Shoucheng Zhang: "Quantum Computing, AI and Blockchain: The Future of IT" | Talks at Google
Thank. You so much dye it's a great pleasure, for me to come here to Google but, also a special, privilege, to be introduced by daya of a friend but also we. Have been constantly exchanging, ideas and, today. I'd like to talk to you to talk to you what about I view, as the, three frontiers, of information. Technology, for the future quantum. Computing, artificial, intelligence and blockchain but, especially, also, the possible symbiosis among, these three major trends I think, in these days in the world there are many experts, in each one of those, subjects. But I think really exciting. Opportunity, is possibly, the conference, or the symbiosis, among. These three, major, trends of the future of the information technology let me starts with a story of the recent, scientific, discovery. A recent, discovery but it had a long history so a. Lot of great discoveries, in. Science also. Relates to some deep changes, in philosophy, we, seem to live in a world of opposites a world, of dualism, we. Have whenever, we have positive numbers we have negative numbers when we have credits we have deaths we, have being and young good and evil angels, and demons, but. In the natural world there's also a counterpart, to these philosophy. Of the opposites, or the duality, so in, 1928. The, great and perhaps one of the greatest theoretical. Physicists, of all time, Paul, Dirac was. Trying to unify, Einstein's. Theory, of special relativity, with. Quantum mechanics in. The process of doing so he was doing some mathematical derivations, he. Had to encounter, operation. Of square, root and then, he remembered from, his high school days that, the square root of 9 is not just 3 because, 3 times 3 is 9 but. Also minus, 3 because, minus 3 times 2 minus 3 is also 9 so, whenever you take a root you, have to take both the positive, and the negative roots, at. That time was very perplexing. What that negative root means and, he actually. In one brain stroke, of genius, he, predicted, that for every matter in the world that's the opposite, matter or the antimatter and, so, when you visit Westminster. Abbey you can try to find the PAC commemorating. The famous Dirac, equation. Until. 2012. One of the most humbling experience. In my life is to receive the Paul Dirac a medal. So. Just I said whenever. You take the square root you have the positive branch, and a negative branch and he, brilliantly. Interpreted. The mid-, branch, to. Be a universal. Law of nature that, for every particle there's, in the universe there's, also a antiparticle. Except. At a time everybody. View this as a beautiful, equation, but. Except at a time of 1928. Where, he made this prediction. There, was simply no antimatter so, for example the antimatter of the electron, will be something that has a positive charge but, there's a mess the, proton has the opposite, charge to the electron, but has 2,000. Times more the mass as, the electron, so, nobody believed him then. You know what he said he said my equation, is so beautiful, you guys simply just go look for it and. People. Did and he was lucky and five. Years later in cosmic, ray radiation. It's very hard to naturally, produce their town Earth but in the cosmic ray radiation, people. Discovered, antimatter, namely the positron, which has exactly, the same mass but the opposite charge of the, electron so, I think this is the one of the greatest. Prediction. Of all humanity. That. Something, conceived of beauty also turn out to be true. Today. We actually use this antimatter, in medical, devices a famous, medical imaging technique called PET scan positron. Emission, tomography was. Actually based on this anti particle the, positron. It. Also captured, the imagination, of Hollywood, so, there's, the, famous novel, and the movie of, da Vinci Code many of you have read the book and saw. The movie but there's also a sequel to it's called, Angels & Demons also played by also, a book by them prompt I also played by Tom. Hanks, basically, the novel depicts the. Epic. Struggle, between between, angels and demons culminating. In the halation, of particles, and antiparticles, so, actually it's the highest, information. Density one can possibly, achieve anywhere. In the universe if, you have antimatter, and which, matter the energy they release is, the most powerful they can ever, be but, it's also a fun, analogy.
Just, As we have NGO we have daemon whenever, we have positive a particle, we have the opposite. Antiparticle. But. Human, curiosity. Didn't stop there so, after the rocks prediction. Viewed. As one of the greatest, prediction, of all time, curiosity. Didn't stop there so there was a another, great theoretical. Physicist, but somewhat, elusive. During. His time, named, Ettore, Maya Rana and, he asked a curious question could there be matter which doesn't have and he made or a particle, which, is its own antiparticle. Particle. Which would, not have its own I will not have antiparticle. Is its own antiparticle is, that possible. So, he asked this question, and he also wrote down a beautiful equation which. Described it but. This time he was not so lucky nobody. Believed, him and nobody found it so. He. Actually got very, disappointed. About that, so, everything's, then it became a mystery, in fundamental. Science so we have in, fundamental. Science a most, wanted list for example, at the list included, what is called a God particle, or Higgs, boson, but in 2012, it. Was discovered, inserting. The laboratory, in Geneva there's. Also the gravitational, wave Einstein. Was less lucky, than Dirac Dirac, only, his, prediction, only took five years for, it to be experimentally confirmed but. Einstein's prediction of, gravitational. Wave took more than 100 years only. Two years ago was discovered, was, Einstein, predicted a 100, years ago so this is such a list and and, also something called the Dark Matter particle which, we still try to find but, also, very, much on the top of the list yes this. Very interesting. Concept of my, araña Fumiya which is a particle which does not have antiparticle. Or is its own antiparticle, but. It's more mysterious maybe, among all those on. The most-wanted list, maybe my Rana for me is most mysterious because, not only my runner Lamia has not been found like. I said he was very disappointed, when nobody believed in, his prediction. And he. Was Italian and, he boarded. A ferry from. Palermo. -, from. Naples. To Palermo like he never reappeared from. That very right so, you become a deep deep mystery, and this, year is exactly, the 80 year of his disappearance. But, we also have some good news to report even. Though he, himself was never found his, particle, now has been found and that's the highlight of my. Talk. Today so, so. Then because, he simply wrote down the equation but so he didn't tell people where to find it so that's why it took 80, years so, nobody knew, where to find them but, my theory, group has stanford predicted. Where, and how. To find this mystery particle, and seeing. During. The. Period of 2010. And 2015. Or. Theory group wrote three, theoretical, papers first, one exactly, to predict, were actually. Quite. Surprisingly. It's not true for this particle to be found in some huge accelerators. But it could be in a tabletop kind, of experiment, very, much like a semiconductor. Device people will usually use so, it's a material called a topological insulator. That I already mentioned introduction. Something I discovered. Ten years ago but, they put, it into it some magnetic dopants. Also the topological insulator, can be something that business theory right and there, you can put in some magnetic dopants, which, could be chromium, and, then on top of it you apply a superconductor. So we predict that in this system, you can find these mysterious my runner phobia, but, that's not good enough not only you have to predict, where to find it but to what to measure in order to find it and there, I think a common sense can, even guide us so, somehow the. Regular particle, is like two sides of a coin whenever. You have the upside you have the downside, whenever, you have the positive particle, you have the antiparticle, associated. With it but, it's my Ronna particle. Is only, only. One side it is only a particle but no antiparticle. So in some vague sense it is half of a usual particle, so this concept, of 1/2 would be very very important, in the, later part of my talk about, quantum computers, so, somehow this Marana, particle, is half of a regular particle, so, but regular particle has some phenomena, of their conductance, like the resistance, or conductance we, usually, measure can, be quantized, in units of, 0 1 2 3 and, so on so, they behave like integers, in. Quantization. As depth so, we. Once had had. A, Eureka moment that, if the Marana particle, is in some sense half of a regular particle, then, they should display, some plateau at half, integer, steps namely, at 1/2 3 1/2 and so on and so forth so, that became, our prediction, that in. This system, you can experimental. A construct but what you measure is this 1/2 step. And last, year in a close collaboration, with experimental. Colleagues. At UCLA, UC. Davis and UC, Irvine so, they exactly, constructed. This system, as we erect, a proposed and they, perform the measurement exactly. According to a theoretical, prediction, and lo, and behold, besides. This, integer, step at 1 something.
At 0 you, see there's a step at 1/2, and this 1/2, is a crucial, idea that, my, Arana particle, being half of a regular particle, you should display was regular, particle display, integer, quantized step my, runner particle, should give you half quantized, step, so, that is really the smoking. Gun it was, celebrated. Last. Year with the publication, in the science magazine, so. In that very exciting, moment I remember, the famous novel. And famous. Movie, I saw, about angels, and demons and I, proclaimed, that, is as if we discovered, a paradise, with only NGOs and no demons so, I call this the NGO particle. So. Now what is it good for, so today. Or. Classical. Computers. Are already very very powerful. But, they are good at doing some things and not good at doing some other things so. If I give you very two very large numbers, and ask the computer, to multiply, they do this in a split, second on Google couch you, were maybe a nano nano a second, but, if you give a number and to ask the computer with a dead number effect arises into, two other numbers giving. The example for example 15 is equal to, 3 times 5, but 11 cannot, be vectorized as a product, of two numbers the only thing you can do just to say 11 is 1 times 11 which doesn't, mean very much, but. Then if I give you a very very large number and if you want to ask whether that were in the large number, it. Can be expressed. Just like 15 as, the product, of two other numbers or it is more like 11, which cannot be expressed as, a product of two numbers the, computer, the, classical computer will have a very very hard time to answer this question the, only way it can do is to, do an exhaustive search it tries to divide this very large number by first, by 2 then, by 3 then 5 by 7 and, so on so forth and then, it takes forever to to, - to, do this exhaustive, search so. What you live do you think about maybe, all of the most important, computational. Problems what we were like a computer, to do with, Google cloud with all the data what, we would like to do is to find some optimal solutions, or something so, when we try to find optimal solution we basically have to enumerate all, possibilities. Computer. All of them maybe there's some function. Optimizing. Function associated with it and you try to find maybe the least path or biggest. Profit, or something like that but. You also have to do an exhaustive search, and that takes very, very long time so, that's why computer. Has a lot to advanced, but. Then enter, the function world what, is the mysterious, world of the punctum world. So. If I have two, slits and I. Use a classical. And to randomly, shoot through these two slits then, obviously a bullet either at, one given time goes through the right or it goes through the left and, on, the back of year two you will see two blobs one, coming, from the right and the other coming, from the left but. Not so if you try to shoot elementary. Particles, through the double slits so. Somehow on, the backgrounds, you don't see two blobs associate. Was the right or one associated was the left you, actually observe a rather intricate. Interference. Pattern. And that, pattern can, only be explained, if, the particle, went through double slits at exactly, the same time it. Went through both the right and the left at exactly, the same time if it didn't do so and if, you knew which, way it went, it wouldn't, lead. To this intricate, interference. Pattern so, somehow the, quantum, worlds the, mysterious, quantum world is, parallel, at one, given time a particle, is both going through the right and going through the left and. Then, people, somehow, started, thinking that's this very difficult problem the, computer, classical, computer has a very difficult time to solve namely. Has to go through C, really it's an exhaustive, search of all possibility, maybe, it can be done by a quantum, computer which is intrinsically. Parallel, so, basically then it can search through all these possibilities, exactly. At the same time and give, you one results, in one step. Of computation, so, there were truly truly be wonderful, and will increase, computational. Power in, such a tremendous, way, so. But in order to construct, such a quantum computer you, first need to have the basic, elementary. Unit which, will be called a quantum bit or a qubit. So. A classical, bit as you have on your classical, computer. One. Bit it's either 0 or 1 but. Just like a quantum, mechanical particle. Can go through double slits at the same time a quantum.
Bit A qubit, somehow. Is a linear superposition between. 0 and 1 it's neither exactly, 0 no, exactly, 1 somehow he lives in, this mysterious. Superposition. State, between, 0, & 1, so in order to do a quantum, computer you need necessary. Have to construct, such, a elementary. Qubit, a quantum bit but. Being quantum mechanical, is also very very fragile in the classical, world if you are very curious to say wow is it really zero is a really, one you try to observe it they immediately, clutch two zero one and you loose this mysterious. Quantum, concept, so, therefore in, all the most of the approaches, that has been proposed, to. Construct, a quantum computer it's, has, a lot and lots of arrows these qubit, it's very very fragile and very unstable and, it's. Very easily collapsing. To a classical, qubit. So therefore it's a it's, a daunting. Number that for one use for logical, qubits you have to use ten to even, perhaps, 100. Error correcting. Bit to, correct a, one, use for qubits, and that's, obviously, it's very very, very difficult, to scale and that's why we don't yet have a truly functional, quantum. Computer, yet, which, can factorize a very big number now. Enter, my, scientific. Discovery. So we discover this mysterious, a very, interesting angel, particle, which, is half, of a regular particle, so, then for, so it's a little bit complicated. Scientific, diagram, but, somehow when you enter, in with one qubit, which is a regular particle, it can, be immediately split, into two, of this Marana vermeer or these, angel, particles. So, then each being, half so one, qubit you already think is the minimal thing you can have but, one qubit, is now stored, in to Angel particles, so, just like one qubit entering here it's partially, start here and partially stopped there, then. If you have local perturbation. It's, very hard for local, perturbation, to, destroy. The, global, these. Two and your particles, together function, as one qubit so, it's very very hard for local. Perturbation, to destroy, this qubit and therefore. It's a very very robust way of doing computation, in. Fact in this experiment, measurement, what, is happening is that this angel particles, are reading with each other so, if you have some, lines and if you try to braid them that, is kind of a digital operation, if you either braid it or you didn't, whereas, in order most other approaches, to quantum computing it's almost an analog, computation. It you can make very easily make little, errors but, if you do what is called a topological operation. Of braiding, then. Then. It's actually very very robust, so, you now approach one.
Qubit Is just one qubit you don't need error correcting qubits, so, these are still after, our discovery, it's is still kind of a new approach so, it's coming, up but, compared, to other approaches. Which may, already have many many qubits but a lot of them are serving as error correcting, qubits, to one useful qubit, I believe, or approach were eventually scale, are much much faster because it's one-to-one. So. This is the. First part of my talk about, quantum, computer but, now let me switch to the second part of my talk, which, is about artificial. Intelligence. When. We look at the human history, it is or. It. Has a long kind. Of, Earth. It, took a very long time for the most intelligent, species. To. Develop on, earth and it took maybe three million years of evolution, but. Finally we became the dominating, species, but. Now we actually face was so our challenge may be a more intelligent species. Namely AI could. Be some, emerging, but, era has been developing, maybe since the 60s so why we. Suddenly have this, such. Rapid, increase, in the, progress, of AI, so, it's Mason basically. Due to the conference, of three, major trends in. Computation. A1, is the most law so, the most law basically, is about computational. Power so, it is it. Doubles, the computational. Power doubles, every 18 months according, to the progress of the most law so, now most. Law is facing, some challenging, that's the bad news but, the, good news is that too maybe we'll have something so, much more powerful, when, then, the most law predicts. Namely, we have Moore's, law has being a quantitative. Incremental. Increase even though it's very very fast but, Ponton computer, can be one quantum jump in the computational. Power because, of this massive, parallelism. Associated. With quantum, computing so on the horizon, were has see both chanting the in terms of computational, power we see both challenges, to the classical, Moore's Law as the, device gets smaller and smaller but, we also see tremendous hope maybe, quantum, computer can can arrive, at a scene. And so when you try to search among. Optimization. Problem you came to one, search for. One rather than a exhaustive, search in, a serial fashion. So. This is something, on the horizon that could really, fundamentally. Be a game-changer but. The other reason why artificial, intelligence, today is exploding. Is because, was the arrival of the Internet and the Internet of Things you, provided. A massive, amounts, of data. And. Machines. Need to learn and they learn only from epic, Terra and. The other is the rapid progress, of the AI algorithm. And this, is, also one. Of the main reason for example the deep neural Nets which, is providing. The main kind. Of engine behind this rapid, growth so. In the field of AI we, always ask this question, when would someday AI surpass, humans, and what, is the objective test so. We're. All. Totally. Amazed so to see the progress Google, has made. Announced. Two years ago about, deep, mind having. Alphago, which, beat a human player in, playing the game so. Aunt I was very fortunate that. Our son Brian. Was also at. Working, at the deep mind these. Kind of projects at the same time at that time so. When. We asked this question so I'd like to revisit a question, that we always have been asking namely. The so-called Turing, tests when, is the objective, tests, that, AI really, passed the human mind so Turing. Proposed, the following test long time ago he says that if we have a human and. Then we're having, a conversation with. A. Something. Behind a curtain either, another human or a, a I machine, and if, you talk for one long, day and afterwards. You cannot tell the difference whether, it's a human behind or whether. It's a machine behind that. May be the day when a I really, reached to, human, intelligence. But I think it's not an objective, test, so first, of all because, the human brain it took a long long time to evolve and a lot of these human. Brain. Has a lot of irrational, emotional, components, and maybe. It can not be so imitated, by the Machine maybe also totally unnecessary for. The machine to imitate, every. Human irrationality, that's. Possible, because, one. Strategy is you talk, to the machine in totally, irrational way, maybe, a rational, machine will be very hard to food a human head to to, see that it's actually a human so. But then what about the Google's, success a deep mind of our goal which, is a game of human and looks, little bit more objective but, still it is a game invented by humans, why should. Intelligence. Test be based. On a game that's, invented, by human so what will be the most objective, test that AI really. Reached, human. Intelligence so. I like to have a proposal, which could possibly replace the, Turing, test and, then, I asked her to play.
A Game of nature namely, ask the machine to, make a scientific, discovery and before. The humans do and maybe. And then we can objectively, such, as a prediction, of my, honor firm young gravitational. Wave some, of the greatest prediction. Of the human scientific, mind and see, if the machine can make a prediction, before. The humans do and when were to an objective experiment. And verified the prediction. We, say this is the day when, machine, surpassed, human, intelligence. So. Can we see whether this is possible or not so I am. A usually. A theoretical, physicist, but I for, the first time I wrote. Rai. Which. Will soon be published so. Basic idea is that let's pick so, first of all we haven't made the progress of making a prediction that humans has not made but. We are idea still be winder history to say that, if humanity, is still at a point where, one great discovery, hasn't yet been made whether. The machine at, the same level, can make that scientific. Discovery. So. We know some great predictions. In theoretical, physics such, as gravitational, wave, Dirac. Antiparticle. And so on but, maybe the greatest scientific, achievements. In chemistry, isn't Mendeleev's. Periodic. Table, so Mendeleev. Looked. At all the chemical, compounds, and he, discovered, in. A brain stroke of genius, the organizing. Principle, of the world namely that, the order materials, that, we see can, be reduced. To. Elements. But these elements organize in itself into. A periodic, table so at that time he, there's. Only some. Limited number of elements, discovered, and whence, he organized, them into a periodic table he, sees some host in the periodic, table and he says oh these elements, must be there you guys look for it so, there was the brilliant, prediction. And I think certainly I will rank this as the greatest, scientific, discovery, in chemistry, maybe of all humanity. So. The question we like to ask ourself is that if we rewind, history that. We. Are in the stage that, periodic. Table has not yet been discovered but. If we feed all the, chemical. Compounds, to a machine what, machine be, able to come up with the discovery, of the periodic table so, that's. Maybe is quite related to all the AI work that's going on at Google. And. We actually call or. Algorithm. At Inuvik so. Once you see the name you immediately see that there must be a lot of connection, to maybe. All the work you guys are doing here namely. This or, the Google Translate, or the natural. Language processing is. Based on a algorithm, called, words to, two, met words, into. A vectorial, form and once, you map words into a vectorial form you, can understand, the. Machine the vector actually encodes. Some semantic, meaning of. The word itself and then, it can discover certain relationships, so, hard as were Tuvok work basically try, to understand, a word in, the context, of other, sentences, if, two words, often occur, together like, king and queen in one sentence the machine will understand, maybe in bacterial, space they're, somehow close to each other so our, idea is, to borrow, this, kind of idea from, the natural language processing and, try to see if it is possible to be used to, make scientific, discoveries, so. We're basically just, like Google. Here would feed all the corpus. Of, texts. Into a machine using, word to back and then discover the meaning of the words and then do translation, and so on so forth we basically feeds, in a totally, unsupervised. Way or, the, list of all chemical, compounds, to, the machine and to see whether the machine can come up with the organizing, principle, and lo, and behold the. Machine or, algorithm, discover the periodic, table because, the periodic table can be viewed as nothing but a tool dimensional. Vector ER arrangements, of other elements but if we can do something like atom, to Veck it, will all similarly, map each, element. Into. Some, some. Vectorial. Form and the, when you collapse this to two dimension, you will exactly, discover, the periodic, table so for example, like. Let's seeing a large corpus, of text, whenever, you see King you see Queen a lot the co-occurrence, a lot but, in, chemistry whenever. You see NaCl. You see KCl, a lot so, you somehow, the machine will understand in a and C and K may be very related, to each other, so in factorial, space there, must be close to each other so based on by borrowing the ideas for natural language processing we. Actually could organize. Its them in totally unsupervised, fashion, the machine actually discovered, the periodic, table so I think we're getting into a very very exciting, time there. That's one. Of the greatest scientific, discovery, can, at least be replicated, by a machine discovery, without, any supervision, whatsoever, but. Once these algorithms. Start, to work then, we can use it to discover new materials, and possibly. Be a, user to discover, new drugs before.
The Humans do. So. Now let me move to the third. Stock pic of my. Of. My talk today and namely, about the blockchain and maybe some, of you are already wondering, what AI, in, quantum computing and blockchain can possibly have, anything in common with each other so, basically, the Internet has always has, provided a tremendous value in. As a communication. Tool to, to. For all of us to communicate, but. Then we have to at some point we have to exchange values, over the Internet but. Whenever we have to exchange value over the Internet we have to agree on a common standards, of value, so therefore the most important, thing when, you try to move to the next stage of the internet development, possibly. Moving into the world of finance for example. The. Key essence of finance is to, have, some consensus. About a, value the, reason why we use goat previously. Is because, compared. To something like Apple, as, a medium of exchange it's because everyone can agree on what one ounce of gold actually means we, can do position measurement to determine. Its content and quality but, it's very hard to do it for one Apple, because there's so many different kinds of apples. So it's not suitable, as a medium, of exchange so, therefore the, key element. Of a, medium of exchange is, consensus. So, if I have very broad distribution, about. The value then it's not suitable to use as a media exchange if we all agree on the value reaching. Consensus, then, it is extremely. Valuable so. The internet taught us one very, important thing is to namely to do things in a distributed fashion but, if they have a very distributed, network how can they possibly agree, on something, so, previously, in human economy, we always thought there has to be some centralized, entity which, is to control a lot of it and get people to agree some. Values, but. When you actually observe the natural world there, is a way for the natural world to reach consensus, so let me give you one example out, of physics, for. Example where. Every day when you walk up and. Walk towards your refrigerator, to get a glass of milk or something you, people usually like to stick a magnet on their, refrigerator so, how does a magnet, really, work so, actually, all materials. Consists of electrons, and electron, works like a compass, it has a North Pole and a South Pole so electron, actually works like, a methods, but, the most of the time they, don't agree on the direction to point to so they all pointing in random directions and, therefore, globally. Macroscopically, they, don't behave like a magnet, but. The magnet their sticks on your refrigerator, somehow. Miraculously. A consensus, has been reached or electrons. Decide to point in the same direction and, that. Is happening without any centralized, entity telling, electrons with what to do somehow, there's. The mechanism, of protocol. Of exchange, somehow, they, miraculously agree. To a point one direction. So, details about, something. Very very profound about, the net natural, world to agree. On something, is what is called a low entropy state and to, be disordered is in a high entropy, state the. Natural trend of the world is to gradually. Always, the entropy has to increase over time the world always becomes more more disordered, but, somehow in a subsystem you, can actually reach hung high consensus, reduce, entropy, but then the Caesaria has to there has to be a cost you, have to dump the extra entropy, somewhere else so, it's consensus. Can't happen in some, self-organized. Distributed. Way but, there has to be a cost associated, with it that seems, consensus, is a state of low entropy you have to dump the extra, entropy somewhere, else yet, I think is the fundamental explanation, of, why blockchain, is working so, blockchain, has distributed, the world of computers. And the early approach to, have managing, a distributed, system of computers, is to ask whether, there's some centralized. Master algorithm. Deterministic, algorithm, possible, which, will coordinate, and. And. Direct. All these distributed. Computers. Even though some of them have. Very long latency very. Broad distribution of latency and some one of them can't, even be hacked and behaved maliciously, whether, this is still in all these circumstances. Master. Deterministic. Algorithm, possible, to tell all these computers. Exactly. What to do and reach consensus, then. There's a famous result in computer, science called official, inch Patterson, theorem which, actually, is a no-go result which, says such, a master, deterministic, algorithm, is not possible, so.
This Actually is the very reminds. Me of the central result of physics namely, the entropy always have to increase if, such kind, of a master algorithm exists. Actually, we have a name for it it's called Maxwell's, demon so, somehow this demon, has very high intelligence for example, if you have a compartment, of a gas and you, have a war dividing, between them and you have a little hole the, Maxwell's. Demon when you sees a high energy particle founder left it opens, the shutter ladies through and to low energy particle. Coming through, and then closest. A shutter and doesn't match so then, if this demon can do all this choreograph. In. Efficient, way then, little bit later this site will be much hotter than this side and then you can extra some walk to it so, such centralized, entity to coordinate, will really be. Able to extract. Energy out of nowhere and this. Obviously is not possible, so, I like to make the analogy of the Fisher Lynch Patterson, theorem with the concept of the Maxwell demon, none, of them are possible, the master, algorithm is not possible, and Maxwell, demon is not possible, so, what's it the solution the solution is provided, by the blockchain so if you want. The. Entire distributed. Internet to agree on some temporal, order, which is the most crucial thing for financial, transactions, which transaction, happen first which, transaction, happens, later you, want to order machines, to vote but, voting at a cost by, solving, what is called a hash puzzle, only, those machine can which can solve a hash puzzle which is very deep culture soft but very easy to verify there, was some machine solve this hash puzzle every machine will agree that yes, this is true and we agree on this, temporal order so, it's such two szostak algorithm, and it actually, requires energy to. Compute, and to, reach. This hash puzzle so therefore in the self-organized, blockchain, consensus, mechanism, we reach consensus. Namely in a state of low entropy but, we dump two extra entropy, somewhere else through. The computation of the hash puzzle and that is very similar to what's happening the physical, world namely, we can in, principle reach, this state of consensus, of low entropy provided. If we dump extra, entropy somewhere else so, I really think this is really one of the most brilliant, invention. Of in, human history somehow. We can have, a natural, and objective, mechanism, in a distributed, world to reach consensus.
But, There's a cost to it namely, you have to do this mining work so, that the extra entropy, can, be dumped somewhere, else so. Once will you have this consensus, mechanism, I think, this offers, a great new opportunity. To. Last new kind of symbiosis, between, blockchain. And AI so. I talked about AI, be in conference, a magic conference of three major trends I alluded, to. To. The computational, power Moore's law and then possibly. Quantum computers, I also, talked about some new inventions. In the algorithm. But, what a AI needs the most is, to have data so, that AI can learn but. Right now oh data are, concentrated. As centralized, platforms. So that's very little incentive for individuals. To contribute. Data because they basically get. Nothing, in return and maybe their privacy could even be violated, so, I envision, the future of the world where, the ownership of that data should be completely, be. Returned to the individuals, so, all my personal data or my behavior data or my online data or my genomics data or, my medical records everything should, be owned. By the individual, and the privacy should be completely, protected, but. Then you say Wow then calculation, possibly realistic, if everybody, keeps their secret, private, and there. Is a beautiful thing called privacy, preserving, computation. And that, will make it possible to, have a data marketplace, so I first of all protected, all my privacy, data but, I can leak information, out, one, bit at a time totally, at mine control and such. A world will, be a data marketplace. There individually. So it's a peer-to-peer marketplace, where. Individually, under their private data and then, there, can, be a bidding and selling, process. Very. Selectively, control by. Performing, privacy, preserving data, marketplace. So. Such a future world of, of. Marketplace. Based. On one principle which I call in math way trusts and. That is possible. That it's. That. You can still preserve, privacy, but, still maintain. And. But still can't do a computation that. Only. Leaks, out very, very selectively, one in piece of information, at, a time so. The famous problem. Is called a secure multi-party computation, or, a millionnaire. Promise, so obviously. Private, wealth is very very private, people, don't like to reveal but, there could be so happened that two millionaires want to compare who is richer but without revealing to each other if they review to each other Wells. They have obviously. They. Will find out but leaks too much privacy data but, there's a computational. Protocol, called. Yass Yass, doubled, circuit, that, they can exchange particle, in the end of the day they only find out one bit of information namely. Who is richer without revealing anything, there's. A idea, of differential. Privacy namely. Adding noise to private. Data so. That they don't become individually. Identifiable. But, if I want to conduct a corrective, survey, I can, add noise in such a way that in the statistical. Aggregate the noise will cancel out so, the statistical, information is completely accurate but no not, much individual. Private data has been leaked because, there's so enough, noise that, individually. Identical, information. It's not there but, but. Overall. Over. Statistical. Information still accurate and then, there's also the idea of zero knowledge proof I, can prove to you for example that I solved, a very difficult game let's say the Sudoku, game but, I want to only give you one bit of information namely, I solve the game but, I don't want to reveal you my entire solution I want, you to keep on trying card and this is also possible through, the zero knowledge proof so.
There's Really a world, where, mathematics will, enter, economics, in a very sensual way in. Making, a data marketplace, possible, so that's why all of us were on or individual, data and then. Google. Cloud and all, these entities. Then, can compute, in. Centralized. They, can compute useful, statistical, information without. Revealing. Without. In having us to be revealed. This privacy death so. I really think about this world where, both AI and, and. Blockchain, combined, can, do great social. Good in this new era of crypto. Economic, science based, on in math we trust because. When you really think about what's, the problem with our society today is because there's discrimination. Against, minority, and there, is a fundamental of, our, society, but, when you really think about AI learning, let's say if my AI everything, is already working accurately, 90%. Of the time but, I want some extra data so that I can go, from 90%, to 99%. But. That I need is not yet another kind of data which looks very similar to all the previous that I have seen I want, data which is. Called to have high mutual, entropy, namely the data that's most distinct, and that, by definition is, owned by the minority. But. Then in such a data marketplace, I will be to the highest for. Those data which most. In. The minority so. Then the economic incentive. Structure, will be aligned or society, will be value, the minority, the most and. That's exactly, what we need to. Do social good, so finally, there's a vision that the ugly duckling, can somehow become a beauty, swamp, because, ugly duckling is not ugly it's different but, now, difference. Will be valid the most minorities. In this fear that a marketplace, will not be discriminated, against, so. I really see this wonderful, new world in. A conference, of three major trends quantum, computing AI and blockchain but, I also see, myself being, coming from academia, and. Opening. Interactions, with. Colleagues. In industry, we, really can enter a new world where, the latest scientific idea, it's really, really fascinating, and, totally, amazing, that these mathematical. Concepts, was, purely invented, by. Mathematicians. In a chat could. Turn out to be so useful so something, like number theory every. Day when we conduct, a transaction, using, HTTP. Uses, number theory in the most essential way so, this is a wonderful, new world where collaboration with, academia in the industry, can, really lead, to great, progress. As. I said the greatest opportunity, of, making. Progress is oftentimes see, a conference, of some, major trends, before. In anyone, who does in their specialized, area couldn't, see the overall, picture and I really think that the, symbiosis among, these three major trends will, be the defining. Characteristic. Of the future of information, technology, thank, you.
Should. I entertain, some questions. So. You talked about consensus. And how proof-of-work systems, achieve. Consensus. By distributing, and by increasing entropy yeah, how, does it how does that work. Yeah, so actually. I think in the end of the day there should always be some, trade-offs. So I think, find, a fan. I see, the future of the blockchain were out and those. Cryptocurrency. Will. Happen in some, what like or, what we have in the current world the current war will have m0, m1 m2 different. Layers so, I believe that the most fundamental layer. Universal. Currency should be completely, based on proof of work because, then the entropy that you dump is extreme, it's a totally, transparent not only it has to be there but there's also totally transparent, I think, that the most basic. And fundamental layer, proof of state will not work because. There's so much possibility of, collusion that you can lose something I'm chained but gain something off. Chain it, can be bribery and so on so, I think we're the, the what the true exciting thing about the block chain world is that at the most fundamental, layer there, can be something that's totally objective and, only, connects to the natural world namely energy, and not. So much about proof of state which human. Irrationality, can. Get involved but. I can't very well imagine I'm the higher layers then, they will prove a role but, the most fundamental layer, such like M 1 or M 0 should. Be, completely. Robust, and I still think the proof of work oh there's, something another. Approach which is called proof of space-time, proof, of space which, is based on storage, and that's, I think it's also it's. A quantifiable. Physical. Resources I think, that the most basically. Human. Things, shouldn't be involved. But. Maybe it is. So. I mostly, think about quantum. Computing may be useful for AI as a search algorithm, so. One, algorithm, for so. All these also. Happening so, one of the most, interesting. Approach. To AI, is the, gang right generative, adversary', and networks so, I don't mean these three trends all always, necessarily, have to work together they. Can actually leads, to progress by competing, with each other so in, one aspect quantum. Computing and blockchain some were competing with each other because a lot of the Crito. Encoding. Algorithm, could be broken by compton but on the other hand i also see that, kontin can help AI in. Doing, the most efficient, search and. That's, what also a I needs to do right. So. This relationship, is, very, much like a, symbiosis, in, your ecosystem, there's. Post competition. And collaboration. Yeah. We cannot just use a human, whale to decade they will always do the same thing they the I think in the process of competition. They will all become stronger. But. There's a metal layer of consensus, to be reached that's like I actually agree, into, this titute distributed, system yeah currently in crypto there's many fragmented.
Pools Of liquidity, quote unquote so how. Do you bridge that gap between where we are now in these so, I think for, example the relationship between the, Bitcoin, blockchain and lightining network very, much fits to this framework, of m1 m2, so. Basically. The, blockchain is completely objective based on proof of work and, so this is the try to reach the most universal. Consensus, among, parties which totally. Don't know each other and they need still need to transact, but, when you really think about business transaction maybe, two of us already have been working, very well as partners in, the last ten, years so why should we still. Use treat, each other as as, totally, strangers. So, what we can do is we enter into each other state. Channel. By, putting or collabos, on the blockchain but, we keep on doing very very fast trading, but we still saddle. Once a months so, this is I think exactly like the relationship, between m0. M1 m2, the, relationship, between writing and Bitcoin is like the relationship between m0, and m1 so. Where. You go above every layer, so. There should, be it's less robust, but. Will be more efficient, but, the trade-off comes, from our history that, were already had a history of trust so if you have business partners they already someone, know each other they, don't absolutely have, to use the most universal. Robust. Later, they, can establish. A higher layer where, they sacrifice. Some. Universality. But in, exchange for efficiency. Yes. No question on the NGO, particle. Yes intra particle is one that's not possible. Yeah. So it's a half a cube it sounds. Like identity, element, in your actual art brewing, or field right identity, element. You know when up is again it itself is a. Node, a more precise analogy, it's like a complex, number can be expressing. In terms of two real numbers so. The, complex. Number is like a particle, the complex conjugate, is like the antiparticle, if. You have the real number the complex conjugate is the same as itself okay. So the. NGO particle is more like a real number, I see how would you now we we think, about. Yeah. What. Would be, neutral. Yeah. Yeah so so yeah so, yeah, well. I think the analogy is just to say that that. So. Here there's one incoming, quantum. Qubits but. Actual, computation before, you do actual computation you're splitting them and by, splitting them they're already kind, of become. Non-local, their entangled. But the classical, noise is not entangle, so, it's impossible to destroy it using, classical noise so, that's why topological. Quantum computer, can be so, much more robust. Yes. Okay so combining. A couple of the themes of your talk if you're. Able to harness, the power of Kwan competing, and if we're able to then secure. Our data through, you. Know privacy, encrypted, ways of being able to share it yeah I'm. Wondering how you see the future of Google because. That seems like a truly, extant all threatened. Can spin up a quantum computer that can do extremely, efficient parallel search yeah and then they can harness everyone's, data I think the only way is to not resist changes. But embrace, changes, right. So. How do you see. Examples. Yeah yeah yeah actually. I have an answer to these. So. In. This, way. Actually. We, can do the following. Construct. That's. A for example my private data I want. To store it in a secure way but still be possible to do some computation so. We know Google Cloud, competes, with Amazon, clouds so, what we can do is that on the Amazon Cloud, I stole completely, random numbers but. On the Google cloud I store. My information. Plus rendering. Information as to our Amazon, Cloud so. If I really can't assume this these two entities are really competing very hot maybe there's no collusion and there's, no way they will secretly. Exchange, but, then you can use the protocol, of secure multi-party computation, to, do a computation which.
Gets, Only one result without, revealing any details, so. In, this world centralized. Entity still, is. Useful, but. In order for this to work you have to assume, that they are competing but. Not colluding, hi. I'm just wondering the use of trim entropy is interesting. Because it seemed to be this mysterious, thing but it's also reap resize that in thermodynamics. You can have a logarithm term in um in classical. Thermodynamics and, then you have Claude, Shannon with information, cereals that have an entropy and, then you make energy using, energy so kinda reminds me of like some. Sort of free energy yeah, yeah it's exactly yeah so so, I think the blockchain world is exactly, extracting, extracting, some free energy out, of it so, so. So you're basically, you're. Achieving something, but, whatever, you achieve the total amount of energy the useful, amount, it's. Only the energy you spent minus, the entropy that. You have to waste so, the subtract so a lot of you, actually today, still see a lot of white papers they, claim to do miraculous things and, these, kind of white papers reminds, me of the proposals, in the 18th century about perpetual mobili. I'm. Just I'm wondering can you extrapolate that now to further then you need a temperature term for the creature to work yeah yeah, yeah, yeah actually. Temperatures. Very naturally, if whenever, you have a conserved, quantity, as such. As conservation, of energy the. Temperature concept. Naturally, a boss, because anytime you have a random, but. Conserved system, it's. The most generic what is called a Boltzmann distribution so, the entropy their temperature, comes in, naturally, but, I think could why. I get, so excited about these is for the first time I see a convergence, between social, science and natural science yah-tchi. Provides. An anchor for the social, scientific, world namely. A defender, my idea, for IM, 0 m1 m2 the. Fundamental, anchor is, now entered, natural. Science we, can precisely, see the entropy it's wasted, so we can see why it consensus, reached and, then you can build more. Human things on top of it but the most basic layer is, now common, bitching social and the natural science and fundamentally. Reduces, to any energy, entropy a information. Thanks. So much for your time so. I think in your talk you were saying that you're gonna see this, first. Layer of one block chain and then further. Layers built on top of that so. What do you think of the various projects, or companies are trying to build their. Own block chain and how does that relate to, your talk so do you think well I think. Yeah. So there has to be some, unique, thing, that you provide so brought. A Bitcoin, blockchain and you seen are really different, so, because. As, a fundamental layer, of trust, you, actually don't want universal, Turing machine because, it can be maybe hacked but, then you have to do some more, transactions. On, top of it and then if, the room looks more natural so, the, evolution, of the blockchain world will, emulate. The, evolution. Of biological. Species you. See for King you see here different species, if. They, long enough maybe they become a different species but, there's always something fundamental, namely. All biological, beings, are based ourselves, so, this kind of basic constructs. Will not change but, to some organization, the, different organisms are different organizations of, different cells that, may change.
Yes. Thank. You for your time so my. Question is when do you think quantum, computing, would be in the application. Like. After, your, findings, and research and. When it is in the application do, you think it's gonna be in the hands of only, big certain, companies, or its visit scale yeah. So so, yeah so I think, quantum. Computing research most, ideally, should, be done in open environment. I, think because. Yeah. Let me just make this statement because I know a lot of companies are trying but. The very nature of company, trying is they, have to protect, shareholder, interest they have to protect a secret but. For something so powerful, and. Its implication, for Humanity, so I know that. I think it should be best, conducted, in Open University research. And. This is exactly what I'm doing so. My approach to quantum. Computer, I have, many, many temptations, to. Do a company, on quantum computers, but, I resisted, that. With. Or without my, invention. I. Think, if you use this old way of if, you use this way, of trying it, will take a long long time can, you just imagine for one useful qubit, you need 70, qubit to serve it I think, it wouldn't scale but, with this approach it was scale. Okay. I think we're about. Wrap. Up I'm gonna ask one last question okay. Yeah. Does it change any. Other. Requirements. Of quantum, computing, like such as, absolute. Zero temperature no, no no it's, a still operates a of most, proposals operated. At low temperature. Unfortunately. Yeah. Yeah, but, our approach could work at room temperature, if. A room temperature superconductors. Discovered. But. That hasn't been discovered yet, we. Wouldn't. Mind that maybe, for some very very hard computation. If you were there's really a qualitative, improvement. We. Can just go it to low temperature. You.