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