:48
in school and then it was done. And at the time, I thought that was a good thing because I didn't

1847
2:46:48 --> 2:46:55
really understand what was good and bad. And obviously, yeah, when my children were small,

1848
2:46:56 --> 2:47:02
I had a very good friend who made a point of I didn't really know him. He was from he was Swedish.

1849
2:47:02 --> 2:47:07
I was in Sweden at the time. And but he was a good friend. And he said,

1850
2:47:09 --> 2:47:14
introduce your take your children to church and introduce them to at least give them the possibility

1851
2:47:14 --> 2:47:24
to read the Bible and stuff like that. And I said, Why, why do you mention that? Nothing else. He

1852
2:47:24 --> 2:47:30
said, because if you don't, he said, your children will create their own God, and their own God might

1853
2:47:30 --> 2:47:40
be a lot worse than the God we know about. So and I thought that was I'm not a Christian. I'm open to

1854
2:47:40 --> 2:47:47
it. But but I was brought up as a Christian Church of England. But I do have noticed in the last four

1855
2:47:47 --> 2:47:57
and a half years that the people who pray and who get comfort from the knowledge that God is with

1856
2:47:57 --> 2:48:03
them, they are our allies, because they are so brave, and they are prepared to take risks that

1857
2:48:04 --> 2:48:10
people who don't believe in God are not prepared to take. And so those are the people I really

1858
2:48:10 --> 2:48:17
respect and who I've regarded as my biggest allies, that almost to a man.

1859
2:48:21 --> 2:48:27
Sorry. Sorry, I wanted to comment on that. Because this reminds me of something that

1860
2:48:28 --> 2:48:36
I've always found rather curious about most entertainment in the form of films and

1861
2:48:36 --> 2:48:43
television shows. And that is if you if you look at what the worst possible thing is that they can

1862
2:48:43 --> 2:48:47
they can think of for their script to drive their plot, whatever it might happen to be,

1863
2:48:48 --> 2:48:55
is death or physical torture. And as far as I can tell, based on my understanding of the world,

1864
2:48:56 --> 2:49:03
the worst possible thing anybody can do is to be disobedient to God, that that is worse than

1865
2:49:03 --> 2:49:06
absolutely anything. There's nothing that even comes close to comparing with that.

1866
2:49:07 --> 2:49:15
And that's why the kind of threats that are levied against honest people don't affect them

1867
2:49:15 --> 2:49:20
quite as much. They might feel the pain, they might actually be harmed, they might not like it, but

1868
2:49:21 --> 2:49:26
they quite often, provided they're strong in their faith, are going to stick to their guns.

1869
2:49:26 --> 2:49:34
The reason is because they know that it is worse to be disobedient to God and his commandment

1870
2:49:34 --> 2:49:40
than it is to suffer whatever it is the bad guys might want to throw at us. That's just my feeling.

1871
2:49:42 --> 2:49:51
Yeah, so I just I've got a couple of points here. So Jim was talking to you about the

1872
2:49:51 --> 2:50:00
the guy who Kirk Moore, the doctor who's being, he said sued, but actually, as I understood,

1873
2:50:00 --> 2:50:09
is he's being he has been indicted by federally criminal criminal indictment, as I understand it.

1874
2:50:09 --> 2:50:17
And so anyway, that's very interesting. He was a brilliant guest. And you would get on well,

1875
2:50:17 --> 2:50:24
very well with him. And I wanted to ask you about the it seems to me that the the thing that makes

1876
2:50:24 --> 2:50:31
the discovery of the algorithms so important in your mind is that it takes away the plausible

1877
2:50:31 --> 2:50:38
deniability, which is often built into these intended crimes. You can see it in the 20s,

1878
2:50:38 --> 2:50:43
you since in the last four and a half years, there's been a lot of criminality, in my opinion,

1879
2:50:43 --> 2:50:50
but always there's this plausible deniability built into it, which actually makes things 10

1880
2:50:50 --> 2:51:00
times worse in my mind. Because it demonstrates the extent of evil. So so with the the interesting

1881
2:51:00 --> 2:51:05
thing about your discovery of the algorithms is that the plausible deniability disappears,

1882
2:51:06 --> 2:51:10
but it's still evil. So I just wondered whether you was in your comments.

1883
2:51:13 --> 2:51:21
Well, the thing that bugs me about it is that while that's true, I really wish I could take this

1884
2:51:21 --> 2:51:27
all the way to the finish line by gaining access to the computers that they're using on the other

1885
2:51:27 --> 2:51:34
side. You know, one thing that is causing a lot of problems for everyone involved with any kind of

1886
2:51:34 --> 2:51:39
election integrity investigations is that the officials on the other side aren't willing to

1887
2:51:39 --> 2:51:44
be transparent. So even though a lot tells us that they must give us certain materials,

1888
2:51:44 --> 2:51:50
they refuse to do it. And if they refuse, there's not much we can do. And what this does is it kind

1889
2:51:50 --> 2:51:55
of reminds me of when I was a little kid. And you've got a bully blocking your way at a certain

1890
2:51:55 --> 2:52:02
point, you know, the only way to get him to move is to push them out of your way. And I feel like

1891
2:52:03 --> 2:52:07
I'm just gonna call them the bad guys generically here, because I don't know who exactly is doing

1892
2:52:07 --> 2:52:14
all this stuff. But the bad guys are essentially daring us to push them out of the way physically,

1893
2:52:14 --> 2:52:20
knowing that we don't want to because then all of a sudden we lose plausible deniability on the

1894
2:52:20 --> 2:52:25
righteousness of our action. So it forces us to deal with them on a level where they have

1895
2:52:25 --> 2:52:32
a really strong advantage. But one good thing I happen to be seeing, or at least I think I'm

1896
2:52:32 --> 2:52:40
seeing is things seem to be turning in our favor now. It's very interesting to see that,

1897
2:52:40 --> 2:52:48
because they really did have all of the advantages. And they seem to be whittled down,

1898
2:52:48 --> 2:52:54
you know, a little bit more every day and bigger and smaller chunks. And I think that eventually

1899
2:52:54 --> 2:53:01
it's going to erode to a point where there's a total loss of faith in leadership. And this is

1900
2:53:01 --> 2:53:09
something I've seen at some big companies I've worked for, where when the subordinates lose

1901
2:53:09 --> 2:53:15
faith in their superiors, the superiors lose all power because they're hamstrung, they absolutely

1902
2:53:15 --> 2:53:22
can't get anything done if the people working for them don't want to do it. And it's kind of like a

1903
2:53:22 --> 2:53:28
strike without going on strike. I've seen this in more than one office, by the way. I've experienced

1904
2:53:28 --> 2:53:35
when I was working at Universal Studios, the people working there somehow intuitively realized

1905
2:53:35 --> 2:53:45
that our bosses were not, they didn't really know what they were doing. As a result, they started

1906
2:53:45 --> 2:53:49
doing less work. And this became a crisis of leadership where I was ultimately offered

1907
2:53:51 --> 2:53:57
their job, along with a couple colleagues of mine. The management at Universal said, would you,

1908
2:53:57 --> 2:54:02
Andrew, and you two guys? So Andrew, I worked for the military, the British military,

1909
2:54:03 --> 2:54:09
and some of my officers became very good friends of mine so we could talk openly. I wasn't military,

1910
2:54:09 --> 2:54:16
I was working for the military, but I wasn't military. And they said, and they told me that

1911
2:54:17 --> 2:54:25
the biggest fear in any officer's mind is when the men turn against the officers. So, you know,

1912
2:54:25 --> 2:54:31
and very often wars are ended. So the Vietnam War, I believe, was one of these, that the officers

1913
2:54:31 --> 2:54:36
were getting shot in the back by their own men. That's when they pulled the plug on the wars.

1914
2:54:39 --> 2:54:44
It's a similar thing to what you were describing. I didn't know that. Yeah, yeah. They get shot.

1915
2:54:44 --> 2:54:51
Yeah, I mean, when the county clerk no longer believes her boss has the authority to do things

1916
2:54:51 --> 2:54:56
that she knows are illegal, and so she refuses to do them or protects herself against it by

1917
2:54:57 --> 2:55:03
storing evidence away, if it's only Tina Peters in Colorado, they might be able to shut her up.

1918
2:55:03 --> 2:55:09
But if it's not just Tina Peters, but, you know, 20 and then 30 and then 40 and then 100 other

1919
2:55:09 --> 2:55:14
county clerks all around the country, that becomes an unstoppable wave. Because the one thing about

1920
2:55:14 --> 2:55:19
human beings that I haven't been noticing, or you have as well, is that we really detest it when

1921
2:55:19 --> 2:55:25
people lie to us. It's really one of the worst things that anyone can do to another person is to

1922
2:55:26 --> 2:55:36
deceive them. And with good reason, because lies put us all at risk. So if a leader is telling

1923
2:55:36 --> 2:55:43
you lies, it's very serious, because all the people he's leading, he's putting at risk of

1924
2:55:43 --> 2:55:50
losing their lives. So human beings sense when they're being lied to. And as you say,

1925
2:55:50 --> 2:55:54
it's extremely important to them, because they need to be able to trust the people who are

1926
2:55:54 --> 2:56:02
supposedly leading them. And now it seems to me that governments are promoting anti-human agendas,

1927
2:56:03 --> 2:56:07
which are against human nature. But they've also betrayed their countries in the last

1928
2:56:07 --> 2:56:11
four and a half years. These governments and politicians. Oh, definitely.

1929
2:56:11 --> 2:56:13
And the guilty of treason.

1930
2:56:13 --> 2:56:21
By the way, as far as I'm concerned, oh yeah. I'll tell you something interesting. When I presented

1931
2:56:21 --> 2:56:27
this material to some people at the Special Investigations Unit in Albany, at the end of

1932
2:56:27 --> 2:56:33
the meeting, one of the guys there said, this is treason. Just right out like that.

1933
2:56:33 --> 2:56:33
Absolutely.

1934
2:56:35 --> 2:56:39
A couple of the other guys nodded their heads in agreement. But the thing is, is that

1935
2:56:39 --> 2:56:46
the truth is something that once somebody realizes that they've been deceived,

1936
2:56:47 --> 2:56:52
they lose all faith in that person. And it is very hard to recover it, if at all. So the most part,

1937
2:56:52 --> 2:56:56
once you've been lied to, it's like a cheating spouse. It's like you never really fully trust

1938
2:56:56 --> 2:57:04
that person again. And I see that is going to probably be ultimately what brings this whole

1939
2:57:05 --> 2:57:12
Altschuh-Karz down. But to get there, all of this information has to get out. So like this stuff I'm

1940
2:57:12 --> 2:57:18
talking to you about, this is just one part of the puzzle. And it's going to reach a certain

1941
2:57:18 --> 2:57:23
group of people. And those people, a subset of them are going to realize, oh, hey, wait a minute,

1942
2:57:23 --> 2:57:26
this realizes I've been lied to. I can't trust them anymore. Okay. So that's great. Now they're

1943
2:57:26 --> 2:57:31
on our side. But there are so many other things that are going on simultaneously. They're going

1944
2:57:31 --> 2:57:35
to reach different audiences. All those things have to happen. The vaccine stuff is terribly

1945
2:57:35 --> 2:57:42
important. And it's causing people to open their eyes. And eventually, I believe our leadership

1946
2:57:42 --> 2:57:48
will lose all of their support from their subordinates. And at that point, the whole

1947
2:57:48 --> 2:57:51
thing is going to fall apart. That's what I think is going to happen.

1948
2:57:51 --> 2:57:57
Sure. But the problem with things falling apart, Andrew, is that, you know, what's going to replace

1949
2:57:57 --> 2:58:04
it? So it's a bit, you know, when the system is broken up and falling apart, there'll be a time

1950
2:58:04 --> 2:58:10
when the people are not governed. And unfortunately, people, human beings, what they are,

1951
2:58:10 --> 2:58:17
you know, they like to be in cults. And the problem is atrocities get committed when there's no

1952
2:58:17 --> 2:58:21
leadership. Do you see when there's a power vacuum? And that's the worry.

1953
2:58:21 --> 2:58:30
Oh, yeah. That's one of the most dangerous aspects of the election problems that we're seeing here.

1954
2:58:30 --> 2:58:35
Like, for instance, I'm convinced, and perhaps I'm deceiving myself to be convinced this way,

1955
2:58:35 --> 2:58:40
but at the moment, I am convinced that every election in New York has been invalid for the

1956
2:58:40 --> 2:58:46
last several years, possibly as far back as the year 2000. Meaning every single person who's been

1957
2:58:46 --> 2:58:51
in office, whether they actually got enough votes from the voters to put them there or not,

1958
2:58:51 --> 2:58:56
there's no way to know. And therefore, they may as well be illegitimate because all of the elections

1959
2:58:56 --> 2:59:01
have been controlled, which means the normal and appropriate remedy would be to take every single

1960
2:59:01 --> 2:59:05
person out of office, which means, as you say, we would have no government at all. We'd have no one

1961
2:59:05 --> 2:59:11
there to deal with making sure that our power is on and the lights are on and the police are getting

1962
2:59:11 --> 2:59:16
paid and all that kind of stuff. I agree that would be a problem. However, sometimes you just

1963
2:59:16 --> 2:59:21
have to do it. So the question is, is this the difference between smoking and not smoking where

1964
2:59:21 --> 2:59:27
you immediately start driving benefits from not smoking? Or is it more like taking out a very

1965
2:59:27 --> 2:59:33
badly placed malignant tumor in your brain? It has to be removed and it has to be removed fully,

1966
2:59:33 --> 2:59:38
but you have to be extremely careful how you do it. Now, it's more like the latter than the former,

1967
2:59:38 --> 2:59:42
but it doesn't change the fact that it has to be removed completely. And I do believe that is

1968
2:59:43 --> 2:59:49
absolutely true. I don't think any part of it should be left. And I hate to put it this way.

1969
2:59:49 --> 2:59:54
I don't want to sound like a tough guy or anything, but I don't know how America survives

1970
2:59:54 --> 3:00:01
if we tolerate this kind of thing. In fact, I find it very interesting that tolerance has become the

1971
3:00:01 --> 3:00:07
tolerance has become the anthem of the bad people. Because essentially what they want is you want,

1972
3:00:08 --> 3:00:17
they want you to tolerate any evil that they might happen to commit. And as such, I would say that

1973
3:00:17 --> 3:00:26
the tolerance is, as a slogan anyway, is a very evil thing. And it's something that we have to

1974
3:00:26 --> 3:00:30
turn around. We cannot say that, let's just say, I don't know what the denomination is all over

1975
3:00:30 --> 3:00:35
the people on this call belong to, but as a Christian or as a godly nation that we are going

1976
3:00:35 --> 3:00:42
to tolerate evil. It's just not going to happen. So Andrew, I think one of the key things in America

1977
3:00:42 --> 3:00:46
in particular, you know, so people who are Americans love America with good reason,

1978
3:00:47 --> 3:00:55
apart from the illegal wars and all that. But anyway, and the so people who are friends of

1979
3:00:55 --> 3:01:03
America feel this way, you know, that I think that one of the key things in America could be

1980
3:01:04 --> 3:01:09
if people like you started mentioning treason, as it should be mentioned at the moment,

1981
3:01:10 --> 3:01:16
at the moment, and after the unbelievable things that have happened in the last four years,

1982
3:01:16 --> 3:01:21
I really think that people understand treason, they know it's wrong. And then but they need

1983
3:01:21 --> 3:01:27
to be reminded, this one of the worst crimes that can be committed, at least in the United Kingdom.

1984
3:01:27 --> 3:01:31
So until quite recently, I think in the last 20 years, the only crime that you could be

1985
3:01:33 --> 3:01:39
get a capital punishment for in the UK, in theory, this was was high treason. But I think that's been

1986
3:01:39 --> 3:01:48
removed now. So it's so according to British, Britain, the worst crime is definitely treason,

1987
3:01:49 --> 3:01:55
worse than mass murder. And so I think we ought to emphasize that because I agree with you that what

1988
3:01:55 --> 3:02:05
you discovered, as I understand it, the key discovery of yours is the is the the algorithms.

1989
3:02:06 --> 3:02:14
And I think that when you mentioned that the reason they need to be interested in it and

1990
3:02:14 --> 3:02:18
outraged is that it's treason. The people who set up this system.

1991
3:02:19 --> 3:02:26
Yeah. So one thing about that particular word is that the people on the other side of this are

1992
3:02:26 --> 3:02:32
trying to redefine. And they're trying to narrow it to such an extent that it becomes very difficult

1993
3:02:32 --> 3:02:37
to say that. And they're also trying to say that saying something is treason is the equivalent of

1994
3:02:37 --> 3:02:43
inciting people to commit murder, because the punishment for treason is execution.

1995
3:02:44 --> 3:02:48
And by the way, I agree with you. But I'm just telling you what the other side of this looks

1996
3:02:48 --> 3:02:57
like. And the thing is, when I look at it, I think that the algorithm actually comes

1997
3:02:57 --> 3:03:04
about as close as we're going to get to showing that, particularly after look, I've only got one

1998
3:03:04 --> 3:03:08
source for this. So I don't I don't like saying this is a fact. So I'm just going to tell you what

1999
3:03:08 --> 3:03:14
the source said. And this has to be independently verified, make me feel happy about it. But

2000
3:03:15 --> 3:03:21
I showed this algorithm. I spent a lot of time with this guy, four hours in person and a couple

2001
3:03:21 --> 3:03:29
hours on the phone. And he said he'd seen this algorithm like it or the same one used in to

2002
3:03:29 --> 3:03:36
control an election in I think it was Iraq. But he said the Middle East, because he said that when

2003
3:03:36 --> 3:03:43
he was with military intelligence, and Saddam Hussein was overthrown, they they went ahead and

2004
3:03:43 --> 3:03:47
support of the election. And the goal was to make sure the bosses didn't come back to power. And so

2005
3:03:47 --> 3:03:54
what they did was they use this tool, which he said resembled the algorithm I found to control

2006
3:03:54 --> 3:03:59
the voter rolls so that they could ensure that the candidate they wanted to win would win,

2007
3:04:00 --> 3:04:08
which would mean that they're using this in foreign countries. Okay. And that gets us a lot closer to

2008
3:04:08 --> 3:04:15
treason, because now we're using something that would be treason there here. And also,

2009
3:04:16 --> 3:04:20
this might not be the greatest of arguments. But Jerome, are you still on the call? What do you

2010
3:04:20 --> 3:04:28
think? Am I right about this or not? Jerome, I think so he's muted though. Jerome, are you there?

2011
3:04:29 --> 3:04:34
He might have walked away briefly. Probably. Does that make sense to you?

2012
3:04:34 --> 3:04:43
It does to me. Yes, absolutely. But I can't speak for everybody on the call. But I just wanted to

2013
3:04:43 --> 3:04:51
what so you mentioned that there was some settlement agreement which was ignored. And that was a fraud

2014
3:04:51 --> 3:04:58
of the courts. Did you are you intending to take that up again, because that seems very important

2015
3:04:59 --> 3:05:05
to me. So if you get nowhere with, you know, if you if you question them about that, and

2016
3:05:05 --> 3:05:10
and nothing happens, then you've got proof that the that the court is being defrauded,

2017
3:05:10 --> 3:05:16
but actually the court doesn't care. I have a feeling that the court doesn't care. And

2018
3:05:17 --> 3:05:24
I also have a feeling that for whatever reason, judicial watch is uninterested in pursuing it.

2019
3:05:25 --> 3:05:30
I did publicize it by writing an article about it. And I have spoken about it to other people.

2020
3:05:31 --> 3:05:37
I have yet to get a reaction from judicial watch. And I certainly haven't had a reaction from

2021
3:05:37 --> 3:05:45
anyone else. But in my opinion, it's definitely worth following up on the explanation given in

2022
3:05:45 --> 3:05:51
the in the court documents, which are signed as you know, truthful and are a condition of the

2023
3:05:51 --> 3:05:57
settlement agreement are as far as I can tell, false by looking at the two databases involved.

2024
3:05:58 --> 3:06:02
Well, not least it shows that the rule of law isn't operating in

2025
3:06:04 --> 3:06:09
as far as that court is concerned anyway. But actually one of the one of the worst things

2026
3:06:09 --> 3:06:13
that happened in America was when lawyers started getting threatened. I mean, never mind

2027
3:06:14 --> 3:06:21
the disbarred which happened to Rudy Giuliani and a few others. But before that happened,

2028
3:06:22 --> 3:06:29
we had attorneys who worked for Donald Trump, who are getting threatened by local prosecutors,

2029
3:06:30 --> 3:06:35
saying that they would go after their law licenses if they worked for Donald Trump,

2030
3:06:35 --> 3:06:39
because what Donald Trump was doing was challenging election. And this is a very bad

2031
3:06:39 --> 3:06:43
thing, nevermind that Democrats do it all the time. But when I saw that, I was thinking,

2032
3:06:43 --> 3:06:48
hey, wait a minute, we have a right to counsel of our choice, right. But if they're threatening

2033
3:06:48 --> 3:06:52
the Council of our choice, then we no longer have the Council of our choice. Absolutely.

2034
3:06:52 --> 3:06:57
And they now deny us that constitutional rights. And I haven't seen a single person

2035
3:06:58 --> 3:07:02
write one word on that subject, which really disappoints me. I've read quite a lot about the

2036
3:07:02 --> 3:07:07
subject. I've read a lot of very popular commentators like Alan Dershowitz and

2037
3:07:07 --> 3:07:12
Jonathan Turley and a number of other attorneys who've opined on the subject, but none of them

2038
3:07:12 --> 3:07:18
have dealt with the fact that this denies us our right to counsel. And I suspect the reason may be

2039
3:07:18 --> 3:07:26
because the right to counsel is limited to, or at least it's described as a part of our criminal

2040
3:07:26 --> 3:07:34
code. And at the time, the cases that Donald Trump was filing were not criminal, they were civil

2041
3:07:34 --> 3:07:39
cases. But eventually they did become criminal cases. And it still isn't being talked about. I

2042
3:07:39 --> 3:07:44
think it's a very important issue because as soon as you start telling lawyers you're going to be

2043
3:07:44 --> 3:07:50
disbarred or you're going to be thrown in jail or you're going to be fined $94 million like Giuliani

2044
3:07:50 --> 3:07:58
was for simply representing your client, there's no way you can have the representation of your

2045
3:07:58 --> 3:08:02
choice with that. Absolutely. Because the lawyers who otherwise represent you are not available.

2046
3:08:03 --> 3:08:11
Yep, so they use threats, I agree, and coercion. And so I was going to ask you whether you think

2047
3:08:11 --> 3:08:16
with a judicial watch they've gone quiet about that or whatever, was it judicial watch?

2048
3:08:18 --> 3:08:20
Maybe they were threatened, unknown to you.

2049
3:08:22 --> 3:08:27
Yeah, I can buy that. I mean, I don't want to cast dispersions on their motives because I don't know

2050
3:08:27 --> 3:08:34
what they are. And I certainly know that I don't want to take unnecessary risk. I don't have the

2051
3:08:35 --> 3:08:40
job right now. I mean, I do occasional freelance work. But I certainly don't make the kind of

2052
3:08:40 --> 3:08:47
money I used to make when it wasn't illegal to be a conservative in the country. And just for an

2053
3:08:47 --> 3:08:55
instance, I used to be a very well-respected, successful university lecturer. And I can't get

2054
3:08:55 --> 3:09:00
a job as a university lecturer in America because I'm conservative and I'm not about to find one of

2055
3:09:00 --> 3:09:06
those diversity statements that they want everyone to come in. Oh, great. As far as I'm concerned,

2056
3:09:07 --> 3:09:14
there's a lot. So you won't sign them? That causes me to self- What? Sorry, what did you say you

2057
3:09:14 --> 3:09:23
won't sign them interested in? Well, they have these forms, they're called diversity and inclusion

2058
3:09:23 --> 3:09:30
statements. And so anyone who wants to apply for a job at a university has to find this thing that

2059
3:09:30 --> 3:09:35
says that you're committed to diversity, that if somebody tells you they're a girl, even though

2060
3:09:35 --> 3:09:41
they're obviously a boy, you're going to pretend that they're a girl, this kind of thing. And I'm

2061
3:09:41 --> 3:09:45
just, that's not something I'm going to do. It's not honest to find something like that. And anyone

2062
3:09:45 --> 3:09:51
who does find it just to get a job is, you know, they're being dishonest. So I either become one

2063
3:09:51 --> 3:09:59
of them in order to get a job or I refuse and I don't get the job. So I know people who sign those

2064
3:10:00 --> 3:10:05
forms willy-nilly because they have no, they told me they sign them because they have absolutely no

2065
3:10:05 --> 3:10:12
intention of honoring what they've signed. So I don't know what- Yeah, but I feel uncomfortable.

2066
3:10:13 --> 3:10:19
Sorry? Yes, I understand that you're- I'm not comfortable doing something like that. If it's

2067
3:10:19 --> 3:10:28
false, I just can't bring myself to find it. So essentially they sign them, they sign them daring

2068
3:10:28 --> 3:10:35
the employer to sue them if they don't, do you understand what I mean? They just don't care.

2069
3:10:35 --> 3:10:41
They just sign it as a ridiculous piece of paper. They have no respect for the piece of paper. They

2070
3:10:41 --> 3:10:48
have no intention of doing what they've said. And I agree. I can understand that you wouldn't

2071
3:10:48 --> 3:10:55
want to compromise yourself. I wanted to ask you, the last question was where you don't,

2072
3:10:55 --> 3:10:58
you know, if you don't want to answer this, that's fine. But where do you get your

2073
3:10:58 --> 3:11:03
unbelievably strong values from, Andrew? We need more people like you.

2074
3:11:05 --> 3:11:13
I can't, I don't really know for sure. But one thing I do know is that I do have an innate sense

2075
3:11:13 --> 3:11:21
of whether something I have said is correct or not. And if I get a sense that it's not,

2076
3:11:21 --> 3:11:28
I tend to investigate it to see why. Okay. And I also made a deal with myself a long, long time ago

2077
3:11:28 --> 3:11:34
that if I ever find out that I said something that was incorrect to someone else, I have to correct

2078
3:11:34 --> 3:11:41
it. So I will make real efforts to like track down the person and, you know, call them up or write

2079
3:11:41 --> 3:11:45
them or see them in person or whatever it is to correct the error. Now, it's not always practical

2080
3:11:45 --> 3:11:50
to do that. But what this means is it's a huge incentive for me not to make the error in the

2081
3:11:50 --> 3:11:55
first place, which makes me a lot more careful with what I say. And it makes me more willing

2082
3:11:55 --> 3:12:02
to investigate things. I'll tell you one thing that's kind of an interesting subject on its own.

2083
3:12:03 --> 3:12:08
But a long time ago, when I first met my wife, she was constantly telling me, you know, Andrew,

2084
3:12:09 --> 3:12:14
that dream you told me about yesterday or last week or whatever just came true. So you're having

2085
3:12:14 --> 3:12:19
dreams about the future. And I thought she was out of her mind and it was ridiculous. And I kept

2086
3:12:19 --> 3:12:24
telling her that. But then that little voice in the back of my mind said, you know, you're telling

2087
3:12:24 --> 3:12:30
her this is not true because it's impossible is not a very good answer. You have to actually prove

2088
3:12:30 --> 3:12:36
it to her and to yourself before you can actually say that honestly. So I finally figured out that

2089
3:12:36 --> 3:12:44
if I kept a journal of my dreams that was dated with every dream, I could prove that she was wrong.

2090
3:12:44 --> 3:12:49
It actually proved she was right. At which point I had to eat my words, which I did.

2091
3:12:50 --> 3:12:56
That's what the other subject is about. I'm kind of well known in the.

2092
3:12:56 --> 3:13:04
Are you able to share the the the the address of the internet address of the substance in the

2093
3:13:04 --> 3:13:10
chat or not the one you're talking about, the relatively hidden one? Yeah, I can. I don't talk

2094
3:13:10 --> 3:13:15
about this too much because some people are made very uncomfortable by people who claim to have had

2095
3:13:16 --> 3:13:22
this kind of experience. But I've had so many of them. I do write about it. I'm doing it right now.

2096
3:13:22 --> 3:13:30
I'm typing it in. If I were you, I would go to the earliest pages, not the most recent ones,

2097
3:13:30 --> 3:13:38
because those are the most spectacular ones. Sorry, when you missed that one, sorry. Pardon?

2098
3:13:38 --> 3:13:44
What did you say? You said I would go to if I were you, I would go to and then that word I missed.

2099
3:13:44 --> 3:13:55
The earlier entries and the reason is because the more recent ones are they just concern

2100
3:13:55 --> 3:14:03
what I consider to be a weaker introduction to the material than if you go to the older one.

2101
3:14:03 --> 3:14:09
I did it because I've had some dreams I think are so important that they basically should be shared.

2102
3:14:09 --> 3:14:18
You know, like 911. I had eight dreams about the 911 disaster in the year 1990 and one in 1989.

2103
3:14:19 --> 3:14:23
And so I printed those off and I sent them to some people at the time.

2104
3:14:24 --> 3:14:29
But they're not all like that. Some of them actually do. Some of them have to do with other

2105
3:14:29 --> 3:14:33
subjects. Like for one, I'm going to leave out all the details and just tell you the punch line. But

2106
3:14:33 --> 3:14:38
basically I was told in the dream that it's better to live a blameless life than to wish for and

2107
3:14:38 --> 3:14:46
receive a comfortable one. Okay. And I think that the logic of that stands on its own. And I've

2108
3:14:46 --> 3:14:51
certainly had other advice like that that I consider equally salutary. Does that make sense?

2109
3:14:52 --> 3:14:58
Yes, absolutely. It does to us anyway. Okay. Andrew, thank you so much for coming on

2110
3:14:58 --> 3:15:05
and for speaking so bravely. And what a brilliant thing you've done. So as I understand it,

2111
3:15:05 --> 3:15:12
let's just nail this down. Are you the first one to have discovered these algorithms in the

2112
3:15:12 --> 3:15:20
voting systems? As far as I know, yes. And I haven't encountered anyone who has said any different.

2113
3:15:21 --> 3:15:27
And actually, I'll go again, if Jerome is here, I think he'd be a better person to hear this

2114
3:15:27 --> 3:15:32
because, you know, I'm talking about myself all of a sudden. But yeah, so Jerome, are you here?

2115
3:15:35 --> 3:15:44
Jerome, I think he's walked away from this. Okay, I'll say this. But from what I'm told by the

2116
3:15:45 --> 3:15:50
the experts, like what I've been told from experts I've talked to on this,

2117
3:15:51 --> 3:15:56
this algorithm is so well hidden that they're astonished that it was ever found at all.

2118
3:15:57 --> 3:16:03
Never mind that it was me that found it. Apparently, they consider this to be almost

2119
3:16:03 --> 3:16:12
a miracle that it was found. Brilliant. Brilliant work by you. Well, after listening to you,

2120
3:16:12 --> 3:16:17
I'll tell you something. So after listening to you for over three hours now, Andrew,

2121
3:16:17 --> 3:16:23
I'm not astonished. Yeah. You're brilliant. You probably don't realize it. Well, maybe you do

2122
3:16:23 --> 3:16:27
partly but not you don't understand how brilliant you are, I think.

2123
3:16:29 --> 3:16:33
So anyway, well, thank you. I appreciate it. What were you going to tell us? Okay, so

2124
3:16:36 --> 3:16:39
I didn't hear that fully, but I'm assuming you're saying goodbye now so I can hang up

2125
3:16:39 --> 3:16:49
or did you have another question? No, I'm saying that you said that you nobody thought that you

2126
3:16:49 --> 3:16:55
would be that anyone could find these algorithms and they were astonished that you had found them.

2127
3:16:55 --> 3:17:02
And I said, well, after listening to you for over three hours now, I'm not astonished. Yeah.

2128
3:17:03 --> 3:17:10
Oh, thank you. Yeah, I think the reason they were astonished it was me is mainly because they were

2129
3:17:10 --> 3:17:14
astonished that anybody found it. Okay, so it just happened to be me. So that's why they're

2130
3:17:14 --> 3:17:19
astonished it was me. Absolutely. The few who've actually gone a little bit farther than that,

2131
3:17:19 --> 3:17:26
to say why in particular is it astonishing that I would, the couple who had any comment on that are,

2132
3:17:26 --> 3:17:33
I think, feel that way because I don't have a math background. And I don't have a background in

2133
3:17:33 --> 3:17:37
cryptography. But on the other hand, Wow, I've got people on the other end of it saying,

2134
3:17:37 --> 3:17:41
that's why you did find it. Because if you did have those backgrounds, you wouldn't. Yes.

2135
3:17:41 --> 3:17:48
Because the way it was implemented was designed to avoid all of the detection

2136
3:17:48 --> 3:17:54
methods used by people with that kind of a background. So yeah, so I think that my

2137
3:17:54 --> 3:17:57
background is... I think they're both copyrights.

2138
3:18:00 --> 3:18:05
Yeah, that wouldn't surprise me. I mean, ultimately, one of the things I became known for in

2139
3:18:05 --> 3:18:12
computer graphics was my ability to fix files that had been screwed up by other people. And what that

2140
3:18:12 --> 3:18:17
meant was I had to analyze very complicated files and find out which parts belong together and then

2141
3:18:17 --> 3:18:25
put them there. Which is very difficult, tedious work that most people don't like to do, but

2142
3:18:25 --> 3:18:31
it doesn't bother me. So I got very good at it and got a reputation for it. And that's essentially

2143
3:18:31 --> 3:18:37
what I was doing here. Very good. Oh, it looks like Jim has another comment. Should I take that

2144
3:18:37 --> 3:18:45
question or are we ending this? Yeah, Jim, go ahead. Thank you very much. I just wanted to know if you

2145
3:18:45 --> 3:18:52
would come on Telegram for a little while and maybe share your sub stack because I can't really

2146
3:18:52 --> 3:18:59
get it through this chat and maybe talk or share some more questions and answers. The Telegram chat

2147
3:19:00 --> 3:19:06
is a place where we can share links and things like that. And maybe in the future, we can put

2148
3:19:06 --> 3:19:11
that Telegram chat on the invitation so that afterward we can talk there if we have more time

2149
3:19:11 --> 3:19:16
or if individuals have more time. So if it's okay with Steven and with Charles,

2150
3:19:16 --> 3:19:24
very appreciative for their moderation. So thanks. I could do that, but not right now. I just say,

2151
3:19:24 --> 3:19:31
you know, I'm working on a project that I have to have finished by Friday and I have been delayed

2152
3:19:31 --> 3:19:38
thanks to excessive number of migraine headaches that had me in bed for about six days. So I want

2153
3:19:39 --> 3:19:44
to do that first. Can I give you some information that may help you with the migraines?

2154
3:19:46 --> 3:19:51
And I have to do that off? Yes. Yeah, do it offline because I actually have to get back to my work.

2155
3:19:53 --> 3:19:59
But that would be fine with it. Okay. And if you can't reach me, you can reach me through the

2156
3:19:59 --> 3:20:09
moderators because consider drinking saffron tea. Consider drinking saffron tea. It increases the

2157
3:20:09 --> 3:20:16
diffusion coefficient through ischemic tissue and the spasms in the vessels are due to ischemic

2158
3:20:16 --> 3:20:24
tissue. So what helps in the acute situation is what may help. This is not advice. Talk to your

2159
3:20:24 --> 3:20:31
physician about it is sipping on some oxygen, which means like, what's this got to do with the,

2160
3:20:32 --> 3:20:37
I don't understand what this has got to do with them. It's just his own headache.

2161
3:20:37 --> 3:20:44
I'll talk to him later on. Thank you very much. Sorry. Yeah. Okay. Sorry. I think I'm going to let

2162
3:20:44 --> 3:20:50
you guys go then. Yes. Thank you. Well, I was trying to find a good time to finish, but I'm

2163
3:20:50 --> 3:20:55
not the usual moderator Charles is, but he's fallen asleep in Australia, I think,

2164
3:20:55 --> 3:21:00
because it was five o'clock in the morning when we started in Australia, in Melbourne. So thank you

2165
3:21:00 --> 3:21:09
very much, Andrew. You're a brilliant guest and I'm so happy that you've spoken to us. Oh, one last

2166
3:21:09 --> 3:21:17
question. So why are people knowing what we think happened in 2020, some of us anyway,

2167
3:21:17 --> 3:21:20
why are people so confident that that won't happen again?

2168
3:21:23 --> 3:21:31
That I think is really peculiar. It reminds me of something, it's kind of tickling the back of my

2169
3:21:31 --> 3:21:37
mind right now. Well, I'll give you a cartoon. If you remember Charlie Brown back when that was

2170
3:21:37 --> 3:21:41
still being published and Lucy, I was pulling the football away from him at the last minute and him

2171
3:21:42 --> 3:21:46
being willing to give it another try because she kept saying, oh no, I'll hold it this time. And

2172
3:21:46 --> 3:21:55
then she pulls it away again. I think it's kind of an endless hope that it will be done right,

2173
3:21:55 --> 3:21:59
because the person actually doesn't know what else to do, but to hope because they have no idea how

2174
3:21:59 --> 3:22:08
to fix it themselves. Absolutely. I think you're right. Have a great day then. And it was my

2175
3:22:08 --> 3:22:12
pleasure to be able to talk to you guys, although hopefully the next time if we ever do this again,

2176
3:22:12 --> 3:22:16
the connection will be better and there won't be all these time lags. Okay. By the way,

2177
3:22:16 --> 3:22:23
Jerome Corsi is a wonderful ally for you, Andrew. Oh, well, thanks. Yeah, I thought it was interesting

2178
3:22:23 --> 3:22:30
and good that he contacted me. So I appreciated that. He once told me in a conversation,

2179
3:22:31 --> 3:22:37
he once told me in a conversation, I'm sure he won't mind because he's got a good sense of humor.

2180
3:22:37 --> 3:22:43
He once told me in a conversation on the phone that he should have got his PhD when he was 15.

2181
3:22:44 --> 3:22:52
So I said, when did you get your PhD? In government from Harvard, by the way. And he said 21.

2182
3:22:54 --> 3:23:00
And I thought, wow, that's pretty amazing. I wanted to do that when I was young. And I think

2183
3:23:00 --> 3:23:06
I probably could have, but I didn't have the money to go to school. I was one of these typical

2184
3:23:07 --> 3:23:13
poverty stricken kids who had zero money. In fact, that's why I left college, because I only

2185
3:23:13 --> 3:23:19
had enough to go so far. And then I had to quit work for a living. Did you know Aaron Schwartz?

2186
3:23:21 --> 3:23:26
Are you familiar with the work of Aaron Schwartz, who was also a computer genius?

2187
3:23:28 --> 3:23:35
It doesn't ring a bell, but it's a fairly common name. So it's possible. Okay. Well, he was the

2188
3:23:35 --> 3:23:45
guy who was indicted by the federal government for something to do with MIT. They accused him of

2189
3:23:45 --> 3:23:50
stealing documents or something from MIT. I hope I haven't got it wrong. And essentially, they bullied

2190
3:23:50 --> 3:24:00
him. That's starting to sound a little familiar. I'm actually rethinking my opinions on a lot of

2191
3:24:00 --> 3:24:09
things, thanks to my discovery of how biased the media is. There was a politician in Illinois who

2192
3:24:09 --> 3:24:17
took Obama's position in the House of Representatives. And then he got thrown in jail. And I thought,

2193
3:24:17 --> 3:24:24
well, he was a bad guy because he was selling Obama's feet. Because that's the story that went

2194
3:24:24 --> 3:24:30
out. But then he got released after being pardoned by Donald Trump. And he went ahead and gave a

2195
3:24:30 --> 3:24:35
few interviews. And those interviews indicated to me that the charges against him were Trumped up.

2196
3:24:36 --> 3:24:42
And the same thing goes for Julian Assange and Edward Snowden, both of whom I was inclined to

2197
3:24:42 --> 3:24:49
believe the official story on until the last few years when all of a sudden my trust in the media

2198
3:24:49 --> 3:24:54
evaporated to nothing. And now I work with them and I'm thinking, well, those guys were real workers

2199
3:24:54 --> 3:25:01
also. But anyway, I do have to run. Sure. There's a wonderful film about Harry Schwartz on YouTube,

2200
3:25:01 --> 3:25:07
I think, which I thoroughly recommend to you. It's brilliant. And he was targeted by the government,

2201
3:25:07 --> 3:25:10
for some reason unknown to us all. But thank you so much, Andrew, for coming on.

2202
3:25:11 --> 3:25:15
No problem. Have a good day. Bye bye. Yes. Bye bye.