Moonshots with Peter Diamandis - How Quantum & AI Will Shape the World’s Future w/ Jack Hidary | EP #123

Episode Date: October 10, 2024

In this episode, Jack and Peter discuss LQMs which are the next stage of AI beyond LLMs. LQMs are growing in applications in the biggest parts of the economy. Jack and Peter then talk about the synerg...y of bringing AI and quantum together for global impact. Recorded on Oct 1st, 2024 Views are my own thoughts; not Financial, Medical, or Legal Advice. 01:57 | The Future of AI and Quantum 31:41 | The Intersection of AI and Brain Imaging 01:04:46 | Decoding the Mysteries of Quantum Jack Hidary is a leading entrepreneur and visionary at the forefront of AI and quantum technology as the CEO of SandboxAQ, raising over $500m in funding. He is the author of forthcoming book AI or Die and the influential textbook; Quantum Computing: An Applied Approach. A serial entrepreneur, Hidary co-founded and led EarthWeb/Dice from inception to IPO, and co-founded Vista Research and sold it to S&P/McGraw-Hill. Jack studied neuroscience at Columbia University and was a Stanley Fellow in Clinical Neuroscience at the NIH where he applied neural networks to brain imaging. Learn more about SandboxAQ: https://www.sandboxaq.com/  ____________ I only endorse products and services I personally use. To see what they are, please support this podcast by checking out our sponsors:  Get started with Fountain Life and become the CEO of your health: https://fountainlife.com/peter/ AI-powered precision diagnosis you NEED for a healthy gut: https://www.viome.com/peter  Reverse the age of your skin with Oneskin; 30% here: http://oneskin.co/PETER    Get real-time feedback on how diet impacts your health with https://join.levelshealth.com/peter/ _____________ Get my new Longevity Practices 2024 book: https://bit.ly/48Hv1j6  I send weekly emails with the latest insights and trends on today’s and tomorrow’s exponential technologies. Stay ahead of the curve, and sign up now: Tech Blog _____________ Connect With Peter: Twitter Instagram Youtube Moonshots

Transcript
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Starting point is 00:00:00 Alzheimer's, 40 years of research, nothing to show for that. Parkinson's, a handful of things to do for those patients. Dementia, an epidemic, cancer, nothing to show. You can use the power of quantum physics to understand and model molecules. Instead of the world of large language models, we've now entered the world, Peter, of large quantitative models, LQMs. People feel like the world is going rapidly and disrupting and reinventing today with generative AI. This is just the beginning, Peter. Everybody, welcome to Moonshots. Today is an extraordinary episode with a dear friend, Jack Hittery. When you're talking about the intersection of AI and quantum,
Starting point is 00:00:47 Jack's the CEO of an incredible company spun out of Alphabet called Sandbox AQ. He's a Brooklyn boy, a graduate of Columbia, where he studied philosophy, physics, and neuroscience. Did his fellowship at the NIH. He and I go back 25 years to when he started VISTA research. Back in 2016, Jack founded the Quantum Group at Alphabet, working with Sergey Brin and Astro Teller at X.
Starting point is 00:01:13 And in March of 2022, spun out Sandbox AQ, taking on the role as CEO, attracting none other than Eric Schmidt as his chairman, raised a monster round of $500 million in one fell swoop, and he's on a rocket ship ride. It's good to see you, Jack. Peter, good to see you, my friend. It's exciting times.
Starting point is 00:01:34 It surely is. You wrote a textbook called Quantum Computing, An Applied Approach. I mean, who writes- Very light reading, very light reading, Peter. But more importantly, you wrote this book as well, which I love, AI or Die. It's really a how-to manual for CEOs. I recommend it for everybody.
Starting point is 00:01:52 And importantly, you're on my board, the XPRI is one of our trustees. Buddy, I want to talk about quantum. I want to talk about the intersection of AI and quantum. I want people to understand why this is so important right now. We've been inundated with large language models and generative AI and it's changing the world, but it's changing a part of the world and the rest of the world is about to be transformed and discovered through this intersection of AI and quantum, sandbox AQ, A for AI, Q for quantum. Go, let's jump in here, buddy.
Starting point is 00:02:31 A lot of discuss. Peter, these are very exciting times, not only for sandbox AQ, for you, for me, for XPRIZE, but for the human race. We as humans are dealing with so many challenges. The challenges that we often talk about We as humans are dealing with so many challenges. The challenges that we often talk about at XPRIZE, visioneering gatherings and other gatherings of folks
Starting point is 00:02:51 that are really concerned about life sciences, the medical challenges that we face as humans, Alzheimer's, 40 years of research, nothing to show for that. Parkinson's, a handful of things to do for those patients, dementia, an epidemic, literally across the world as our population gets older. In cancer, some success stories, in breast cancer, for example, much higher survival rates now, earlier detection,
Starting point is 00:03:19 understanding of the multiple subtypes of breast cancer. Yet another cancers like pancreatic cancer, nothing to show. Yeah, glioblastoma. GBM, glioblastoma, nothing to show. Steve Jobs died of pancreatic cancer, billions in the bank account, nothing to do. Now, years later, still nothing, still nothing. So, massive challenge is in the world of medicine, massive challenges in the world of energy. We all hope for a transition to a cleaner, more efficient energy posture for our world, yet we've made halting, halting progress at best towards that. So a lot of challenge, and the question is, what are the tools at hand, Peter, that we can use, that we can marshal to address these challenges.
Starting point is 00:04:05 And one of the reasons I started Sandbox AQ is for deep impact at scale. And you and I have always talked about that. Impact is good, but scale is what's critical. Impacting the lives of billions of people, the world's biggest problems or the world's biggest business opportunities. And that's what Sergey and others had challenged me to take up as we got going with Sandbox AQ. As we look at these two particular tool sets, AI and quantum, initially, Peter, they might seem quite different. Like, wow, one comes from computer science and inspired by the brain, neural networks are inspired by biological neural networks, okay, that's one interesting tool. And then physics on the other hand, and now you particularly talk about quantum
Starting point is 00:04:50 physics, how does that relate? How are these two things related? Well, actually, Peter, there's a fundamental core nexus, a wormhole, if you would, that brings us together between AI and quantum. And that is, both of them are modeling the world around us. Both of them are taking huge swaths of data and compressing them down to manageable units in a way that we can actually leverage them to make a prediction, to have an output that is useful for us in addressing these kinds of challenges. So they seem quite literally worlds apart, AI and quantum, but there's a fundamental core commonality. So let's start if you will.
Starting point is 00:05:37 Let's jump in with the large language models and generative AI and what does that mean? How do you think about the limitations and what it's given us? Peter, let's dig into that. So before large language models, we actually had architectures of neural networks, these artificial representations that are inspired by the brain architecture. Our brains, as we know, have about 86 to 100 billion neurons and then trillions, possibly hundreds of trillions of connections known as synapses in the brain or connections or weights or parameters in an artificial neural network. So, loosely inspired by the brain, certainly not an exact depiction of what happens in the brain, very loose, but nevertheless, we call it an
Starting point is 00:06:25 artificial neural network. And prior to large language models, we had architectures such as RNNs, recurrent neural networks. And these had the ability to actually make a pretty good prediction. If you said the dog ate the blank, it actually would make a good guess that the dog probably ate the bone or the homework, but probably not the dog ate the house, right? And so it was pretty good. The main drawback of our advance is they're super slow, just not fit for purpose. You cannot be doing what people do today, which is doing interviews on Zoom and in real time asking the LLM to help it do an interview as you see people happening right now.
Starting point is 00:07:09 But certainly they showed that it was possible to have this kind of prediction. And along came a paper by colleagues at Google. Eight of them wrote a paper in 2017 called Attention Is All You Need. And what they realized in this paper, what they demonstrated in this paper is that these new GPU architectures, we could take advantage of the parallelizability, took me 10 minutes to practice that word, parallelizability of the GPU architectures
Starting point is 00:07:38 in order to actually get a lot of throughput to actually make this both the training and the inference, both the training on large inference, both the training on large corpuses of words and then the real time use of those models. That's what we call inference. Both of those could be sped up massively. And so that's in fact what happened. These GPUs, the G and GPU of course, Peter, for graphics, not meant initially for language models or anything like that, initially meant to give us beautiful graphics in doom and you know, all these kinds of things.
Starting point is 00:08:11 And Nvidia, people may not realize, is actually a 30 year old company. This is a company that's, it's a 30 year overnight success, let's put it that way. And so it's an exciting moment because in 2017, the marriage of these new architectures known as transformers, another way of basically putting these artificial neural networks together combined with the power of these GPUs really led to this revolution that we have with OpenAI and Anthropic and Google Gemini and Meta Llama.1 3.2 and so and so forth. All this came from some initial work done over many decades. Of course, AI is not new. We can go back many, many years. I like to go back to 1943, a paper by McCullough and Pitts,
Starting point is 00:09:00 a neuroanatomist and a mathematician. Quite a strange bunch. We don't have time for that today, but maybe another episode of this podcast, we'll talk about McCullough and Pitts. But they realized that when you open up the brain, you don't see a CPU, you don't see a memory, you don't see the kind of architecture that you think of a von Neumann kind of computer after Johnnie von Neumann,
Starting point is 00:09:23 you see something very different. And that's in fact, was the beginning of these kinds of neural network architectures. But fast forward to we now have these things, languages are now being, language models are being trained. But what is really happening under the hood, Peter? And how does this, you know, Jack, you tell us that it's connected somehow and has some similarity to what's happening in physics. Well, let's look some similarity to what's happening in physics. Well, let's look at it. What's happening in a large language model, Peter, is that you take a huge corpus of words, billions of words, the words in Wikipedia and the paragraphs in Reddit and the posts on social media, some true, some false.
Starting point is 00:10:00 So it's garbage in, garbage out. But with all the garbage, you bring it all in and you present it to these language models and you train them. And what you hope that is happening is what we call generalization or learning. You hope that it hasn't just memorized that entire corpus. If it just memorizes it, well, no learning has actually happened. Similar with a toddler. If we have a toddler and we take them around our neighborhood and we say, hey, let's go into this car, let's go into this bus, let's do this, let's do that. And later a different car comes by,
Starting point is 00:10:32 you want that toddler to say, that's a car. It's a different kind of car, it's red, it's not blue, it's larger, not smaller. It's different than the initial car that the kid went in, but the kid knows that it's a car. How does a kid know that? That's what learning is about. That's what Eric Kandel won a Nobel Prize for and many others did
Starting point is 00:10:51 to understand what learning, how learning works in the brain. And now we can replicate that in these neural networks. And so one way to think about neural networks is a compression algo. What it's doing is it's taking a huge body of stuff often represented in our world, language in this case could be images, could be movies, even videos, and it's compressing them down to their essence,
Starting point is 00:11:13 the essence of car-ness and the essence of bus-ness. And it's saying, okay, I've seen enough of these and I understand what some of the core elements are. So if you put a prompt in saying, show me a car driving down the street that's red but upside down and it's singing a melody from Taylor Swift. There are now models that actually will do that now because we've taken not only text but also video and images and train these models and they've extracted some of the essence of what's happening. And so that's a form of compression. It's a shorthand
Starting point is 00:11:51 that's now embedded in the weights of that model, embedded in the parameters of that model. And some of these language models, as you know, Peter, can get up to four or 500 billion parameters, a trillion parameters. And it looks like now we'll hit two trillion, maybe even two and a half trillion parameters in some of the newer models that are coming out right now. There are limitations though, right? There are limitations of what they can do. Yeah, so that's the good news.
Starting point is 00:12:17 We found a way to compress all of say, various languages down into this model. The problem is there is no equation for the English language. There's no equation for the for Mandarin or for any language. And so the ability of the language model, it's really limited to mixing and matching what it found on the on the internet. And so, yes, it can make a new essay, but that essay essentially is regurgitating bits and pieces of what came before. That's really what's happening. It's a probabilistic engine that is spitting out stuff that it hopes and we hope makes some coherent sense.
Starting point is 00:12:59 And with enough training and human feedback, humans in the loop feedback, then we can actually make that happen more often than not. There's still a lot of hallucinations, of course. So we're really limited in terms of we can't really go beyond what is really in that corpus. So that's language models. And the question is, what else could we do with this kind of architectures, the architectures of neural networks and in general with artificial intelligence? Well, let's think about the world beyond words. And so, okay, language models, a lot of words, great, easy training set, low hanging fruit. That's really why it started with that.
Starting point is 00:13:37 But it turns out the majority of our world, Peter, is not words, but numbers. The majority of our world, if you think about a medicine, a drug, it's described by numbers, by certain configurations of carbons and hydrogen, and maybe we throw in a nitrogen or throw in some sulfur and things like that, and we make different medicines. We think about biology, that's numbers. We think about physics, we think about battery chemistry to store energy. those are numbers, right? And there's no amount of training and words that's going to help you design that next battery because you need to know about the laws of chemistry and physics. And we need to have the exact nature of
Starting point is 00:14:16 those laws, not a guess, not some paragraph in a textbook, but the actual mathematics of this ballgame. And so there are, and I think this was the insight, right, that brought about the creation of Sandbox AQ. There are laws of physics. They go back a hundred years. There are some fundamental laws of quantum physics, and we all know Newtonian physics, F equal MA, you know, the- Ball of inertia.
Starting point is 00:14:44 What we learned to describe the velocity of a cannon ball shooting out of a cannon. But there are a series of laws of physics known as the quantum equations. I just wrote them down because I want to discuss them a little bit. Schrodinger's equation, Heisenberg's uncertainty equation, Planck's equation, Born's probabilistic interpretation equations. Was the ability of computation to model these equations accurately what brought about the birth of sandbox AQ. It was the realization that we could have the compute, we could bring the future forward, Peter. That's really what you and I have been doing our whole lives.
Starting point is 00:15:32 What we love doing. Bringing the future forward. And this was a realization that we could bring the future forward to compute these laws at scale with impact, with deep impact at a scale that would impact billions of people. Everybody, I want to take a short break from our episode to talk about a company that's very important to me and could actually save your life or the life of someone that you love. The company is called Fountain Life and it's a company I started years ago with Tony Robbins and a group of very talented physicians. You know, most of us don't actually know what's going on inside our
Starting point is 00:16:09 body. We're all optimists. Until that day when you have a pain in your side, you go to the physician in the emergency room and they say, listen, I'm sorry to tell you this but you have this stage three or four going on. And you know, it didn't start that morning. It probably was a problem that's been going on and you know it didn't start that morning. It probably was a problem that's been going on for some time but because we never look we don't find out. So what we built at Fountain Life was the world's most advanced diagnostic centers. We have four across the US today and we're building 20 around the world. These centers give you a full-body MRI, a brain, a brain vasculature,
Starting point is 00:16:46 an AI enabled coronary CT looking for soft plaque, a DEXA scan, a grail blood cancer test, a full executive blood workup. It's the most advanced workup you'll ever receive. 150 gigabytes of data that then go to our AIs and our physicians to find any disease at the very beginning when it's solvable. You're gonna find out eventually. You might as well find out when you can take action. Fountain Life also has an entire side of therapeutics. We look around the world for the most advanced therapeutics that can add 10, 20 healthy years to your life and we provide them to you at our centers. So if this is of interest to you, please go and check it out.
Starting point is 00:17:28 Go to fountainlife.com backslash Peter. When Tony and I wrote our New York Times bestseller, Life Force, we had 30,000 people reached out to us for Fountain Life memberships. If you go to fountainlife.com backslash Peter, we'll put you to the top of the list. Really, it's something that is for me, one of the most important things I offer my entire family,
Starting point is 00:17:52 the CEOs of my companies, my friends. It's a chance to really add decades onto our healthy lifespans. Go to fountainlife.com backslash Peter. It's one of the most important things I can offer to you as one of my listeners. Alright, let's go back to our episode. Before we start in discussions of the quantum equations that affect our world on atomic and molecular level, there's a set of Newtonian equations that drive everything from a car going down the street to a kid playing ice hockey to rockets flying into space.
Starting point is 00:18:27 Those Newtonian equations are very deterministic. They're very clear. You can write them out and you can send a rocket to the moon back in 1969 with the computational power that is found on, I don't know, I can't find a computer smaller than- The basic watch, yeah. ... Describe that. So those have governed and limited what we've been able to model thus far, right?
Starting point is 00:18:57 We can model large chunks of atoms moving, but not model on a subatomic molecular basis. So speak to me about that. Yeah. So this is a fundamental point. As you mentioned, back in the 60s, we had the ability to calculate where that rocket would go. And literally computers were doing that. In those days, what we call computers are human beings who are computing.
Starting point is 00:19:23 That was the initial computer was a human being and their job was called a computer like a lawyer does law, call them a lawyer. These were computers doing that. There were some also actual computing machines helping out on the side. And the reason why they could do that with such small amounts of memory and compute
Starting point is 00:19:42 is because equations really are great compression vehicles. If I want to say, hey, that you mentioned a cannonball, Peter, I have that cannonball shooting out of the cannon. It's going to take this parabolic type of pathway as it shoots out, hits its apex and comes down again. And so I could, on the one hand, take lots of notes about, oh, here it is at time 0, time 1, time 2, time 3. Take lots of notes where it is. Or instead of all that data, I could just summarize it very, very succinctly in an equation
Starting point is 00:20:16 where I have the starting parameters and then I could predict anywhere along the parabola, I could tell you immediately where it is and where it's going to be. So that equation with some of those starting conditions plugged in and the variables gives me a very succinct. I've compressed a huge amount of data into a small number of bits of information. Back to Shannon, back to Claude Shannon, what he taught us in 1948 in his landmark journal article about Shannon entropy, using the word entropy in a novel way, not in exactly the traditional way we used it in physics, but in a way that said, what is the surprise factor we have in looking at this body of information?
Starting point is 00:20:59 If you have random numbers, you know, kind of in a grayscale image, there's no compression possible. It's random,, there's no compression possible. It's random. Therefore, there's no pattern. But when you have a parabolic pathway of a cannonball, oh, Isaac Newton says, I can tell you all about that information in a very, very succinct form. And we do that all the time in the Newtonian world, again, to send rockets up, to understand the dynamics of cars, even hydrodynamics and fluid dynamics with airplanes and testing in wind tunnels or even virtual wind tunnels, how the airflow will happen over that curved shape of the wing, very complex dynamics, but that's still in what we call the classical world,
Starting point is 00:21:44 the pre-quantum world. And so now, Peter, you bring up all these interesting equations and now we're able to say, hey, we did this on the macro scale, on the classical scale, but how about those electrons? How about those photons? How about those molecules? At these scales, we've got to use different equations. And that's what the quantum grates gave us. Heisenberg and Schrodinger and even Einstein 1905 paper, the one he got the Nobel Prize for Peter was not relativity.
Starting point is 00:22:16 It was the photoelectric effect in 1905, part of his honest mirabalas, his miracle year that he had one of those papers. And it was inspired. People may not realize, by the way, a little side fun science note here a ballast, his miracle year that he had one of those papers. And it was inspired. People may not realize, by the way, a little side fun science note here in the Moonshot podcast. Why did Einstein, here's a fun question for everyone out there, science nerds and geeks alike. Why did Einstein write these four plus ones, actually five papers in one year on seemingly a disparate set of topics. I'll leave it to the end.
Starting point is 00:22:46 We'll come back at the end and we'll find out why that is the case. But he did write the photoelectric paper and that led to his Nobel Prize, but it was building on Max Planck 1901. 1900 gives the talk in the Prussian Academy of Sciences,01, publishes the paper and really forever changes the world because what Max Planck realizes is that to resolve some of the key crises happening in physics at the time, I know people feel the tension that we feel right now, the crises of the late 1800s in physics,
Starting point is 00:23:22 the ultraviolet catastrophe, there's so many, there's actually four or five different crises happening at the same time in physics. It turns out all of those could be explained by the fact that we were still wed to a Newtonian way of thinking. And when it came to the subatomic world, we needed to actually abandon that
Starting point is 00:23:43 and move into a new regime and have a different view on how the world works. And Max Planck would kick that off with a sense of how black body radiation working was again, one of the key crises at the time. Einstein, a young Einstein in his twenties read that paper and then wrote his paper in 1905 with homage, explicit homage to Max Planck, his senior, and saying that Max Planck explained that for black body radiation, I, Albert Einstein, will explain it for a photon, a packet of light. And this ushered in along then with Schrödinger and Dirac and Heisenberg, and of course, Niels Bohr. All of these greats who each won Nobel prizes
Starting point is 00:24:29 helped us understand the dynamics of how things work fundamental to our universe. It's not people often say, oh use quantum only describes things at the smallest of scale. Well, yes and no. It's describing things at every scale. It's just that we don't have to go through the trouble of using the quantum equations when you have something the size
Starting point is 00:24:51 of a rocket ship. It does describe the rocket ship. In fact, because that's what's happening is the rocket ship is made up of all these little atoms and electrons. So let's move away if we could. Maybe one thing we could also do in this podcast is we'll help society to move away from the phraseology of, oh, quantum only describes things at the small scale. Actually it describes everything. Everything is quantum, but they're particularly useful when we're thinking about the small scale.
Starting point is 00:25:17 And we can generalize with Newton's laws every time. That's right. That's right. We can approximate using Newton's laws. Exactly. Exactly. Correct. So now coming back to your fundamental question. So we talked about how neural networks compress a lot of stuff in our world down to a much smaller format so we can manage it, manipulate it and make some output of the generative AI, for example, in language or in images
Starting point is 00:25:42 or things like that. But now let's turn to physics. Physics does the same thing. As we talked about Newton laws can do that, say for a parabola or rocket ship going to orbit or going around the moon, but now we could also compress something even more fundamental. We can say, what is the behavior of that electron? And let's talk now about valence electrons.
Starting point is 00:26:03 I know it's bringing back nightmares for listeners back into into high school chemistry but but the valence electrons are the ones on the outer edge and those are the ones we really are concerned about when we want to make a new drug. Peter as you well know as a doctor when we want to make a new drug and we want to say what molecule would fit into that target in the body. To give an example to our listeners today, if God forbid someone has melanoma, someone has non-small cell lung cancer, bladder cancer, a variety of cancers, we now have a new class of cancer drugs that go take us beyond the horrific regime of chemotherapy and radiation. They take us into immunotherapy,
Starting point is 00:26:46 the ability to use our own immune system to fight these cancers. And the key to that happening is a molecule, actually a synthetic antibody, but not an antibody created by our own internal adaptive immune system, but one that we synthesize in the world. And this antibody doesn't have the function that normally antibodies have of helping us directly to fight a particular
Starting point is 00:27:15 disease or pathogen. What this does is it locks in to a particular receptor known as the PD-1 receptor on a T cell, on an immune cell, it locks into the T cell's PD-1 receptor and protects that receptor from being hit by a tumor, by a ligand, by a molecule coming out of the tumor that normally shuts down T cells and puts them to sleep like hypnosis around the cancer. And this protects it, like the Romans had their nice shields. Imagine now the T cells armed with this nice shield and goes into battle. But to develop that molecule that would fit lock and key
Starting point is 00:27:56 into that T cells PD-1 receptor, we have to have ultimately an understanding of how those electrons at the outer edge of that antibody and the electrons in the outer edge of the PD-1 receptor, how they interact. And now for the first time, just in the last two, three years, we now have that ability. Let me have you pause here one second because it's important for people to realize, I mean, we do all of our work in the world of bits, but we are physically individuals living in the world of atoms. And when you want to start looking at the functionality on a cellular surface or in a chemical reaction or in a new battery chemistry, those are all atoms. We've been
Starting point is 00:28:46 able to model them in classic computation thus far, but it's approximations and it's massively computer heavy. Are you talking about being able to get to a deeper level of fundamental modeling than we've ever been able to do with our computers today because we have had deep mind with Alpha Fold and Alpha Fold 3 and most lately it was Alpha Prodeo being able to help us predict new proteins. But that's using classical computer models versus the work that you're doing today. Can you differentiate those two? Sure, that's correct, Peter. So when we look at what's been done before in trying to model biological systems as an
Starting point is 00:29:34 example, a lot of good work has been done, but unfortunately it did not involve the physics itself. And so people would look at libraries of proteins and what's fundamental to proteins as we know is their conformation, the way that they're folded like an origami. And when you have a string of amino acids, the building blocks, the Lego blocks of proteins and you string them together,
Starting point is 00:29:57 they're gonna fold in a certain way. And that folding is fundamental to the use, to the application of that protein. In fact, when we have a misfolded protein, that's a whole nother ball game and that leads to diseases of all kinds. Stan Prusner, who won the Nobel prize and is at UCSF, an incredible body of work, showed how misfolded proteins can lead to complete disaster in the brain and other organs as well. So when we think about the prediction of folding based on a string of amino acids, one can just look at lots of examples and based on those examples, train a neural net on what
Starting point is 00:30:38 would happen. And that's in fact how a number of the methods that we use out there do that, like alpha fold and others. But now we have the ability, Peter, to go beyond that. Because while alpha fold does a very, very good job, it doesn't actually get down to the very specific ways in which it will, if that protein will interact. Looking at the structure of the protein, the folding, is only the first step. We must now get to the dynamics of the protein. How will it act on other things? How will other things act on it? And again, we come back to electrons and electrons are described
Starting point is 00:31:14 by quantum mechanics. And so if we want to understand how an electron on one molecule would interact with the electron on the receptor, on the target. We've got to get down to that level. And that's the level now, finally, that we at SandboxAQ have been able to model things at. And that is a big breakthrough. That means that we can have- And that's- Yeah.
Starting point is 00:31:36 I mean, that is amazing because that applies across all material science, all biology. What was that moment in time? So I mean, when you joined Alphabet to head this division, did you have this in mind already? Or was this sort of something that unfolded as the computational power came online? I mean, help me understand that moment of creation. I'm just super curious. My first area of focus was actually in AI. As you know, I was applying AI decades ago now
Starting point is 00:32:07 to brain imaging as you opened up with and specifically to fMRIs, to dynamic brain imaging. Dynamic brain imaging, as readers may, as listeners may know, is not the same as just a static CT image. Just to put it on a light box, let's take a look at it. You're talking about gigabytes and gigabytes and gigabytes of data taken over a period of time.
Starting point is 00:32:30 Looking at blood flow of your right. Looking at blood flow, for example, where I'll give somebody a test and ask them to move their finger. If they're moving this left finger here, index finger, this is the exact spot is roughly about right here in my brain right now, moving this finger this minute right now. I can see your homunculus right now. Yeah, right there.
Starting point is 00:32:51 It's right there as we're speaking and listening to each other. As we all know, we're using Broca's area here, number 44 to speak. And then I listened to you, Peter, I'm losing Wernicke's area back here. And so, you know, when we look at these MRI images, the human eye can only see so much. And so my team and I began to train neural networks, primitive ones at the time, but neural networks nonetheless, to see if we could glean more information
Starting point is 00:33:18 from these medical images. And sure enough, we were able to do that. And that shows the robustness of this idea of a brain-inspired neural network that even with a very, very primitive compute we had at the time building computers ourselves literally by hand, we could actually make that work. Fast forward to today, one thing I realized is with AI, yes, language would be important, but the quantitative world, Peter, would actually be as if not more important. The majority of our world is quantitative in nature.
Starting point is 00:33:53 The majority of our world is governed by numbers. And so rather than spend a lot of time developing large language models, we became very interested in the quantitative models. And that took a number of forms. But then realizing of course from the background of physics that we needed to figure out a way to take these quantum equations that you were just discussing, Schrodinger's equation, Heisenberg's formulation, all these interesting equations, we needed to do that at scale. And the conventional wisdom at the time, Peter, was that we would need a quantum computer
Starting point is 00:34:27 to do that. We'd have to wait two or three decades to get a quantum computer, and specifically a quantum computer that was fault tolerant, that was error corrected. Right now- Low error rate, yes. Exactly. You and I are speaking from computers right now that are error corrected. There are literally mistakes that pop up in computers. How does that happen? Well, actually, Muons,
Starting point is 00:34:49 cosmic rays, can actually hit your computer and cause a bit to flip. And so we have various error correction schemes in our phones, in our laptops, in our computers, in our watches that allow for error correction for transistors for bits, zero and one type bits. It's not that hard because you can take a vote if you want to of multiple bits and over represent the bit you want with many bits. Bits are so cheap to make, why not have lots and lots of extra transistors? But in the world of quantum computers, it's not that simple. These are very, very sensitive devices, which is one reason why, by the way, you can flip the script and make instead of a quantum computer, you can turn it to a quantum sensor. Maybe that will leave that till another time in
Starting point is 00:35:35 this podcast to talk about. But basically, quantum computers are very sensitive to perturbation from the outside world. And so they do need this error correction. That is hard. That error correction is hard, Peter. We knew at the time that it would take years and years before we'd have an error corrected quantum computer. Let me pause you here one second because there's a really important distinction here for everybody to understand what Sandbox AQ is doing because you're a
Starting point is 00:36:05 software company, you're not building quantum computers and there are a multitude, there are dozens of companies building quantum computers and we could talk about what the horizon for those are. Yes. But the important point to make here is that you can use the power of quantum physics of the equations to understand and model molecules and such without having quantum computers. That's correct. The computational power. Yeah. And by the way, Peter, when quantum
Starting point is 00:36:33 computers one day do get scaled and do get, and we encourage and we have relationships with more than a dozen of the quantum computing hardware companies out there, it'll add more fuel to our fire. Yeah. It'll give us even- And of course, and Google is one of the leaders. I was just at Hartmut's lab seeing their- Beautiful, beautiful work. Beautiful golden chandeliers. Beautiful work, yes. And they just announced some great progress
Starting point is 00:36:54 on reducing the error rates in quantum computers, and that's all fantastic. But I think the point here is that the same computational power in those GPUs that gave us the large language models, you've been able to build algorithms that you can use on those GPUs to approximate or to solve these classical equations of Schrodinger and Niels Bohr and Planck and Heisenberg to help you model the actual world of atoms and electrons and ions
Starting point is 00:37:27 today without quantum computers. That's correct. And that is going to give us incredible insights into the physical world across health, materials, environment, everything. Peter, is giving us, is giving us right now. This is happening in real time. And that's what's so exciting. And when you look at, again, coming back to the fundamental idea of information, right?
Starting point is 00:37:54 Of what does it mean to take a part of the world and represent it in an equation, in a dynamics, in a modeling, in a simulation. You're talking about, again, it's very similar to what we did with language. We talked about large language models. We took a corpus of billions of words and we compressed it down and embedded that information into the weights, into the weights of a neural network. Again, we didn't memorize those words. That's not what we did there. We embedded information into a space,
Starting point is 00:38:27 into an information space that encodes all that stuff. We're doing something similar here. We're taking the dynamics of a certain molecule and we're describing it in a much shorter way using these equations given to us over a hundred years ago by the quantum grates. And that allows us then to make predictions, very precise predictions about, okay, Peter, let's say you work at a large pharma company and you say, Jack, I heard you have this wonderful
Starting point is 00:38:56 platform. I'll give you a molecule that we're thinking about that might hit this particular target in the body. Maybe it hits glioblastoma, as an example, this brain cancer, horrific disease. And we'll take that, we'll make a digital twin of that, Peter, and we'll make 10 million, 100 million, maybe a billion permutations on that drug. We'll add a methyl group that is adding a carbon and a few hydrogens. We'll add a nitrogen. We'll add an amine group, we'll add this, we'll take away that. Each one will be a slight variation on the theme that you initially started with based on your research.
Starting point is 00:39:33 These are AI simulations in quantum. That's right. So first there, we take the quantum equations, we run those, and that becomes the data set. So we're generating our data set, and that's what we use to train the AI. Okay, let's pause right there. That's a fundamental point. That's really important here. That is a fundamental point.
Starting point is 00:39:52 Yeah. If you were trying to discover these molecules that are useful in cancer, Alzheimer's and so forth, trying to get those with a large language model, the data doesn't exist in the corpus get those with a large language model, the data doesn't exist in the corpus of data that the large language model is controlling. That's right. It's outside the data set.
Starting point is 00:40:11 Outside the data set. It's impossible for them to discover it if they didn't have the data in the first place. That's correct. And so you've got to generate the data that these quantum models can then assimilate and generalize. So let's double down on that for me. Yeah, let's double click on that. So basically what's happening here is instead of the world of large language models, we've now entered the world, Peter,
Starting point is 00:40:36 of large quantitative models, LQMs. LQMs are about starting with equations to generate data. That is one that's the most efficient way to generate data. And the most accurate way to generate data is with the equations themselves. The equations are the bedrock of the universe. They are the fundamentals. They're upon which everything is built and created. That's correct.
Starting point is 00:41:00 The fact that humans, by the way, just taking a step back and the fact that human beings have uncovered the quantum equations of the universe is stupendous. Deserves a moment of silence. Okay, moment observed. And so, this is absolutely incredible. And by the way, as many of our viewers may know, the quantum equations are not one of, they're the most tested set of equations that we've ever had in the corpus of science. And they were so, and the people wanted to doubt them so much. They were so much- Even Einstein.
Starting point is 00:41:38 Einstein hated them. He hated them. There was the, God doesn't play dice. He hated them. God doesn't play dice. He hated them. He railed against them until his death in the 1955 because although he was one of the creators of it, he couldn't comprehend given a classical view that he held onto how this world could even be described by these equations. Ironically, one of his best known papers, 1935, known as E EPR Einstein, Podolsky, Rosen, the three authors of it was an attempt to derail quantum mechanics as a science. It ended up becoming a cornerstone
Starting point is 00:42:13 of the science describing the phenomenon of entanglement. But coming back to your key question, Peter, which is what's happening now in the world of quantitative AI, rather than having to use a corpus of data on the outside world, which contains a huge amount of garbage and false info and good info all mixed together, like in the language world, we start with the pristine equations themselves. We take a theme and make variations on that theme. We take a molecule, make variations on it. We take a battery chemistry, which we're dealing with ions now. And ions, again, are subject to the quantum laws. And we're saying, okay, this is a lithium ion battery.
Starting point is 00:42:54 But we've been stuck with lithium ion chemistry, Peter, for 40 plus years. And we actually need to start moving beyond that, need to think about what other chemistries would give us batteries that may be cheaper, maybe more lightweight. The heaviest thing in a car, in an electric vehicle is the battery. But batteries actually are going way beyond just electric vehicles. The bigger market, the bigger application for batteries is not in cars. It's stationary. It's in every building in the world needs to ultimately have an energy storage system that accepts electrons when they're cheaply available and then uses them when they're in demand.
Starting point is 00:43:31 And that arbitrage that day trader like arbitrage of buy when low and use when high, right? That is going to impact the world of energy beyond anything we've ever seen. The one I'm waiting for is room temperature superconducting. That's what I want. Quantum to deliver us. Yes, well, that's the kind of modeling
Starting point is 00:43:53 we now are beginning to embark on. So when we think about the drugs that we need, the medicines that we need, we think about diagnostics, biomarkers that we want to have. Right now, as our audience may know, there is no marker in the world for the progression of Parkinson's is a marker that tells you whether you have Parkinson's or not, not very helpful since it's probably very obvious. But in terms of whether it's progressing or you've halted it with some treatment, there
Starting point is 00:44:19 is no so we need new biomarkers, we need treatments, we need better battery chemistry, we need new biomarkers. We need treatments. We need better battery chemistry. We need cheaper solar energy as cheap as it's become. The fact now that the underlying substrate of solar silicon is competing with the semiconductor industry, it does not bode well for the inexpensive nature of solar. In fact- But perovskite's coming. Perovskite's are exciting, but what's the problem? Perovskites, Peter, they're not stable. To be bankable, to be financeable, solar panels need to have a 25-year guarantee, a 25-year life, shelf stable, roof stable.
Starting point is 00:44:55 In fact, perovskite technology only takes us out about a year in terms of stability. And so there's a need to actually model that at the quantum level as well. There is a company, Paranova, I'll tell you about it sometime soon, that's doing a heck of a lot better than that. Good, we hope so. We want that to happen.
Starting point is 00:45:11 We want that future. Everybody, I want to take a short break from our episode to talk about a company that's very important to me and could actually save your life or the life of someone that you love. The company is called Fountain Life. And it's a company I started years ago with Tony Robbins and a group of very talented physicians. Most of us don't actually know what's going on inside our body. We're all optimists.
Starting point is 00:45:35 Until that day when you have a pain in your side, you go to the physician in the emergency room and they say, listen, I'm sorry to tell you this, but you have this stage three or four going on. It didn't start that morning. It probably was a problem that's been going on for some time. But because we never look, we don't find out. So what we built at Fountain Life was the world's most advanced diagnostic centers. We have four across the US today and we're building 20 around the world. These centers give you a full body MRI, a brain, a brain vasculature,
Starting point is 00:46:09 an AI enabled coronary CT looking for soft plaque, a DEXA scan, a grail blood cancer test, a full executive blood workup. It's the most advanced workup you'll ever receive. 150 gigabytes of data that then go to our AIs and our physicians to find any disease at the very beginning when it's solvable. You're going to find out eventually. You might as well find out when you can take action. Fountain Life also has an entire side of therapeutics. We look around the world for the most advanced therapeutics that can add 10, 20 healthy years to your life and we provide them to you at our centers.
Starting point is 00:46:47 So if this is of interest to you, please go and check it out. Go to fountainlife.com backslash Peter. When Tony and I wrote our New York Times bestseller Life Force, we had 30,000 people reached out to us for Fountain Life memberships. If you go to fountainlife.com backslash Peter, we'll put you to the top of the list. Really, it's something that is for me one of the most important things I offer my entire family, the CEOs of my companies, my friends. It's a chance to really add decades onto our healthy lifespans. Go to fountainlife.com backslash Peter.
Starting point is 00:47:27 It's one of the most important things I can offer to you as one of my listeners. All right, let's go back to our episode. So back to the core question that you have, the fundamental breakthrough now of realizing that what's happening in the world of physics, in this case, quantum mechanics, is that we're summarizing essentially a massive dynamics in the world of physics, in this case, quantum mechanics, is that we're summarizing,
Starting point is 00:47:46 essentially, a massive dynamics in the universe with these core equations. We're taking a molecule that has infinite degrees of freedom. It can move in any way, form, or manner. There's anything that can happen to it. And then we're focusing like a laser on the business end of that molecule. We're not going to the business end of that molecule. We're not gonna model every electron in that molecule. We're gonna limit ourselves to the valence electrons, that is the outer electrons. And within the valence electrons, we're gonna limit ourselves to the business end
Starting point is 00:48:15 of the molecule that might be hitting the actual target that we're going for in the body. By constraining ourselves down to that portion of the problem, we can make it tractable in today's GPU-based computers. In fact- When did this become possible, Jack? When did it become possible for you to do this with the compute and the algorithms? Because it hasn't been that long.
Starting point is 00:48:40 We had the first breakthrough exactly three years ago. Just three years ago. Yeah, so just three years ago is when we realized this is going to change the world. This is something that's going to fundamentally change how we do things. And again, while most of the world was focused on language, and again, God bless the applications for college students writing essays an hour before the deadline for language models. But we realized that this was going to be a fundamental change in one, how we thought about AI and how we thought about the use of quantum in the real world. We've always had quantum in textbooks.
Starting point is 00:49:15 We have many quantum innovations. The MRI machine is a quantum in nature. The laser is quantum in nature. Lots of quantum. We've used them, but we haven't been able to really predict and utilize them. At scale. At scale, yeah. At scale on any arbitrary quantum system.
Starting point is 00:49:35 And that's now where we've come to. And that means that the world now has a superpower now, a new superpower. Humans have a superpower. This generation of humans is the first to have the superpower to do this at scale on real world types of systems. Every quantum textbook in the world usually has a chapter one, two, and three describing the equations you just rattled off, Peter, and then has a chapter five or six that says,
Starting point is 00:50:01 let's use it on an actual case. And what is the case? One hydrogen atom, one proton, one electron. Now, as far as I know, hard to cure cancer with just an atom of hydrogen. And so we got to get to real world system. And that's what happened. In the last number of years, our team and I, we've worked on real molecules from labs and UCSF Nobel Prize winning labs. We've worked on molecules from large pharma companies, spinouts, all kinds of folks working on battery chemistry with a company called Navonix, a public company that does battery chemistry, working on new materials for the US Army. The US Army wants to lightweight the tanks. Car companies that may announce soon want to lightweight their vehicles so that they're
Starting point is 00:50:49 more fuel efficient. Well that's new material science. We need new materials to make that happen. Materials are made of atoms and those atoms have those electrons that we talked about. And so we've got to fundamentally rethink now- By the way, I think how it materials. Material sciences is like the most under appreciated area of technology, right? Everything that is new and breakthrough, I bow down to material scientists and the work that they do. And
Starting point is 00:51:17 this notion of the materials genome, right? The idea that we understand the fundamentals of certain limited number, like, limited number, a fraction of a fraction of 1% of materials that are possible and we use them. But given the work that you're doing, we're able to expand this understanding that will head towards fundamental across every industry is going to be transformed by this. Yeah. And Peter, you get into a fundamental point here, which is the compute we're talking about now, both AI, quantum, this quantitative AI we're talking about, it's not just about doing things faster. Often people write, oh, they're doing things faster. And by the way, faster is good.
Starting point is 00:52:02 I mean, yes, let's get to the medicine faster. That's great. But here's the more fundamental point. We're actually exploring a bigger landscape. We're able in the case of medicine to explore a bigger biochemical space than can otherwise be explored. In the case of material science, explore a bigger space. If you're looking at batteries, battery chemistry, there's about 19 elements of the periodic table that you could, in different combinations, build a battery for the electrolyte, for the anode, cathode, for the membrane, all these core four elements. And so we actually can now start to explore a much bigger space. If we were limited before, as we were, to building prototypes by hand and testing each one,
Starting point is 00:52:46 how many could you possibly build, right? Even if you're Willy Wonka and have Oompa Loompas around, you're limited to the number of batteries you could possibly prototype. But now that we have the actual quantum equations in the system and you could run it at scale, you could explore a much bigger material science landscape. Gone are the local minima and maxima that we've been living with. Yeah, we were really in a cul-de-sac to use a more suburban term for a minimum or maximum, but yes, yes, exactly right. So, let's talk about what the implications of this are. Our favorite subject, you and I both love health and longevity and reminding people the way that we've discovered drugs in the past, we'd go into the Amazon, we'd chop down
Starting point is 00:53:36 bushes and trees and dig up dirt and we'd crush them up and we'd try and find unique molecules and we'd test them, a molecule at a time. And that led to today's devastating drug industry, which is riddled with failures. What do you hold as the average drug development time and cost, a decade and $3 billion? Exactly, right now it's about seven to 10 years of preclinical work that is developing first a target.
Starting point is 00:54:04 You gotta start with a target in the body. What are you going for? is developing first a target. You got to start with a target in the body. What are you going for? You got to validate that target. And then you've got a drug that target. You got to design a drug that fits like a lock and key into that target. That's about eight to 10 years. Then you go into clinic, even with, and God bless the FDA has actually done a pretty good job trying to compress down the phase one, phase two, phase three trials. You often could do a pivotal phase two now where the phase two becomes the, in a sense, like the phase three. Sufficient data to give you an approval by the FDA. To get out there. There's breakthrough pathways for drugs now, particularly for orphan drugs,
Starting point is 00:54:40 particularly for orphan diseases and rare diseases. And so when we think about the time it takes, eight to 10 years preclinical, four or five years minimum in clinic, and here's the kicker, here's the most sobering of the statistics, 90% failure today in clinic, 90 out of 100 drugs that go into clinical phase one trial, phase two, phase three, never see the light of day, never come out again. You know what's equally sobering for me? When you get prescribed a drug because you have a particular problem, chronic disease,
Starting point is 00:55:17 whatever it might be, you expect that that drug works for you. But do you happen to know what percentage of people that drug were prescribed for them actually works? Please. It's like 20%. Wow. The fact of the matter is the FDA is making sure it's not harmful, right? That's the results we get out of a phase one, phase two side of the equation, but the FDA
Starting point is 00:55:41 is approving a drug if it helps a sufficient number of people, not everybody. And so I think it's insane. But the hope now is not a drug that statistically might work for enough people for the FDA to approve it, but I want a drug that works specifically for me. I want a drug that is coded for my molecular design and genetics and so forth. Right. And Peter, this is now the precipice of where we're going because because of that two and a half, three, three and a half billion dollars per drug program, drug companies, biotech companies have not had the ability to do more than just one big cannon shot and just hope it works and hope it goes to enough
Starting point is 00:56:26 population to then advertise and pay back the cost of that. Which is why the drug costs are so extraordinarily expensive. Because also the few successes have to pay for all the failures. But now let's talk about a world where it costs one-tenth the amount to make a drug, 300 million instead of 3 billion. Let's say it costs half the time to make that drug. Let's say we take the rate of success in clinical trial from 10% to 50, 60%. This is all possible now. And so, if we look at that world, then a biotech company, a lab, a set of researchers can say, you know what, we can actually now make a drug that's targeted to this very specific
Starting point is 00:57:11 sliver of the demographic of the population that has this particular genome sequencing or other characteristics. And we can start to get to that future that you just described that says, instead of having one size fits all kind of drugs that end up not fitting many people at all, we can now start to really understand how we can make these more targeted drugs that really match well with key cohorts out there in the population. We've been doing that in part, right?
Starting point is 00:57:43 So we've got companies like in Silicon Medicine, we've got Alpha Prodeo, but again, they're using classical computers and AI large modeling. How far are you from using the equations of quantum physics through the power of Sandbox AQ to help companies with this? Well, Peter, two fundamental parts of that question. We're doing it right now. And fundamental to our success and our velocity in this is that we are not a biotech company. That's fundamental. When you look at the landscape and you say, who else is using computation to help with drug design? Actually, there's quite a few companies using computation. But here's the thing, Peter, they're all biotech companies. They're all companies themselves who have the burden, the overhead of trying to bring a drug to market, who have pipelines and clinical expenses and all
Starting point is 00:58:37 that overhead, not just of money, but of time and management attention. We do none of that. We focus just on doing this advanced set of calculations in partnership with labs and with drug companies. And that- So you're a software company that is supporting a multitude of different industries. Right, exactly. And so that allows us to now have the world-class talent that can do this.
Starting point is 00:59:03 We have quantum physicists on board, chemists, medicinal chemists, biologists, doctors, physicians advising us. We have all these specialties all coming together, AI specialists of all kinds, modelers of all kinds, mathematicians in our midst coming together to make this happen. That is not something that a drug company can really do because they've really got to hit their bottom line, which is making the final drug. We focus on working with dozens and ultimately hundreds and thousands of drug companies in the same way
Starting point is 00:59:37 that Oracle is a database used by every vertical out there. Ultimately, our platform will be used by many, many verticals out there today. It's being used by the biopharma community. It's being used by the chemicals community, by Dow Chemical, by others. And then we'll start moving into material science, batteries, energy, all these different areas as well. So business model really counts. And when you want to focus and say, Peter, what are you going to be the best at in the world? What are you going to be the best at in the world?
Starting point is 01:00:09 What is your company going to say, this is our territory? This is where we're going to really focus? You have to make a choice. You can't be all things to all people. And so we made the choice to be best at this kind of computation, this software. And our software runs today on the GPUs and it's architected so that it could also run on the quantum computers of tomorrow.
Starting point is 01:00:33 And that is the future we're heading to, Peter. We're heading to a meshed hybrid cloud world that meshes CPU as a basic controller, GPU workhorse, absolute workhorse, with QPU, quantum processing unit. And bringing those together is a core part of that future. Real quick, I've been getting the most unusual compliments lately on my skin. Truth is, I use a lotion every morning and every night religiously called One Skin. It was developed by four PhD women who determined a 10-amino acid sequence that is a synolytic that kills senile cells in your skin. This literally reverses the age of your skin and I think it's one of the most incredible
Starting point is 01:01:17 products I use it all the time. If you're interested, check out the show notes. I've asked my team to link to it below. All right, let's get back to the episode Jack talked about the future of of clinical trials here because I think you know, I've heard you Sort of visioner This notion of running in silico human trials That drops the cost not by 50 percent, but you basically a thousand-fold eventually, so that you know
Starting point is 01:01:48 when you introduce this drug into humans, because it's run in quantum models, because it's run in simulation, that you know it's going to work. Just like the first time the team at SpaceX launched the Dragon Falcon combination to the space station, they didn't kind of hope it would actually get there and dock accurately. They'd run the simulation so many times in such accuracy that they knew it was going to work. So is that the future for drug development? Yeah, we still have to do the clinical trials. There's no way around that. But as you pointed out, we can go into the exam with the answers, right? That's very exciting.
Starting point is 01:02:26 And so if we can go in right now, the 90% failure, what does that tell us, Peter, the 90% failure of clinical trials today? It tells us. And by the way, this is having gone through phase one and phase two and failing in phase three, which is insane, right? So you've had enough of a success in phase one,
Starting point is 01:02:46 in phase two, and you know it didn't hurt anybody. To show some safety and efficacy, yeah, that's right. But then at the end of the day, it didn't help enough people. Exactly. So given that 90%, what that tells you is that we have the ability to add a lot of value here, right? There's a, you know,
Starting point is 01:03:03 we're not talking about some optimized system. Can I still invest in the company, buddy? Exactly. There's a lot of value to be created here. If you look at highly optimized systems in our world, those are different stories. If you look at a Stirling engine, one of the most efficient things on this planet,
Starting point is 01:03:21 not a lot of value you can add to a Stirling engine. But if you look at the clinical trials value you can add to a sterling engine, right? But if you look at the clinical trials that you're pointing to, this is where we can add a lot of value. By modeling exactly what's going to happen and adding to that model every year, more and more of the variables. And so initially looking at those valence electrons and how they're going to hit there, and then looking at maybe some dynamics. If it's a protein, let's say you're talking about a small molecule, less than a thousand Daltons with carbon, say, being 12 Daltons. So small molecules, as you know, Peter,
Starting point is 01:03:53 but to share with our listeners, are things like aspirin or Tylenol or things like that are small molecules. But when they interface with a much larger beast, like a biologic such as a protein or an antibody or things like this, then there's a lot of stuff going on. And so we're now adding functionality to the system that allows us to do that kind of stuff, protein against protein, small molecule with proteins. This is complex stuff and it's getting more and more capable every few months. And so this is the kind of work that will ultimately lead us to a much better
Starting point is 01:04:26 sense of the answer before we walk into that clinical trial. Let me emphasize, we'll still need the clinical trial because we do want to have that final real world confirmation, but we'll have so much information and modeling before that, that will move up that success rate very, very dramatically. You know, I need to dive into quantum computing a little bit with you. Sure. Because while you're on a rocket ship, when we add quantum computing to your rocket ship, it becomes a warp drive and you're a starship all of a sudden, not just interplanetary,
Starting point is 01:05:01 you're going to the stars. So quantum computers have been around for a little bit. And as you've said, we've got dozens of companies and their qubits, their equivalent of their bits are atoms or photons or ions, lots of different approaches. Can we talk about where they are today in your estimate and where they would need to get to to be functional for sandbox AQ to use?
Starting point is 01:05:30 Yeah, great question. And you talk about, they've been around for a little while. Let's be more specific. Paul Benioff, yes, a cousin of Mark Benioff, Paul Benioff, I want to give some kudos out to Paul Benioff. He is the one who's had the first paper describing a quantum computer. That was in 1979. He's passed away recently, just in the last few years. But I want to give credit out there because often histories of quantum computing gloss over him and talk about Richard Feynman. Yes, Feynman did popularize the idea of what a quantum computer can do. Feynman did popularize the idea of what a quantum computer can do, and we owe that to Feynman for helping get the idea out. But I do want to give some credit to what credit is due with Paul Benioff for having
Starting point is 01:06:13 kicked us off. And one interesting history of science question is, John Yvonne Neumann, we mentioned it before in Von Neumann Architectures, John Yvonne Neum Norman being at the nexus of computing and physics. Why wasn't he the one to conceive of a quantum computer? It's an interesting question. Maybe another session. We'll come back to that. But we still get to come back to Einstein's five papers. Yes, we're going to come back to that as well. So we have many. It's good to give answers on this podcast, but also to plant questions with the audience as well. So, so, so back to back to quantum computers now. So you ask about the different modalities of quantum computers. Yes, indeed. There's seven major ways to build a quantum computer. And as
Starting point is 01:06:58 you pointed out, we can build them either with natural qubits or synthetic qubits. A natural qubit, an example there would be a neutral atom. That's one of the dark horses in this race. One that hasn't got as much attention, but is scaling very rapidly. This is where you take a neutral atom, not an ion. So it's not an ion trap computer, but it's neutral. It's not, doesn't have any charge. You manipulate it with lasers. Several
Starting point is 01:07:25 people won Nobel prizes such as Steve Chu and others for showing us how to manipulate various entities with lasers. We use those techniques. Near absolute zero. Exactly. We use those techniques to set the atom, which now becomes the qubit into a certain state. And again, let's remember that quantum bits or short qubits can be in the state of zero or one, just like the transistor could be in zero one, a bit can be zero one,
Starting point is 01:07:55 but they can also be in super positions in combinations of zero and one. And that gives us an infinite palette to draw from. And that qubit we can say is going to be some part zero, some part one, or a third this, two thirds that. We can have different combinations. Everything in between. And everything in between. And so we represent these qubits in very different ways than just the normal transistor. And so that neutral atom we can manipulate into one of those states.
Starting point is 01:08:25 We could read that state and then we can operate on that state. And that's what's critical to a quantum computer, the ability to initiate a state on a qubit, operate a set of operations on those states. And then read it out at the very end. And so those are critical things in a quantum computer. And now, in fact, neutral atom quantum computers are scaling faster than almost every other kind of quantum computer.
Starting point is 01:08:53 Doesn't mean the other ones are out, but advantages to neutral atoms are that they're basically room temperature, easy to transport, quite compact, and you're starting with a easy to transport, quite compact. And you're starting with a compartment of gas, let's say rubidium, as an example, where you already have hundreds of millions of these neutral atoms in the actual container. And to do things that are useful with quantum computers, we generally know that we're going
Starting point is 01:09:21 to need to do the error correction. Let's use, for the sake of this conversation, a ratio of a thousand to one, a thousand physical qubits to one error corrected or logical qubit, right? A thousand to one ratio. Really important for folks to recognize because you hear about all these quantum computers that have a hundred qubits, a hundred and seven qubits. Exactly, Peter. Right. They're not the functional error corrected qubits.
Starting point is 01:09:47 That's correct. Yeah. That's correct. So where are we today in this race? So today we're at a few hundred of these physical qubits. Really, some papers have claimed to make one logical qubit, but we're really not at the point of having a set of logical qubits, of error corrected qubits that we could really manipulate at this time.
Starting point is 01:10:11 Now that will change very rapidly. If you look at the photonic side, using photonics, that's another promising approach exemplified by PsyQuantum, both in California and Australia, and also Photonic, a company in Canada, as well as a number of labs working on photonics. The Chinese, by the way, are making good progress as well in photonics. Pan Jianwei, the leader of the quantum program in China, himself is a photonics oriented physicist. And so that was the first quantum computer that he started to build. My favorite science fiction stories are always about massive quantum computers buried under Beijing that brought about AI super intelligence.
Starting point is 01:10:57 Yeah, yeah. Well, there are quantum computers deep, deep inside these universities that are run by Pan Jiangwei, but they're not in Beijing. They're about two hour fast train ride from Shanghai in a different place. But yes, they are there. But in any case, so you have photonics. And one of the advantages that the photonics people will tell you is that we can mass produce these qubits using silicon photonics. We can use some of the same techniques of the semiconductor industry. In fact, PsyQuantum uses global foundries, uses one of the big fabs out there to mass produce this by hopefully the millions.
Starting point is 01:11:35 And so if you want to do something like crack RSA, right? Let's say you want to crack the encryption protocols that are used throughout the world that are the bedrock of our economy. The reason why we have- You want to crack my Bitcoin wallet? Yes, Peter. That's what we want to do.
Starting point is 01:11:51 We want to crack right in and break the chain. In fact, you want to do that with either RSA, which has been around since 1978, since R, S, and A, Ravesh, Sham Shamir and Edelman gave us RSA. If you want to crack ECC, elliptic curve cryptography. So blockchain, you mentioned blockchain, Bitcoin, Ethereum, these are all based on either RSA or ECC. If you want to crack those, but more even bigger than just blockchain,
Starting point is 01:12:22 every ATM transaction, every wire transfer, every e-commerce using a credit card on Amazon, every single transaction, every WhatsApp. When you're on WhatsApp, it says encrypted end to end on the WhatsApp messages there. What is that encryption? That is RSA and ECC. So if you want to correct that, estimates are we'll need roughly 5,000 or so logical qubits. Maybe we can get away with 4,200, but let's just say 5,000 error corrected qubits, which using our thousand to one ratio, Peter, let's go back then and say 5 million physical qubits. So we're at a few hundred physical qubits today and we're going to need 5 million of these things. So, Jack, I know you're a betting man and you predict the future, actually you implement and create the future.
Starting point is 01:13:11 When are we going to get there? When do you think we're going to have actual quantum computers that you're going to want to use at Sandbox AQ? I would say that- Min-max. Yeah. I would say that by year 2029, which is only five years from now, we'll start having the building blocks of about a thousand to 5,000 physical qubits in these modular Lego blocks.
Starting point is 01:13:37 Then what'll start to happen is people will daisy chain these blocks up using fiber optic connection and modulators that allow the physical instantiation of those qubits inside the block to be coordinated with a quantum state without collapse, without observation, without collapse, with the quantum state in the adjacent Lego block. When you can start to daisy chain them all together and then thus create a mega computer made of lots of these Lego blocks, let's say for example, they each had a thousand of these physical cubits and then I got a thousand of these Lego blocks together. Now I've got a million cubits and therefore I have a thousand logical qubits. Amazing. Now, of course, 2029 is when Ray's predicting whatever AGI is back then. And of
Starting point is 01:14:33 course, the reality is we're going to be using some variations of digital supercomputing to help us build these quantum computers. And then those'll, you know, those digital super computer, digital AIs, super AIs will become resident on these. It's an exciting five years ahead. And Peter, just to finish. So that'd be like, that'll take us about five years. That'll take another two, three years of the engineering to put all that together, make it error corrected,
Starting point is 01:15:01 bring it all in. So let's talk about the year, maybe 2031, 2032. I think it's going to be a very critical year. Yeah. Insane. And so- And Peter, I think we should note that while we're talking about quantum computers, there's whole worlds of quantum technology that take us beyond computing. Quantum sensing is one of those critical things. Quantum sensors are here today. We don't need air correction.
Starting point is 01:15:28 We don't need millions of qubits. They're here today. They're flying on planes right now, helping us to navigate when GPS is jammed, when GPS is denied by countries and by bad actors. They're right now being tested in hospitals to diagnose how your heart is given the magnetic field of the heart that is magneto cardiography, MCG versus ECG.
Starting point is 01:15:52 These are all the areas that Sandbox AQ is pioneering. That's right. Which we're going to need to come back and dive into those. We'll come back to it in another podcast. Absolutely, because it's extraordinary. I mean, quantum computers at this level in the next five, six years change the game. I mean, people feel like the world is going rapidly and disrupting and reinventing today with generative AI.
Starting point is 01:16:20 This is just the beginning, Peter, just the beginning. This is super exponential on steroids. I don't have enough superlatives to explain how fast it is. Peter, let's come back if we could in summary to one of the core points I hope we can have viewers and listeners take away from our conversation today, which is information. Information not in a generic sense, but in a very specific technical understanding of that word. The way that Claude Shannon understood that word, the way we understand it in a field
Starting point is 01:16:51 we call QIS, quantum information sciences. The way we understand information now in neural networks, where we're taking a large body of data and we're representing it by a smaller amount of bits. Marshall S. Lindenberg Learning. Stan Mallow Right. And that learning, that generalization leads to information that represents that larger data set that we started with in the first place. The same in physics, where we can take dynamics in the world, be it Newtonian with a rocket ship, quantum, electrons, molecules, and we can take very complex behavior and dynamics and summarize it
Starting point is 01:17:49 in a small number of pieces of information called equations, called dynamics dynamics and this fundamental ability of humans to search and look for summarization, conciseness, compactness, compression. This is fundamental to the breakthroughs that we're now seeing both in the AI world and we are now seeing in the application of quantum physics for the first time at scale in compute on GPUs that we've never seen before. This fundamental insight that information is the building block of our universe. This fundamental insight that information in this sense of Claude Shannon's entropy of information, and we can then quantize that into the quantum theory of information. Maybe in another podcast we'll have time to talk about that. This is fundamental to the human race will completely revolutionize our existence on
Starting point is 01:18:36 this planet and hopefully other planets as well. A beautifully put. Before we break away here, I have two questions. The first, simulation theory, yes or no? Are we living in a simulation, buddy? Let me say this, that if we are in a simulation, then the beings who created the simulation, kudos to them. They've done a pretty good job. I'll put it that way. Okay. Second. And we should also answer the Einstein question.
Starting point is 01:19:11 That's the second question. Okay, there you go. So it turns out Einstein, as a young man, read a book by Henri Poincaré, the mathematician and physicist, that in that book, Juan Carré put out a series of challenges, challenges about Brownian motion, challenges about light, challenges about how the world works. And it turns out that four or five of those challenges are the ones that Einstein decided to tackle as a 20-something,
Starting point is 01:19:44 sitting in Bern being a third class patent clerk at the patent office. The only job he could get due to his friend's dad who got him the job. This is what he decided to tackle, but he was inspired by this book. And for some reason, histories of Einstein often lost over why he wrote this on subject matters that seem to have nothing to do with each other in that year of 1905. So we have, we have Henri Poincaré. So just like David Hilbert did in the year 1900, Henri Poincaré in his book, very often contributions to our society can take the form of not just the answers, but the questions. David Hilbert put out a challenge in the year 1900 of key mathematical problems, some of which still
Starting point is 01:20:32 vex us today. The Clay Mathematics Prizes more recently do the same thing, but updated them for our mathematics of the last few decades. Henri Poincaré put that challenge out there, and a young man called Einstein took up that challenge. So I leave the listeners few decades, Henri Poincaré put that challenge out there and a young man called Einstein took up that challenge. So I leave the listeners with this, Peter, what are the questions we want to pose to ourselves as challenges to our colleagues around the world, to young people today, to our kids, to the next generation? Let's focus on the questions and not just the answers. our kids, to the next generation. Let's focus on the questions and not just the answers. I love it.
Starting point is 01:21:06 Ladies and gentlemen, none other than Jack Hittery. Jack, you are an extraordinary entrepreneur. I am so blessed to call you a friend. Thank you for all the work that you do. Thank you, my friend. Thank you, Peter. Great to see you. Love you.
Starting point is 01:21:19 Yeah, great to see you. Love you too. And let's do this again real soon. Take care.

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