REFERENCE

See if we can't figure this out for the good of all involved. It is the 28th of December 2022 and we're going to look at neuromythologies and what role they play in neuroscience and driving the money to neuroscience. We looked at a video a few days ago by a guy who worked for the army. And I found it very insightful, the rabbit holes that his work has drawn me down, mainly because he mentioned he worked for the Human Brain Project, a pile of money that my neuroscience career brought me close to and intermingled with, but I was never actually paid by them. But I worked for a few months, a couple different times in the laboratory of the guy who was the custodian of that whole grant. And so I thought it would be a cool thing to do today to check that out.

Just a few things to let you know about. If you don't know about Sasha Latypova's substack, I would like you to check it out. If you look at the article called The Mouse King, you will find a nice reference to us at some point. A brilliant presentation, she called my presentation to the UK Doctors for Medical Ethics. You can find on the website here that you're on now. The sync on that video is actually pretty good. I'm now going to be recording all these videos and just posting them afterwards because the sync is starting to get annoying. So let's see if we can check a little bit of this out.

First I wanted to give you a little reminder of where we are. I think it's always really important to have a reminder of where we are. And in this reminder, I thought I would play a little Bill Burr. I really like Bill Burr, he's about my age. He did a standup monologue for SNL two years ago. And I believe this was maybe they'll give us a October 11th, 2020 October 11th, 2020 was when that paper came out authored by Rochelle Walensky and Debbie, whatever her name is over in Scotland, and a bunch of other kind of mid level bench warming virologists and epidemiologists at the time came out with this declaration that we didn't know what was going to happen because we weren't sure that our, our immune systems would be able to handle the virus. We weren't sure whether we would make natural immunity. And since there weren't any treatments, this was going to be a ongoing, you know, earth wide disaster. And most of the proclamations in that paper, if not all of them turned out to be completely false, 100% opposite what we knew beforehand, it was one of those points in time that I hammered on for quite some time because that article featured so many people that rose to prominence in social media, rose to prominence on TV, their their whole, you know, visibility skyrocketed while I lost my job and couldn't couldn't stream on YouTube and this kind of thing. And just to keep in mind that people are still getting added to Twitter, but not me, I'm applying, appealing every day on both of my accounts. I'm not hearing anything. I heard that Walter Chestnut got back on Twitter. So hopefully we'll learn more about spike protein toxicity.

Listen to this stand up from Bill Burr. And remember that it's October 2020 when Rochelle Walensky and others declared that we weren't sure what was going to happen because we were still not we were not sure at all whether our immune systems would be able to cope with this virus. And worse yet, we were really worried that we wouldn't make any memory to it. How is the sync right now? Is the sync OK? Because I think Twitch is doing it to me. I've reviewed several. I've reviewed several of my streams and basically it's just one point where the something happens and it's good.

OK, I'm going to keep going. I am so excited to be here. I have been doing stand up forever and this has always been a lifelong dream of mine to come here and host Saturday Night Live. So thank you so much for coming out during during these difficult times. You guys all look like surgeons with your masks on. Makes me feel comfortable that you're wearing masks. I like people who wear masks. That's good. You're listening to the eggheads, the people we all cheated off of in high school, right? Keep listening to them. And then if you don't wear a mask, that doesn't bug me either, right? Take out your grandparents, you know, take out your weak cousin with the asthma. I don't care. It's your decision. There's too many people. It's a dream come true if you're that dumb and you want to kill your own family members by all means, do it stops you from reproducing. So not wearing a mask is dumb. Not wearing a mask endangers your family members. If you're dumb enough not to wear a mask, go ahead and do it.

This is on Saturday Night Live in October of 2020. You have to realize we are three years into this drama and they have been pulling us hard in different directions. They have been pushing us hard in different directions. They have been bamboozling us with the television and with social media for three years. We need to find the compassion and the understanding and the psychic wedge to use to get inside of the heads of our family and friends to break them free of this nonsense. What does Bill Burr know about respirators? Bill Burr is making jokes that he's told to make, making jokes that were approved for him to make. If you don't think that's true, I can't help you. It gets worse.

It's literally a dream come true. There are too many people. Speaking of dreams come true, did you see Rick Moranis got sucker punched on the Upper West Side? New York is back baby. New York is back baby, New York is back, yes.

But he's from Philadelphia.

We lost our edge there for a minute. City started looking like a giant bed bath and beyond and then bam, oh Rick he took one in the chops. It had to happen. It had to, that's what happens when you stick an M&M store in Times Square, alright.

The band behind him all wearing masks. If you saw in the beginning they showed you that the people that were sitting in the front there was only a small table worth of people sitting in the front. What is that? Potato chips? Cheese. Cheese? Licker. Thank you. Hello. Hello.

This has to balance itself, get the peep shows back in Times Square, old people can walk safely 40 blocks away. I don't know, I'll probably get cancelled for doing that joke, you know. How stupid is that cancel thing? They're literally running out of people to cancel. They're going after dead people.

Why does he have to use the word literally there? What does it mean in that context? It doesn't mean anything. We all have to start, we all have to start upping our game a little bit. Upping the game that we call communication. We need to start requiring accuracy from people on television, requiring accuracy from people that we talk to on the street so that we can take the language back. Speak out to people, be a grammar cop, especially when it comes to vocabulary. Don't let people use vocabulary willy nilly that they don't know how to use, including me. Correct me if I use the word penultimate to mean something other than the second to the last. Correct me if I say something that can protect you from a respiratory virus that can't protect you from a respiratory virus. Ladies and gentlemen, if we look back in time, back to 2020 and 2021 and carefully examine the messaging and the behavior, we will be able to really put into focus where we are right now. How intolerable it is that the people that are still in charge are the same people who pushed this messaging relentlessly, coercively. We can't forgive anybody. He should have known better as an American.

They're trying to cancel John Wayne. It's like, yeah, dude, God did that 40 years ago. They're all up in arms. They're like, did you hear what he said?

It's almost cognitive dissonance if you think about it, because he's saying that canceling is crazy. But wearing masks is something that we should enforce. And if you're so dumb, how insulting is that? To have no regard for school closure, no regard for masks in schools, which at this time was rampant across the country if your school was lucky enough to be open at all, an entire generation of children permanently scarred by adults who didn't know better, adults that couldn't make their own decision, adults that took commands from the television instead of doing what I know was in the best interest of my child. And my child knew was in best interest of him or her. And that's what the disconnect is. If you get parents to behave in such a way that makes their kids go, Hey, what's what are you doing? You can successfully start a rift between children and their most trusted adults by simply making that adult defer to another adult. By simply making that adult so scared that they admit that they don't know what's going on and we just have to do what the TV tells us, honey, do not underestimate the depth and the breadth of the psychological operation that you have experienced, the amount of damage that they have done and the amount of damage that they continue to do by not reversing course by not admitting that this was wrong, but by instead sticking to this narrative of millions of people died, but at least 10 million people were saved by the shots. So goodness, thanks. Thanks. Goodness.

And in that interview and playboy in 1970, can you believe that? It's like, yeah, he was born in 1907. That's what these people sounded like. You never talk to your grandparents and brought up the wrong subject and all of a sudden it went off the rails. Just keep making the cookies. Yeah. You don't bring up race or religion with your grandparents. You keep it simple. Anyway. I don't know.

Oops. He lost his train of thought.

My grandparents are older. I don't know. Plowing ahead.

Yeah. Plowing ahead with the jokes. I'm allowed to tell.

Let's talk. Let's talk white women here. Shall we? Let's talk white women, white women, the amazing, amazing your accomplishments over the last few years.

Isn't this painful?

The way white women somehow hijack the world.

It's very, very hard to watch because it isn't Bill Burr, but it's Bill Burr doing what he's told on Saturday Night Live during what he thinks is a crisis. What he has been briefed as it's a crisis. We need your messaging to be spot on tonight, Bill, because we are in the middle of a crisis. We are approaching what could be a winter of severe disease and death. We don't have vaccines yet, except for you. We don't have them for everybody at bill. So you've got to go out there and you've got to make this message strong. You've got to put your spin on it, but Bill, we're counting on you. That's what you're witnessing there. That's what I see now in retrospect in October of 2020 on Saturday Night Live. That's what I see.

So if I think about Henry Markham, I think about a guy that I actually worked for in 2005 and six and seven or four and five and six, something like that. I got a grant from the Free University in Amsterdam to study abroad with Henry Markham. At that time, Henry Markham was one of the few people on earth who had multiple rigs, meaning microscopes with micro manipulators around them on which you could record more than one neuron on a brain slice. And so he specialized in paired recording. So recording from neurons to see how they're connected to one another and look at the synaptic dynamics. And he became pretty, you know, locally in that sense, colloquially in the, in that field quite famous. He worked with Burt Sackman in Heidelberg, who got the Nobel Prize for patch clamp physiology, which is the, the use of glass pipettes to record from neurons and other cells that have electrical currents and measure single ion channels. And so he was one of the people that I was happy to have studied with during my PhD while I was working in the Netherlands. I also got to go to the EPFL in Switzerland in Lausanne to work with this guy. That was again, before he gave this talk, before he was awarded $1 billion for the Human Brain Project, he had what was called the Blue Brain Project, which was a IBM sponsored supercomputer that he was given custodial ownership of at Lausanne under the pretense that he would feed it biological data that would allow them to recreate a many, many compartment, thousands of neurons, thousands of synapses.

You'll hear him explain it model of a cortical microcircuit under the pretense of making a, a silicon based version of it that would allow us to adapt the circuit architecture of the brain to a circuit architecture of a learning circuit in a computer. And because biology obviously is better, whatever we get out of the brain and put into this chip will necessarily be faster, better, smarter, whatever. And so the principle was, is that if you build it, they will come. And so he was going to get this supercomputer and then he was going to have all these recordings where he filled all these neurons, looked at how they were connected, and then put all this real physiological data, this real structural functional relationship data of the neocortex into this thing. Let's listen to him talk about this in 2015 for the World Economic Forum. I’ve shaken this guy's hand a few times. He's come to my posters a few times. I've had beer with him a few times. I've been in his lab many, many, many hours. Would he recognize me on the street? I believe he would, but he wouldn't probably know where I was from. And if he would know where I was from is because I'm so funny looking, but it's not because I did something super great in his lab or something like that. I was a competent physiologist when I was in his lab and I definitely made the impression of competence, but that's about it. I'm excited to watch this with you.

We're going to go back to our roots to solve one of the biggest challenges that we face today, which is the volume of data that we're producing. I'm going to show you how we're going to have to go back to our roots as well to solve the next challenge. I'm going to make myself smaller at which we process the data, the amount of data that we produced over the past hundred years. We are today producing it in a 10 to 20 minutes. So every 10 to 20 minutes to okay.

So part of the thing that you're seeing laid down here and I'll stop it this quick just to make sure that we get this right. Part of the thing you're seeing laid down here is this idea that we're getting so much data so fast we compare this called big data. We need big AI and deep learning in order to get this big data down into something that we can use. And because we're coming into this singularity of technology and singularity of data collection and singularity of all this stuff, it's all going to magically like some kind of wizard's ball just come together all in one swirling spark that will explode into like human trans humanism utopia with supercomputers and flying cars and all this other stuff. And so what he's talking about here is part of the sales pitch that he's been pushing for up to this point already 10 years or more that somehow by measuring ever more detailed the structure function relationships of individual parts of the brain we're eventually going to be able to figure out how the brain produces consciousness and and in so doing we're also going to extract from that this structure function relationship that will put us leaps and bounds ahead of wherever we are now in computational understanding of how computers work because we just don't know how to build circuits like the brain does. So that's what the idea is here. That's where Neuralink comes from. That's the same train of thought. This is 2015. And remember that guy that we did the other day, Dr. Giordano, he worked for the Human Brain Project. He worked for this guy's dual use program portion of the $1 billion grant to simulate the human brain, simulate the human brain. Imagine how arrogant it is to say that you're going to simulate the human brain with a supercomputer. Imagine how absurd it is even to claim that that's your plan. But here we go.

Today we produce the same amount of data we produced over the past 100 years. In the next 10 years, we'll produce that in five seconds. What is absolutely clear to almost every technologist out there is that we as humans can no longer read and digest this information. We need help. We need serious help. And actually most of the technologies, if you really dig into it, the ones that are taking the leading edge are the technologies that are getting that help. The essential help is in the form of algorithms. And in the past, we could write algorithms quite tractably. You could have a very good mathematician or computer scientist, a theoretician, and you could develop your algorithm. An algorithm is really a series of steps followed by rules in each step. Calculate this. Take this data. Merge it with this data. Come up with this decision. It's a decision tree. And then you come out with a digested form or an interpretation of that. So it's basically taking your zeros and ones and transforming them into something that you find useful. Now there are basically three kinds of algorithms. This is stated very simplistically today that can go beyond the kind of algorithms that we used to use in the past. Basically we need very sophisticated algorithms. And we actually need machines to help us build those sophisticated algorithms. The one that is very popular today is deep learning. Many of you probably have heard of it. This is what Google is going into. It's with Microsoft, Facebook. Most of the people are using these deep learning strategies. What deep learning really is is a series of neurons or nodes with successive layers. And when the information comes in, the next layer has to combine that information in a certain way. And in the end, you can train one of these nodes to recognize all the different features and the conditions of a face. So it becomes a face detecting node. And if you show it enough images of faces or pictures with faces in it, you can train and develop the algorithm. Actually the algorithm is so complex that you cannot actually understand it. But the machinery can run the algorithm. So this is becoming a very powerful tool. And it will be a very powerful tool that lives in the cloud that you access. And when you want to recognize something, you won't necessarily know or realize that actually it ran through this deep learning algorithm to decide what you were looking at or a certain pattern of information. It's going to become more and more important because the trend is that everything is becoming digital. Our self is becoming digital, our health is becoming digital. Being able to recognize patterns so that we can make decisions on that is going to becoming increasingly important. The second is what you can think of as brain-inspired design. Although deep learning is partly brain-inspired design, it's a very structured network. But a brain-inspired design is more of a massive set of interconnections. It's a concept of what the brain could be doing, and we try and mimic that concept. So IBM's Watson, for example, is probably a very good idea of kind of cognitive computing where we look at the brain and we see that it's got sensory areas and it's got reasoning areas and decision-making areas and reward areas. And we mimic those mathematically and try and get the machinery to make these decisions. So Watson can take all of the millions of pages of Wikipedia, for example, and it can actually run it through this kind of conceptual model of the brain and it can make decisions on them. And it's actually incredibly powerful and very useful. The third direction is the emerging direction, and of course this depends now on much more concrete information about the brain, which you could think of as the brain-derived design. Mimic the brain as accurately as possible. After all, it is the product of four billion years of evolution. It has to have the intricate connectivity in the circuits. To get to brain design, you need to understand a lot more about the brain, how it's put together, how the neurons are structured. And the essence is really that you have neurons and you have a lot of cables. You have enough cables in your brain to wrap around the moon a couple of times in just your brain. So there's a lot of cables that are connecting and forming this intricate network. And what it's really doing is carrying out an algorithm through these different networks. Just to give you an idea, you also have synapses that have to connect these neurons. And these are synapses in a piece of the brain the size of a pinhead, 40 million synapses that have to connect about 30,000 neurons in a small piece, just the smallest possible piece that you could look at. They are the messengers between cells. And by controlling these messengers, you can control the algorithm.

But as you can obviously realize, there's nobody can program this. You have to let it learn. And that's why the brain is so powerful, that actually learning involves adjusting the algorithm that is happening at all of these different synapses. We are beginning to be able to piece together how these neurons are fitted together, how they connect together, and how they function. And this is a real simulation on a supercomputer. And the color that you see are voltage fluctuations. What you're really seeing is the brain carrying out algorithms. But they're very, very sophisticated algorithms that you have to... And as you learn, you adjust your communication between your different nodes so that you can actually execute that algorithm better and better and faster and faster. This is just an example of the kind of circuitry that you get in such a tiny piece. Again, it's about the size of a pinhead. You have about 7 million connections, 40 million synapses that are connecting them together. And we're starting to get a good idea of the blueprint circuits. And these circuits could be printed into silicon chips and then run, mimicking closely how the algorithm that has come out of evolution. You can, of course, take that further. Again, this is a simulation of a region of the brain that is where you have now about 5 million cells, and they're interacting together, executing the algorithm. Now of course, you can see that this is very different from what you would look at if you looked at a computer processor and how they are transmitting information. Here there's much more patterns are forming, and these patterns are reflecting the kind of algorithm that is being executed in order for the animal to make a decision about what it is it's seeing or to be motivated to change its goal or to achieve a goal. But you can take this now further towards a whole brain simulation, and this is the beginning of a whole brain simulation at still a very simple neuron level, and one can synthesize these branches because these branches are really the result of the brain evolving to be able to execute more and more sophisticated algorithms, but also to be able to learn. So actually one of the reasons why the brain is so complex is that it had to advance the algorithms, but it also had to allow them to become adaptable. So you could throw the brain into a different environment and the algorithms would change. So what we are doing to be able to allow these algorithms to change is to embed them into virtual object structures, or virtual mice, or virtual animals, or a car, as I'll show you in a second. So to do that you actually have to, we're still at a stage where you still have to run the brain simulation on a massive supercomputer, going towards the human brain, this will be a billion euro supercomputer or half a billion euro by then, and you actually still need supercomputers to be able to create this virtual environment with virtual robots that would be controlled by this brain. So this is just an example that you would then take this and today already we can couple this, in this case it's just a plate that has to balance the ball, in this case it's a very small brain circuit that already is able to drive, self-drive a car, and as these become more and more sophisticated you will have a single chip that will be able to be plugged into your car and allow you to self-drive it as we go further into the future. So this is an example of a robot in the Human Brain Project, one of the things that we're doing, the European Human Brain Project, is that for many years researchers at the Technical University in Munich have been evolving physical robots, and this is one of the most advanced latest robots called Rob Boyd, and he's actually a showcase, he goes around to stages and to schools and people can interact with him and talk and question and there's a lot of intelligence that's gone into this, but now what we will try and do is to see how we can add intelligence to that by using these brain-derived circuits that closely mimic how the brain functions. So basically why would you want to go from these fantastic chips that we use today, which are fast and highly reliable, to a brain-like circuit which is somewhat much slower in some sense, they communicate in the range of hertz and not gigahertz, and very messy, appearing very messy. Well, there are many different reasons which you can see here, it's adaptive, it's iterative, it's self-learning, you can throw it into an environment and the algorithm will find its way until it can learn. It's contextual, which means you change, switch off the light and it will adapt so that the algorithm can still operate and it can become very personalized, it can be adapted to you. The most important technological reason really is power and cost efficiency. There are ways to get these circuits onto computer chips that now will, it will be about 10,000 or 100,000 less energy to run them than the conventional chips. This is an example of a project run by Karl Heinz Meyer at the University of Heidelberg where they have for many years developed the technology to be able to print these circuits, and program these circuits with neurons, and then they can put them onto a chip and they build them onto a wafer, and then you can build them onto a big kind of brain-like computer. So this is not just an idea, this has already happened, there's a lot of happening and I'll show you many examples. There's a small board you can get today, it's a USB stick, and you can actually start doing brain-like programming. The state of art is this chip called the Brain Scales chip developed in Heidelberg where you now can have four million neurons, and this is some of the most advanced neurons you can put onto silicon, with a billion plastic chips. So you have a very large network that would cost, it would be incredibly expensive energy-wise to run simulations of that, and this can do it 10,000 times faster than you can simulate it on a computer, and actually 10,000 times more cost-efficient. Most important, it becomes highly user-configurable, so you can, even a non-expert can start programming these circuits. The second generation is developed by Steve Ferber at Manchester University. He was the architect of the ARM processor, which is in all your cell phones here. He has taken a different route to neuromorphic computing, all of this is called neuromorphic computing, where he takes commodity hardware, which is the ARM processor, and it is configured to be able to act as neurons and connections, and then they can embed this, and they've reached up to about 50,000 chips or a million cores. This in principle today can already capture a trillion neurons. That's more than the human brain, but that's only with one input to each neuron, so they could actually go to about a billion neurons with close to 100 billion synapses, and as we go further in the next 10 years, we will have the hardware to capture the scale of the numbers that they are in the brain. The two projects that they are, those are both run in the human brain project, but there are other projects at Stanford. The Bohan group has built an amazing neuromorphic chip, where they basically have only neurons sitting on the chip. They can build this into a board, they can build a million neurons into it, it's about 100,000 times more energy efficient than any chip out there, and then they have the connectivity that you can configure the brain's connectivity, they have that outside on the conventional system, which is something that makes it again energy expensive and not easy to scale, but nevertheless this is one of the other exciting developments that have happened in the past few years. IBM has developed a very exciting chip as well, which basically allows you to put about a million neurons together. The more neurons you have, the more sophisticated the algorithm is. The more sophisticated the connectivity is, the more sophisticated the algorithm is. The more plasticity you put on it, the more you can adapt the algorithm. So these are one bit synapses, so it's really just a binary system, but you can still scale this to very, very large systems today, and they've built a very nice software on layer on top, where you can actually now program these to start building the algorithms that you need for whatever big data challenge you need. This can run at real time on very, very large data sets. Qualcomm has also produced a chip. It's a bit of a secret project. We don't know exactly what is inside this chip. It's called a neural processing unit, and it will be added to the normal thing in your cell phone, they say, where you have the normal compute processor unit, you have a graphics processor unit, and you have some other processors, as well as a neural processing, and that allows you to do this kind of human-like analysis on the data, very low cost, very high volumes of data, very fast. So there are already these five major initiatives, different directions, some of them complementary, some of them have certain strengths and other weaknesses, but they exist today, and they're being evolved at an incredibly high speed in order to make it possible for us to digest and make decisions on big data, we're talking about petabytes of data or exabytes of data in the future, as fast as possible. So there's clearly hope that not only can we deal with the volume with DNA storage, but we're going to be able to deal with the speed of making decisions on such massive volumes of data. There are many applications. In the airports, you will probably find in the future a neuromorphic chip, which is sensing and analyzing odors, and it will make a decision whether there is a threat, the brain-like capabilities of an owl to detect sound location is being implemented into neuromorphic systems so that you can easily have a device that would be able to track the positions of anything that's happening in terms of sound around in the world. You have something like Watson, where you have a massive amount of actual knowledge and data that's coming together, but in the end to make decisions on that.

Did he sell you on it? Has he sold you on the idea that he can, with the three-dimensional cartoons of the scaffolding of neurons and all the flashy pictures, has he convinced you that he knows how the brain works? Has he actually, he's actually told you, right, that they can't look and see what these deep learning algorithms, what the architecture of them is and how that relates to the problem that they purport to solve. And so how will making a cartoon version of the architecture of the brain, as best as we can discern it into a computer chip, result in something that will have a meaningful relationship to either understanding the brain or pushing forward what we do with computers because inside of that thing is going to form an algorithm in a black box that we can't open. So when the AI finally figures out how we don't know, we don't know how to play Go any better except for by playing go against the AI, we're not going to understand what the AI knows or doesn't know or how the AI knows it about anything. So it's a lot of smoke and mirrors. It's a lot of smoke and mirrors.

With the palm of your hand on your phone, you would need to still use a neuromorphic process to decide what is it that you want to know and not just what is possible to get out of the system. Neurologography is another possible technology that could be supported by neuromorphic computing. A big development is that in the brain, the whole aspect of intervening by putting in circuits to adjust brain circuits in neuroprosthetics for Parkinson's disease or other types of disease or epilepsy, they would have neuromorphic processes to help make decisions as to when to stimulate, where to stimulate. There's a whole range of others. I think the most exciting thing is that what we're really going to feel probably more concretely than anything else is that we're just not going to need to go through all these settings that you have to put your preferences. Your preferences in an iPhone or in whatever application you go into, it's going to get more and more complicated and you get very irritated. I fill in my preference. I don't want this. I don't want this. In the future, you will have these accompanying chips that will adapt to the world, not because you tell it to, but because it observes what you do. I think that's going to be something that is very exciting about having this new kind of solution to how we're going to deal with the massive speed of big data.

Imagine, this is the kind of charlatan. I worked in his lab, but I'm not at all afraid to say that he's a charlatan. Throughout neuroscientists and neuroscience, there is charlatanism everywhere. People overselling what they're doing, overselling what their data means, overselling the impact of their work in order to get the next grant. This guy's just one of the best ones. Henry Markham got a grant for $1 billion from the EU. Let's see an updated version of his talk from four years ago. “That state has he done with...” Did I not play it?

Actually, it's an amazing privilege for both Camille and myself to be part of Frontiers because the privilege of working with so many...

I don't know why he calls it a privilege. He started the Frontiers Journal, which is a set of journals that he's kind of the owner of or the head of the corporation that does it, him and his wife. His wife had a reputation in the laboratory. She was his post-doc before she became his wife. It's his second wife, as I believe, and when we watch her speak, you'll know what I mean.

Amazing minds, all facets of science. It's actually something that normally when you become a scientist, you do your one thing, study a molecule, and you may get a Nobel Prize, but this is so much richer to be able to have this global involvement and global perspective. The other confession that I have to say is to Jean-Claude, because I stole an idea from him, so I should lose a token in the blockchain currency, because Jean-Claude had a very creative idea in his title he called...

He should lose a token in the blockchain currency? Did he say that?

Geez, Jean-Claude. That's clever. So, I decided to give it a new title, Homo Digitalis. When are we going to have Homo Digitalis?

Homo Digitalis. No thanks.

What I find absolutely fascinating about the universe is that it evolved a brain with a consciousness. Now, without a brain, without consciousness, no one would know there's a universe. We wouldn't even know there is a universe. We may be just part of that universe, we may even be able to react to things in that universe, but nobody would know there's a universe. It's as if the universe evolved the brain so that someone would know that it's there. There would be no pain, there would be no concepts, there would be no creativity, there would be no art. We wouldn't be able to build a myth, a story, and we need to build myths so that we can discuss with strangers because we share a myth. The last summit that we had here, the book was Homo Sapiens, The Brief History of Mankind by Yuval Nuharari.

Who? What?

One of the things that we learned is that the reason why we suddenly have “Sapiens, The Brief History of Mankind” by Yuval Nuharari, we wouldn't be able to build a myth, a story, and we need to build myths so that we can discuss with strangers because we share a myth. The last summit that we had here, the book was Homo Sapiens, The Brief History of Mankind by Yuval Nuharari, and there one of the things that we learned is that the reason why we suddenly exploded out of the caves and started joining forces is because we developed a technique to trust strangers, and that technique is a myth. It's a concept. You believe in a star god, I walk around the corner, I see that you're believing in a star god, I believe in a star, we're friends. We can trade.

Do you hear what this man is explaining to you in a nutshell? That's why we do office hours because I take notes and try to build a weekend stream. This is what I would do anyway to try and build the next stream, but instead trying to watch it with you a little bit to take notes with you. In the last video he was talking about neuroprosthetics, which is the Neuralink thing, and I've told you many times in passing that I think that's nonsense. Here he's explaining something a bit more complicated, which is how myths allow people, social groups, to trust other social groups that they don't necessarily know. Because you believe in the same star god that I believe in, or you don't. Do you see where we're going with this one? Do you believe in the gain of function virus or don't you? Do you believe in the gain of function virus, the natural virus, the virus, the vaccine, the transfection, the countermeasure, the government? Do you believe in the television gods or not? They are fracturing our society based on a new mythology, a new mythology that you and I and GigaOhm Biological and a few other people have been on top of in a way that no one else has been. And that is a mythology about a certain tiny combination of genes that can be assembled in a laboratory and spontaneously lead to billions of people infected, millions of people dying and a global crisis that requires the transfection of billions of people around the world and the continued rollout of transfection as a countermeasure to prevent the ongoing crisis caused by this novel form of death.

It is a mythology, ladies and gentlemen, it is a mythology they are using to divide us and control us. And they are going to turn up the screws, they're going to turn the screws, they're going to turn up the thermometers or the heat or whatever you want to call it. They're going to make the water boil. This is four years ago before the pandemic, a guy that I used to work for in neuroscience, the head of a of a relatively benign set of journals. Now after he gave up doing neuroscience, because neuroscience was this little thing, now he gets so much more, so much more deep experience with science, being the head of a journal. And here he is telling you about a mythology that he himself is part of curating. Do you not see?

So without this magical structure, without this ability to form consciousness, basically we wouldn't know this universe and we wouldn't have the magic that we have there today. So the challenge as a neuroscientist when you come in and you see this sort of scale or the magnificence or the grandeur of this importance of this structure is that you have to ask how did it, how does it do it? How does it actually build the, how does it solve this problem of creating ideas, consciousness eventually? I'm not going to explain how that happens and not that I know how it happens, but it is a magnificent structure.

Look at the wizardry that happens here. Are you seeing the wizardry? It's the same thing they did with the virus. They had the graphics ready to go, the three dimensional stuff ready to go. Sure. They rolled out Bill in his dorky sweater over the lighted table with some, with some tactile viral forms and a little DNA double helix. Henry and the WEF and the people that are running this show have much better graphics than that.

And what I wanted to do in my research was to try to find-

I'm not saying Henry's running the show by the way he's not.

One route, a path where you can answer.

What I'm saying is, is that Henry Markram is the, one of the pinnacle examples of what an academic neuroscientist, academic biologist becomes if they maximize their advancement in the system. He went from being the custodian of a blue brain project where he used a supercomputer to model the brain based on reconstructions of neurons and lots and lots of recording time. That resulted in almost nothing, but some pretty pictures and some calcium signaling models of a tiny pinhead of the neocortex. That's it. Hundreds of dollars, hundreds of thousands of man hours of time, lots and lots of lectures like this. Now, 10 years after he branched out into heading the Frontiers set of journals, he's giving a talk about how mythology is used to greet strangers and about how mythology is so important in controlling the behavior of humans while he is the head of a series of scientific journals which is responsible for curating public scientific knowledge. It's really, really fascinating to me.

It's not about being able to necessarily even answer that question.

The blue brain project was in Lausanne, but it was using an IBM computer.

At the end of the tunnel... Blue brain is not anything like blue balls. There's no cure for blue balls. There's a cure for blue brain, you just don't fund it.

That answer is really to be... That path is to build a brain and to step inside of it and to see how it reacts to the world.

See, that was one of the things that was really cool or said to be cool about the blue brain project. It produced a three-dimensional model that the computer could fly through of the circuit that you were studying as if that helped anybody get it, like… It's terrible.

Not only see how it reacts to the world, but to see how it converts that state as Edan was talking about, the activity of neurons and of chemicals and of molecules interacting and genes and synapses. How does that state all transfer into a perception? You may think that you see with your eyes, but that's only because your brain is so good. It's deceiving you. You are seeing with your brain, you're not seeing with your eyes. Your eyes are just sending in actually clues, fragments, tiny, tiny fragments of the world. It's actually amazing what you see because 99.9% of what you see is not coming into the brain. The brain is actually interpolating and building this magnificent world in front of you. How does it do it? So for me, the criteria of when we will understand the brain is when we can hit a button, run an equation, E=mc², and it will convert it back into a perception.

That doesn't even make any sense. The terrible explainer of what the brain really does when he says that you're not seeing with your eyes, you're seeing with your brain and that most of what you're seeing is not even really collected by your eyes. That's all, it's all nonsense. He's trying to relate the idea that the fovea of your visual system is the sharpest part of the image and the rest of the image isn't sharp and that your conscious brain is aware of that lack of sharpness but chooses to sort of pretend that it's not there. Because wherever you point your fovea becomes instantly in focus and instantly detailed so you can assume that the details are available, they're just not readily being sampled. And so as a result of your eyes scanning the room, of entering the room, of processing the shape of the room, the sound of the room and what you see, your brain builds a cognitive map that's present when you close your eyes and that's part of your total experience of being in the room. And so yes, you're not seeing with just your eyes because what you're seeing is actually the cognitive experience of the room and the way the echoes echo off the wall and echo off the monitor and then don't echo off the curtain wall and echo off the carpet but not off of the black floor or whatever. And these are all details that your brain incorporates into the conscious experience of the room that he's distilling down in a most inept way by saying that you don't see anything with your eyes, you see with your brain. That's like the worst attempt at teaching what the brain does that I've heard in recent memory and yet here he is curating a set of journals, one of which is frontiers of neuroscience. So it's extraordinary really, if you think about it, how many excellent scientific communicators there aren't. And in that whole room here, are these people now upset and like, what are we listening to this guy for or are they also so ill equipped to understand the majesty of the human brain and the human experience and the human body and its interactions with the environment that they just are, they are blown away by his language. That's my assumption, but it's terrible. It's falls terribly short of conveying his own understanding of what the brain does. Never mind trying to give some of that understanding away by using semantics that means something.

…when we will know we've understood the brain. The idea of building a brain is a very old one, it's not original. This amazing Spanish medical professional became an anatomist and for years he just stared under a microscope and he drew every cell, almost every cell, this is a hundred years ago, about the time of Einstein. He was drawing, creative, very creative, because this was not a copy, it was a drawing. It was what he was seeing in his head as he was looking down a microscope. He was drawing cells.

And what does that mean really? Well, the focus of a microscope is dependent on focal length, right, so he had to focus up and down through the three dimensional preparation that he and his students had created and then try to translate that into 2D image that someone could see on the wall. What's Henry doing here?

He drew almost every kind of cell that you can find in the brain today. For a hundred years of neuroscience, we've almost not seen any new cell that he did not see. So this was really the Darwin of the brain. He explored and he drew it all down. But what do you do with piles and piles of paper?

You should look up a guy by the name of Fritjof Nansen, he is a polar explorer from Norway who was also a neuroscientist and drew the nervous system of many marine invertebrates that he was studying, a warrior explorer biologist many, many years before this guy.

And today we have the possibility to do that. We can do it in digital form. You can leverage all the computing power, the neuroinformatics, the different strategies and we can start to build a digital, digitize the brain and build a digital reconstruction of the brain. So how do you do it? One way is just start scanning the brain. If it's stained in different ways, you stain for proteins, you stain for genes and you digitize them. So you can identify.

Here we are collecting gigabytes, terabytes of data, no connectivity at all. Ooh, there are all these genes and all these cells and they're all arranged just like this and we can do it over many, many, many, many, many, many brains and then realign them. They've been talking this game for decades. Ever since I got into neuroscience, this was going to be like within five years, all the problems were going to be solved and I started in neuroscience in 1998. It was like I had already gotten in too late. You know, most of this stuff is going to happen in the next few years. They just figured out oscillations and sooner or later we're going to be able to just do transcranial stimulation and control the oscillations and fix everybody's problems. You got in just a little late, really, and since then, really nothing, a lot more money thrown at the problem. What is that ringing, dinging noise? Is someone doing that in the stream? That is weird.

Where all the cells are, how many cells they are by looking at different kinds of states. (What is that dinging noise? Where is that coming from?) You can identify the types of cells that they are in the brain. (Do you guys hear the dinging noise?) What this is already giving us today is, this is a mouse brain, it's our experimental run on the human brain, is that we have a full-scale atlas of every one of the 740 million neurons that are in the mouse brain, including all the non-neuronals, the support cells, 737 brain regions. We now have an atlas of where these cells are, how many cells they are, what kind of cells they are.

Completely useless data, completely useless, spent hundreds of thousands of euros, if not millions of euros, useless data.

To a certain level, and we will get deeper and deeper into what kind of cells they are, because they get infinitely more and more differentiated. So the next thing that you need is an atlas of all their shapes. It's like us, we all have different shapes, it's weird, it's wonderful, we have different shapes, neurons have different shapes. They are also incredibly amazing creatures. Yidan is the father of neurons in neuroscience. He's the one that has really pioneered how we understand neurons.

He didn't talk so much about Yidan Segev, so he's talking about a modeler of neurons.

Because for him it's pretty much done, he's understood neurons and he's working with us in the Blue Brain Project now to see what happens when they all talk together, what happens when you build them together. So we build an atlas.

There are no axons on this picture, axons are the connection from this neuron to lots of other neurons. Axons can be millimeters or even centimeters long. There are no axons in this picture, so I get that you want to understand, but this is like showing trees without showing their roots. And the roots are much bigger on these trees than they are on the trees out in your backyard. It's extraordinary really, the kind of bamboozlement that I too engaged in as I talked about where neuroscience was going to go and how fast it was going there and how you had to get on the train now or you're going to miss out.

Of all the types of neurons, but you very quickly realize that you run into a wall because you can't possibly draw all these neurons. It took my lab about 15, almost 20 years to draw, to digitize a thousand of these neurons, but we have to do it for billions, so you can't do that. But now we can use computational tools and we can synthesize these neurons.

Computational tools to model and synthesize the network of neurons, so we're not going to simulate the brain after all. You see? You see? It's the same synthetic biology shortcut that's taken on every other field. We can't do it the old fashioned way, so we're going to use AI, deep learning and synthetic biology to make an approximation of it and sooner or later it's going to work. It's no different in virology, ladies and gentlemen.

We can take snapshots of neurons and synthesize 3D models of them. We're moving to be able to take an iPhone and scan all of Cajal's images and build 3D models. We can take a hundred years of research and turn them into 3D models, but you can also now computationally clone them because once you understand one oak tree you can build as many as you want. So today we can actually now use this atlas, go to a particular brain region and load these new…

So do we really imagine that certain neurons are identical to one another? Because I know that's not true. Even though there are pyramidal cells and different kinds of interneurons, different kinds of interneurons based on what genes they express, or what shape they have, or yada yada yada. Every one of those individual neurons in that class that you call class 1, 2, 3 tells Z is a different neuron than all the other neurons in its class. And understanding why that difference emerges and what genes are differentially expressed between those groups of neurons, you could subdivide these classes infinitely. So again, we're simplifying the model in order to get something to work, in order to make the gears all mesh, but we're not actually solving the problem we're purporting to solve. This is a lot of Jedi mind tricking, a lot of, yeah, this isn't the droids you're looking for kind of crap here. Wait till you see his wife's speech at the same meeting.

And start building them. (Here we go.) Here's an example where you can just start loading these computationally synthesized neurons that look just like the real thing and start piecing together the circuit.

But there were no axons in that picture, it was just dendrites. So the only the receiving antenna is no, no wires. What the? It's just so disingenuous. He knows that. He knows that he's not showing the axons. He's not showing where that most of those neurons don't talk to one another, but talk to neurons that aren't even in that local circuit. That's the beauty of the neocortex. It's not a local bundle of neurons. It's all making a local connection and then sending its connection out to the next. It's an interconnected circuit that's interconnected with lots of other circuits and we don't understand it.

The next thing you have to solve, which appears to be an intractable problem is how do you connect them? Your brain has an intractable problem. How do you connect them?

Then if you have an intractable problem of how to connect the billions of neurons with trillions of synapses that you're modeling with a piss poor… What I'm trying to suggest to you here, ladies and gentlemen, is this kind of academic blowhard science, big talk that has gotten us into this trouble that has allowed public health as a field to be weaponized against us, to be used against us because academic scientists have now been trained to stay in their corner, to stay in their little field and definitely not question the people that give these kinds of talks because Lord only knows we are a few years away from the singularity where computers solve all of the problems where we have all the data we ever need and we can collect all the data that we don't have that we need in a snap of your fingers, which is what he just basically explained in the last video. Now let's see if we watch his wife's talk at the same conference if we see anything else of interest. Oh, I didn't have that up. I got to look at this frontiers and then maybe I can find it this way or not. Oh, that's her, that's Camilla, but I want to see this talk where she does this. So let me, Future Computing, this was this way, I'm going to go back.

That state as Edan was talking about, the activity of neurons and of chemicals and of molecules interacting and genes and synapses, how does that state all transfer into a perception?

There she is. So this is at the same conference and I want you to understand what I'm playing this for. I'm playing this because she's going to sell this idea that biology, supercomputers, chemistry, all the scientific pursuits are exponentially learning and the stuff that we don't know is exponentially disappearing and at some moment it's just going to be humans are gods. You heard Henry Markram cite the useless eaters guy from Israel, you heard him cite the book and then you heard him say that the mythology is how people learn to interact with strangers. And so what I'm trying to tell you here is that they know, they know that mythologies are how societies are ruled and they have been crafting this transhumanist mythology for decades, through many different disparate academic fields from neuroscience to chemical sciences from physics to climate science. It's all part of this movement, all part of this bamboozlement that has been occurring over time. I can even give you a little microcosm of it. How often neuroscience has focused on genes and genetic causes for autism and knockout gene models for autism as opposed to environmental causes for autism is extraordinary. There are millions of dollars for genetic models of autism, millions and millions of dollars, virtually nothing for environmental cause models of autism or the development of them. There's no interest in funding in that at all. Listen to how Camilla Markram, a ridiculously mediocre scientist turned innovator and journal editor with her husband, Henry Markram, the snake oil salesman that I just showed you in the last two videos, listen to how she describes her work and the role that Frontiers plays in it.

Celebrating our 10th anniversary with us, we're going to have a whole array of amazing speakers today that are going to take us into the future. But before we step into this future, I thought that we should take a look back and see what science has actually brought to us. Okay, we live in exceptional times and often enough we don't even appreciate it. So now what we're going to do is a little morning exercise. Can you all please just get up? We're going to do a little experiment to see what science has given us already today. Okay, good. Up and down, up and down. So you can sit down again if your birthday falls on an even day of a month. So if your birthday is on the 2nd, 4th, 6th, 8th, 10th, 12th, 14th, 16th, 22th, 24th, 26th, you can sit down. Remain standing if it's 28th and 30th just for statistical reasons, okay. So 200 years ago, child mortality was 40%. That means that four out of 10 children did not survive their first five years of life. And it was pretty much like that for the 200,000 years of Homo sapiens existence on this planet. Okay, we're going to do another one. You can sit down if you're over 35. You understand where we are. Now we see the young chickens. Henry's standing. Sorry everybody. But just 100 years ago, the average life expectancy was not more than 35 years of age. I should actually have as well a seat here because I would have not been alive long enough to give this lecture. But then again, if you look around in the room, there's journal managers still standing, but that's about it. I think this Frontier story would not work so well in this way. Okay, so let's relax. The few remaining ones that are standing can sit down again, take a deep breath, and let's all be happy to be here in this room. So what has actually happened? Why are we all here? It's really all thanks to science, saved our lives many times over and over again. The biggest killers in the past used to be infectious diseases. And in fact, it was really scientific discoveries.

She is a neuroscientist married to a neuroscientist, a guy who used to do the exact same work that I did, which is work under a microscope recording from brain slices to measure how neurons talk to one another. She used to work on an autism model in mice where they used valproic acid to create the behavior in mice, not a very well funded project. It crashed and burned, disappeared. She is a neuroscientist by training, including a postdoc. Now she's telling us after 10 years of working at Frontiers that infectious disease is the problem. I find this dubious at least crazy sketchy.

Biology, chemistry, public health, agriculture as well, that has made us live much longer. So here I want to show you some of these discoveries. I'm just going to overlay these two graphs and then let's start.

I'm not underestimating the progress that they've made with neural nets. What I'm underestimating is and what I'm calling out is the idea that neural nets can be a incorporated into a somehow interactive manipulation of our brain. Neural nets are just a way of, of organizing zeros and ones in a computer that resembles something that we understand of how neuronal networks work. But the fact that those programs based on that architecture are capable of solving massive amazing problems does not bridge the gap between those, those programs doing things useful and interacting with the brain and augmenting its function. And these two things are often told in the same narrative in the same talk and interchangeably used to threaten us, to coerce us into believing sooner or later, they're going to put something in your head. You might as well accept it because we're going to learn how the brain works and then you're going to have to accept it when in reality they're still just using psyops. They're still using psychology. They're still using eye tracking. And so I'm not saying that neuronal nets aren't useful. I'm not saying that computers aren't going to get better in the future. I'm saying that regardless of what happens in that sphere, the brain development, the human immune system, and so many more aspects of human sacredness are going to be untouchable by our technology, irrespective of what these people say. That's what I'm saying.

So it all really started with very basic discoveries. About 170 years ago, an Austrian doctor called Ignaz Semmelweis, he discovered that simply washing or he discovered that childbed fever is actually a contagious disease and then simply washing your hands can prevent it from spreading and saving lives.

This is what Joseph Lee reminded us of is that when polio went away, it was when they actually figured out that someone else's shit needs to get in your mouth before you can get polio and that someone else has to have polio. And so if you stop getting people's poop in your mouth, you greatly reduce the transmission rate of polio and the number of kids in iron lungs.

Shortly after, Louis Pasteur discovered the germ theory of disease and then we learned how to combat germs and bacteria with antibiotics. A hundred years ago, pneumonia and tuberculosis used to be one of the biggest.

I find it almost annoying that she uses the word germ because germ is not a scientific term. It's like a street term. We know that there are bacteria, so why not call them bacteria? Germs, what are they exactly? She doesn't know, but she's giving a speech because she's been told to. This is a controlled message. You can look in the past now and see it for what it is. This was four years ago. One year before the pandemic, a mediocre neuroscientist is giving a talk about infectious disease.

Today, it is pretty much tamed with antibiotics. At the beginning of the 19th century, Karl Lahnsteiner discovered blood groups. With that, he opened the road towards blood transfusions. Shortly after, Levinson discovered that adding citrate to blood allows it storage. Then, these two discoveries basically saved more than a billion lives. I, before I started this presentation, did not know about these two scientists. I bet you also did not know about them.

How is she giving a talk about scientists she didn't know before she gave this talk? What does that even mean?

Huge lifesavers. And today, we have vaccines that protect us against infectious diseases. 200 years ago, Jenner developed the first vaccine against smallpox. And thanks to this discovery, smallpox became the first human disease to be completely eradicated from planet Earth in the 1980s. That alone saved, this vaccination, saved 530 million lives.

That's a pretty extraordinary statement to make. As a neuroscientist who has barely any publications, who couldn't really debate her way out of a wet paper bag when it came to anything to do, she had a reputation of being a whoosh! I am shocked, quite frankly. I didn't think that this little exercise would uncover so much nonsense, but here we are.

And then in the 1950s, Salk discovered or developed a vaccine against polio, saving another 120 million lives. And today, we have lots and lots of vaccines that protect us against infectious disease from tuberculosis to diphtheria, measles, huge lifesaver. And today, we even have vaccines against cervical cancer.

We even have vaccines against cervical cancer, Gardasil. You see what's happening here, ladies and gentlemen, do you see how long this has been going on? Do you see why we need to look back in the past? Do you see why Mark Kulacz, Housatonic Live is such a pimp, such a Jedi master, such a king among kings? Because that guy has been on this and cataloging this. And there's much more to catalog because there are much more, many more parallel threads. It's the reason why I have some of my supporters that I do some of my bigger supporters, some of my more regular supporters, some of my skateboarding supporters, because they were aware of this undercurrent already for years having been used in these Ted Talk presentation conferences to get everybody jazzed up about transhumanism, to get everybody jazzed up about skill and flow state. This is spectacular, spectacular to see how on message of the WEF, the WHO and the UN, these people who are supposedly running a Frontier startup journal, holy crap.

The other huge killer in the past used to be famines, but then with steam and electricity, we had the industrial revolution. They brought about the industrial revolution, and with the industrial revolution, we had actually enough safety and security to grow our population. So at the onset of the industrial revolution, there were a billion people on this planet. And within just 130 years, we doubled to two billion, and then just within one lifetime, we went up to seven billion people on this planet. And it was really two discoveries that made it possible to feed so many people within such a short time. Again, at the beginning of the 19th century, it was two German chemists that discovered a way how to synthesize nitrogen and use it as a fertilizer. And the Haber-Bosch process saved, again, nearly three billion lives since then. Huge discovery. And then in the 1940s, Norman Borlaug developed ways of how to produce high-yield, disease-resistant wheat. And it's estimated that that discovery, triggering the Green Revolution, saved more than 250 million to one billion people. I think it's quite clear that science is saving lives over and over, and there is a great news attached to that. There's more of us on this planet, and we are making scientific discoveries really at an exponential rate. Today there's eight million scientists. You see how that has grown. We're commanding over a worldwide R&D budget of $2.3 trillion. So we are making with this money more discoveries. We're producing more data, and we're summarizing these discoveries in research articles. Last year, 2.4 million research articles were published. And this creates a beautiful research and innovation cycle. Research and innovation lead to growth, economic growth, and then governments and the industry have more money to reinvest back into our research labs. Sounds all great, but here obviously is the bad news and why we're all here in this room. The current publishing system is severely bottlenecking this innovation cycle and prosperity cycle. So let me show you how.

She called it a prosperity cycle. The publishers are holding this back by not getting articles out quicker, by not making it 2.4, but 5 million articles a year. And she's going to help with that. Frontiers is going to help with that.

Already seen these slides, but we have as well a lot of new faces in the room. As I said, last year, 2.4 million articles were published. And still today, 80% to 90% of these articles are published behind expensive subscription paywalls. Clean energy solutions are just not widely available. Doctors who are treating us today do not have access to the latest medical studies.

So they're allowed to push their journal as not having a subscription paywall as long as they push agenda 2030, as long as they push infectious diseases, as long as they push the many, many, many vaccines that we have that work great, including the ones for cervical cancer. Do you not see how easy it is this quick quid pro quo? Something for me, something for you, I'll help your journal grow. I'll support your business model. I'll help you fight against the subscription paywall. Just make sure that at every conference you talk about these things. She said that before she gave this talk, she didn't know who those two guys were that invented the Bosch process that invented nitrogen or the ones that, who gave it to her then? Who told her to say those things as a benchwarming neuroscientist? Who told her to say these things? That's what you need to ask yourself because everybody that was at this talk, everybody that was at a talk that she's given like this has been given this information. Here it is, vaccines work, infectious disease is scary.

Even as the researchers, we can't access all of the latest research because our universities can't afford to pay the subscription fees to all the journals, not even the richest universities. The scale of the problem is actually quite massive. Scholarly journals usually have an embargo period of at least one year, often much longer. And the calculation is quite simple on 2.4 million articles that sums up to 2.4 million years of delay to access last year's science, if you add each article. And this is the other reason why we're here today. You know that in order to publish these 2.4 million articles, it's estimated that at least half of them go through bouncing cycles. They're bounced around from one journal to another before they do get published. It's the rejection cycle. And we all know in this room that it takes at least six months, but often much longer to get your article published. So again, summing it all up, the accumulated delay to publish valid science is another 600,000 years. And if you add it all up, the delay to access our latest science and publish our latest science is 3 million years. It's quite astonishing.

I mean, if you add it up in a very disingenuous, silly way, you get 3 million years. If you realize that most of the people who need to read that science through their employers are subscribed to those journals, that's how it works. Then it's not like the people that need to read the science can't read the science. It's more or less just the publishing companies grifting off of the institutions that feed them their publications, which is a problem and a waste of money. But having these people publish science is no better than having, say, Mark Zuckerberg and bioRxiv publish science if it's still going to be a gatekeeper, if it's still going to go under this peer review process that takes many, many, many months. They're not really proposing anything new here other than the fact that they don't have a paywall, which I'm still dubious about. I think they do sometimes.

Because we've all experienced it firsthand, and that's why we as well started Frontiers about 10 years ago and why we have this large community of all of you that participate. This is an open science platform, and in open science, everybody can access the latest science for free. In the old subscription model, our universities pay so that we can read the papers, whereas in the new open science model that's been already around for like 15 years or so, universities pay so that our papers get processed, and then everybody in the world can read for free. In fact, it's much cheaper to publish in open science. Currently an open access article costs an average 1,500 euros, whereas a subscription article costs somewhere in the range between 4,000 and 5,000 euros. I'm not going to go too much into this because Fred is actually going to tell us a little bit more how we think about article processing charges, how they compare to subscription articles. But let's put it this way, if universities would decide to switch to the open access model overnight, they could actually overnight save in the range of 6 to 8 billion euros to do more research. And we could also completely eliminate these inefficiencies and delays in the current publishing model. In open science works, and it works actually extremely well, so here I'm going to show you a couple of slides. What we've done here is we've taken the 20 largest publishers, and we've looked at how many articles they've published in the last three years. So these are recent articles. So behind paywalls, there were around 4 million articles published in the last three years, whereas there were around 600,000 open access articles published. And then we looked at their citation rates. Citation rates basically mean how much science is built on top of an article. And you can see that open access articles, on average, get more citations than subscription articles. So these are the latest articles. And here we've taken just this graph and expanded it across these 20 largest publishers that are out there today. And what you can see is that you can see this advantage in the open access articles over and over again. So in the last three years, for example, the American Physical Society has gotten a higher citation rate on their open access articles than on their subscription articles. The same is the case for the Oxford University Press. Higher citations on their open access than subscription nearly doubled the citation rate on Springer Nature's journals on open access articles. Here's Frontiers at the same rate as Springer Nature. And over there, you find Elsevier. Their subscription articles are still getting more citations than their open access articles, but about a little bit less than plus open access articles and certainly less than both Frontiers and Springer Nature's open access journals. That's within the last three years. Open science, open access, already today has more impact on creating new science than subscription science. Now, if we would just imagine if we were to make it all open, how that would accelerate innovation. And these are the views and the download rates from all over the world based on Frontiers articles 90,000 published so far. And we see that they already got more than 400 million views and downloads really massively from all over the world and as well from innovation hubs such as Silicon Valley and Shenzhen. And here we are zooming in on a particular gene editing article in the Silicon Valley. And you can see, yes, people at Stanford are reading it, but there's as well a lot of people here in Palo Alto and at Google that are reading this particular article. And that is because companies today depend on science and innovation and on access to the latest science for their productivity. Today's economy is a knowledge economy. And in this context, we have this rapid and efficient access to the latest science is important for economic growth. And today, it is even more important to have this access to the latest science because the digital revolution has brought us to a completely new level. And I'll elaborate a little bit on this. Computers today are hyperabundant, and what's more, they are actually connected to each other. Nearly every household in the West has access to the internet. Computers are as well becoming more powerful. They have more memory. They're becoming faster. While at the same time, the prices are actually shrinking. And that's the reason why you find computers today not just in your PC or computer chips, not just in your PC, but really in all kinds of devices from phones to drones to even your washing machines nowadays.

The internet of things!

And that in turn has led to an explosion of data production. So what we see here is the data exchange on the internet. And you can see it's exponential growth. More powerful computers, more data have in turn led to the rise of artificial intelligence.

More data. A lot of it is just pictures. Pictures with more pixels, movies with more pixels, create cameras with more pixels, create an exponential increase in the amount of data that gets transmitted, ads get transmitted. It's just, it's a lot of bamboozlement here, ladies and gentlemen. You have to know that. You have to see it for what it is when they show these exponential graphs. But they've been doing it for a while. People are confused. People believe this Theranos act.

Now, these are algorithms that start making sense out of the data and in fact become more and more clever the more data becomes available. And in the last five years, what we have seen is the rise of amazing artificial intelligence products. I'm fine. Oh, you can give me some water. I'm getting, it was too loud yesterday. So Henry thinks I'm getting a horse. Okay. So AI, for example, has given us autonomous cars, cars that can completely drive by themselves. Artificial intelligence now as well talks to you and can even plan your day. And artificial intelligence can as well now start beating us at computer games. But let's imagine if we could actually use open science in combination with artificial intelligence and the latest cutting edge technology to accelerate our scientific process. And we'll hear some of the presentations today that are actually already doing that. But in order to do that, we actually really now need this access to our latest articles and to our latest data as well. And that's not just so trivial. In Frontiers, for example, our articles are not just free for humans to read. They're as well free for machines to read. And in fact, we annotate the articles in a way that it's very easy for machines to mine them and extract meaning out of them. And then what we as well need...

Interesting for Frontiers to be interested in feeding scientific articles to AI, isn't it? Isn't it an interesting thing? I find it interesting.

This video is not playing, but it doesn't matter. What we as well need is access to our latest data. (genes!, data!) All of you who sit in this room, you know that in your labs you're producing data in abundance. But what we're not doing is putting this data systematically into databases and then sharing it with others. But the benefits of sharing data...

Sharing data. Yeah, we can share scientific data, but what we'd really like you to do is share your data. Share your data with us. That's what we would like. But, you know, for now, we'll just talk about sharing scientific data and talk about AIs being able to trawl over all of this stuff and organize it and mine it.

Huge. They're enormous. If you take the Human Genome Project, for example, at first people as well tried to patent genes, lock them away behind paywalls, just as we are so well accustomed to do with our articles. But then it was all made open and the benefits were huge. It allowed geneticists to create the first gene editing therapies for cancer. And for every dollar that was invested in the Human Genome Project, 145 were made in return. Pretty much triggered the…

145 were made… What is that, profit? What is she talking about?

…the entire biotech industry, which is today worth billions of dollars every year. And Open Science is today being led by Europe. And we have as well amongst us the visionary and the pioneer behind the Open Science Cloud. That's Jean-Claude Bürgelmann. I'm very happy that he's speaking today about the European Open Science Cloud, which is an initiative to bring together all of Europe's data onto common platforms and allow people then to access it and to share it. But we can as well use all of this now, Open Science, cutting-edge technology, artificial intelligence to help us as well address the major challenges that we face as humanity today. The UN has formulated 17 Sustainable Development Goals. And I think they can be more or less summarized in four challenges that we are facing today. So what are these challenges? Well, the first one is the one that we've already alluded to quite a lot at the beginning. We want to live...

Are you kidding me? Frontiers? The journal set run by Henry Markram, the egomaniac responsible for the Blue Brain Project and the Human Brain Project, a billion-dollar 10-year grant from the EU to simulate the human brain, which failed miserably, now running Frontiers, the journal set, was before the pandemic telling us about the UN's sustainable goals, was talking to us about human homo digitalis, and is the number one challenge is eradicating disease. I rest my case, ladies and gentlemen, these secret meetings and these private meetings and these conferences and all the stuff that's been going on for the last 10 years has been bent on making sure that this Scooby-Doo operation worked. Bent on making sure that whether you accepted masks or not, you eventually accepted them. Whether you accepted RNA or not, you're eventually going to accept it. Whether you accepted that it was a novel virus or a lab leak, you've accepted that there's a virus and that it's gain of function and that it's still out there. And in order for this Scooby-Doo to have occurred as effectively as it occurred, they needed to brainwash everyone, including neuroscientists, including neurobiologists, including chemists and physicists, all of whom have been to a conference where this kind of nonsense was sputted out. When I say that this is a benchwarming neuroscientist, I mean that this is a postdoc who did nothing for her boss, but marry him. And now they're running a journal together for the last 10 years, a set of journals purporting to solve a problem in science, which is open access to the data while at the same time being required to push all of the sustainable development goals of the UN, and push all of the transhuman agenda, if not directly indirectly in all of the things that they do. How can you not see it for what it is? The only thing she hasn't said is equity. I'm sure it's coming.

And we want to eradicate disease, right? 10,000 diseases today already have cures, but 20,000 still do not have cures. So let's see what some of our speakers actually intend to do about this. We have Marlene Temerman today, and she is going to talk about how to bring health services to some of the most vulnerable on our planet, women and children in Africa. We have Thomas Hartong, who's going to be talking today, and he has built the world's largest toxicological database that is machine readable, and his AI algorithms can actually already predict the toxicity of drugs, in some cases even better than animal experimentation. We have Henry. He's bringing together the world's neuroscience data in brain models and brain simulations, and he's going to talk to us how we can use these brain simulations to understand brain diseases better and as well find new cures for them. We have Yidan. He's going to talk about what makes us human and how we can use artificial intelligence or whether artificial intelligence is going to bring us to a new level of superhumanity. We have Tony amongst us as well. He's going to be talking about aging. At the beginning, we saw already today, well, in the last 200 years, we've extended our lifespan from 30 years on average to over 80 years, and Tony studies aging, and he has found ways of how blood from young mice given to old mice can make them young again.

Blood from young mice can make old mice young again sounds like a great idea to me. Transhumanism, here we come!

And then we have as well Paolo Vinais amongst us. He's using the multi-genomics approach to study the effects of air pollution and climate change on our health, and he's actually going to ask one of the most crucial questions, I believe, of the century. Can we actually live healthy lives without a healthy planet? And this is the second challenge. We got to feed all the people on this planet. Today we are 7.6 billion people, and the United Nations estimates that within 50 to 70 years, we're going to stabilize, not keep on growing, but really consolidate and stabilize at 11 billion people. So now we need more than ever science and technology to feed these extra four, and all of us, in fact. So how are we going to do that? Well, previous scientific advances have given us pesticides and fertilizers. That's not a bad thing. It saved all of us, and that's why we're in this room today. But now we need science and technology to produce more using...

It's interesting, she consistently says science saved us all, as opposed to like created the conditions to permit us to live, or I don't know, it's a weird... Something's weird about it.

And using less of those. Artificial intelligence, for example, already today, can scan fields, images of fields, and predict pests on crops, thus minimizing the use of pesticides. And more than ever, as well, we continue to need plant scientists and genetic engineers to develop these high yield crops to feed people using less land, less fertilizers. And then there's meat. We all love meat. We had some yesterday. We're going to have some more. I'm a little bit quirky about that. But here it comes. We all love meat, but eating meat is actually problematic. For many reasons, we got to feed the cattle takes up a lot of land, to have the cattle takes up a lot of land. But as well, cows are methane emitting. It's one of the biggest drivers of climate change. Not to talk about all the animal suffering that is involved in all of that. So Mark Post has actually pioneered a way of how to grow meat from culture itself in the lab. And in fact, when he started that, the cost of a lab-grown burger was $300,000 about five or six years ago. Today, it's only $11. So bringing this to the supermarkets could form a completely new basis of our meat consumption conscience, with a good conscience as well. And this is the third challenge.

Why should we have a bad conscience about eating meat? Because the animals are mistreated? Then let's treat the animals better. What would be better about growing lab meat? Not much. The waste products, the amount of waste is necessary, the amount of time that goes into it. Never mind the possible side effects on your health, if that's the meat you eat, because we don't understand really how our food is processed and how it's extracted and how that, you know, the value of these things is… It's just very hard for me to look back in time and see that I was part of this machine, that I was integrated into this machine, that I went to these conferences, that I listened to these talks, that I believed these mantras, and how quickly I've snapped out of it over the last three years, how violently I have been thrown out of the truck, out of the moving truck into the street full of traffic, and had to go from being a person who would think that this talk made sense to being a person who questions the childhood vaccine schedule in the United States, and can get sick to my stomach if I think about the next conversation that I'm going to have with my pediatrician. This is the reality of the world that we're living in right now, ladies and gentlemen, everyone's going to have to go through this baptism of fire or remain forever captured in the mythology of the virus. And I know a lot of people are going to hate me when I say that, but that's what this is. It is a mythology of a virus, of a coronavirus. It is a bamboozlement based on exaggeration, based on misdirection, and based on outright lies. And we are going to need years to fight our way out of this, years to document it correctly. And we are going to need all of us to do it. Mark is right. We need more people on this task, but I hope this has been a little helpful in terms of looking backwards and thinking about how this could have taken place across the spectrum of academic science, across the spectrum of biologists, how so few are yet sticking their head above the parapet, not very many people are, are risking their career to speak out about this yet. And it is just shameful, but it is because there has been decades of preparation, decades of cognitive conditioning on these principles that they are now turning the screws on. That's how I see it. That's what I really see happening here. And what scares me most about it is, is that I don't, I don't see an easy way to wake a lot of people up about this stuff. I just don't see an easy way to wake people up about this. That's, that's what scares me the most.

Ladies and gentlemen, this has been a GigaOhm Biological and Office Hours presentation. Thanks for joining me. Hope you had a little bit out of it. Hope you got a little bit out of it. Again, remember, how's the sync right now at the end of the stream? My plan is to use this as study time as I'm preparing for the stream either on, I don't know if it's going to be Friday nights or Saturday nights. I'm still working out the schedule for 2023, but rest assured, it's going to be a lot more that you can see coming and a lot more of me on doing the actual work that you can see. So thanks very much for joining me and I'll see you again soon.