How Quantum & AI Will Shape the World’s Future w/ Jack Hidary | EP #123

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 lqm people feel like the world is going rapidly and disrupting and Reinventing today with generative AI this is just the beginning theater everybody Welcome to moonshots today is an extraordinary episode with a dear friend Jack hit we're talking about the

intersection of AI and Quantum Jack's the CEO of an incredible company spun out of alphabet called sandbox AQ he a Brookland 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 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 to see you jack Peter good to see you my friend it's exciting times it it surely is you know 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 and importantly you're on my board at The X prise 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 you know transformed and discovered through this intersection of AI and Quantum sandbox AQ a for AI Q for Quantum go let's let's jump in here buddy it's Peter these are very exciting times not only for sandbox

AQ for you for me for X prise but for the human race we as humans uh are dealing with so many challenges uh the challenges that we often talk about in x-prize visioneering Gatherings and other uh Gatherings of folks 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 uh across the world as we as our population gets older in cancer some success stories in breast cancer for

example much higher survival rates now earlier detection understanding of the multiple subtypes of breast cancer yet in other cancers like pancreatic cancer nothing to show gbo blastoma 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 uh we all hope for a transition to a a cleaner more efficient energy uh posture for our world yet we've made halting halting progress at best uh towards that so a

lot of Challenge and the question is what are the tools at hand Peter that we can uh use that we can Marshall to address these challenges and one of the reasons I started sbox AQ is for Deep Impact at scale and you and I have always talked about that impact is good but scale is the lives of billions of people trans you know the world's biggest problems or the world's biggest business opportunities and that's what SRA and others had challenged me to take up as as we got going with sandbox AQ and 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 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 physics how does that relate how are these two things related well actually Peter there's a fundamental core nexus a wormhole if you will that brings us together between Ai and Quantum and that is that is both of them are modeling the world

around us both of them are taking huge swats 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 let's start if you will yeah let's let's jump in with the with the large language models and generative Ai and and and what does that mean how do you think about um the limitations and

what it's given us uh Peter let's dig into that so before large language models we actually had architectures of neural networks these artificial representations that are inspired ired 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 neuronet 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 artificial neural network and prior

to large language models we had architectures such as rnn's 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 uh and and so it was pretty good the the main drawback of our NS is they're super slow just not fit for purpose you cannot uh be uh 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 as you see people happening right now 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 demonstrate in this paper is that these new GPU architectures we could take advantage of the parallelizability took me 10 minutes to practice that word paraly ability of the GPU architectures uh in order

to actually get a lot of throughput to actually make this both the training and the 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 and so that's in fact what happened these these gpus the g& 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 kind of things and Nvidia people may not realize is actually

a 30y old company this is a company it's it's a 30-year overnight success let's put it that way uh and and so it's an exciting moment because in 2017 the marriage of these new architectures known as Transformers another way of of basic basically uh putting these artificial neural neural networks together combined with the power of these gpus really led to this revolution that we have with open Ai and anthropic and Google Gemini and meta Lama 3 3.1 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 uh I like to go back to 1943 a paper by Mulla and pits um 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 Mulla and pits but they realize 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 um you know architecture that you think of a van noyman kind of computer after Johnny V noyman you

see something very different and that's in fact uh was the beginning of these kind of neural network architectures but fast forward to we now have these things languages uh are now being uh 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 at it what's happening in in a lang 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 fall 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 memorize 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 you want that todler 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 kandell won a Nobel Prize for and many others did 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 the essence of hardness and the essence of bustness 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 We There are now models that actually will do that now because we've taken not only text but also video and images and and trained 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 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 4 500 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 yeah so so so that's the good news we found a way to compress all of say you know various languages down into this model the problem is there is no equation for the English language there's no equ 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 um 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 make some coherent and with enough training and you know 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 in that Corpus so so that's language models and the question is what else could we do with this kind of architectures the the architectes of neural networks and in general with artificial intelligence well let's think about the 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 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 into 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 in 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 those laws not a guess not uh

some paragraph in a textbook but the actual mathematics of this there are and I think this was the Insight right that brought about the creation of s box AQ there are laws of physics uh they go back a 100 years there are some fundamental laws of quantum physics and we all know Newtonian physics F equal Ma you know uh you know the learn to what we learned to describe the you know velocity of a cannonball shooting out of a a cannon but there are a series of laws of physics um known as sort of the

the quantum equations and I I just you know wrote them down cuz I want to I want to discuss him a little bit um you know sher's equation Heisenberg's uncertainty equation Plank's equation borne's probabilistic interpretation equations yeah was was the you know was the ability of computation to model these equations accurately what brought about the birth of sandbox AQ it was the 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 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 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

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are are very deterministic they're very clear you can write them out and you can send a rocket to the moon back in you know 1969 with the computational power that is you know found on I don't know I can't find a computer CL yeah to describe that um so those those have governed and limited what we've been able to model thus far right we can model Lar L chunks of atoms moving but not model on a you know subatomic molecular basis so speak to me about that yeah so this is a fundamental Point as you

as you mentioned you know back in the 60s we had the ability to calculate uh where that rocket would go and literally computers were doing that in those days what we call computers are human beings who are Computing right that was the initial computer was a human being and their job was called a computer like a lawyer does law called my lawyer these were computers doing doing that there were some also actual Computing machines helping out on the side and and the reason why they could do that with such small amounts of memory and compute

is because equations really are great compression Vehicles if I want to say hey that you mentioned a cannonball Peter I I have that Cannon bable shooting out of the cannon it's going to take this a parabolic uh type of pathway as it shoots out hits its uh Apex and comes down again and so I could on the one hand take lots of notes about oh here it is at Time Zero time one time two time three take lots of notes where it is or or instead of all that data I could just summarize it very

very succinctly in an equation where I have the starting parameters and then I can predict anywhere along the parabola I can tell you immediately where it is and where it's going to be so that equation with some of those starting conditions plugged in in the variables gives a very suc 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 if you have random numbers uh you know kind of in a grayscale image 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 in the neonian world again to send Rockets up to um understand the

Dynamics of cars even h Dynamics and uh fluid dynamics with airplanes and and testing in Wind tunnels or even virtual wind tunnels how the air flow will happen over that curve shape of the wing very complex Dynamics but that's still in the what we call the classical world the pre Quantum world and so now Peter you bring up all these interesting equations and 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 greates gave us Heisenberg and schroden even Einstein 1905 paper the one he got the Nobel Prize for Peter was not relativity it was the photo El electric effect in 1905 part of his Hest Moralis his miracle year that he had one of those papers and it was inspired people may not realize by the way a little SCI fun science note here uh in the moonshop 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 desperate set of topics I'll I'll leave it to the end we'll come back at the end and we'll find out why that is the case but um he did write the photoelectric paper uh and that led to his Nobel Prize but it was building on MOX plank 1901 19 00 gives the talk in the Prussian Academy of Sciences 1901 publishes the paper and uh really forever changes the world because what MOX plank realizes 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 crisis of the late of the late 1800s in physics the um ultraviolet catastrophe the the so many it'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 need to actually abandon that and move into a new regime and have a different view on how the world

worked and MOX plank would kick that off with a sense of how black body radiation working was again one of the key uh crises at the time Einstein a Young Einstein in his 20s read that paper and then wrote his paper in 1905 with homage explicit homage to MOX plank his senior and saying that MOX plank explained that for black body radiation I Albert Einstein will explain it for a photon a packet of light and and this ushered in along then with Schrodinger and derck and Heisenberg and of course Neil's bore all of these greats

who each one nob is um 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 of a rocket chip it does describe the rocket ship in fact because that's what's happening because 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 will 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 uh but they're particularly useful when we're thinking about the small we can generalize with Newton's Laws every time every place that's right that's right we can approximate us new 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 a much smaller format so we can manage it manipulate it and make some output of a generative AI for example in language or in images 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 a rocket ship going to orbit or going around the moon but now we can also compress something even more fundamental we can say what is the behavior of that electron and let's talk now about veence electrons I know it's bringing about nightmares

for our listeners into into high school chemistry but um but the veence 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 um Peter as you will 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 listen ERS today if God forbid someone has melanoma someone has nonmoral 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 the ability to use our own immune system to fight these Cancers and key to that happening is a molecule actually u a synthetic antibody not an antibody created by our own internal um adaptive immune system but one that we synthesize in um the the world and this antibod doesn't have the function that normally antibodies have of um helping us directly to fight a particular disease or pathogen what this does is it locks

in to a particular receptor known as the pd1 receptor on a t- cell on an immune cell it locks into the te- cell's pd1 receptor and protects that receptor from being hit by a tumor by a Lian by a molecule coming out of the tumor that normally shuts down te- 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 te- cells armed with this nice shield and goes into battle but to develop that molecule that would fit lock and key into

that um t cells pd1 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 pd1 receptor how they interact and now for the first time just in the last two three years we now have that ability this 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 in the world of bit but we are physically individuals and living in a 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 and we've been able to model them in classic computation thus far right but it's it's approximations and it's massively computer head so are you talking about being able to get to a a deeper level of fundamental modeling than we've ever been able to do with our computers today because we have had you know deep mind uh with Alpha fold um and Alpha fold 3 and most lately was it was

Alpha prodeo being able to help us predict um 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 uh in trying to model biological systems as an 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 confirmation 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 they're going to fold in a certain way and that folding is fundamental to the use to the uh application of that protein in fact when we have a misfolded protein that's a whole another ball game and that leads to diseases of all kinds right Stan PR 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 uh and other uh other organs as

well so when we think about the pred 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 um on what would happen and that's in fact you know how a number of the uh 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 in

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 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 sandbox AQ have

been able to model things at and that is a big breakthrough that means that we can have yeah that I mean that is 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 to head this division what 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 Creations I'm just super curious my my first area of focus was actually an AI as

you know uh I was applying AI decades ago now uh to brain Imaging as you opened up with and specifically to fmis to Dynamic brain Imaging Dynamic brain Imaging uh as readers may as listeners may know is not the same as just a static CT image you know just to put it on a light box let's take a look at it um you're talking about gigabytes and gigabytes and gigabytes of data taken over a period of time blood flow of your looking at blood flow for example where I'll give somebody a a test and ask

them to move their finger if they're moving this left finger here index finger this the exact spot is roughly about right here in my brain right now moving this finger this minute right now I can see I can see your homunculus right now yeah right there it's right there as we're speaking and listening to each other as we all know we're using broker area here number 44 to speak and then I listen to you Peter I'm losing waru's area back here um and and so you know when these when you look at these F images

the human eye can only see so much and so um my team and I began to train neural networks primitive ones at the time but neural networks nonetheless to see if we can glean more information from these uh medical images and sure enough we were able to do that and that shows the robustness of these this idea of a brain inspired neural network that even even with the very very primitive compute we had at the time building computers ourselves literally by hand uh we could we could actually make that make that work fast forward to

today um what one thing I realize 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 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 in 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 um all these interesting uh equations we needed to we to do that at scale and the conventional wisdom at the time Peter M was that we would need a quantum computer yeah 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 um mistakes that pop up in computers how does that happen well

actually muans cosmic rays uh can actually hit your computer and cause a bit to flip and so we have various error correction schemes in our phone in our laptops in our computers and our watches that allow for error correction for transistors for bits zero and one type bits it's not that hard because you know you can take you can take a vote if you want to of multiple bits and over represent the bit you want with many bits we have I mean 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 uh which is one reason why by the way you can flip the script and make a instead of a quantum computer you could turn it to a Quantum sensor maybe that we we'll leave that till another uh time in this in this uh podcast to talk about but but basically quantum computers are very sensitive to perturbation from the outside world and so they do need this this error correction and that is hard that error correction is hard Peter

and so uh we knew at the time um that it would take years and years before we' have an error corrected quantum computer let me pause you here one second because it's a really important distinction here for everybody to understand what sandbox AQ is doing because you're a 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 comput power yeah and by the way Peter when quantum 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 of course and Google is one of the leaders I was just at Harm's lab seeing their beautiful golden chandeliers beautiful work and they've just announced some uh some some great progress on reducing the error rates in quantum

computers and that's all fantastic ftic but I think the point here is that the same computational power in those gpus that gave us a large language models you've been able to build algorithms that you can use on those gpus to approximate or to solve these you know these classical equations of schroer and Neils bore and Plank and Heisenberg to help you model the actual world of atoms and electrons and ions today without quantum computers and that is going to give us incredible insights into the physical world um across Health materials environment everything Peter not not

will is giving us is giving us right now right now this is happening in real time uh and that's what's so exciting um and when you look at again coming back to the fundamental idea of information right of what does it mean to take a part of the world and represent it in an equation in a Dynamics in a 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 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 much shorter way using these equations given to us over 100 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 platform uh I'll give you a a molecule that we're thinking about that might hit this particular Target in the body maybe it hits Glo blastoma as an example this you know brain cancer horrific disease and um we'll take that we'll make a digital twin of that Peter and we'll make 10 million a 100 million maybe a billion permutations on that drug we add a bethl group that is adding a carbon and a few a few hydrogens We'll add a nitrogen We'll add an amine group We'll add this

we'll take that each one will be a slight variation on the theme that you initially started with B Sim 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 let's let's pause a fundamental point a fundamental point is fundamental point the the 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 of data that the large language models crawling it's outside the data set outside 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 you know these Quantum models can then assimilate and generalize so specifically Peter 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 of large quantitative

models lqm and lqm are about starting with equations to generate data that is one the 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 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 uh deserves a moment of silence yeah okay moment observe um and and

so this is it 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 they people wanted to doubt them so much they were so much even Einstein Einstein hated them he hated them God doesn't play dice he hated them he railed against them until his death in uh the 1955 because although he was one of the creators of it he couldn't comprehend given a classical view

that he held on to how this world could even be described by these equations ironically one of his best known papers 1935 known as e Einstein Podolski Rosen the three authors of it was an attempt to derail quantum mechanics as a science it end up becoming a Cornerstone of the science describing the phenomenon of entanglement but 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 but you know we've been stuck with lithium ion chemistry Peter for 40 plus years right and we actually need to kind of 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 an electric vehicle is the battery but batteries actually are going Way Beyond just electric vehicles the 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 and that Arbitrage that day trader like Arbitrage of buy when low and use when high right that is going to

impact the the world of energy uh beyond anything we've ever seen I'll tell you the one I'm waiting for is room temperature superc conducting that's I want to deliver us yes well this that's the kind of modeling 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 there's a marker that tells you whether you have Parkinson

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 is no so we need new buyer markers 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 for the inexpensive nature of solar in fact but perovskites coming perovskites are exciting but what's the problem perovskites Peter they're not stable to be bankable

to be financeable solar panels need to be have a 25-year guarantee a 25e life shelf stable roof stable 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 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 we want that to happen 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 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 body we're all optimists until that day when you have a pain in your side you go to the physician or 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 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 an AI enabled coronary CT looking for soft plaque Dex a scan a Grail blood cancer test a full executive blood workup it's the most advanced workup you'll ever receive 150 gabt

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 so if this is of interest to you please go and check it out go to Fountain life.com back/ Peter when Tony and

I 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 decomp will put you to the top of the list really it's something that is um 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 decomp 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 so back to the core question that you have the fundamental breakthrough now of realizing that um what's happening in the world of physics and this case quantum mechanics is that we're summarizing 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 model every electron in that model in that

molecule we're going to limit ourselves to the veence electrons that is the outer electrons and within the veence electrons we're going to limit ourselves to the business end of the molecule that might be hitting the actual Target uh 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 when did this become when did this become possible Jack when did it become possible for you to do this with the compute and the algorithms because we first had the Breakthrough 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 we we we have many Quantum Innovations the MRI machine is a Quantum in nature the uh the laser is quantum in nature lots of quantum we've used them but we haven't B to really predict and utilize them than at scale SC at scale on any arbitrary Quantum system and that's now where we've come to and that means that the world now has a superpower down a new superpower humans have a superpower this generation of humans is the first to have the superpower to do this at scale

on realworld types of systems every Quantum textbook in the world usually has a chapter 1 two and three describing the equations you just rattled off Peter and then has a chapter five or six that says let's use it on an actual case and what is the case one hydrogen atom one proton one electron now um as far as I know heart to cure cancer with just a 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 um 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 nanx a public company that does battery chemistry working on new materials for the US Army the US Army wants to lightweight uh the tanks car companies uh that may announce soon want to lightweight their vehicles so that they're more fuele efficient well that that's new Material Science we need new materials to make that happen materials are made of atoms and those atoms are have

those electrons that we talked about and so we've got to fundamentally rethink now way I think Material Science is is like the most underappreciated area of Technology right everything that is new and breakr I I I bow down to material scientists and the work that they do and this un notion of the materials genome right the idea that we understand the the fundamentals of certain limited number like you know a fraction of a fraction of 1% of materials that are possible right and we use them but given what the work that you're doing we're able

to expand this understanding that will head towards um you know fundamental across every industry is going to be transformed by this yeah and see Peter you're getting to 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 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 uh battery chemistry there's about 19 elements in the periodic table that you could in different combinations build a battery from for the electrolyte for the anode for the cathode for the membrane all these core for 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 how many could you

possibly build right even if you're Willy Wonka and have oal loomas around you're limited to the number of batteries you could possibly prototype but now yeah 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 cult toac uh to use a more Suburban term for minimum or maximum but yes yes exactly right so let's talk about what the implications of this are

um our favorite subject you and I both of Health and Longevity and you know reminding people the way that we've discovered drugs in the past we'd go into the Amazon we' chop down bushes and trees and dig up dirt and we'd crush them up and we'd try and find unique molecules and we test them molecule at a time and that led to today's devastating drug industry which is riddled with failures you know what do you what do you hold is the average drug development time and cost a decade and and $3 billion dollar exactly right

now it's about 7 to 10 years of preclinical work that is developing first the target you got to start with a Target in the body what are you going for what you got to validate that Target and then you've got to drug that Target you got to design a drug that fits like lock and key into that Target that's about 8 to 10 years then you go into Clinic even with and and God bless the FDA is actually done a pretty good job trying to compress down the phase one phase two phase three trials you

often can do a pivotal Phase 2 now where the phase two becomes the in a sense like the pH sufficient data to give you an approval by the to get out there there's breakthrough path you know uh uh Pathways for drugs now particularly for Orphan drugs and sorry sorry particularly for Orphan diseases uh and and rare diseases and and so when we think about uh the time it takes 8 to 10 years preclinical four 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 a 100 drugs that go into clinical Phase 1 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 whatever it might be you expect that that drug works for you but do you happen to know what percentage of uh people that drug words prescrib cribe for them actually works please it's like 20% wow the 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 is approving a drug if it helps a sufficient number of people not everybody yeah and so you you know I think it's it's insane but the hope now is not a drug that statistically might work for enough people for 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 2 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 population to then advertise and pay back the cost of that which is why the drug costs are so extraordinarily expensive exactly because also the successes the few successes have to pay for all the failures but now let's talk about a world let's talk about a world where it costs

one tenth the amount to make a drug 300 million instead of three 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 sliver of the demographic of the population that has this particular genome sequencing or other characteristics and we

could start to get to that future that you just described that says instead of having one size fits all of drugs that end up not fitting many people at all we can now start to really understand how we could make these more targeted drugs that really match well with key cohorts out there in the population now we've been doing that in part right so we've got companies like in silico medicine we've got Alpha proteo but again they're using classical computers and AI large you know modeling um how far are you from using using you know

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 and clinical expenses and all 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 you're a software company that is supporting a multi to different Industries right exactly and so that allows us to now have the worldclass talent that can do this we have Quant physicist 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 them 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 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 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 biofarma community it's being used by the chemicals

Community by Dow Chemical by uh others and uh 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 do be the best at in the world what are you going to be the best at in the world 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 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 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 in it 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 the 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 cytic that kills scile cells in your skin and this literally reverses the age of your skin and I think it's one of the most incredible products I use it all the time uh 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 talk about the future of of clinical trials here because I think you

know I've heard you um uh sort of visioner this notion of running in silico human trials that drops the cost not by 50% but you know basically a thousandfold eventually so that you know when you introduce this drug into humans because it's run in Quantum models because it's run um in simulation that you know it's going to work just like the first time you know the 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 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 um and and so if we can go in right now the 90% failure what does that tell us Peter the 90% failure in clinical trials today it tells us and by the way this is 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 and phase two and you know show some safety and efficy but then at the end of the day it didn't help enough people exactly so 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 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 right if you look at um you know a Sterling engine one of the most efficient you know things on this planet not a lot of value you can add to a Sterling engine right but if you look at the 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 veence 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 um with carbon say being 12 doton so small molecules as you know Peter 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 and so this is the kind of work that will ultimately lead us to a much better 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 because while while you're on a rocket ship um 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 you're going to the Stars so quantum computers have been around for a little bit and as you said we've got dozens of companies and they their CU bits 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 uh for sandbox AQ to use yeah great question and you talk about they've been around for a little while let's be more specific Paul benof yes a cousin of Mark benof Paul Ben want to give some Kudos out to Paul benof 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 um history of quantum Computing gloss over him and

talk about Richard fan yes fan did popularize the idea of what a quantum computer can do and we owe that to to fan for helping get the idea out but I do want to give some credit to where credit is do with Paul Benny off for having kicked us off and one interesting History of Science question is Johnny Von noyman we mentioned it before in Van noyman architecture is John Johnny Van noyman 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 we still we're still gonna come back to to Einstein's five papers before yes we're gonna come back to that as well so we have many um it's good to give answers on this podcast but also to plant questions with the audience as well um so so back 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 you pointed out we can build them either with natural cubits or synthetic cubits a natural

cubid 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 a neutral atom not an ion so it's not uh an ion trap computer but it's neutral it's not doesn't have any charge you manipulate it with lasers several people won Nobel prizes such as Steve Chu and others for showing us how to manipulate various entities with with lasers we near absolute zero exactly we use those techniques to set uh the atom

which now becomes the Cubit into a certain State and again let's remember that Quantum bits or short cubits can be in the state of zero or one just like the transistor could be in 01 a bit can be 01 but they can also be in superpositions in combinations of zero and one and that gives us an infinite palet to draw from and that Cubit we can say is going to be some part zero some part one or a third this and two3 that we can have different combinations and everything in between and so we represent

uh these cubits in very different ways than just the normal transistor and so that neutral atom we can manipulate into one of those States uh we could read that state and then we could operate on that state and that's what's critical to a quantum computer the ability to initiate a state on a cubit operate uh a set of operations on those States and then at the very end read it out uh at the at the very end and 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 uh Quantum 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 compartment of gas um let's say rubidium uh 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 to need to do the error correction let's use for the sake of this convers a ratio of a

th000 to1 a th000 physical cubits to one error corrected or logical Cubit right a thousand rati really really important for folks to to recognize because you hear about all these quantum computers that have 100 cubits 12 physical cubits exactly Peter right they're not the functional error corrected cubits yeah corre and when you so so where are we today in this in this race So today we're at you know few hundred of these um physical cubits uh really uh some papers have claimed to make one logical Cubit but we're really not at the point of having

a set of logical cubits of error corrected cubits that we can really manipulate at this time now that will change very rapidly if you look at the photonic side using photonics that's another promising approach exemplified by squantum uh both in California and Australia and also 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 uh panjan way the leader of the quantum uh program in China himself is a photonics oriented physicist and so that was the first

uh quantum computer my favorite my favorite science fiction my favorite science fiction stories are always about you know massive quantum computers buried under Beijing that brought about AI super intelligence and yeah yeah well there there are quantum computers deep deep inside these universities that are run by panan way but they're not in Beijing they're about two hour fast train ride from Shanghai uh in a in a different place but yes they are they are there um but in any case uh so you have photonics and one of the advantages that the photonics people will tell

you is that we can mass-produce these cubits using silicon photonics we can use some of the same technique of the semiconductor industry in fact site Quantum uses Global foundaries uses one of the big Fabs out there to mue this by hopefully the millions 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 we're

g to crack right in and and break the chain um and and if you want to do that with either RSA which has been around since 1978 since RS and a Raves Shamir and Adelman uh gave us RSA if you want to crack ECC elliptic curve cryptography so uh blockchain you mentioned blockchain uh Bitcoin you know ethereum these are all based on either RSA or or uh ECC if you want to crack those but more even bigger than just blockchain every ATM uh transaction every wire transfer every e-commerce using a credit card on Amazon every

single transaction every WhatsApp when you 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 cubits maybe we can get away with 4,200 but let's just say 5,000 error corrected cubits which using our thousand to1 ratio Peter let's go back then and say five million physical cubits so we're at a few hundred physical cubits today and we're going to need five 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 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 I would say that minmax yeah I would say that by year 2029 uh which is is very you know only 5 years from now we'll start having the building blocks of about a th000 to 5,000 physical cubits in like these modular Lego blocks and 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 cubits 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 could 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 um 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 th logical cubits and so amazing now of course 2029 is when Ray is predicting you know whatever AGI is and and of course the reality is you know we're going to be using some variations of digital supercomputing to help us build these quantum computers and then they'll you know those digital super computer or digital AIS or super uh AIS will become resident on these it's an exciting five years ahead and Peter just to finish so that'll be like that'll take us about five years then it'll take another two three years

of the engineering to put all that together make it error corrected 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 uh insane and and and Peter I think we should note I think we should note that while we're talking about quantum computers there's whole worlds of quantum technology to take us Beyond Computing Quantum sensing is one of those critical things Quantum senses are here today we don't need error correction we don't need millions of cubits 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 uh Bad actors uh 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 so these these are all the areas that sandbox AQ pioneering that's right which we're going to need to come back and come to it those absolutely because it's it's extraordinary I mean quantum computers at this level in the next five six years um change the game I mean

people feel like the world is going rapidly and disrupting and Reinventing today with generative AI um this is just the beginning Peter just the beginning this is a this is super exponential on steroids I have no I have enough don't have enough superlatives to explain how Peter let's come back if we could in summary to one of the core points I hope we um 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 we call qis Quantum information science es 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 learning right and that learning that generalization leads to um 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 the rocket ship Quantum electrons molecules and we can take very complex

behavior and Dynamics and summarize it in a small number of pieces of information um called equations called 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 um in this sense of clud 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 that the human race will completely revolutionize our existence on this planet and hopefully other planets as well 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 um let me say this that if we are in a simulation then the um 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 now we should also answer the Einstein question that's the second question okay there you go so it turns out Einstein as a Young Man read a book by HRI Kare uh the mathematician and physicist that in that book Juan Kare put out a series of challenges challenges about brownie in motion challenges about light uh 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-some sitting in Burn being a

third class patent clerk at the patent office the only job he can 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 loss over why he wrote this on subject matters that seemed to have nothing to do with each other in that year of 1905 so we have hre Punker so just like David Hilbert did in the year 1900 hre Punker in his book very often contributions to our society can take the form

of not just the answers but the question s David Hilbert put out a challenge in the year 1900 of key mathematical problems some of which still Vex us today the clay mathematics prizes more recently do the same thing but updated them for our mathematics of the the last few decades HRI Pon put that challenge out there and a young man called Einstein took up that challenge so I I leave the listeners with this Peter what are the questions we want to pose to ourselves as challenges to our um 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 I love it ladies and gentlemen none other than Jack hit 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 great to see you love you too and uh and let's do this again real soon take care [Music]