Latest / The Occult Rejects / The Unreasonable Effectiveness of AI in Computer Vision with Ashish Part 2

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The Unreasonable Effectiveness of AI in Computer Vision with Ashish Part 2 · The Occult Rejects

• 0:00
  Speaker 1: But again, as I was saying, the most important thing
• 0:02
  Speaker 1: about the occupancy network for avs is a collision avoidance system.
• 0:06
  Speaker 1: It implicitly has this. Now that that is not to
• 0:09
  Speaker 1: say that it will automatically go on the correct path. No,
• 0:14
  Speaker 1: it just means it will consistently choose a collision avoidant path. So,
• 0:21
  Speaker 1: as you can see here, this car went out of control.
• 0:23
  Speaker 1: It almost collided in the right corner, but the collision
• 0:25
  Speaker 1: avoid the occupancy network avoided it moved over to the left.
• 0:28
  Speaker 1: This is clearly the wrong side of the road, but
• 0:30
  Speaker 1: it avoided another collision and it stabilized. And that's the value. Originally,
• 0:35
  Speaker 1: Initially that was the major value, and it was sufficient
• 0:38
  Speaker 1: to sell it into the av industry just for this alone,
• 0:41
  Speaker 1: because there is software alone doing this. I'm going to
• 0:48
  Speaker 1: skip this because this is going to get really technical. Well,
• 0:55
  Speaker 1: AI people might care about the leaderboard results. I just
• 0:58
  Speaker 1: want to point out we're in the competitive race. Thank
• 1:01
  Speaker 1: god we have in Vidia. That's the listing you see
• 1:03
  Speaker 1: at the top on this leaderboard right here. But then
• 1:06
  Speaker 1: you can see who all the competitors are and how
• 1:10
  Speaker 1: many there are in China. There are At the time
• 1:14
  Speaker 1: I was doing this I'm sure it's way higher than
• 1:17
  Speaker 1: this now, but at that time in twenty twenty three,
• 1:19
  Speaker 1: in China there are more than one hundred and sixty
• 1:24
  Speaker 1: electric vehicle manufacturers. I'm sure the number is way higher
• 1:27
  Speaker 1: than that now, but there's more than one hundred and
• 1:30
  Speaker 1: sixty EV manufacturers in China. In academia, China, Taiwan, East
• 1:40
  Speaker 1: Asia in general, they are super active and competitive on
• 1:46
  Speaker 1: all of the leaderboards in AI, especially related to computer vision.
• 1:53
  Speaker 1: Thank god we have in video. In Vidia consistently is
• 1:57
  Speaker 1: at the top of the leaderboards from America, and then
• 2:00
  Speaker 1: there are basically nobody else in America, literally nobody, It's
• 2:06
  Speaker 1: just in video and then chiny China all the way down. Effectively,
• 2:10
  Speaker 1: that's what we're dealing with. And the reason for this
• 2:13
  Speaker 1: is because of the massive overinvestment in lightar for decades.
• 2:17
  Speaker 1: There's been more than a decade that this has happened.
• 2:20
  Speaker 1: And this is like, I cannot stress enough how dumb
• 2:25
  Speaker 1: lightar is in an AV system. It does not make
• 2:28
  Speaker 1: any sense and I have no idea how anyone rationalized
• 2:32
  Speaker 1: it in the first place. This is a video game reference.
• 2:35
  Speaker 1: I just wanted to show you what I meant by
• 2:37
  Speaker 1: reducing the size of the vauxels. So this is and
• 2:41
  Speaker 1: this is done in real time. So again I'm trying
• 2:43
  Speaker 1: to demonstrate that this thing called an occupancy network and
• 2:47
  Speaker 1: radiance fields, this can be rendered robustly in real time,
• 2:52
  Speaker 1: and this is extremely important. And what you're looking at here,
• 2:55
  Speaker 1: again it's a video game reference, but what you're looking
• 2:57
  Speaker 1: at here is you're seeing depth calculation that is being
• 3:01
  Speaker 1: done and it's being color graded based off of that.
• 3:04
  Speaker 1: So blue is farther away and then yellow red would
• 3:07
  Speaker 1: be closer. So what you can see is that in
• 3:10
  Speaker 1: video games, to save memory, they're trying to focus on
• 3:13
  Speaker 1: the field of view and they only render what's in
• 3:16
  Speaker 1: your field of view and then ignore everything else. And
• 3:18
  Speaker 1: then they render only what's in your field of view
• 3:21
  Speaker 1: to save memory and compute, and this is really cool.
• 3:24
  Speaker 1: And then they this is kind of my home base,
• 3:26
  Speaker 1: and this is kind of a lot of what I
• 3:27
  Speaker 1: do or was doing. But they did even cooler stuff
• 3:31
  Speaker 1: than this. They like even realize that they could reduce
• 3:35
  Speaker 1: they could increase vossal size really far out and save
• 3:38
  Speaker 1: a lot of memory by not having to do as
• 3:40
  Speaker 1: many points when you're really far away, and that and
• 3:43
  Speaker 1: you don't lose anything by doing that. And you see
• 3:45
  Speaker 1: this in video games too, where whenever you're some character
• 3:49
  Speaker 1: looking over a long field of view, everything near you
• 3:52
  Speaker 1: is high resolution, and then the further you go in
• 3:55
  Speaker 1: the like look in the video game visually, it's lower
• 3:59
  Speaker 1: quality result solution out there further out because you don't
• 4:02
  Speaker 1: lose anything. And that's the same thing they're doing in
• 4:05
  Speaker 1: avs to save memory as well. So this is a
• 4:10
  Speaker 1: video game reperd Oh wait are you talking?
• 4:11
  Speaker 2: Sorry, sorry, I think you just described dreams.
• 4:17
  Speaker 1: Like actual dreams.
• 4:19
  Speaker 2: Think about it. It only renders in front of you,
• 4:22
  Speaker 2: in front of your field of vision.
• 4:26
  Speaker 1: Yeah, that's actually uh so all of the the you're right.
• 4:31
  Speaker 1: So one thing I skipped over in the neuroscience part
• 4:35
  Speaker 1: is that the the our entire experience in reality happens
• 4:41
  Speaker 1: in the hippocampus. And this is something that's important to understand,
• 4:46
  Speaker 1: is that we experience the world through our senses. Yes,
• 4:51
  Speaker 1: that's true, but in terms of the mapping, like everything
• 4:54
  Speaker 1: that I'm showing you what the car sees on the
• 4:56
  Speaker 1: screen and creating that, we do that exact same thing
• 4:59
  Speaker 1: in the and it happens in the hippo campus. And
• 5:02
  Speaker 1: so that's that virtual representation actually does happen in the
• 5:07
  Speaker 1: brain too, and that's where we live. We live in here.
• 5:10
  Speaker 1: That's everything in our universe happens right there, and that's
• 5:14
  Speaker 1: our entire experience. So we're experiencing whatever our hippocampus recreates
• 5:18
  Speaker 1: for us. So another thing that's important here everyone probably
• 5:24
  Speaker 1: knows by now the GPUs are in heavy demand and
• 5:27
  Speaker 1: they for some reason do a lot for AI, so
• 5:31
  Speaker 1: that might be relevant. So what a GPU stand for
• 5:34
  Speaker 1: graphics processing unit and it and it's not just computer
• 5:39
  Speaker 1: vision that GPUs do really well on any AI. Math
• 5:43
  Speaker 1: just happens to be really efficient on a GPU, especially
• 5:48
  Speaker 1: compared to a CPU. And uh and the quint Yeah exactly.
• 5:53
  Speaker 1: I mean there's i a will have to do a
• 5:56
  Speaker 1: finance podcast in the future. The uh, the magnitude of
• 6:02
  Speaker 1: difference between the performance on a GPU versus the performance
• 6:06
  Speaker 1: on a CP was massive. And so it's interesting that
• 6:11
  Speaker 1: not only is the generalized math for a AI really
• 6:16
  Speaker 1: powerful on specifically GPUs for some reason, graphics processing units,
• 6:22
  Speaker 1: but this math that I'm talking about, point clouds, occupanty networks,
• 6:26
  Speaker 1: rate tracing, radiance fields, neural written nerve, all of that
• 6:32
  Speaker 1: is extremely efficient on a GPU. By comparison to a CPU.
• 6:38
  Speaker 1: In fact, as you can see here, one billion points
• 6:41
  Speaker 1: per second compared to thirteen million points per second on
• 6:46
  Speaker 1: a CPU. That's the magnitude of difference that we're talking
• 6:48
  Speaker 1: about huge. And of course this is all out you know,
• 6:53
  Speaker 1: twenty twenty three data, so way way more powerful.
• 6:57
  Speaker 3: Now.
• 6:58
  Speaker 1: Everything I'm showing this actorate as a twenty twenty three
• 7:01
  Speaker 1: and has only gotten better since real time rendering. I've
• 7:07
  Speaker 1: kind of hit on it a lot. So another thing
• 7:10
  Speaker 1: is can it can consistently be done robustly? And this
• 7:15
  Speaker 1: paper published by Meta National Taiwan University, which publishes such
• 7:20
  Speaker 1: an absurd number of really good papers Kaiser and University
• 7:25
  Speaker 1: of Meryland, this is robodime or sorry, robust dynamic nerves.
• 7:32
  Speaker 1: And what they're showing here is that you can do
• 7:36
  Speaker 1: all of this NERF stuff that I'm talking about very robustly,
• 7:40
  Speaker 1: and in real time. They're dislocating the camera, they're creating
• 7:44
  Speaker 1: jitter and noise like zoom in and zoom out, they're
• 7:50
  Speaker 1: changing your position. It still works incredibly well. What you're
• 7:56
  Speaker 1: seeing on here. Let's take probably the horses, the'siest one
• 8:00
  Speaker 1: to understand. So on the bottom here this is heat mapped,
• 8:04
  Speaker 1: and it's heat mapped for depth, meaning red closer to you,
• 8:08
  Speaker 1: blue farther for you. And what you can see with
• 8:11
  Speaker 1: this horse is you can see in real time the
• 8:14
  Speaker 1: depth calculation that is happening robustly. So remember what I
• 8:19
  Speaker 1: was saying about the point clouds, and you saw what
• 8:21
  Speaker 1: you saw from Weimo and all that. This is the
• 8:24
  Speaker 1: depth calculation with a vision based system on the horse,
• 8:29
  Speaker 1: and you can see it starts out red and as
• 8:32
  Speaker 1: the horse goes further back, the depth calculation changes relative
• 8:36
  Speaker 1: to the rest of the environment. And it does it
• 8:38
  Speaker 1: in real time. It transitions cleanly. You've got someone doing
• 8:42
  Speaker 1: a windmill and you can see the depth calculation. You
• 8:44
  Speaker 1: can see as the foot goes back, or the leg
• 8:46
  Speaker 1: goes back, the range goes further back. It can do
• 8:49
  Speaker 1: it fast, it can do it at speed, It can
• 8:52
  Speaker 1: do it in chaotic environments. So this is just a
• 8:55
  Speaker 1: visual demonstration of how robust the depth calculation actually is,
• 9:00
  Speaker 1: which was the strong point of why you would invest
• 9:03
  Speaker 1: fifty thousand dollars in a light our based system over
• 9:06
  Speaker 1: one hundred dollars camera. Nope, you can do it. And again,
• 9:11
  Speaker 1: who's published every paper. I'm showing here all academically accepted,
• 9:16
  Speaker 1: all major publications, all considered major papers in academic world.
• 9:22
  Speaker 1: So not a single paper here is anything controversial. Every
• 9:26
  Speaker 1: single paper is academically accepted and understood at the highest
• 9:30
  Speaker 1: level of regard and fully in production and fully in
• 9:37
  Speaker 1: production and in a variety of ways. Yeah. Absolutely, all
• 9:40
  Speaker 1: this stuff is well known, especially in East Asia. The
• 9:43
  Speaker 1: amount like people are so brainwashed about China here, the
• 9:46
  Speaker 1: propaganda so absurd. If you guys saw the ai that's
• 9:49
  Speaker 1: happening in China, would we would look like an idiot.
• 9:53
  Speaker 1: We do look like complete idiots compared to China right
• 9:56
  Speaker 1: now and East Asia, Taiwan especially. So I'm gonna show
• 10:04
  Speaker 1: this is a more modern version. This is not the
• 10:07
  Speaker 1: most modern, but this is modern roughly in twenty twenty three.
• 10:10
  Speaker 1: And this is the Tesla's occupancy network. And you can,
• 10:14
  Speaker 1: again compared to the point cloud representation you get from LDAR,
• 10:19
  Speaker 1: look at the level of detail that you're getting on
• 10:23
  Speaker 1: the Tesla based system. This is in real time. This
• 10:26
  Speaker 1: is not a camera overlay. This is what the car
• 10:29
  Speaker 1: sees and reconstructing in real time on board. This is
• 10:34
  Speaker 1: the occupancy He's now there's a speaker that's talking on this,
• 10:37
  Speaker 1: and he's turning things on and off so that you
• 10:41
  Speaker 1: can see the various details. But look at the calculator,
• 10:46
  Speaker 1: the quality and resolution of the points, and there's so
• 10:50
  Speaker 1: many of them. It looks like it's more dense to
• 10:53
  Speaker 1: me than the one that Weimo was showing at the
• 10:56
  Speaker 1: very same time, at the very same conference. It's worth
• 11:01
  Speaker 1: seeing this one again. Now this is even more mod
• 11:04
  Speaker 1: there's even more advancement that has already been made, and
• 11:07
  Speaker 1: so this is not the modern version, but in twenty
• 11:10
  Speaker 1: twenty three, you can see like just how much of
• 11:12
  Speaker 1: a difference this is. And it's just it's impressive, and
• 11:17
  Speaker 1: it's like a video game. I mean, anyone that's done
• 11:20
  Speaker 1: video games knows that this is pretty much a video game.
• 11:26
  Speaker 1: So I want to read this slide because it's the
• 11:32
  Speaker 1: conclusion slide is written exactly right. And then I got
• 11:35
  Speaker 1: one more major thing after this, and then we can
• 11:38
  Speaker 1: go into discussion. But I'm just gonna read this real quick.
• 11:42
  Speaker 1: It appears that all major av industry participants on any
• 11:46
  Speaker 1: modality all over the world are converging to three things
• 11:50
  Speaker 1: still true to this day. But this was what I
• 11:53
  Speaker 1: was showing in twenty twenty three. Occupancy equals point clouds
• 11:57
  Speaker 1: equals radiance fields equal VR representation. These are all exactly
• 12:03
  Speaker 1: the same thing. They're all the same thing, and all
• 12:06
  Speaker 1: augmented by semantics meaning words help. Lllm's help, but they're augmented.
• 12:10
  Speaker 1: LLLM is an augment, not the foundation. Number two, perception
• 12:15
  Speaker 1: accuracy is increasingly measured by how correctly perception recreates reality
• 12:21
  Speaker 1: in virtual space. And number three, the task of AV
• 12:27
  Speaker 1: perception has become building a localized digital twin of the
• 12:32
  Speaker 1: real local environment in real time. That's what the task
• 12:37
  Speaker 1: of perception in robotics basically has become. So feature rich
• 12:43
  Speaker 1: representation is best. What this means is high resolution data
• 12:46
  Speaker 1: is better. And you've got to understand. I had to
• 12:50
  Speaker 1: explain that to a whole room full of PhDs, that
• 12:55
  Speaker 1: higher resolution data is better an entire room of PhDs.
• 13:01
  Speaker 1: I had to explain that too. At ASU, the perception
• 13:07
  Speaker 1: task has complexity proportional to the maximum perceivable radial distance
• 13:12
  Speaker 1: from ego, and the minimum required length of this radius
• 13:16
  Speaker 1: increase with speed increases with speed.
• 13:19
  Speaker 3: What this is.
• 13:19
  Speaker 1: Saying is the faster you go, the farther down you
• 13:24
  Speaker 1: need to see. But the slower you go, you don't
• 13:27
  Speaker 1: necessarily need need to see as far. And the complexity
• 13:33
  Speaker 1: of this whole VR representation task is determined by the
• 13:37
  Speaker 1: radial length the minimum required radius, and that complexity increases
• 13:43
  Speaker 1: the faster you go. And so if you're going fast,
• 13:48
  Speaker 1: the complexity is not growing linearly, it's growing squared. And
• 13:54
  Speaker 1: that's relevant if you were hearing what I was saying
• 13:56
  Speaker 1: about light our degradation over range and the forecasting window.
• 14:02
  Speaker 1: That's number of humans.
• 14:03
  Speaker 2: Two.
• 14:04
  Speaker 1: It's true for humans too, exactly number three. Recreating the
• 14:08
  Speaker 1: world as humans see it requires perception systems to see
• 14:13
  Speaker 1: real photons the way humans see them. And I should
• 14:20
  Speaker 1: have mentioned this earlier. The road systems are designed for vision,
• 14:24
  Speaker 1: not lightar. The road engineers have designed roads for vision,
• 14:29
  Speaker 1: not lightar. This is the level of dumb we're dealing with.
• 14:33
  Speaker 1: PhDs don't mean anything. This is the level of dumb
• 14:35
  Speaker 1: we're dealing with.
• 14:36
  Speaker 2: There's very little braille on the roads. But if there
• 14:39
  Speaker 2: was a lot more people driving, uh huh.
• 14:43
  Speaker 1: I'm sure. So. I conclude success on the perception task
• 14:50
  Speaker 1: converges to real time virtual rendering of localized reality as
• 14:55
  Speaker 1: humans perceive it. And because of the nature in which
• 14:59
  Speaker 1: humans perceive reality, mimic real perception must at least be
• 15:04
  Speaker 1: inferred from real photons. Again, I have to make the
• 15:08
  Speaker 1: fundamental argument that reality is perceived from real photons and
• 15:12
  Speaker 1: not fake photons, which is what lightar is. Real photons
• 15:17
  Speaker 1: is vision actual reality. Fake photons is lightar. So I
• 15:24
  Speaker 1: have one more thing, and this is what really ties
• 15:29
  Speaker 1: it all together. So this is the actual formal end
• 15:32
  Speaker 1: of the presentation. But I want to show this because this,
• 15:37
  Speaker 1: in my opinion, if you've been paying it, if you
• 15:39
  Speaker 1: made it this far, this is the honeypot at the end.
• 15:44
  Speaker 1: This is a foundational paper in computer vision. This is
• 15:50
  Speaker 1: one of the most referenced papers in computer vision, one
• 15:55
  Speaker 1: of the most popular Pepper papers in computer vision, and
• 15:58
  Speaker 1: considered maybe up there with one of the most important
• 16:02
  Speaker 1: papers ever in computer vision. Academically recognized, widely accepted and
• 16:08
  Speaker 1: understood and everything all around the world. Thousands and thousands
• 16:13
  Speaker 1: of times, maybe tens of thousands of times this specific
• 16:16
  Speaker 1: paper has been referenced. This is a paper it's called
• 16:20
  Speaker 1: the Unreasonable Effectiveness of deep Features as a perceptual metric.
• 16:25
  Speaker 1: This was published by open AI and Adobe Research in
• 16:30
  Speaker 1: twenty eighteen. Back when Elon was still there at open AI.
• 16:35
  Speaker 1: If you don't mind, I'm just going to read the
• 16:37
  Speaker 1: conclusion as is because it's pretty profound our results. So
• 16:42
  Speaker 1: this is a paper about computer vision, and it explores
• 16:45
  Speaker 1: the reason why color is important. That's why this is
• 16:49
  Speaker 1: such a famous paper. Our results indicate that networks trained
• 16:55
  Speaker 1: to solve challenging visual prediction and modeling tasks end up
• 16:59
  Speaker 1: learning a represent of the world that correlates well with
• 17:02
  Speaker 1: perceptual judgments. A similar story has recently emerged in the
• 17:07
  Speaker 1: representation learning literature. Networks trained on self supervised and unsupervised
• 17:12
  Speaker 1: objectives end up learning a representation that is also effective
• 17:16
  Speaker 1: at semantic tasks. Earlier, I was saying being able to
• 17:19
  Speaker 1: see a red wagon oops allows you in language to
• 17:23
  Speaker 1: be able to describe a red wagon better. That's what
• 17:26
  Speaker 1: this is saying. Interestingly, recent findings in neuroscience make much
• 17:32
  Speaker 1: the same point. Representations trained on computer vision tasks also
• 17:38
  Speaker 1: end up being effective models of neural activity. In Macaca
• 17:42
  Speaker 1: visual core text, macakay is a type of monkey that
• 17:45
  Speaker 1: has a brain very similar to the human. So this
• 17:50
  Speaker 1: is important because this is the first thing that says
• 17:52
  Speaker 1: computer vision and the visual cortext might be related. Moreover,
• 18:00
  Speaker 1: and roughly speaking, the stronger the representation is at the
• 18:03
  Speaker 1: computer vision task, the stronger it is a model of
• 18:07
  Speaker 1: cortical activity. Our paper makes a similar finding. The stronger
• 18:13
  Speaker 1: a feature set is at classification and detection, the stronger
• 18:16
  Speaker 1: it is as a model of perceptual similarity judgments, as
• 18:19
  Speaker 1: suggested in Table four. Together, these results suggest that a
• 18:22
  Speaker 1: good feature is a good feature. Features that are good
• 18:25
  Speaker 1: at semantic tasks are also good at self supervised and
• 18:27
  Speaker 1: unsupervised tasks, and also provide good models of both human
• 18:33
  Speaker 1: perceptual behavior and macake neural activity. This last point aligns
• 18:39
  Speaker 1: with the rational analysis explanation of visual cognition, suggesting that
• 18:45
  Speaker 1: the idiosyncrasies of biological perception arise as a consequence of
• 18:49
  Speaker 1: a rational agent attempting to solve natural tasks. This is
• 18:53
  Speaker 1: their way of saying, artificial intelligence needs to exist in
• 18:58
  Speaker 1: the real world and understand the real world, and that
• 19:03
  Speaker 1: is what is being found that computer vision is enabling
• 19:07
  Speaker 1: us to do. And on top of that, just happens
• 19:11
  Speaker 1: to be that when you do that in AI, comprehend
• 19:16
  Speaker 1: the real world through vision, you end up mimicking the brain,
• 19:21
  Speaker 1: whether you know it or not, it ends up becoming
• 19:24
  Speaker 1: the brain. There go further refining the degree to which
• 19:31
  Speaker 1: this is true is important question for future research. So again,
• 19:35
  Speaker 1: the human brain is lit I mean sorry. Computer vision
• 19:39
  Speaker 1: is when you do it correctly, it literally mimics the brain.
• 19:45
  Speaker 1: This is important to understand everything. There's all kinds of
• 19:52
  Speaker 1: ridiculous shit that I've heard about the brain and the
• 19:55
  Speaker 1: concept of consciousness. It's quantum and microtubiles and and pulling
• 20:01
  Speaker 1: in consciousness from the ether and all this shit. No,
• 20:04
  Speaker 1: every single thing. One hundred percent of everything we know
• 20:09
  Speaker 1: in the universe is mathematical. One hundred percent of everything
• 20:14
  Speaker 1: we know in the universe is mathematical. The brain is mathematical.
• 20:21
  Speaker 1: And computer vision experts implicitly understand the visual cortex in
• 20:27
  Speaker 1: the brain, whether they know it or not, because the
• 20:30
  Speaker 1: visual cortex is doing a step by step function from
• 20:35
  Speaker 1: V one through V six. In the visual cortex, this
• 20:39
  Speaker 1: is a step by step sequential function, and people in
• 20:44
  Speaker 1: computer vision are doing exactly those steps without even knowing it.
• 20:49
  Speaker 1: They're memory, They're mimicking the brain. I actually have no
• 20:53
  Speaker 1: idea why there's some giant wall between neuroscience and computer vision,
• 20:57
  Speaker 1: but computer vision people probably understand the brain better than
• 21:00
  Speaker 1: most people without even knowing it.
• 21:04
  Speaker 2: I would agree, because the first things that all life
• 21:08
  Speaker 2: had to figure out was the world around them, and
• 21:12
  Speaker 2: all intelligence is built off of that same basic premise
• 21:16
  Speaker 2: every single time. It's how do you relate to the
• 21:18
  Speaker 2: world around you?
• 21:20
  Speaker 1: Correct?
• 21:20
  Speaker 2: That includes light and all the rest, any sort of
• 21:23
  Speaker 2: sensory impulse, any kind of information.
• 21:26
  Speaker 1: Yep. So that's actually what that paper is saying, is
• 21:29
  Speaker 1: that a rational agent, whatever that is, So you can
• 21:32
  Speaker 1: either be a conscious human or a AI. You know something,
• 21:37
  Speaker 1: any AI. It turns out that a rational agent that
• 21:40
  Speaker 1: successfully interacts in the real world is doing is going
• 21:46
  Speaker 1: to end up making neural activity in the brain. And
• 21:49
  Speaker 1: by the way, I just want to make sure that
• 21:52
  Speaker 1: I distinguish that this is not saying that computer vision
• 21:56
  Speaker 1: becomes human. This is saying that computer vision and becomes
• 22:01
  Speaker 1: the brain. And that's a big difference. So everything in
• 22:06
  Speaker 1: this we're all life in this world, is going to
• 22:10
  Speaker 1: have some variation of the brain. And we know that
• 22:14
  Speaker 1: different animals and different species all up and down the
• 22:17
  Speaker 1: animal kingdom, they all have varying different kinds of success
• 22:21
  Speaker 1: at perceiving the world. For example, bats, they have eyes
• 22:28
  Speaker 1: anyone ever wonder why bats have eyes when they don't
• 22:30
  Speaker 1: really use them. And so one of the things is
• 22:33
  Speaker 1: that the eye evolved first, and then bats literally got
• 22:39
  Speaker 1: stuck in a cave. And when they got stuck in
• 22:42
  Speaker 1: a cave, they had to switch to echolocation, and so
• 22:45
  Speaker 1: the value of the eye went down, and then they
• 22:48
  Speaker 1: evolutionary prioritize sonar. But one of the issues is that
• 22:53
  Speaker 1: they have problems with reflective and smooth surfaces, and they
• 22:56
  Speaker 1: have all kinds of issues and that's why they got
• 22:58
  Speaker 1: forever stuck in a cave. They're literally stuck in a
• 23:01
  Speaker 1: cave now because they went from vision to prioritizing audio sonar.
• 23:09
  Speaker 1: So so you can see how and that they have
• 23:13
  Speaker 1: issues with flying near mirrors. If they end up flying
• 23:18
  Speaker 1: towards the mirror, they'll end up running into it. Windows
• 23:20
  Speaker 1: are a problem. That's why birds sometimes have problems and
• 23:23
  Speaker 1: stuff like they don't understand glare that well, the glare
• 23:27
  Speaker 1: is and the concept and sonar, so you you'd be
• 23:30
  Speaker 1: surprised at how many like you can probably see glare
• 23:32
  Speaker 1: on my glasses. So that's how you know that there's
• 23:35
  Speaker 1: right you can see like that kind of stuff. That's
• 23:37
  Speaker 1: like obvious. You don't even think about it, but you've
• 23:39
  Speaker 1: got to think. You've got to understand it. Bad has
• 23:40
  Speaker 1: no concept of this, And so you can see how
• 23:43
  Speaker 1: all these different qualitia, subtle qualita of color and texture,
• 23:49
  Speaker 1: all these subtleties are complicated and are lost when you
• 23:54
  Speaker 1: don't have vision, but create massive exponential benefit when you do.
• 23:59
  Speaker 1: So the really big important thing about that's going on
• 24:06
  Speaker 1: in the world right now is the energy consumption that
• 24:11
  Speaker 1: is being used for AI, and it's going to grow exponentially.
• 24:14
  Speaker 1: I wanted to show this graph. This is from a
• 24:16
  Speaker 1: presentation from Rayker as well, the guy who predicted this singularity.
• 24:19
  Speaker 1: If you've ever heard of it, If you've ever heard
• 24:21
  Speaker 1: of the word singularity, it comes from this guy, Raker
• 24:24
  Speaker 1: as well, and he has this chart, and I just
• 24:28
  Speaker 1: wanted to show this chart briefly just so people can
• 24:30
  Speaker 1: understand what is happening with compute. So this is a
• 24:33
  Speaker 1: log ten scale that means it's increasing by multiple of
• 24:37
  Speaker 1: ten every step up. So even though this line looks
• 24:42
  Speaker 1: roughly linear, this is actually actually an exponential graph because
• 24:46
  Speaker 1: it is going up in factors of ten. So what
• 24:50
  Speaker 1: you're seeing towards the end here is you're seeing the
• 24:54
  Speaker 1: beginning of what is called an S curve, and this
• 24:58
  Speaker 1: is an exponential curve in compute. So there was this
• 25:02
  Speaker 1: thing called Moore's law, and Moore's law has been had
• 25:06
  Speaker 1: been in effect for very a very long time, and
• 25:10
  Speaker 1: what it states is that compute was expected to double
• 25:13
  Speaker 1: every two years roughly, and that comes from the size
• 25:17
  Speaker 1: of the transistor. So in other words, they were able
• 25:20
  Speaker 1: to they were expecting to be able to cut the
• 25:23
  Speaker 1: size of the transistor by half every two years, so
• 25:28
  Speaker 1: that means on the same amount of space you fit
• 25:30
  Speaker 1: about double the number of transistors, so you double your compute.
• 25:33
  Speaker 1: That was a simple logic that was used, and that's
• 25:35
  Speaker 1: what Moore's law is. Since then, because of this guy
• 25:39
  Speaker 1: named Jensen Wang from Nvidia CEO of Nvidia, he's been
• 25:45
  Speaker 1: doing this GPU thing and he's been blowing Moore's law
• 25:48
  Speaker 1: out of the water consistently every year for like decades,
• 25:51
  Speaker 1: and so now it's like way the hell blown out
• 25:54
  Speaker 1: and you're seeing an exponential curve starting right at the
• 25:57
  Speaker 1: tail end there, and this is going to keep keep
• 26:00
  Speaker 1: going because GPUs do all these tasks that I've been
• 26:03
  Speaker 1: talking about in this presentation way better than any any
• 26:08
  Speaker 1: CP or anything like that, and he's just crushing it.
• 26:11
  Speaker 1: He's blown like at this point, More's law is completely
• 26:13
  Speaker 1: blown out of water, obliterated. I think if I recall correctly,
• 26:17
  Speaker 1: the mass on his rate of growth in compute is
• 26:21
  Speaker 1: something around one point seven x per year compared to
• 26:25
  Speaker 1: two x every two years. So that and that's probably
• 26:29
  Speaker 1: a conservative He's probably going to go faster than that.
• 26:33
  Speaker 1: So and that's important because it's not necessarily it's not
• 26:35
  Speaker 1: related to the size of the transistor anymore. So there's
• 26:40
  Speaker 1: a lot of like horrible stuff that has happened in AI,
• 26:44
  Speaker 1: and just like every other subject like AI of course
• 26:50
  Speaker 1: is right for all kinds of distortion and then abuse.
• 26:55
  Speaker 1: And after I gave this presentation at ASU, so many
• 26:59
  Speaker 1: horrible things happened to me. It's kind of hard to
• 27:03
  Speaker 1: talk a lot about it because there are a lot
• 27:05
  Speaker 1: of problems we're talking about it, but just know it
• 27:08
  Speaker 1: was really bad. One I can talk about, though, is
• 27:14
  Speaker 1: there was there was this open AI whistleblower in November
• 27:20
  Speaker 1: of twenty twenty four that was assassinated in California, and
• 27:25
  Speaker 1: I think more people should watch this. I hope people do.
• 27:29
  Speaker 1: But basically there was a government operation that was run
• 27:33
  Speaker 1: on me, totally illegal and all this stuff, and it
• 27:35
  Speaker 1: was pretty horrible, and it seems to resemble a lot
• 27:39
  Speaker 1: of elements of MK ultra. And it's very similar to
• 27:41
  Speaker 1: what I've heard veterans about UAP that our UAP whistleblowers
• 27:45
  Speaker 1: talk about happens to them. But it seems to be
• 27:49
  Speaker 1: that being a whistleblower is the fastest way to get
• 27:52
  Speaker 1: yourself killed in America today. And this just keeps happening.
• 27:58
  Speaker 1: And I really want to bring up this guy because
• 28:01
  Speaker 1: this guy was doing was was doing something very important
• 28:07
  Speaker 1: at that time, and he was and he ended up
• 28:11
  Speaker 1: dying in November of twenty twenty four, and this was
• 28:13
  Speaker 1: around when a lot of the same stuff was happening
• 28:16
  Speaker 1: to me and many others. And I'm bringing this up
• 28:20
  Speaker 1: because it's just important to understand that there have been
• 28:26
  Speaker 1: like everyone is aware of all the public assassinations that
• 28:28
  Speaker 1: are happening and all this stuff, but less people are
• 28:31
  Speaker 1: aware of how many assassinations and how much corruption is
• 28:35
  Speaker 1: happening in academia. Just last month, another MIT professor who
• 28:39
  Speaker 1: was working on clean energy was just murdered. And this
• 28:43
  Speaker 1: is consisted. If you know about Bob Lazaar, you know
• 28:45
  Speaker 1: about what happened to him in academia. If you know
• 28:49
  Speaker 1: about Nikola Tesla, you know what happened to him, And
• 28:53
  Speaker 1: all this stuff over and over again. In America specifically,
• 29:00
  Speaker 1: people who find things in academia or elsewhere are often
• 29:05
  Speaker 1: just killed for whatever reason. And it's not just one subject.
• 29:10
  Speaker 1: It is all across academia, across all of the STEM subjects.
• 29:16
  Speaker 1: Anything in STEM, this is where you end up getting
• 29:20
  Speaker 1: if you do, if you figure something out by accident,
• 29:23
  Speaker 1: you still get a lot of horrible stuff happens to you.
• 29:27
  Speaker 1: And so I wanted to bring this up because I've
• 29:29
  Speaker 1: personally seen this happen. I've been subjected to it by
• 29:34
  Speaker 1: the US military, and I've seen many other people in
• 29:39
  Speaker 1: academia that it's happened to and an ai And on
• 29:43
  Speaker 1: top of that, this appears to be almost identical to
• 29:48
  Speaker 1: what UAP whistleblowers, like veterans from the military who have
• 29:53
  Speaker 1: talked about UAPs. It seems almost identical to what they
• 29:56
  Speaker 1: say happens to them. It's almost when I watch them all,
• 30:01
  Speaker 1: they are all saying the same mk ultra stuff is
• 30:03
  Speaker 1: happening to them, and it's really horrible because I've seen
• 30:06
  Speaker 1: it happen personally, and you would be surprised. They don't
• 30:10
  Speaker 1: do just one thing to you. They do thousands and
• 30:13
  Speaker 1: thousands of things to you, continuously, different things, NonStop. They
• 30:18
  Speaker 1: have control of every device in your life. They have
• 30:20
  Speaker 1: control of a huge number of people all around you.
• 30:22
  Speaker 1: They will manufacture your environment. They will artificially manufacture every
• 30:26
  Speaker 1: kind of accident imaginable. They will artificially manufacture the people
• 30:30
  Speaker 1: in your life. You can't trust anyone because they're always
• 30:33
  Speaker 1: doing it. And this is an absurd number of resources
• 30:36
  Speaker 1: that they invest in this task, and this is an
• 30:39
  Speaker 1: absurd amount of taxpayer dollars that are going specifically to
• 30:43
  Speaker 1: the task of the US military murdering and harming Americans
• 30:47
  Speaker 1: on American soil. And this is extremely important for people
• 30:51
  Speaker 1: to know. Every single person in America needs to understand.
• 30:55
  Speaker 1: For eighty plus years, the active elements of the US
• 30:59
  Speaker 1: military are actively harming people in America, Americans on American
• 31:05
  Speaker 1: soil who do these things to try and figure anything out.
• 31:10
  Speaker 1: And it happens so frequently and so commonly, and I
• 31:14
  Speaker 1: know it's not just academic and veterans. I've heard of
• 31:17
  Speaker 1: it happening in a variety of other subjects, all kinds
• 31:20
  Speaker 1: of assassinations. People forget like, well, I shouldn't get into that,
• 31:25
  Speaker 1: But people forget like how many how many assassinations have
• 31:29
  Speaker 1: been happening, and those are just the public ones, and
• 31:32
  Speaker 1: you don't even know about all the ones that you
• 31:34
  Speaker 1: don't know who they are, and there are so many.
• 31:37
  Speaker 1: So I hope that there are other people that you
• 31:40
  Speaker 1: guys can find that have been suppressed in academia and
• 31:45
  Speaker 1: retaliated against. And it's so consistent this pattern of behavior,
• 31:49
  Speaker 1: even doctors, especially during COVID Over and over again, we're
• 31:54
  Speaker 1: talking about massive amounts of retaliation for telling the truth
• 31:58
  Speaker 1: about anything in medicine, and they've been heavily oppressed. Doctors
• 32:02
  Speaker 1: have lost control of medicine. Like doctors don't do medicine,
• 32:05
  Speaker 1: insurance dos does. Insurance is basically dictating your medical care,
• 32:09
  Speaker 1: not the doctors. The doctors don't have a choice, they're
• 32:12
  Speaker 1: stuck in the system.
• 32:14
  Speaker 2: Sheesh. I had a horrible thought earlier. I thought, what
• 32:18
  Speaker 2: if it's the insurance industry that's actually behind this whole
• 32:23
  Speaker 2: light our scam because they don't want collisions on anybody's record,
• 32:27
  Speaker 2: So it's the collisions that's leading them on this you
• 32:31
  Speaker 2: know whole you know, insurance perspective of the universe.
• 32:35
  Speaker 1: I can address that computer vision. When you have a
• 32:40
  Speaker 1: camera based system, something else that you get for free
• 32:44
  Speaker 1: that you don't get with the light our system is
• 32:46
  Speaker 1: that you get a dash cam for free, and so
• 32:49
  Speaker 1: I actually have on video recording. Like I was saying
• 32:53
  Speaker 1: that one of the things that they end up doing
• 32:55
  Speaker 1: to is a form of like the government attacking you,
• 32:58
  Speaker 1: is that they try to artific actually manufacture a lot
• 33:01
  Speaker 1: of accidents. So in twenty twenty four, there were a
• 33:04
  Speaker 1: lot of accident like new close calls unreasonably statistically impossible,
• 33:09
  Speaker 1: Like just over and over again, how many different accidents
• 33:12
  Speaker 1: I almost got into and had to avoid. I even
• 33:14
  Speaker 1: have them on video, and like you could just see
• 33:17
  Speaker 1: I have multiple camera angles. And even after they missed
• 33:20
  Speaker 1: me because I avoided it, they came, they tried to
• 33:23
  Speaker 1: hit me again, and like you know, it's it's like ridiculous,
• 33:26
  Speaker 1: how how much how many things happened. But anyway, they
• 33:28
  Speaker 1: answer your question, you get a dash cam for free,
• 33:31
  Speaker 1: so insurance companies would care. And I actually what I
• 33:36
  Speaker 1: had proposed at ASU is I had actually proposed a
• 33:39
  Speaker 1: black box option with cameras, like a similar thing that
• 33:41
  Speaker 1: they do in airlines. Part of my PhD was like
• 33:45
  Speaker 1: not like a small part of my PhD was also
• 33:47
  Speaker 1: proposing this because it's so cheap. You could have flash memory.
• 33:50
  Speaker 1: Like it's so cheap, like one hundred dollars, it's a
• 33:53
  Speaker 1: whole system for you, Like that's it. Yeah, and it
• 33:56
  Speaker 1: would be a black box. It would be small, very fast.
• 33:58
  Speaker 1: You could do it on flash memory, so would happen
• 34:00
  Speaker 1: instantly even with the collision. Yeah, not bad. Cameras have
• 34:05
  Speaker 1: so many different advantages that are not really like. First
• 34:09
  Speaker 1: of all, in my opinion, we should standardize on video data.
• 34:14
  Speaker 1: It is high resolution data. So of all the data
• 34:18
  Speaker 1: that you could have, video data, high quality, high resolution
• 34:22
  Speaker 1: video data is the most data valuable or sorry, most
• 34:28
  Speaker 1: valuable data. Sorry, most valuable data is the high resolution data.
• 34:33
  Speaker 1: So we should standardize on video data. And one of
• 34:36
  Speaker 1: the things that is advintageous from a business perspective about
• 34:39
  Speaker 1: standardizing on video data is that let's say there's a
• 34:42
  Speaker 1: regulatory problem and you have to like submit the records
• 34:45
  Speaker 1: like the black box or whatever. Well, video data is
• 34:48
  Speaker 1: common and uniform, so there's no proprietary thing in cameras, Like,
• 34:53
  Speaker 1: there's no proprietary thing in like a video, So no
• 34:58
  Speaker 1: business will ever have to worry about giving away a
• 35:01
  Speaker 1: trade secret if they gave away the raw video footage,
• 35:05
  Speaker 1: because that's not where any of the trade secrets happen.
• 35:08
  Speaker 1: All the trade secrets happen on the back end with
• 35:10
  Speaker 1: all the software. So standardizing on video data means that
• 35:14
  Speaker 1: you can offer that data with no harm to your
• 35:17
  Speaker 1: personal proprietary IP. You're just giving the video rob video footage,
• 35:22
  Speaker 1: which is universal, and that's not something you can say
• 35:26
  Speaker 1: with light R. You would end up having to give
• 35:27
  Speaker 1: away some proprietary information with light our data if you
• 35:30
  Speaker 1: if you gave it away.
• 35:32
  Speaker 2: Yeah, interesting point though, we'll.
• 35:36
  Speaker 1: Go more to it. Yeah, but that's that's those are
• 35:39
  Speaker 1: that's the big stuff, guys, like computer visions where I
• 35:42
  Speaker 1: real AI is it's not ll m's or chagpt or whatever. Yeah.
• 35:47
  Speaker 2: I had an interesting thought while you were describing the bats.
• 35:50
  Speaker 2: So have you ever read the book The Time Machine
• 35:53
  Speaker 2: by is she?
• 35:54
  Speaker 1: Wells, Oh yeah, definitely. Uh huh.
• 35:56
  Speaker 2: So you've got the morelocks and the elloy right, and
• 35:59
  Speaker 2: there are underground and they're kind of blind, you know,
• 36:03
  Speaker 2: their eyes have degraded, but they've got a great sense
• 36:05
  Speaker 2: of smell. They can smell other people, and it's like
• 36:08
  Speaker 2: you're dealing with the Warlocks in a lot of ways.
• 36:11
  Speaker 2: They're sort of blind to what they what's right in
• 36:15
  Speaker 2: front of them. You know, they just opened their eyes,
• 36:17
  Speaker 2: but they've got so much invested at this point they
• 36:21
  Speaker 2: can't even see the forest for the tree. So it
• 36:23
  Speaker 2: really is fitting that it's all about sensory data and
• 36:27
  Speaker 2: these people are so fucking blind.
• 36:30
  Speaker 1: Yeah, I mean, this is not a mistake. Guys, like
• 36:33
  Speaker 1: you got to understand, nobody is this dumb. It is
• 36:36
  Speaker 1: not possible to be in a room full of PhDs
• 36:38
  Speaker 1: that are all collectively a room full of aphds are
• 36:42
  Speaker 1: not that dumb. This is happening on purpose. This is intentional,
• 36:46
  Speaker 1: and this is mostly a money game, and that's why
• 36:48
  Speaker 1: it's happening. It's really mostly about money. Yeah. The other
• 36:54
  Speaker 1: thing to kind of know, again, from the neuroscience side,
• 36:57
  Speaker 1: this is a little bit morbid, but it makes a point.
• 37:00
  Speaker 1: It is valid. What is understood is that brain dead
• 37:07
  Speaker 1: equals dead. This is understood in neurology. Brain dead equals dead.
• 37:14
  Speaker 1: Any other part of the body you're still alive for
• 37:17
  Speaker 1: some time if you lose it. But brain dead equals dead.
• 37:22
  Speaker 1: So again I was saying, I've heard all kinds of
• 37:23
  Speaker 1: dumb shit about consciousness. Everything in this one hundred percent
• 37:28
  Speaker 1: of everything we know in this universe is math. So far,
• 37:33
  Speaker 1: one hundred percent of everything we know in this universe
• 37:36
  Speaker 1: is math. So all of the consciousness aspects happen in
• 37:42
  Speaker 1: the brain, I'm sure, Like, don't get me wrong. Your
• 37:45
  Speaker 1: body in its entirety is a perception sensor. The whole
• 37:49
  Speaker 1: body is a perception sensor, and that is relevant to
• 37:52
  Speaker 1: the brain. But in terms of your actual consciousness, where
• 37:55
  Speaker 1: does it actually happen. It's in the brain. It is
• 37:59
  Speaker 1: a mathematical concept. And computer vision is showing that they
• 38:04
  Speaker 1: already understand this and that for some reason there's just
• 38:09
  Speaker 1: a big wall between what is already published and understood
• 38:13
  Speaker 1: in computer vision for many years, but for some reason,
• 38:16
  Speaker 1: neuroscience isn't picking it up.
• 38:24
  Speaker 4: One thing I didn't want to ask you about, kind
• 38:26
  Speaker 4: of going back, I guess to like the stuff that
• 38:28
  Speaker 4: was happening to you. Uh, you went, you were talking
• 38:30
  Speaker 4: about ASU, right, that's where you were at. Isn't like
• 38:33
  Speaker 4: the DD and the part of my energy there's not
• 38:36
  Speaker 4: like the military, actually like fun projects at that police too.
• 38:39
  Speaker 1: Yeah. I was in one.
• 38:40
  Speaker 3: Oh there you go.
• 38:43
  Speaker 1: So yeah. So in my undergrad my computer science undergrad
• 38:52
  Speaker 1: at ASU, I had to like do a Capstone project.
• 38:57
  Speaker 1: So how all of this happened was there was this guy.
• 39:03
  Speaker 1: Before I say his name, let me just look something up.
• 39:13
  Speaker 1: I just need to make sure that I can say
• 39:17
  Speaker 1: this real quick. Yeah. So I needed to do a
• 39:25
  Speaker 1: Capstone project. And at the time, the CIA was at
• 39:32
  Speaker 1: ASU and they were so the Capstone project and computer
• 39:36
  Speaker 1: science at a SU you have to do something for
• 39:40
  Speaker 1: real organization or business in the real world, and there
• 39:44
  Speaker 1: are a number of projects that you can apply to
• 39:46
  Speaker 1: do your Capstone project. And one segment of those projects
• 39:51
  Speaker 1: that was available to do was working for the CIA,
• 39:56
  Speaker 1: and I was doing something called making a digital twin,
• 39:59
  Speaker 1: which is sounds more than it really is. It's not
• 40:02
  Speaker 1: that big of a deal. This part is not really
• 40:04
  Speaker 1: that big of a deal. But the point is, initially
• 40:08
  Speaker 1: for my undergrad I built this little digital twin project,
• 40:12
  Speaker 1: which is not a big deal for the CIA, and
• 40:15
  Speaker 1: my project manager at the time his name was John Lewis,
• 40:19
  Speaker 1: and at the time he was the CTO of the
• 40:22
  Speaker 1: CIA after I did that project. Coincidentally, John Lewis retired
• 40:31
  Speaker 1: out of the CIA, moved from Washington, DC to Phoenix, Arizona,
• 40:36
  Speaker 1: and in twenty twenty three he started teaching a class
• 40:39
  Speaker 1: at ASU which I took as I started doing this
• 40:45
  Speaker 1: AI thing in twenty twenty three, so that happened. Another
• 40:49
  Speaker 1: thing that happened was that two people moved in right
• 40:54
  Speaker 1: next door to me. One of them was a former
• 40:57
  Speaker 1: seventy fifth Ranger with a Q clearance, and then the
• 41:01
  Speaker 1: other one was a former US Air Force officer turned
• 41:08
  Speaker 1: DoD contractor at Booz Allen. And if you can believe it,
• 41:12
  Speaker 1: so they told me that this DoD contractor her name
• 41:15
  Speaker 1: was Mickey Adaci and the former seventy fifth Ranger was
• 41:19
  Speaker 1: Cisco Salsato Francisco Salsado. So they befriended me. And this
• 41:26
  Speaker 1: is all. They moved in right next door to me,
• 41:29
  Speaker 1: literally next door to me, all around the same time.
• 41:31
  Speaker 1: All this is happening at the same time, John Lewis
• 41:34
  Speaker 1: from the CIA moving and me taking his class and
• 41:37
  Speaker 1: even being my project manager, I should also mention this
• 41:41
  Speaker 1: while I'm doing this. At the beginning, I didn't say this,
• 41:44
  Speaker 1: but this my project advisor for this project that I
• 41:48
  Speaker 1: was doing here. This is doctor Kiddombi. This is also
• 41:54
  Speaker 1: a NITSA engineer, National Highway and Transport Safety Association, So
• 41:59
  Speaker 1: he was my project advisor and he's fully aware of
• 42:02
  Speaker 1: everything I said here, including the Waimo stuff. So this
• 42:08
  Speaker 1: is again and I have the date on the slides
• 42:10
  Speaker 1: here when all this is happening, so if you can
• 42:15
  Speaker 1: believe it, so they told me this DoD purchaser, her
• 42:18
  Speaker 1: name's Mickey Adacci for Booz Allen. They told me at
• 42:21
  Speaker 1: that time she was purchasing light ar based systems for
• 42:24
  Speaker 1: the US Navy. And if you can believe it, as
• 42:28
  Speaker 1: dumb as light ar is on vehicles on cards, it's
• 42:34
  Speaker 1: even dumber, even more dumb to do it on a ship.
• 42:40
  Speaker 1: Why offers nothing of value at all on a ship.
• 42:44
  Speaker 1: There's no reason to do it. So these people moved
• 42:48
  Speaker 1: in next door to me, they befriended me, and they
• 42:51
  Speaker 1: started asking me a lot of stuff. And then after
• 42:54
  Speaker 1: I gave this presentation at ASU, lots of horrible stuff
• 42:59
  Speaker 1: happened to me, and I've been put under surveillance. As
• 43:03
  Speaker 1: far as I know, I'm still under surveillance. But they
• 43:05
  Speaker 1: started hiding it at first. They don't even hide by
• 43:07
  Speaker 1: the way, when they put you under massive amounts of surveillance,
• 43:10
  Speaker 1: which is an absurd number of people that they put
• 43:12
  Speaker 1: on you to surveil you. They don't just put like
• 43:15
  Speaker 1: one or two people on you. They'd like follow like
• 43:18
  Speaker 1: I have a crowd of people following me around and
• 43:20
  Speaker 1: they change out all the time. I went to the
• 43:22
  Speaker 1: Grand Canyon to get out in the middle of nowhere,
• 43:24
  Speaker 1: just to see what would happen. And they don't hide like.
• 43:27
  Speaker 1: The point is to make sure that you know that
• 43:29
  Speaker 1: they're there because what they do to you, and part
• 43:32
  Speaker 1: of the mk ultrapart is that they try to make
• 43:35
  Speaker 1: you go psychotic. Literally, if they can't get you to
• 43:38
  Speaker 1: commit suicide, they'll try to make you go psychotic. And
• 43:41
  Speaker 1: they also do this through a variety of methods in intelligence,
• 43:45
  Speaker 1: but they also do it with drugs. They do a
• 43:47
  Speaker 1: variety of different things, and the idea is one of
• 43:51
  Speaker 1: the things they do is they try to manufacture as
• 43:53
  Speaker 1: many catastrophes in your life as possible, because the idea
• 43:56
  Speaker 1: is to make you like, if they can't get you,
• 43:58
  Speaker 1: to put you in a psych world, which thankfully I
• 44:02
  Speaker 1: have a lot of medical experience, and so I could
• 44:04
  Speaker 1: handle what they were doing to me. But at the
• 44:06
  Speaker 1: same time, if they fail at that, then they just
• 44:08
  Speaker 1: try to get you to commit suicide by making your
• 44:10
  Speaker 1: life as horrible as possible. I've applied to more than
• 44:14
  Speaker 1: two hundred jobs over the past two years and I
• 44:17
  Speaker 1: haven't been able to get one interview. And prior to that,
• 44:22
  Speaker 1: I was able to get an interview at almost every
• 44:24
  Speaker 1: job that I ever applied for. And I have like
• 44:27
  Speaker 1: really good resume. I'm not saying I'm the best, but
• 44:30
  Speaker 1: for sure in the top fifty percent. And somehow I
• 44:32
  Speaker 1: was a person, maybe the only person that got a
• 44:35
  Speaker 1: computer science degree, and my employability went down instead of up.
• 44:40
  Speaker 1: And so that's pretty ridiculous. And they do a lot
• 44:45
  Speaker 1: more horrible things than that to you. They manufacture all
• 44:48
  Speaker 1: kinds of horrible shit. They have access to every single
• 44:51
  Speaker 1: device that you ever have, it doesn't matter what, and
• 44:54
  Speaker 1: they will do all Like one of the things they do,
• 44:56
  Speaker 1: like I'm pretty uniquely trained on being able to distinguish
• 45:00
  Speaker 1: between algorithmic behavior and real behavior, and like I'm like,
• 45:05
  Speaker 1: thank god, because they do all kinds of algorithm and
• 45:08
  Speaker 1: stuff they insert. They have access to every kind of
• 45:11
  Speaker 1: app you might have any kind of audio. I couldn't
• 45:13
  Speaker 1: listen to music anymore because one of the things they
• 45:15
  Speaker 1: do is they in that subtext into.
• 45:19
  Speaker 2: A Benford's law. Do you use Benford's law to do
• 45:23
  Speaker 2: analysis of the type of numbers they keep popping up?
• 45:28
  Speaker 1: Well, I'm not familiar.
• 45:30
  Speaker 2: Oh. Bedford's law is a mathematical tool that they use
• 45:33
  Speaker 2: to determine whether or not it's a made up number
• 45:36
  Speaker 2: or a natural number, based upon the probability of the
• 45:41
  Speaker 2: numbers in different positions. So like a lawe is more
• 45:44
  Speaker 2: common than a nine, so you're gonna have ones pop
• 45:47
  Speaker 2: up way more often, and so based off of those probabilities,
• 45:50
  Speaker 2: you could tell whether or not a person's going to
• 45:52
  Speaker 2: be lying about a number or not. If you apply
• 45:55
  Speaker 2: Benford's law, you can actually see whether or not you're
• 45:58
  Speaker 2: getting outside influence.
• 46:01
  Speaker 1: Oh, I'll have to look up at that. I mean,
• 46:03
  Speaker 1: I know of a similar concept in cryptography. One of
• 46:07
  Speaker 1: the things one of the earliest forms of decryption, like
• 46:10
  Speaker 1: breaking encryption, like early early on, this is nothing advanced,
• 46:14
  Speaker 1: but way early on. One of the first things they
• 46:16
  Speaker 1: would do is they would do statistical analysis analysis on
• 46:19
  Speaker 1: the letters and then they would match it to the language,
• 46:22
  Speaker 1: the occurrence in the language, and so they would just
• 46:24
  Speaker 1: look at the statistical analysis of how often something appears,
• 46:28
  Speaker 1: and then they would look at it in the language
• 46:30
  Speaker 1: it was the occurrence, and they would see if they match,
• 46:32
  Speaker 1: and then they would translate that way. So that was
• 46:34
  Speaker 1: actually one of the early things that they would do,
• 46:36
  Speaker 1: which is a similar concept to what you're saying. No,
• 46:41
  Speaker 1: I haven't done that, but I mean I don't need to. Like,
• 46:44
  Speaker 1: like I said, for the most part, they don't even
• 46:47
  Speaker 1: really try to hide anything. Like the issue that they
• 46:51
  Speaker 1: go for is that if you even try to talk
• 46:53
  Speaker 1: about it, then they'll use it as evidence of psychosis
• 46:56
  Speaker 1: and then they'll But like in the military, like the
• 47:00
  Speaker 1: military has reduced the clinical definition of psychosis to whether
• 47:05
  Speaker 1: or not they agree with you and the UAP, like
• 47:10
  Speaker 1: every single person in the military that has ever talked
• 47:13
  Speaker 1: about aliens basically, I mean except for David Fever, I
• 47:16
  Speaker 1: guess was the one person I've heard that doesn't get
• 47:18
  Speaker 1: retaliated against. But almost everyone else all the other military
• 47:21
  Speaker 1: people that have talked about aliens or said anything about
• 47:24
  Speaker 1: it have discussed massive amounts of retaliation and fears for
• 47:27
  Speaker 1: their life and fears for their family and all this stuff,
• 47:30
  Speaker 1: and they have to go through rigorous psych and all
• 47:32
  Speaker 1: kinds of stuff like that. The math says the probability
• 47:36
  Speaker 1: aliens exist is one hundred percent. Yet every single person
• 47:41
  Speaker 1: in the military that ever talks about aliens somehow needs
• 47:43
  Speaker 1: a psych exam over and over again. And it's like
• 47:46
  Speaker 1: they're not really doing a psych exam, they're doing mk
• 47:48
  Speaker 1: ultra over and over and over again. Just like I
• 47:52
  Speaker 1: was saying in academia, people who talk about aliens end
• 47:55
  Speaker 1: up getting put in a psych ward. The clinical definition
• 47:59
  Speaker 1: in Meta Sin for psychosis has nothing to do with
• 48:03
  Speaker 1: whether or not you believe in aliens. You can talk
• 48:06
  Speaker 1: about a aliands all day, every day and be totally
• 48:09
  Speaker 1: wrong about it, You're still not psychotic. It has no
• 48:12
  Speaker 1: relevance to the clinical definition of psychosis. Yet in the
• 48:16
  Speaker 1: military it does, and that tells you everything you need
• 48:21
  Speaker 1: to know. For the past eighty years, what is happening.
• 48:24
  Speaker 1: That is only one thing that they've weaponized in medicine
• 48:28
  Speaker 1: is psychosis. They've actually weaponized everything. And of course, like
• 48:33
  Speaker 1: I don't just to say it, I don't have any
• 48:35
  Speaker 1: brain disease. I never have. My dad's a neurologist, and
• 48:38
  Speaker 1: a psychiatrist. My mom's a pharmacist with a bunch of degrees.
• 48:42
  Speaker 1: I have a bunch of degrees. I know what I'm
• 48:44
  Speaker 1: talking about.
• 48:47
  Speaker 2: Right. It seems like the retaliation industry in the United
• 48:50
  Speaker 2: States is actually like a huge thing. It's like it's
• 48:53
  Speaker 2: big subcontracted out but all these people aren't state employees.
• 48:58
  Speaker 2: But basically, you can hire a bunch of people to
• 49:00
  Speaker 2: go out and just harass and mess with people and
• 49:05
  Speaker 2: it's perfectly legal able higher.
• 49:07
  Speaker 1: The cost of treason in America is near zero. That's
• 49:09
  Speaker 1: why they're doing it so regularly. It's it's a it's
• 49:13
  Speaker 1: a regular and common thing. The number I've seen from
• 49:15
  Speaker 1: Cash Hotel last month published by the are produced by
• 49:19
  Speaker 1: Cash Metel, the FBI director. He said, in the last
• 49:22
  Speaker 1: few years, more than two hundred and seventy thousand Americans
• 49:26
  Speaker 1: have been surveiled. That's in the last few years alone.
• 49:29
  Speaker 1: And that number is a gross underestimate. I know for
• 49:32
  Speaker 1: a fact it's way higher than that. So this is
• 49:36
  Speaker 1: a big problem. Yeah, over and over again. So many
• 49:40
  Speaker 1: people are getting murdered for this stuff. And hopefully it
• 49:43
  Speaker 1: doesn't happen to me, but we'll see.
• 49:46
  Speaker 4: You know, it's interesting that you mentioned that because it
• 49:48
  Speaker 4: was like people that we covid, well, I didn't covid technically,
• 49:52
  Speaker 4: Lisa Covid of ladyburg Lake with smiley face kills a
• 49:56
  Speaker 4: lot of these kids that were showing up dead like
• 50:00
  Speaker 4: attached to like people that are associated with the government
• 50:03
  Speaker 4: in like weird fucking shit like this, kids getting knocked
• 50:09
  Speaker 4: off to tell them to shut up with some.
• 50:13
  Speaker 1: I mean, yeah, I mean I don't know all of
• 50:15
  Speaker 1: the like every single kid, but I am aware that
• 50:17
  Speaker 1: kids are not immune from the corruption that is happening
• 50:22
  Speaker 1: with the US military. There's so many people that talk
• 50:25
  Speaker 1: about corruption in politics. I don't understand why people don't
• 50:28
  Speaker 1: talk about corruption in the military, because that's a way
• 50:30
  Speaker 1: more dangerous game. Corruption in politics is almost exclusively a
• 50:35
  Speaker 1: money game. The corruption in the military is about power
• 50:39
  Speaker 1: and control. And you've got to understand, like the Americans
• 50:42
  Speaker 1: really need to understand how frequent, common regular that the
• 50:47
  Speaker 1: US military is weaponized against Americans on US soil. If
• 50:52
  Speaker 1: you don't believe this, look at the Twitter files. This
• 50:54
  Speaker 1: is a massive sigh of it's extremely dense evidence of this,
• 51:00
  Speaker 1: extremely done. And that's just one thing. That's just one thing.
• 51:03
  Speaker 1: Nothing about the vact. Uh. You know, the covid era
• 51:06
  Speaker 1: makes any sense. Not a single thing about it makes
• 51:09
  Speaker 1: any sense. All the doctors that were talking about the
• 51:11
  Speaker 1: truth were retaliated against heavily all kinds of suppression and control.
• 51:16
  Speaker 1: We've seen it in Europe. We know that they're controlling
• 51:18
  Speaker 1: free speech and you go to prison for it. Uh,
• 51:21
  Speaker 1: it's ridiculous how much has been compromised in America.
• 51:24
  Speaker 3: Yeah.
• 51:24
  Speaker 2: And all the viruses that were created were created for
• 51:30
  Speaker 2: and by the government, right, I mean, none of this
• 51:33
  Speaker 2: stuff was independent. All of the genetic material came out
• 51:36
  Speaker 2: of for Ddrich and was used in the private sector.
• 51:39
  Speaker 2: All of this stuff was originally a military project.
• 51:42
  Speaker 1: Mm. Yeah, I mean mk Ultra was a military project intelligence.
• 51:48
  Speaker 1: But when I say military, i'm including intelligence. By the way,
• 51:51
  Speaker 1: intelligence is the apparatus through which a lot of this happens.
• 51:54
  Speaker 1: Like during the Edward Snoden era, the end, there was
• 51:56
  Speaker 1: a US court the rule that the NSA was illegal,
• 51:59
  Speaker 1: and that court ruling still holds to this day. There
• 52:04
  Speaker 1: is not one person at the NSA that is not
• 52:07
  Speaker 1: fully and completely aware that they are working for a
• 52:11
  Speaker 1: criminal organization whose primary function. The reason why the NSA
• 52:18
  Speaker 1: even exists in the first place, is primarily to commit
• 52:22
  Speaker 1: treason against Americans on American soil. It is literally built
• 52:27
  Speaker 1: for that purpose. It is the functional reason why it exists.
• 52:31
  Speaker 1: And the US Court is it's still illegal, but it's
• 52:34
  Speaker 1: one of those laws that we just pretend doesn't exist.
• 52:38
  Speaker 2: There's a lot of those.
• 52:40
  Speaker 1: There's a lot of them. Every single amendment in the
• 52:43
  Speaker 1: Constitution has been violated for this purpose, including enslavement, by
• 52:47
  Speaker 1: the way. But yeah, this is what I'm trying, Like,
• 52:51
  Speaker 1: it's really important that Americans understand that the US military
• 52:55
  Speaker 1: is not really defending them over the past few years,
• 52:58
  Speaker 1: like the line. By the way, I'm not trying to
• 53:00
  Speaker 1: I understand that there are good people in the military,
• 53:03
  Speaker 1: but I'm just talking about the math alone, the numbers.
• 53:07
  Speaker 1: The primary line for the US military is to defend
• 53:11
  Speaker 1: threat from threats foreign and domestic. But there were over
• 53:17
  Speaker 1: the last few years. We're seeing over and over again
• 53:20
  Speaker 1: every country has had free reign in America with impunity.
• 53:24
  Speaker 1: So what threat were they protecting us against while they
• 53:27
  Speaker 1: were surveilling two hundred and seventy thousand Americans, which is
• 53:31
  Speaker 1: a gross underestimate of the real number. That's just what
• 53:33
  Speaker 1: the FBI is willing to say.
• 53:36
  Speaker 2: It seems like any level of organization more than a
• 53:41
  Speaker 2: church group, is able to infiltrate and you know, basically
• 53:46
  Speaker 2: take over government organizations.
• 53:49
  Speaker 1: And we have no there was a military coup. I mean,
• 53:55
  Speaker 1: Eisenhower warned about the excuse me, sorry, Wisenhower warned about
• 54:01
  Speaker 1: the military industrial complex. I was mentioning that my neighbor, Mickeyadacci,
• 54:05
  Speaker 1: was a boozellandd contractor at the time that was purchasing
• 54:09
  Speaker 1: light our systems. That doesn't make any sense, Like, at
• 54:12
  Speaker 1: a fundamental level, it doesn't make any sense, especially when
• 54:15
  Speaker 1: there's millions of teslas on the road doing more than
• 54:18
  Speaker 1: what any of the military vehicles can do. And by
• 54:21
  Speaker 1: the way, I learned in the process of them running
• 54:23
  Speaker 1: this operation on me. I learned through what they were
• 54:27
  Speaker 1: doing to me. And just so you know, I learned
• 54:30
  Speaker 1: through what they were doing to me what their capabilities are.
• 54:33
  Speaker 1: And you wouldn't believe how primitive it actually is. They're
• 54:37
  Speaker 1: doing the dumbest shit you could imagine. It's you think
• 54:40
  Speaker 1: that they have like super advanced AI robots that are
• 54:43
  Speaker 1: indistinguishable from humans. You think they have like advanced super tech.
• 54:48
  Speaker 1: You see all this shit about them being like they
• 54:51
  Speaker 1: have whole star fleets that are like in operating in
• 54:54
  Speaker 1: other galaxies, and you see so much of this dumb shit.
• 54:57
  Speaker 1: No they don't, they're really domin.
• 55:00
  Speaker 2: Here's a new proposition. So you were talking about all
• 55:03
  Speaker 2: of the scientists who have died, all of the inventors
• 55:05
  Speaker 2: who have died. What if that's because these other people
• 55:09
  Speaker 2: were so far behind them that they couldn't let anybody
• 55:13
  Speaker 2: else get in front of them. So we keep thinking
• 55:16
  Speaker 2: it's because they have something hidden that they're trying to
• 55:18
  Speaker 2: keep from the public. What if it's just trying to
• 55:21
  Speaker 2: stop somebody from growing a better garden.
• 55:25
  Speaker 1: Yeah, that's called sabotage. What Americans trying to make their
• 55:29
  Speaker 1: lives better? It's somehow worthy of killing them. That's not Yeah,
• 55:36
  Speaker 1: I mean what you're saying is exactly what they're doing. Though,
• 55:39
  Speaker 1: So we are facing something called a knowledge deletion campaign.
• 55:44
  Speaker 1: So you know, hopefully what you got out of this presentation,
• 55:48
  Speaker 1: or people got out of this presentation, is that the
• 55:51
  Speaker 1: real version of AI is actually really cool. But all
• 55:55
  Speaker 1: these apocalypse scenarios and all this stuff, the way that
• 55:59
  Speaker 1: happens is what dumb humans. The apocalypse scenarios happens with
• 56:02
  Speaker 1: dumb humans. You don't need to fear artificial intelligence. Artificial
• 56:08
  Speaker 1: intelligence is math. That's it. It's just math. You're blaming
• 56:12
  Speaker 1: the gun and not the human. The actual real problem
• 56:16
  Speaker 1: is artificially dumb humans, and we are being manufactured in
• 56:20
  Speaker 1: America and in the West to be artificially dumb. If
• 56:23
  Speaker 1: you've seen what they're doing in archaeology, which we've talked
• 56:25
  Speaker 1: about and a lot of you are familiar with, if
• 56:27
  Speaker 1: you've seen that they're hiding all life in the universe UAPs,
• 56:31
  Speaker 1: which are common, if you've seen what I'm talking about
• 56:34
  Speaker 1: in AI, we have multiple vectors of knowing that we
• 56:38
  Speaker 1: are being made artificially dumb. We are all The amount
• 56:43
  Speaker 1: of knowledge that we have is this big, and the
• 56:47
  Speaker 1: amount of knowledge of discoveries already made and suppressed is
• 56:53
  Speaker 1: much larger than the amount of knowledge that we have
• 56:57
  Speaker 1: that they're the governments are drip feeding to us. So
• 57:00
  Speaker 1: there are discoveries that have been made all the time.
• 57:03
  Speaker 1: Like way back in the Nickela Tesla day, we had
• 57:06
  Speaker 1: Nicola Tesla. They were trying to hide electromagnetism. This is
• 57:10
  Speaker 1: part of how we get to the propulsion tech with UAPs.
• 57:12
  Speaker 1: Because they're based on principles of electromagnetism. And we know this,
• 57:17
  Speaker 1: and Bob Blazar talks a lot about this, that they
• 57:19
  Speaker 1: were trying to hide all of these principles, anything related
• 57:23
  Speaker 1: to energy, high energy production, anything related to the propulsion tech.
• 57:27
  Speaker 1: That's what they were originally initially trying to hide. And
• 57:34
  Speaker 1: then that expanded to basically all of academia, and now
• 57:38
  Speaker 1: everything in academia is classified automatically. First, everything reality is classified.
• 57:45
  Speaker 1: The entire classification system is being used as a weapon
• 57:48
  Speaker 1: against Americans in order to hide all these activities. And
• 57:51
  Speaker 1: that's the purpose of classification, classifying everything, classifying literally everything,
• 57:57
  Speaker 1: and by the way, classifying everything, it's like that's actually understood.
• 58:01
  Speaker 1: They will admit that everything is by default classified first
• 58:06
  Speaker 1: and then not classified if they ever get around to it,
• 58:10
  Speaker 1: which they never do.
• 58:13
  Speaker 2: Well, not for the important ship.
• 58:16
  Speaker 1: Yeah, which is almost all of it, like most of it.
• 58:20
  Speaker 1: What they're what they're declassifying is not really that important. Yeah,
• 58:26
  Speaker 1: and it's again, it's not just a propulsion tech, it's
• 58:28
  Speaker 1: not just energy. It's across the board in all of academia,
• 58:33
  Speaker 1: across the board. Prioritizing STEM that's what I would know
• 58:39
  Speaker 1: about I don't know about outside of STEM. I'm sure
• 58:41
  Speaker 1: they're doing a lot there. I know about stuff that
• 58:43
  Speaker 1: they do in finance, and obviously all the corruption happens
• 58:46
  Speaker 1: in finance. Actually, I like, you know, my background's in finance,
• 58:49
  Speaker 1: and I've got to probably at this point do another
• 58:52
  Speaker 1: talk about finance in the future. But I just want
• 58:55
  Speaker 1: to put this on the screen for people to see,
• 58:57
  Speaker 1: and I hope people bookmark this website. This is the
• 58:59
  Speaker 1: ustclock dot org. And what you need to understand about
• 59:04
  Speaker 1: this is we have thirty eight and thirty eight point
• 59:08
  Speaker 1: six trillion dollars in debt. We make about five point
• 59:11
  Speaker 1: three trillion dollars in revenue. We spend two trillion dollars
• 59:16
  Speaker 1: on medicare. These are the top four line items in
• 59:18
  Speaker 1: the federal budget. We spend two trillion on Medicare, we
• 59:23
  Speaker 1: spend one point six on Social Security, about a trillion
• 59:27
  Speaker 1: on military, and about and more than that, more than
• 59:32
  Speaker 1: what we spend on the military, about a trillion dollars
• 59:35
  Speaker 1: on interest from our thirty eight and then point six
• 59:38
  Speaker 1: trillion dollars in debt, more than what we spend on
• 59:41
  Speaker 1: the military every year. Is how much we're paying and
• 59:45
  Speaker 1: just interest alone, nothing else. Interest alone. Interest is the
• 59:50
  Speaker 1: third largest line item in the federal budget, behind Medicare
• 59:55
  Speaker 1: and Social Security. Interest. So when we have a huge
• 1:00:02
  Speaker 1: amount of fraud happening on the order of trillions of
• 1:00:06
  Speaker 1: dollars in some total, and your taxpayer dollars are going
• 1:00:10
  Speaker 1: towards weaponizing asset taxpayer funded assets against Americans, and most
• 1:00:16
  Speaker 1: of this trillion dollars that's spent on the military doesn't
• 1:00:21
  Speaker 1: go towards defending Americans, and a huge amount of it
• 1:00:25
  Speaker 1: goes towards harming them. There's nothing about this that is like.
• 1:00:30
  Speaker 1: First of all, mutually assured destruction is literally spelt mad,
• 1:00:33
  Speaker 1: and it's the dumbest thing, the idea of being that
• 1:00:37
  Speaker 1: the for the past eighty years, one of the highest
• 1:00:41
  Speaker 1: priorities of the US military has been to enable and
• 1:00:45
  Speaker 1: create every system possible to destroy the world in five
• 1:00:49
  Speaker 1: minutes or less. And they've done it. And that's what
• 1:00:53
  Speaker 1: mutually assured destruction was about. Philosophically, that's what it was about.
• 1:00:57
  Speaker 1: And once they created it, they weren't going to use it,
• 1:01:02
  Speaker 1: but they created it and kept building it more and
• 1:01:04
  Speaker 1: more and more. And there's even people like just so
• 1:01:08
  Speaker 1: you have some understanding of how they do this. There
• 1:01:11
  Speaker 1: are people in the military who go into these hundreds
• 1:01:16
  Speaker 1: of underground bunkers, like little guy little bunkers, and in
• 1:01:20
  Speaker 1: there they have like two people in each little room.
• 1:01:24
  Speaker 1: And once they're and they spend maybe sometimes up to
• 1:01:26
  Speaker 1: years in this little underground thing with no windows, and
• 1:01:31
  Speaker 1: there's one other person with them at their little post
• 1:01:33
  Speaker 1: where they're at. And what they're doing in this little
• 1:01:36
  Speaker 1: bunker is they have a little machine and they have
• 1:01:40
  Speaker 1: a signal that comes in message or whatever, and then
• 1:01:43
  Speaker 1: they're supposed to do something if they receive that, and
• 1:01:47
  Speaker 1: they do this across hundreds of different ones, and this
• 1:01:50
  Speaker 1: is how nuclear launch codes are sent. And they do
• 1:01:53
  Speaker 1: this regularly because they're conditioning them to do it without question.
• 1:01:57
  Speaker 1: So they send tons and tons of fake ones through
• 1:02:00
  Speaker 1: to make sure that you're doing it, and they make
• 1:02:02
  Speaker 1: sure you do it over and over again. But most
• 1:02:04
  Speaker 1: of them are fake. But then the idea is that
• 1:02:07
  Speaker 1: when one that is real comes through, you won't know
• 1:02:09
  Speaker 1: which bunker it is, and you won't know if it's
• 1:02:11
  Speaker 1: a real one or a fake. When you're just when
• 1:02:13
  Speaker 1: you see the signal, you're supposed to do your task.
• 1:02:15
  Speaker 1: And that's what launches the thing, and you won't even
• 1:02:17
  Speaker 1: know where it's going, what it does, or if it's
• 1:02:19
  Speaker 1: even real or not. And this is how they condition
• 1:02:22
  Speaker 1: you in the military to just do launch nuclear because
• 1:02:26
  Speaker 1: one of the things they would be concerned about is
• 1:02:29
  Speaker 1: this idea of like a noble person that would stop,
• 1:02:32
  Speaker 1: you know, oh no, we should not launch the news.
• 1:02:34
  Speaker 1: They're worried about that. So this is their method of
• 1:02:36
  Speaker 1: getting around that. They just and every day they send
• 1:02:39
  Speaker 1: these things all the way through and when everyone comes,
• 1:02:42
  Speaker 1: they have to be there to like do the thing,
• 1:02:45
  Speaker 1: to launch them and whatever pretend to launch them. They
• 1:02:47
  Speaker 1: have no most of the time they're not, right. Yeah,
• 1:02:51
  Speaker 1: so that's that's kind of like you got to understand,
• 1:02:54
  Speaker 1: this is what our tax dollars are going towards. They're
• 1:02:58
  Speaker 1: not going towards helping you anyway. They're literally being weaponized
• 1:03:02
  Speaker 1: against you to make you dumber. We are actively this
• 1:03:06
  Speaker 1: is what the point of making all this distortion in
• 1:03:08
  Speaker 1: academia is about. It's designed specifically for the task of
• 1:03:13
  Speaker 1: making the population dumber, not keep them where they're at.
• 1:03:18
  Speaker 1: They go back in time, they rewrite history or delete it.
• 1:03:22
  Speaker 1: And this has happened actively. You were mentioning gaina function
• 1:03:25
  Speaker 1: research initially is what you were talking about. When we
• 1:03:27
  Speaker 1: were saying stuff about that, people forget this was on
• 1:03:31
  Speaker 1: the du wasn't commonly understood, and now it's been white.
• 1:03:34
  Speaker 1: In the nineteen nineties, there was a lab that was
• 1:03:38
  Speaker 1: off the coast of I think it was Virginia, but
• 1:03:40
  Speaker 1: it might might have been Maryland, I can't remember. It
• 1:03:42
  Speaker 1: was on an island and this lab was doing gata
• 1:03:46
  Speaker 1: function research in the nineteen nineties on the Bola virus,
• 1:03:50
  Speaker 1: and they were trying to make the bowl of virus airborne.
• 1:03:54
  Speaker 1: They were trying to make it a respiratory disease, and
• 1:03:56
  Speaker 1: they succeeded. And what they did what ended up having
• 1:04:00
  Speaker 1: happening is that this actually got into the vents of
• 1:04:02
  Speaker 1: the lab and there was a whole bunch of monkeys
• 1:04:04
  Speaker 1: in this lab and they all got infected. And in
• 1:04:07
  Speaker 1: the nineteen nineties, this was under during the Clinton era.
• 1:04:11
  Speaker 1: They had to send in a whole team to destroy
• 1:04:13
  Speaker 1: this lab and completely obliterate it. And at the time,
• 1:04:16
  Speaker 1: the way that you had dealt with ebola is that
• 1:04:19
  Speaker 1: you burned everything, including the people, and then you poured
• 1:04:23
  Speaker 1: chlorox in a moat around it. And then made sure
• 1:04:26
  Speaker 1: that no, and then buried all that. And that's what
• 1:04:29
  Speaker 1: they that's how you dealt with Ebola at the time,
• 1:04:31
  Speaker 1: and they and then the United States made an airborne
• 1:04:35
  Speaker 1: version of it and succeeded in the nineteen nineties. That's
• 1:04:39
  Speaker 1: called gain of function research. And that is because once
• 1:04:43
  Speaker 1: that gets out, there's no borders on that. If that
• 1:04:46
  Speaker 1: even got out one time, if that was ever deployed
• 1:04:49
  Speaker 1: even one time, there's nothing that stops that from infecting
• 1:04:52
  Speaker 1: the whole world. And there's nothing that stops it on
• 1:04:54
  Speaker 1: the local border either or anything like that. There's nothing
• 1:04:56
  Speaker 1: you're gonna do about it. You're done once it goes airborne.
• 1:05:00
  Speaker 1: That so that's the kind of shit that your tax
• 1:05:02
  Speaker 1: dollars are going towards. And such an enormous amount of
• 1:05:06
  Speaker 1: money and resources in this country is dedicated to specifically
• 1:05:11
  Speaker 1: the task of harming humans. Again, to this day, intelligence
• 1:05:17
  Speaker 1: is not part of the definition of life for some reason.
• 1:05:22
  Speaker 3: To this day, that's pretty evident.
• 1:05:29
  Speaker 4: Yeah, yeah, we have.
• 1:05:36
  Speaker 1: A lot to really figure out in this country, that's
• 1:05:38
  Speaker 1: for sure. Because China is not this dumb. I promise
• 1:05:41
  Speaker 1: you they're not. Oh yeah, on multiple levels. I worked
• 1:05:45
  Speaker 1: in Hong Kong for a short time when I was
• 1:05:46
  Speaker 1: working for a hedge fund, and I promise you they're
• 1:05:49
  Speaker 1: not this dumb back They had robots, robots all over
• 1:05:52
  Speaker 1: the place in twenty ten. They were thinking about this
• 1:05:55
  Speaker 1: stuff way in advance, and they have They've deployed robots
• 1:05:59
  Speaker 1: all over age. Everyone that looked at China or Japan
• 1:06:02
  Speaker 1: knows this. China is even better. So if you think
• 1:06:04
  Speaker 1: Japan is advanced and the stuff that you see there
• 1:06:07
  Speaker 1: looks super advanced, well just see, you guys got to
• 1:06:10
  Speaker 1: look at what they're doing in China. Don't believe the propaganda.
• 1:06:13
  Speaker 1: They are way more advanced than we are. They're doing
• 1:06:16
  Speaker 1: stuff that we can't do.
• 1:06:18
  Speaker 3: Well.
• 1:06:18
  Speaker 2: One of the things I like to look at is
• 1:06:20
  Speaker 2: sort of like this holistic look at how different energies
• 1:06:25
  Speaker 2: affect different societies. And if you look at the ancient
• 1:06:29
  Speaker 2: decrepit energy of our Congress, right, it shows you exactly
• 1:06:35
  Speaker 2: where they're sort of like energetic field is. It's sort
• 1:06:39
  Speaker 2: of old, decaying, it's crumbling, and it refuses to get
• 1:06:43
  Speaker 2: out of the way for new energy to come in.
• 1:06:45
  Speaker 3: Right.
• 1:06:46
  Speaker 2: This is sort of like the case all across the countries,
• 1:06:49
  Speaker 2: these old decrepit you know, dinosaurs who couldn't manage anything.
• 1:06:53
  Speaker 2: I mean, they were all puppets to begin with, are
• 1:06:56
  Speaker 2: now trying to hold onto power as they slip to
• 1:07:00
  Speaker 2: the void because they're so corrupt. There's no way that
• 1:07:03
  Speaker 2: they could leave any of their empire to anybody else,
• 1:07:06
  Speaker 2: as if they would want to in the first place.
• 1:07:08
  Speaker 2: They hate everybody.
• 1:07:11
  Speaker 1: I mean, once people get power, it often corrupts. We've
• 1:07:14
  Speaker 1: heard that this is what happens all the time with power.
• 1:07:18
  Speaker 1: It is correct.
• 1:07:18
  Speaker 2: It's a lifestyle for them. They don't even think about
• 1:07:20
  Speaker 2: the power, like wielding power is just a lifestyle choice
• 1:07:24
  Speaker 2: for them.
• 1:07:27
  Speaker 1: Well yeah, I mean we can have a discussion about that,
• 1:07:30
  Speaker 1: but I kind of want to go even higher level
• 1:07:33
  Speaker 1: than that and talk about what the net result is
• 1:07:38
  Speaker 1: present day as a result. So I just this is
• 1:07:42
  Speaker 1: more in the financiality use this. This is kind of
• 1:07:44
  Speaker 1: where I'll end off, but I kind of want to
• 1:07:46
  Speaker 1: mention this because it's in my opinion, this is what
• 1:07:51
  Speaker 1: another one of those things that every American needs to
• 1:07:54
  Speaker 1: know about. So one of the things that has happened
• 1:07:58
  Speaker 1: as a result of whatever sabotage happened in America, this
• 1:08:03
  Speaker 1: whole thing for many many years, this has extreme consequences,
• 1:08:10
  Speaker 1: and they've already happened. And I want to just briefly
• 1:08:14
  Speaker 1: mention why this is important and relevant for every single
• 1:08:18
  Speaker 1: American to understand. So here's what happened as a result
• 1:08:22
  Speaker 1: of all the sabotage. I know we're talking about fraud
• 1:08:25
  Speaker 1: in the news right now and money game. These are
• 1:08:27
  Speaker 1: all money games. I'm talking about real danger. Power and
• 1:08:32
  Speaker 1: control is the type of corruption that happens in the military,
• 1:08:36
  Speaker 1: and I want to say, what is the real danger
• 1:08:39
  Speaker 1: for that kind of corruption? Money corruption is one type.
• 1:08:43
  Speaker 1: This is a different type, more dangerous. So what happened
• 1:08:47
  Speaker 1: over the last many years. Like I know, we're talking
• 1:08:50
  Speaker 1: a lot about China and stuff, but I think people
• 1:08:52
  Speaker 1: have not really looked at what China is actually doing.
• 1:08:57
  Speaker 1: More than eighty percent of Australia yep, more than eighty
• 1:09:02
  Speaker 1: percent of Australia is mining country. Almost all of the
• 1:09:08
  Speaker 1: population lives on the East coast within two hundred miles.
• 1:09:14
  Speaker 1: The rest of Australia is divided into four sections like
• 1:09:19
  Speaker 1: a cartel. Literally a square block divided into four sections.
• 1:09:24
  Speaker 1: And there this is all mining country. And there is
• 1:09:28
  Speaker 1: a congo line of barges going from Australia to Southeast
• 1:09:32
  Speaker 1: Asia NonStop. The Congo Line is so large that it's
• 1:09:37
  Speaker 1: visible with the human eye from space. That's how large
• 1:09:41
  Speaker 1: the Congo Line of ships of barges filled with mining
• 1:09:45
  Speaker 1: ore from Australia is going to Southeast Asia every day.
• 1:09:51
  Speaker 1: Something else that is not understood. China owns ninety seven
• 1:09:56
  Speaker 1: percent of the known rare earth deposits on Earth, all
• 1:10:03
  Speaker 1: of them ninety seven percent. If you understand technology, you
• 1:10:07
  Speaker 1: understand how important rare earths are to technology. The rest
• 1:10:11
  Speaker 1: of the world is fighting for those three percent. Similar
• 1:10:15
  Speaker 1: action to what I just described in Australia is also
• 1:10:18
  Speaker 1: happening in South America with China doing that. Do you
• 1:10:21
  Speaker 1: remember those Somali pirates? These are that problem was solved
• 1:10:28
  Speaker 1: by China. China has military bases all down the East
• 1:10:33
  Speaker 1: coast of Africa, and they're also learning Mandarin as their
• 1:10:40
  Speaker 1: first language now in a lot of these places in
• 1:10:42
  Speaker 1: East Africa. Another thing that is well known is the
• 1:10:46
  Speaker 1: new Silk Road that China built through the Mideasts. There's
• 1:10:50
  Speaker 1: a new silk road. So everyone's concerned that China is
• 1:10:55
  Speaker 1: about to take over Taiwan. But I want everyone in
• 1:10:59
  Speaker 1: America to understand something India is the largest bipopulation democracy
• 1:11:08
  Speaker 1: in the world one point four billion people. They pride
• 1:11:12
  Speaker 1: themselves in calling themselves the largest democracy in the world.
• 1:11:18
  Speaker 1: India is also surrounded by Chinese military power on all
• 1:11:25
  Speaker 1: sides for thousands of miles in every single direction. There
• 1:11:31
  Speaker 1: is not one direction around India that is not surrounded
• 1:11:36
  Speaker 1: by Chinese military power for thousands of miles deep. Every
• 1:11:44
  Speaker 1: other country in Asia is aligned with China, including Russia,
• 1:11:50
  Speaker 1: because we put all those sanctions on them, and Russia
• 1:11:54
  Speaker 1: is selling all of their oil because nobody in the
• 1:11:56
  Speaker 1: West is allowed to buy it, so they just sell
• 1:11:58
  Speaker 1: it to China. And if you understand anything about the
• 1:12:01
  Speaker 1: US dollar being the reserve currency of the world and
• 1:12:03
  Speaker 1: how so much of that power comes from pricing oil
• 1:12:06
  Speaker 1: in USD, if you understand that, then you also understand
• 1:12:12
  Speaker 1: the significance of more oil in the world being priced
• 1:12:15
  Speaker 1: in you on and not in USD, and that is
• 1:12:19
  Speaker 1: cheap oil. Every other country in this block is aligned
• 1:12:25
  Speaker 1: with China for thousands of miles, and so is Africa.
• 1:12:29
  Speaker 1: And Australia may say they're not, but functionally they are.
• 1:12:35
  Speaker 1: By the numbers they are. I understand politically they're not,
• 1:12:39
  Speaker 1: but functionally because eighty percent of Australia is mining. It
• 1:12:43
  Speaker 1: all is just going to China.
• 1:12:48
  Speaker 2: I'm sol consumer is a soil consumer. So that's that's
• 1:12:52
  Speaker 2: a perfect example right there.
• 1:12:54
  Speaker 1: So while all this sabotage was happening in America, nobody
• 1:12:59
  Speaker 1: understood anything about what they were what China was doing
• 1:13:02
  Speaker 1: in the world, and everyone in America got really focused
• 1:13:06
  Speaker 1: on Taiwan, which I'm not saying is not important. They're
• 1:13:08
  Speaker 1: really advanced and they're super important for semiconductors and all
• 1:13:11
  Speaker 1: this stuff. But this is the largest population democracy in
• 1:13:15
  Speaker 1: the world. And when you start adding it all up,
• 1:13:19
  Speaker 1: how much of the how much land mass on Earth
• 1:13:22
  Speaker 1: does China already present day control, and how much population
• 1:13:28
  Speaker 1: on Earth does China already past tense control. It turns
• 1:13:35
  Speaker 1: out when you add it all up, that percentage of
• 1:13:38
  Speaker 1: land mass on Earth and that percentage of population once
• 1:13:42
  Speaker 1: you include what will end up happening to India no
• 1:13:45
  Speaker 1: matter what. At this point, that's more than fifty percent
• 1:13:49
  Speaker 1: of the planet right there alone. And nobody has a
• 1:13:54
  Speaker 1: clue about any of this that's going on. And I
• 1:13:56
  Speaker 1: promise you, even in the military and intelligence until twenty
• 1:13:59
  Speaker 1: two twenty four, they probably didn't know either because, like
• 1:14:03
  Speaker 1: I said, when they were running the operation on me,
• 1:14:05
  Speaker 1: I started figuring out how what their capabilities are. And
• 1:14:08
  Speaker 1: it turns out they're really dumb. They're dumber than you think.
• 1:14:12
  Speaker 1: They're doing the dumbest shit you can imagine. So one
• 1:14:16
  Speaker 1: of the problems is that India doesn't have any trait,
• 1:14:19
  Speaker 1: real way to get to the West. I mean, I'm Indian,
• 1:14:23
  Speaker 1: by the way, I've been to India many times. India
• 1:14:26
  Speaker 1: loves America. They want to be American. They all they
• 1:14:30
  Speaker 1: wave American flags, they celebrate Fourth of July like it's ridiculous,
• 1:14:35
  Speaker 1: Like how this is the largest democracy in the world,
• 1:14:39
  Speaker 1: one point four billion people, and it is surrounded by
• 1:14:43
  Speaker 1: China in every direction for thousands of miles. Not one
• 1:14:48
  Speaker 1: person in America knows anything about this dumb shit. That's
• 1:14:53
  Speaker 1: how when I said that we are being programmed to
• 1:14:55
  Speaker 1: be artificially dumb in America, I really mean it. This
• 1:15:00
  Speaker 1: this is an actual thing. It's a real thing. It's
• 1:15:03
  Speaker 1: an intentional thing. It is that. This is reality. This
• 1:15:07
  Speaker 1: is base reality. I promise you, I'm the only person
• 1:15:09
  Speaker 1: living in reality right now. This is what's happening, and
• 1:15:13
  Speaker 1: no one knows anything about it, And This is how
• 1:15:15
  Speaker 1: much of the planet China has conquered without firing one bullet,
• 1:15:20
  Speaker 1: not one bullet. They've already achieved this, and they don't
• 1:15:24
  Speaker 1: have to do anything in military conflict to achieve it.
• 1:15:27
  Speaker 1: And I'm talking about past tense. This is already done,
• 1:15:31
  Speaker 1: This has already happened. All that needs to happen is
• 1:15:34
  Speaker 1: India just needs to trade with China, primarily settle everything
• 1:15:38
  Speaker 1: in you on, and then nothing else matters ever again
• 1:15:41
  Speaker 1: with US dollars. And India has every reason to do
• 1:15:44
  Speaker 1: it and no reason not to because.
• 1:15:47
  Speaker 2: Japano's already indicated. Japan has already indicated that they might
• 1:15:50
  Speaker 2: be more friendly towards China. Now, I mean this is yeah,
• 1:15:54
  Speaker 2: what are these domedoes falls? The other ones are going
• 1:15:57
  Speaker 2: to fall too?
• 1:15:58
  Speaker 1: Yeah. South Korea is also trying to do a lot
• 1:16:00
  Speaker 1: of shipbuilding for America and all this stuff. I mean, yeah,
• 1:16:03
  Speaker 1: they're trying because they don't want to be conquered effectively.
• 1:16:08
  Speaker 1: But it's already too late, and people are going to
• 1:16:10
  Speaker 1: start saying this.
• 1:16:11
  Speaker 2: Everybody, including Americans, are sick and tired of American rule.
• 1:16:16
  Speaker 2: They're just really bad managers in Washington, DC. And that's
• 1:16:19
  Speaker 2: that's really.
• 1:16:20
  Speaker 1: The stereotype of Americans. The stereotypes of Americans have had
• 1:16:24
  Speaker 1: around the world outside of the country, like the rest
• 1:16:27
  Speaker 1: of the world. How the rest of the world perceives
• 1:16:29
  Speaker 1: America and other countries for the last twenty years is
• 1:16:33
  Speaker 1: the dumb American. It is the most common description across
• 1:16:39
  Speaker 1: the world numerous countries of what their perception is of Americans.
• 1:16:45
  Speaker 1: When they come to those countries, they think of them
• 1:16:49
  Speaker 1: stereotypically as dumb Americans. This is what the stereotype has
• 1:16:53
  Speaker 1: been all around the world for the last twenty years,
• 1:16:56
  Speaker 1: roughly close to it. So this is why all this
• 1:17:01
  Speaker 1: shit matters. Because China is graduating three hundred thousand world
• 1:17:06
  Speaker 1: class engineers every single year, world class And when I
• 1:17:11
  Speaker 1: was at ASU, they were making every engineer as dumb
• 1:17:15
  Speaker 1: as possible. I had to explain to a room full
• 1:17:18
  Speaker 1: of PhDs in computer science that higher resolution data is better.
• 1:17:24
  Speaker 1: Do you need a degree to understand that or not?
• 1:17:26
  Speaker 1: And you think I have to explain that shit to
• 1:17:28
  Speaker 1: anyone in China. They're teaching China. They're teaching AI to
• 1:17:33
  Speaker 1: elementary school in China. Elementary school is learning AI in
• 1:17:39
  Speaker 1: China today. That's already happened past tense again. So this
• 1:17:45
  Speaker 1: is what it means to be artificially dumb. I'm not
• 1:17:48
  Speaker 1: saying that Americans are dumb. I'm saying that we've been
• 1:17:51
  Speaker 1: programmed to be artificially dumb, and that is the problem.
• 1:17:57
  Speaker 1: You need to fear artificially dumb human more than you
• 1:18:01
  Speaker 1: fear artificial intelligence. Artificial intelligence is math. You don't need
• 1:18:04
  Speaker 1: to fear intelligence. It's it's the dumb you have to fear.
• 1:18:09
  Speaker 1: We have this concept in Hindi that I mean if
• 1:18:12
  Speaker 1: I I've said a lot of heavy shit, so let
• 1:18:14
  Speaker 1: me let me try some less heavy shit. Everyone has
• 1:18:19
  Speaker 1: heard this word Buddha. Buddha an enlightened one, right, but
• 1:18:24
  Speaker 1: there's always this heavy distortion or difficulty in understanding what
• 1:18:28
  Speaker 1: does Buddha mean. So maybe a better way of understanding
• 1:18:33
  Speaker 1: this is through its opposite. We have another word in Hindi.
• 1:18:38
  Speaker 1: It is called Buddoo, same word with the U at
• 1:18:41
  Speaker 1: the end, Buddha and Buddhoo. Booddoo is the polar opposite
• 1:18:49
  Speaker 1: of Buddha. Buddhoo is a is the definition. The definition
• 1:18:55
  Speaker 1: of this word is like a dim idiot. That's what
• 1:18:58
  Speaker 1: a Buddhoo is. A dim person, very slow idiot, that's
• 1:19:05
  Speaker 1: what boodoo is. So if you don't understand what Buddha is,
• 1:19:10
  Speaker 1: which is not your fault. They've distorted a lot everything
• 1:19:12
  Speaker 1: about this, then you can just learn what Buddha is
• 1:19:16
  Speaker 1: by knowing what it's not. And Buddha is the polar
• 1:19:20
  Speaker 1: opposite of Bodhoo. So don't be Bodhoo. That'll just get
• 1:19:26
  Speaker 1: you to Buddha right there. You'll become Buddha that fast.
• 1:19:29
  Speaker 1: Just don't be Bodhoo. You don't have to know what
• 1:19:33
  Speaker 1: Buddha is, just be not Budhoo.
• 1:19:37
  Speaker 2: So how do we how do we get around the stupidity?
• 1:19:40
  Speaker 3: Though?
• 1:19:40
  Speaker 2: I mean, is there a cure for this kind of Uh?
• 1:19:45
  Speaker 1: I've been trying to do shit about this for a
• 1:19:47
  Speaker 1: long time, but my whole give a fuck is completely
• 1:19:51
  Speaker 1: out the window because of how much horrible shit has
• 1:19:54
  Speaker 1: happened to me and how often I've been hearing about
• 1:19:57
  Speaker 1: it happening to other people like that. There's a fund
• 1:19:59
  Speaker 1: amount the problem is not foreign threats, although there is
• 1:20:02
  Speaker 1: that problem, not saying that doesn't exist, but the existential
• 1:20:06
  Speaker 1: threat that America has is sabotage, is treason. Our US
• 1:20:09
  Speaker 1: military has been weaponized against Americans on American soil for
• 1:20:13
  Speaker 1: eighty years. There was a military coup that happened during
• 1:20:16
  Speaker 1: the Eisenhower era and was solidified. The military solidified their
• 1:20:20
  Speaker 1: power with the Kennedy assassination, and they've been using all
• 1:20:23
  Speaker 1: kinds of rationality and justification for why it's appropriate to
• 1:20:28
  Speaker 1: weaponize the military against America. And when I say military,
• 1:20:33
  Speaker 1: I'm including intelligence. I'm mostly referring to intelligence, not soldiers
• 1:20:36
  Speaker 1: on the ground fighting the war like not like that.
• 1:20:39
  Speaker 1: I'm mostly referring to syops and intelligence and special ops,
• 1:20:42
  Speaker 1: certain special ops, especially the ones.
• 1:20:45
  Speaker 2: About all the experimentation, all the experimentation since the nineteen fifties,
• 1:20:49
  Speaker 2: it's just been non stop, and I think that includes
• 1:20:52
  Speaker 2: COVID that was military experimentation. Yeah, they've looked at us
• 1:20:57
  Speaker 2: like cattle. I mean, there's no other way to put Yeah.
• 1:21:01
  Speaker 1: I mean Americans are definitely considered sacrificial, and they're allowed
• 1:21:05
  Speaker 1: to be sacrificed. I mean, if you want to hear
• 1:21:07
  Speaker 1: one of the darkest chapters in history, you got to
• 1:21:09
  Speaker 1: look at the polio vaccine. That I'll tell you everything
• 1:21:12
  Speaker 1: you need to know, but that's another one of the things.
• 1:21:14
  Speaker 1: I mean, you got to look at that whole era,
• 1:21:15
  Speaker 1: the sixtieth era was just just the things we publicly
• 1:21:19
  Speaker 1: academically understand. There was the food pyramid. There was lead
• 1:21:24
  Speaker 1: based pain. There was sugar trying to say blame saturated fats.
• 1:21:32
  Speaker 1: There was thank you for smoking. There was the JFK assassination.
• 1:21:38
  Speaker 1: There was the moon landing. There was did I say
• 1:21:44
  Speaker 1: the food pyramid already? Yeah, there was that. I mentioned
• 1:21:46
  Speaker 1: the polio vaccine. All of these deceptions, unleaded gas, the gas,
• 1:21:52
  Speaker 1: think tanks and all that, they all happened in this
• 1:21:54
  Speaker 1: same time frame. All these different deceptions, they were all
• 1:21:59
  Speaker 1: happening at the same time, and there were so many
• 1:22:02
  Speaker 1: people being assassinated, including JFK, not just JFK, but that's
• 1:22:07
  Speaker 1: the big one. And that tells you how much like
• 1:22:10
  Speaker 1: you have to understand that military has a monopoly on violence,
• 1:22:15
  Speaker 1: and we're so desensitized to people getting murdered in America,
• 1:22:19
  Speaker 1: Like it is happening NonStop, and it's not just about
• 1:22:23
  Speaker 1: it murders one thing, but like also just destroying people's
• 1:22:26
  Speaker 1: lives like Bob Blazar and the stuff that they did
• 1:22:29
  Speaker 1: to him, Like this is so common and regular that
• 1:22:34
  Speaker 1: this is why everything that I just showed on this
• 1:22:37
  Speaker 1: screen and everything I'm talking about. This is why it happened.
• 1:22:40
  Speaker 1: It's not like there is a foreign threat that is
• 1:22:43
  Speaker 1: doing all this to us for the last eighty years. No,
• 1:22:45
  Speaker 1: this is our own people doing it to us. It's
• 1:22:47
  Speaker 1: called friendly fire.
• 1:22:52
  Speaker 2: Well, if you look at the other countries that are
• 1:22:55
  Speaker 2: as influenced, if not more influenced by our deep state
• 1:22:58
  Speaker 2: than we are. You look at Mexico and you see that.
• 1:23:01
  Speaker 2: You know what was it like thirty of the other
• 1:23:04
  Speaker 2: presidential candidates were all marked the same election cycle. So
• 1:23:10
  Speaker 2: it's it's not just us, it's our influence everywhere. It
• 1:23:13
  Speaker 2: makes people stupid and it you know, ups.
• 1:23:19
  Speaker 1: It's publicly now and that the CIA did two military
• 1:23:22
  Speaker 1: coups in Ukraine and that's why we're in this war.
• 1:23:27
  Speaker 1: Two back to back coups in Ukraine. And that's what
• 1:23:30
  Speaker 1: happened when they destabilize the country because we destroyed their elections,
• 1:23:35
  Speaker 1: and that was the United States doing that, and that's
• 1:23:37
  Speaker 1: publicly known. We know that that's how this all happened, right,
• 1:23:43
  Speaker 1: And of course that's not the only country. We've done
• 1:23:45
  Speaker 1: kuhs and we do coups all over the place, including
• 1:23:48
  Speaker 1: our own country.
• 1:23:50
  Speaker 3: Yeah, there's been a couple.
• 1:23:51
  Speaker 4: Then we go to the Philippines basically like take their
• 1:23:53
  Speaker 4: leader out and just install our own band picked one.
• 1:23:57
  Speaker 1: A couple of times, it's seems like what we're doing
• 1:24:00
  Speaker 1: in Venezuela right now.
• 1:24:02
  Speaker 3: Yeah, yep, fucking crazy.
• 1:24:07
  Speaker 1: Yeah, man, So once and I just kind of want
• 1:24:11
  Speaker 1: to finish this point with India is that I wish
• 1:24:14
  Speaker 1: more people knew about India. I mean, I'm not just
• 1:24:17
  Speaker 1: saying that because I'm Indian, but it's because there's actually
• 1:24:23
  Speaker 1: there's so much that India had to offer, and the
• 1:24:27
  Speaker 1: West would have benefited so much from Indian talent, and
• 1:24:33
  Speaker 1: we never made it so that we had any real
• 1:24:37
  Speaker 1: significant trade relations with India that would matter enough. And
• 1:24:42
  Speaker 1: now the only answer and most logical answer for India
• 1:24:46
  Speaker 1: is to end everyone else in Asia really is to
• 1:24:49
  Speaker 1: align with China, because America is still acting like a
• 1:24:53
  Speaker 1: complete idiot and the rest of the world can't wait
• 1:24:56
  Speaker 1: around anymore. And so it's been five years, six years
• 1:25:00
  Speaker 1: going on six years of idiocy that America is just
• 1:25:05
  Speaker 1: completely incompetent about, and the rest of this world is
• 1:25:08
  Speaker 1: like trying to figure out what to do. You know,
• 1:25:11
  Speaker 1: another thing about Ukraine that everyone needs to know because
• 1:25:15
  Speaker 1: we talk about in archaeology. There's this guy, there's this
• 1:25:18
  Speaker 1: channel called Lend of chem for Egypt who has done
• 1:25:21
  Speaker 1: a lot of really good work in Egypt and documenting stuff,
• 1:25:25
  Speaker 1: and he documents that the haber Bosch process is likely
• 1:25:28
  Speaker 1: happening in one of the pyramids, which is the process,
• 1:25:31
  Speaker 1: the chemical process on how we make fertilizer. And this
• 1:25:35
  Speaker 1: is this is extremely important because this was invented in
• 1:25:39
  Speaker 1: the past I think one hundred or one hundred and
• 1:25:41
  Speaker 1: fifty years, and that was what allowed us to feed
• 1:25:45
  Speaker 1: the world. Without the haber Bosch process, we wouldn't be
• 1:25:49
  Speaker 1: able to feed the world. We wouldn't have enough fertilizer. Well,
• 1:25:53
  Speaker 1: what people don't know about Ukraine is that most of
• 1:25:56
  Speaker 1: the fertilizer in the world comes from Ukraine. Specifically, it
• 1:26:03
  Speaker 1: comes from the Don Boss region. And everyone at this
• 1:26:09
  Speaker 1: point needs to know about the done Boss region, which
• 1:26:13
  Speaker 1: is somewhere if I recall correctly, around here somewhere, and
• 1:26:18
  Speaker 1: the done Boss region, like I said, is where most
• 1:26:21
  Speaker 1: of the fertilizer in the world comes from. So the
• 1:26:24
  Speaker 1: World Health Organization reported when this war started, we impacted
• 1:26:30
  Speaker 1: food supply all around the world. This is part this
• 1:26:32
  Speaker 1: has happened during the COVID era, and this is part
• 1:26:34
  Speaker 1: of why the prices of food went through the roof
• 1:26:37
  Speaker 1: because at that time, when that war started, the dome
• 1:26:40
  Speaker 1: Boss region was immediately put into conflict, and the World
• 1:26:45
  Speaker 1: Health Organization reported that starvation after that increased by four
• 1:26:51
  Speaker 1: hundred million people that year. The world starvation rate increased
• 1:26:58
  Speaker 1: by four hundred million people when this war started and
• 1:27:02
  Speaker 1: put the Donbass region into conflict. This is another thing
• 1:27:07
  Speaker 1: that Americans don't understand about war. So that's not four
• 1:27:13
  Speaker 1: hundred million people in Ukraine, that's four hundred million people
• 1:27:16
  Speaker 1: in the world that are starving because of that war
• 1:27:20
  Speaker 1: just that year alone. So that's what happens when you
• 1:27:24
  Speaker 1: create conflict and don't have a fucking clue what you're doing,
• 1:27:28
  Speaker 1: which is currently the status of America and their military
• 1:27:31
  Speaker 1: and intelligence. So they staged two coups in that country,
• 1:27:36
  Speaker 1: started this war, and then put the global food supply
• 1:27:39
  Speaker 1: into jeopardy because there wasn't enough fertilizer, and four hundred
• 1:27:42
  Speaker 1: million people starved in that year because.
• 1:27:45
  Speaker 2: Of this and the world's largest ecological intentional disaster when
• 1:27:50
  Speaker 2: they blew up the pipeline, and.
• 1:27:53
  Speaker 1: That was also that too, yeah, and I mean yeah,
• 1:27:57
  Speaker 1: and so we blew up the pipeline as well, and
• 1:27:59
  Speaker 1: that all stopped energy and made energy prices volatile. So
• 1:28:03
  Speaker 1: food prices are volatile. Energy prices are volatile. And inflation
• 1:28:08
  Speaker 1: went through the roof way higher than what they what
• 1:28:10
  Speaker 1: the government's telling you it was. And in that era,
• 1:28:13
  Speaker 1: inflation was about twenty to twenty five percent. This is
• 1:28:15
  Speaker 1: understood by people in finance in reality, but this is
• 1:28:19
  Speaker 1: not what is stated publicly. Because if you've messed this up,
• 1:28:24
  Speaker 1: if you have an inflation number that is wrong, all
• 1:28:26
  Speaker 1: your money will go away, it will lose it. So
• 1:28:28
  Speaker 1: people in finance care, so they have to know what
• 1:28:31
  Speaker 1: real inflation is. Despite the propaganda of the government which
• 1:28:34
  Speaker 1: was saying it was somewhere between six percent or nine percent. No,
• 1:28:37
  Speaker 1: anybody that was doing six percent inflation thinking it was
• 1:28:41
  Speaker 1: six percent inflation in finance that you lost money. No,
• 1:28:44
  Speaker 1: you didn't. If you were doing twenty percent inflation, twenty
• 1:28:47
  Speaker 1: five percent inflation and you were able to beat that metric,
• 1:28:50
  Speaker 1: then you were making money in that era. Yeah, So
• 1:28:54
  Speaker 1: all of that is happening at the same time. Inflation
• 1:28:56
  Speaker 1: on the s dollar, energy destabilized, food DISTA, all the
• 1:29:01
  Speaker 1: same and then more.
• 1:29:02
  Speaker 2: Inflation actually makes you dumber too. So if you think
• 1:29:06
  Speaker 2: about it, over time, because of inflation, you're gonna use
• 1:29:09
  Speaker 2: cheaper and cheaper materials, which is gonna take away a
• 1:29:13
  Speaker 2: lot of the artistic flare that you would have had
• 1:29:15
  Speaker 2: with that other stuff before. So this is why you've
• 1:29:17
  Speaker 2: got all of these buildings in Middle America that look
• 1:29:21
  Speaker 2: fantastic and out of like storybooks and stuff, and they
• 1:29:23
  Speaker 2: don't build that anymore. It's because inflation has made people dumber.
• 1:29:29
  Speaker 1: I have to do a podcast on finance. It'll be
• 1:29:31
  Speaker 1: a whole subject, but something to understand. One hundred percent
• 1:29:35
  Speaker 1: of currencies over the past hundred years, every single currency
• 1:29:40
  Speaker 1: over the past hundred years, including the US dollar, every
• 1:29:44
  Speaker 1: single one has inflated ninety nine percent or more and
• 1:29:50
  Speaker 1: many collapsed of course over that time. So again, every
• 1:29:54
  Speaker 1: single currency in the world, bar none, including the US dollar,
• 1:29:58
  Speaker 1: over the past hundred years has lost ninety nine percent
• 1:30:02
  Speaker 1: of its value or more, every single one. And yeah,
• 1:30:10
  Speaker 1: there's nothing that will there's nothing you can do. The
• 1:30:13
  Speaker 1: damage is already done. And so that's the really sad part.
• 1:30:16
  Speaker 1: So earlier I was saying, my give a fox are
• 1:30:18
  Speaker 1: gone because it's too late. It's just fucking too late.
• 1:30:24
  Speaker 1: This is there's there's a consequences to being dumb, and
• 1:30:28
  Speaker 1: this is this is that's what, this is, what it is.
• 1:30:30
  Speaker 1: So here we are, damn.
• 1:30:32
  Speaker 4: Right, Yeah, all right, you got anything pretty much done
• 1:30:39
  Speaker 4: now at this point, right, I think.
• 1:30:41
  Speaker 1: Yeah, yeah, I'm done. We'll see if someone who comes
• 1:30:44
  Speaker 1: to show me.
• 1:30:46
  Speaker 3: Fuck, it doesn't even.
• 1:30:49
  Speaker 2: Matter if they take Taiwan like it makes no difference
• 1:30:52
  Speaker 2: whether or not they invade, because they already owned the
• 1:30:55
  Speaker 2: entire planet. Why why would they even need Taiwan.
• 1:30:58
  Speaker 1: They were never going to invade Taiwan. That was a
• 1:31:00
  Speaker 1: distraction what I would saying, they were never gonna invade Taiwan.
• 1:31:05
  Speaker 2: Yeah, b keeps saying it for the next twenty years.
• 1:31:10
  Speaker 1: Though, what China is actually doing, and what they've actually
• 1:31:14
  Speaker 1: been doing for the last twenty years is what I said.
• 1:31:17
  Speaker 1: This is what China's been doing. They want India, Taiwan.
• 1:31:21
  Speaker 1: They already control Taiwan. They don't need it, they don't
• 1:31:23
  Speaker 1: need to invade it. There's nothing to gain there for them.
• 1:31:28
  Speaker 1: They own ninety seven percent of the rarer it's on Earth.
• 1:31:30
  Speaker 1: They have all the mining, they have all the manufacturing,
• 1:31:33
  Speaker 1: they have more advanced tech than US now, and they're
• 1:31:36
  Speaker 1: not dumb. They keep building NonStop like most advanced shit
• 1:31:42
  Speaker 1: ever that You should see the buildings there. You should
• 1:31:44
  Speaker 1: see their drone fleets, like their drone performances are incredible.
• 1:31:48
  Speaker 1: You should see the new cities that they're building all
• 1:31:50
  Speaker 1: over the place. I think they have ten cities with
• 1:31:53
  Speaker 1: thirty million people or more and they're all they don't
• 1:31:57
  Speaker 1: They're super advanced. I've lived in Hongko, I've been to China,
• 1:32:01
  Speaker 1: I've been to Shanghai, I've been to Beijing. I understand
• 1:32:04
  Speaker 1: this stuff. I've seen it firsthand. Yeah, it's pretty nuts.
• 1:32:09
  Speaker 1: And every like you got to understand in trade negotiation
• 1:32:17
  Speaker 1: with Trump is talking about like terraces and all this stuff,
• 1:32:20
  Speaker 1: making a big show and fuss about it. China doesn't
• 1:32:22
  Speaker 1: do any of that stuff. They still own most of
• 1:32:25
  Speaker 1: the world. That's because they're not dumb. They literally have
• 1:32:30
  Speaker 1: all of the best shit, and everybody wants it. That's
• 1:32:33
  Speaker 1: why everyone is doing business with China and not us.
• 1:32:37
  Speaker 1: It's because, like it used to be that when we
• 1:32:39
  Speaker 1: would buy quote unquote Chinese products, it would come with
• 1:32:42
  Speaker 1: this stigma of like low quality, cheap garbage kind of shit. Nope.
• 1:32:48
  Speaker 1: They make way better phones than us, way better robots
• 1:32:52
  Speaker 1: than us, way better AI software, their drone fleets, their
• 1:32:55
  Speaker 1: buildings are way crazier than ours, way cooler than ours,
• 1:32:58
  Speaker 1: and they can they build them way faster than us.
• 1:33:01
  Speaker 1: Like they built a hospital in two days, and not
• 1:33:04
  Speaker 1: just like some little shape, like a really big one
• 1:33:06
  Speaker 1: all fully fixed and figured out and everything. They do
• 1:33:09
  Speaker 1: it in two days. They have. They've been building energy
• 1:33:13
  Speaker 1: infrastructure for decades, We're just starting.
• 1:33:17
  Speaker 2: It just seems like the whole Ukrainian conflict really did
• 1:33:20
  Speaker 2: kick kick China's economy back into gear because you know,
• 1:33:25
  Speaker 2: that was this cheap source of energy from Russia. Russia
• 1:33:29
  Speaker 2: was trying to get rid of it, so like, yeah,
• 1:33:32
  Speaker 2: on Russia.
• 1:33:33
  Speaker 1: So they made it so that Russia couldn't do business
• 1:33:36
  Speaker 1: with the West, So who else they're gonna go to?
• 1:33:38
  Speaker 1: And China's is happy to take their cheap oil, of course,
• 1:33:41
  Speaker 1: and of course not what that means is that anybody
• 1:33:44
  Speaker 1: that is aligned with China also and does all the
• 1:33:46
  Speaker 1: trade and you on and has you on balances, well
• 1:33:49
  Speaker 1: then they will also get access to cheap oil. And
• 1:33:52
  Speaker 1: of course you must understand the significance of this, so
• 1:33:56
  Speaker 1: there's like literally no reason for any country in the
• 1:33:59
  Speaker 1: world to do business with China, and every reason to
• 1:34:03
  Speaker 1: do business with China, and also every reason to not
• 1:34:07
  Speaker 1: do business with America. All three things are true at
• 1:34:10
  Speaker 1: the same time presently for pretty much everyone.
• 1:34:15
  Speaker 2: Right, if I get sum it all up to finish,
• 1:34:17
  Speaker 2: it's like America's offer is way more expensive gas, right,
• 1:34:23
  Speaker 2: China's offer is We've got cheaper gas and better everything else.
• 1:34:27
  Speaker 1: And with trade tariffs now also trade is also more expensive,
• 1:34:31
  Speaker 1: so the tariff thing also made everything more expensive comparatively relative.
• 1:34:35
  Speaker 2: Only one who wasn't putting tariffs ought everybody was China.
• 1:34:39
  Speaker 1: Yeah, they don't care. They want everyone to do business
• 1:34:42
  Speaker 1: with them. They make everything. That the reason why it's
• 1:34:47
  Speaker 1: a problem for America is because we don't make much.
• 1:34:50
  Speaker 1: That's why we have to do all this trade stuff,
• 1:34:52
  Speaker 1: and that's why it matters so much. But China makes everything,
• 1:34:55
  Speaker 1: so they're the They're the supplier of the world, and
• 1:35:00
  Speaker 1: America is the demander. That's the issue the rules they
• 1:35:05
  Speaker 1: probably yeah, well, yeah, we manufacture a lot of problems.
• 1:35:12
  Speaker 1: So I didn't need to be a total banner. I
• 1:35:13
  Speaker 1: wasn't supposed to go into all the all the like
• 1:35:16
  Speaker 1: finance stuff, but this this stuff people need to know
• 1:35:19
  Speaker 1: and I wanted to document it somewhere.
• 1:35:21
  Speaker 3: That was great. Yeah, I don't apologize at all.
• 1:35:26
  Speaker 4: Do you want to let anybody know? Is there any
• 1:35:28
  Speaker 4: places that like you can? Did you want to promote
• 1:35:30
  Speaker 4: or anything shows.
• 1:35:31
  Speaker 1: My ex got banned? Sure? Whatever? So I mean, I'm
• 1:35:36
  Speaker 1: social media literate. I was trying to do the X
• 1:35:39
  Speaker 1: thing and now it's still even a month later, I'm
• 1:35:42
  Speaker 1: still banned, and I don't know what to do about it.
• 1:35:43
  Speaker 1: I can't do anything about it. I appealed. They just
• 1:35:46
  Speaker 1: said one word spam. I post less than once a
• 1:35:50
  Speaker 1: day or my average amount of posts is like less
• 1:35:53
  Speaker 1: than once a day, and somehow I'm banned for spam.
• 1:35:56
  Speaker 1: They don't tell me what what what I was spamming,
• 1:35:58
  Speaker 1: or why or what. There's no recourse. I just clicked
• 1:36:00
  Speaker 1: the like, sent in an appeal. They said, appeal denied.
• 1:36:05
  Speaker 1: Didn't tell me why. Just that's it done. So maybe
• 1:36:09
  Speaker 1: I just need to start over with another one. I
• 1:36:12
  Speaker 1: don't know. I haven't really figured it out. I've been
• 1:36:14
  Speaker 1: dealing with a lot of problems, so social media was
• 1:36:16
  Speaker 1: just not that high on my priority list. But maybe
• 1:36:19
  Speaker 1: I should make it a little bit of a priority list.
• 1:36:22
  Speaker 2: Well, man, you've got a great story. You could take
• 1:36:25
  Speaker 2: that too much, bigger podcast. We're really glad you brought
• 1:36:28
  Speaker 2: it here. But you know, I mean, you keep out it.
• 1:36:30
  Speaker 2: You get to the bigger and bigger audiences and people
• 1:36:33
  Speaker 2: are gonna actually think about this stuff, that actually consider
• 1:36:35
  Speaker 2: what's what's actually happened where we're at.
• 1:36:39
  Speaker 1: I think if you know anyone that wants to talk
• 1:36:41
  Speaker 1: about this stuff, oh.
• 1:36:42
  Speaker 4: Yeah, I could definitely push you off to some shows
• 1:36:44
  Speaker 4: man for sure.
• 1:36:45
  Speaker 1: Oh yeah, sure, absolutely happy to do it. Like I said,
• 1:36:50
  Speaker 1: I mean, I cover a lot of topics, archaeology, AI, finance.
• 1:36:54
  Speaker 1: I'll have to do one on finance with you guys
• 1:36:56
  Speaker 1: at some point.
• 1:36:56
  Speaker 3: Yeah, had somebody in the chat eveld say they really
• 1:36:59
  Speaker 3: really like you to do that?
• 1:37:00
  Speaker 1: Yeah, okay, yeah, I definitely.
• 1:37:03
  Speaker 3: Then somehow we got to figure out a way to
• 1:37:04
  Speaker 3: talk about the eyeballs. Just I don't know how well
• 1:37:08
  Speaker 3: a discussion on.
• 1:37:09
  Speaker 1: That, okay. I mean I'm not, by the way, I'm
• 1:37:12
  Speaker 1: illiterate on the occult. I'm learning from you as I
• 1:37:15
  Speaker 1: watch yourself a little bit here and there.
• 1:37:17
  Speaker 4: But yeah, thank you, No, that's good. Just even just
• 1:37:21
  Speaker 4: knowing how it works would be interesting to talk about.
• 1:37:23
  Speaker 4: But yeah, so all right, yeah, I guess you don't
• 1:37:26
  Speaker 4: have anything to plug. Headless want you to let everybody
• 1:37:28
  Speaker 4: know what's up with your endeavors.
• 1:37:32
  Speaker 2: All right, Well, you can find me at the Headless
• 1:37:34
  Speaker 2: Giant on YouTube and on Twitter and on Instagram. And
• 1:37:38
  Speaker 2: if you have any sort of stranger, esoteric, scary, spooky stories,
• 1:37:42
  Speaker 2: you can send them to me at the Headless Giant
• 1:37:44
  Speaker 2: podcast at gmail dot com and we will read those
• 1:37:46
  Speaker 2: on Thursdays. So check that out, thank you very much.
• 1:37:50
  Speaker 2: Oh also got the trialogus coming up on Sunday, so
• 1:37:53
  Speaker 2: check those out.
• 1:37:54
  Speaker 3: Thank you.
• 1:37:55
  Speaker 4: Oh yeah, thank you very much. I listened to tomorrow morning.
• 1:38:00
  Speaker 4: Check out Jules is the book club thing that he
• 1:38:02
  Speaker 4: does wor with the reading he does. I don't know
• 1:38:06
  Speaker 4: about how less I can speak for myself. I should
• 1:38:08
  Speaker 4: be there, so definitely check it out. Yeah gee, thank
• 1:38:12
  Speaker 4: you again for coming on. That was really that was
• 1:38:15
  Speaker 4: that you brought the present?
• 1:38:17
  Speaker 3: Hell yeah?
• 1:38:18
  Speaker 1: Yeah absolutely, yeah, frust it up for you this time?
• 1:38:22
  Speaker 3: Yeah no, no, that was great. That was great. Hopefully
• 1:38:25
  Speaker 3: I really.
• 1:38:25
  Speaker 1: Appreciate the opportunity to talk about this stuff. Again. I've
• 1:38:28
  Speaker 1: never heard anyone talk about it. I really needed a
• 1:38:31
  Speaker 1: way to get this stuff out there. You know, it's
• 1:38:35
  Speaker 1: not normally what I do. I don't normally do a
• 1:38:38
  Speaker 1: whole lot of interviews, but it seems like that's what
• 1:38:41
  Speaker 1: I got to do now. And I tried to do
• 1:38:43
  Speaker 1: it the conventional way, but of course that didn't work.
• 1:38:46
  Speaker 1: So this is the new path.
• 1:38:50
  Speaker 2: No, thank you, you got mind blowing stuff. But I
• 1:38:52
  Speaker 2: think a lot more people would really appreciate that message
• 1:38:54
  Speaker 2: because I think, you know, there's we're discovering things about
• 1:38:57
  Speaker 2: human intelligence. It's not being talked about.
• 1:39:00
  Speaker 1: Absolutely, There's been discoveries made all over the place NonStop,
• 1:39:03
  Speaker 1: like way more than people realize, and just a lot
• 1:39:06
  Speaker 1: of it is suppressed. And part of it is just
• 1:39:08
  Speaker 1: not having an avenue because academia is a gatekeeper. And
• 1:39:12
  Speaker 1: so if you don't have like if academia is blocking you,
• 1:39:15
  Speaker 1: which is most people, then what else can you do.
• 1:39:18
  Speaker 1: If I didn't happen to run into you at the
• 1:39:20
  Speaker 1: Cosmic Summit, like, how would I get this out there?
• 1:39:22
  Speaker 1: And so yeah, I really appreciate that this has been
• 1:39:25
  Speaker 1: a really good opportunity for me, and I really want
• 1:39:27
  Speaker 1: to want to get more people to see this stuff
• 1:39:29
  Speaker 1: at least.
• 1:39:32
  Speaker 4: You're doing a great job man. Definitely, thank you and
• 1:39:36
  Speaker 4: thank you everybody in the chat.
• 1:39:37
  Speaker 3: That's what's up. I appreciate you all spending this leave
• 1:39:39
  Speaker 3: Friday night with this. That's what's up.
• 1:39:41
  Speaker 4: Absolutely yeah, there's definitely some good stuff being sitting there
• 1:39:44
  Speaker 4: and again head listeners, sheish, thank you very much for
• 1:39:47
  Speaker 4: coming on. Hopefully we'll get you on again and until
• 1:39:50
  Speaker 4: the next one, everybody be well.
• 1:39:52
  Speaker 3: Later