Generalist AI beyond Deep Learning

welcome everyone from and hello from rainy California uh today we're hosting an exciting event on the future of General STI Our Guest today are professors Christoph fundra malsberg and Michael Levin Michael Levin is a distinguished professor at the department of biology and the principal investigator at the Levin Lab at Tufts University Christoph Van Der malsberg is a senior fellow at the Frankfurt Institute for advanced studies and the visiting professor at The Institute of neuro-information neuroinformatics at dth Zurich and finally our host today's Dr joshabach my colleague and the principal engineer at Intel Labs my name is Tanya grinberg I am an AI scientist at Intel Labs also we would like to express our thanks to Intel labs for sponsoring and hosting this event Series so without further further Ado let's get started the audience is going to be muted by default for this event if you would like to ask a question or make a comment please post post your thoughts in the chat you should have access to it we will have a hopefully longer than usual q a session at the end of today's session so we should be able to go through many of your questions all right um Joshua the floor is yours okay uh I've proceed without slides for now um let's see the question that we want to discuss is how we can build intelligent systems that move beyond the present limitations of deep learning and this is quite a bold proposal because you don't know if deep learning has any limitations at least I don't know I don't know any proof that demonstrates that there is a certain thing that the methods of deep learning cannot do it seems to be that the present generation of models which is widely successful generative AI is built on the Transformer algorithm which is a variant of the deep learning mechanisms that have been existing since the perceptron and the the limitations of this thing are not clear right if we basically get the meaning in the limit we need to use massive data in compute to get there and these models have difficulty to become fully coherent but it's not obvious that such a limitation cannot be overcome with a loss function or this just using the existing regimes and scaling them up even further right they cannot prove that um there's a combination of codecs and the existing methods and some real time and some online learning uh we won't be able to build a system that is a good enough artificial general intelligence and is able to discover the next generation of algorithms for us and this idea that deep learning in the way that it exists with basically the algorithms that we have right now with slight changes to the loss functions and so on that this is sufficient now this is called the scaling hypothesis and on the other hand most people who work in the field have the sense that there is a problem with deep learning in the sense that it's brute forcing the job it seems that biological organisms are able to make sense of the world with much much less data and dramatically less compute and there is uh of course contention about how much compute a brain has and how much compute an organism has and very often the calculation is made in such a way that we discuss how much how many gpus you need to emulate a group of neurons and depending on how you look at then you run the urine might be something like a four or twelve layer neural network or it might be something even more complicated if you look uh down to modeling every synapse and sometimes and uh on the other hand relatively rarely we ask how many brains you would need to run Mac OS because uh the way our nervous system works is highly redundant and stochastic and much of what the individual neuron is doing is probably only relevant to the individual neuron in its survival itself because after all the neuron is a single-tailed animal so if you take the job of an individual for instance in a corporation that individual is going to contribute a lot of its intellectual capability to the corporation but it's going to be a tiny fraction of the total ability of the individual that it needs to survive by itself and to communicate with its immediate neighborhood so uh just asking to emulate a single neuron is is and then multiplying this with the number of neurons is probably not the right way to do it and it's not clear how many neurons you would need to run an M list as far as I know there is no simulation so far that is using a closed neural model and or that is using a small group of neurons in Vivo that are being trained to do analysts more generally I think we need to answer the question about intelligence is and for me this question has evolved over the years in my current answer is that intelligence is the ability to construct a pass through a space of computable functions it's a slightly um fancy way of saying that intelligence is the ability to make models because at the end of the day models are computable functions that are designed in service of control so we have a system that is making uh some kind of Regulation task or performing some kind of Regulation tasks and it when it's controlling the future it's an agent and to control the future and being an agent you will need to construct some kind of control model that is counter factual you need to be able to represent states that are not here yet because the future is not there and that means you need to have a system that is able to perform arbitrary causal Transitions and this causally an insulation from the substrate to represent something that is not the case yet that is basically what is a computer and if for the computer it does it represents functions which means mapping from State descriptions to other state descriptions and the computer can do this in completely arbitrary ways and this is this basic idea of building a system of computable functions and when you uh come up with a way to enumerate the computable functions to list them all then you can search them and if you want to learn something you need to in some sense enumerate organize the space of computable functions in such a way that you'll find those functions that you are interested relatively early on and this question of how you can construct a space of the computable function that is tractable that is able to converge to a solution for the problem at hand that is the main issue right making computer is relatively easy especially if you have something like a cell because it's already contains a computer and they can also organize themselves into higher level computers and become less stochastic and so on while doing this but the big difficulty is how can we find the functions that we are interested in and so when we look at Deep learning what is deep learning um the first thing that we have to note about deep learning is that deep learning is the only thing that currently works at scale it's the only class of algorithms that is able to discover arbitrary functions in a reasonable amount of time and reasonable being orders of magnitude more time and more data than a human being does it's of course the training time for something like um a stable diffusion stability AI model right this serial diffusion is a model of two gigabytes and it contains the whole of the art and you can get every celebrity you want you can get every spaceship for popular culture you can get dinosaurs everything is in these two gigabytes and training this takes weeks and uh this sounds that's very little compared to the years that it takes to train the human brain but during these um weeks and or now it comes down closer to days this thing is uh going through hundreds of millions of images many more than a human being could process in their lifetimes and find correlations between using uh dramatically large server Farms so how does deep learning work first of all it's differentiable Computing which means it is representing all the functions in such a way that they form a continuous space in which neighboring variations of the functions uh still lead to interesting results and you can move by small nudges to the space of functions to get the function closer to what you want that's different from discrete programs program and source code if you change a few bytes and the source code the program will no longer work if you change the neural network uh by a slight bit then it's still going to give you useful results and to do this the neural network is describing the functions via weighted sums of inputs that are arranged into chains basically in layers and some non-linearities which are in a way to make if sentence to make conditional breaks in this network and this description via multiplications and sums is sufficient to represent all the functions that we want and we train this network by setting it up in such a way that it has the potential to build enough things so it has enough links between the nodes that it sums values up and then it just changes the weights which means the multiplying factors for the inputs of every node in a systematic way and it some sense approximates all the functions via real numbers and if you think about alternatives to deep learning the main uh thing that comes to mind is to build up computations from deterministic discrete operators such as Boolean logic or simple automata like cellular automata and as a result we get finite Turing machines and in the finite case dynamical systems and Turing machines and automata are the same thing computationally speaking they're all Turing machines as long as you don't run out of resources so every digital computer in reality is a dynamical system because the physics that the digital computer super means on is somewhat continuous at from the perspective of the individual transistors and so on and we just try to find a region in physics that makes the transistor reliable enough as a discrete unit and deterministic from the perspective of The Logical language that is implemented on the arrangement of transistors but underneath there is an analog system that is just noisy and that is a system that is discrete on the other hand every dynamical systems in physics at the lowest level is discrete again right if you zoom in what you see is nothing continuous what you see is individual atoms and individual charges and so on and they are discrete again and and there is some discussion about whether everything has to be discreeted at the level because of the nature of languages itself and I think that's the case I think that um the discovery of the last century that was most important in philosophy is that those languages which assume that the bottommost layer can be truly continuous they run into contradictions but this only matters if you are really interested in modeling the bottom most layer if you just think about computation uh it doesn't really matter if you start out with a dynamical system or this discrete system as long as you are willing to allow that every dynamical system has only finite resolution so there's only finitely many bits that you can manipulate at any given moment and uh this equivalence between the continuous mathematics and the discrete mathematics has been shown basically in both directions you can use a computer and then by just by using more bits you can approximate continuous system with any degree of fidelity as you want in the same way as digitized music can sample the space of audio functions below the level of resolution that your medium can provide so you will get to something that is equivalent in the opposite direction is also has also been shown correct State and has been gone to the travel to translate this interpreter into a diophantine equation with 17 000 variables right so this paper is called the complete arithmetization of evil it's uh the evaluation function of this which uh did it in 1987 it's uh quite beautiful and I don't think that there's something people actually want to read and this uh formula was generated um with some generative procedure he did not write these 900 000 characters by hand that went into this but it's been shown you can interpret write down the list of interpreter and continuous uh mathematics so the alternative to deep learning might be to basically construct functions from the ground app using automata and in in a way this is also what people have done because uh all our computers on which we run deep learning are discrete automata people started to build this continuous arithmetic they built end or a multi Edition multiplication and so on from discrete logical units and this is clearly practical because it was done with a relatively few people in relatively few years made the search process to build up continuous arithmetic from discrete logical operations is not very large and if you want to get to discover a deep learning algorithm as a special case over discrete automata from scratch it's the existence group has been shown by the computers in the Deep learning mechanisms that we have because relatively few people use relatively little brain power compared to where we want to get to to discover all these Solutions right so you by self-play you could in the same way as computers played a goal discover a arithmetic but using discrete systems so in in many ways this is going to be equivalent and I suspect it might be interesting to start from this automata direction again and build up learning and there are a few people which work in this area but so far nobody has come up with an alternative that scales up in the same way as deep learning and then there is a slight uh the different approach that we might be looking at and it doesn't use a normal turing machine but a non-deterministic turing machine a multi-phase system and that is because systems in the lowest level of physics and I also suspect systems that are implemented on brains are non-deterministic systems and this doesn't just mean that they're noisy or that they're random in many ways a non-deterministic turing machine is a paradigm from computer science that describes your state machine in such a way that not every state has exactly one success or state it's not sufficiently constrained to have only one successor State our turing machine that we normally use and this includes our digital venomen computers and so on they're defined in such a way that every state has exactly one possible successor state if there's the branch in the computer that's because uh it is depending on some environmental variable that is not relevant in the program which means some input of the program but given the same input a turing machine is going to produce the same output and it's going to do this along exactly one path and not the destroying machine in the computational sense is also going to give you the same output but it's going to do this on many paths because the constraints are not so narrow that you go into one state after every state but you can go into multiple States machine just goes into all of them branches and these branches don't necessarily meet again right there is no way that they're connected again it's basically an understood turing machine is implementing some kind of Multiverse but it turns out that many of these Multiverse branches that the known as the turing machine goes into have the same bit combination in them because there are only so many states that are reachable and so by looking at the Dynamics between these bits used can get to the same point in the universe on multiple paths and if the universe that we live in the physical universe that we live in is such a multi-based system it's still going to be possible for Observer that lives inside of it despite it not knowing which pass it goes down determines statistical properties over the regional past which means you cannot know in your Universe which uh slit the double in the double slit experiment the photon goes through but you can predict the patterns that many many photons are going to make on the other side and the things that we can predict in our universe are of that nature their statistical properties over all the trajectories that can happen then that we are part of and how would something like this look in the brain like in the brain obviously the brain can only hold a finite amount of state but if you have redundancy what you can do is if you don't tell the urine to go into one particular State as a as a result of its present state but into a range of possible States and you uh this happens randomly it means that there are many trajectories that activations can take in the brain at the same time and they're going to meet in the same physical substrate and they're going to accumulate and you're going to sum up in some sense and uh there could be some basically some voting over different paths and this means that a system like the brain could via transmitting activation try many many things in parallel and uh stochastically with some degree of Randomness and as a result uh perform computations efficiently that can not be efficiently done with a linear machine right from a computational perspective you want to simulate this on the linearity on the sequentially operating machine it's going to be highly inefficient because you're trying to do the same thing over and over again and this Randomness is going to delete some of the bits that you've been computing but if you're a computational units are very cheap like in the brain and the Brain time is expensive because taking one more step means that you're going to be slower and your interaction with nature but parallelism is cheap just by doubling uh the cell count Once More by dividing the brain cells you get twice as many elements if you do this quite a few times you end up with billions of elements and these billions of elements can perform many of these paths and parallel and this is something that to my knowledge has not been seriously attempted yet to get to work for a learning system there are some people which are thinking seriously about this if you're interested Maya on a panel here and he is describing the mental representations as superpositional States using such a multi-base system and it's also an idea that has occurred to Stephen Wolfram who is thinking of the universe as a multi-based system and who's open to the idea that the brain might be a basically a bounded complexity multi-based system so uh this did we see that there are opportunities to build Solutions in uh that are inspired by biology that we haven't tried yet and if we look at the inspiration that happens so far from biology into artificial intelligence despite uh many claims to the contrary it almost never happens I think that the last contribution of biology to the Transformer was heavy and learning and uh everything else that people got to work since then was mostly not happening because people looked at Neuroscience results and implemented them and then ended up with a better learning machine learning algorithm and if you take the idea is that neuroscientists currently have about how the brain works to my knowledge you cannot Implement them in such a way that they learn and control an organism even a very simple organism like C elegans if you take the connector of c elegans and translate this into a computer model and run it it's not going to put you as coherent form Behavior right the C elements is a small worm and it has only uh 300 I think 309 neurons please correct me uh if I'm if this number is off and as a result if you take this connect home and run it into a digital simulation it's not going to produce the behavior that we want because uh presumably we have not caught up on all the functionality of the individual neurons in the context of that worm could be that there's stuff in the summer of the cells that does not visible in the connector or that we made mistakes in digitizing the connect home or I don't have enough resolution to see all the vehicles that use different neurotransmitters in the connect home to get the functionality right but for systems at scale that go beyond an organism with a few hundred um neurons something like cortical column and so on we don't really have working models at the moment the descriptions that the neural biologists have at the moment if we translate them into computer models are not performing the same things as our digital models are doing they're not able to discover these functions it's because these models are still incomplete and they're not ready yet for being used there's a more fundamental problem that Conrad coding has highlighted in the paper where he used the methods of neuroscientists to reverse an engineer a micro processor right if you take a normal microprocessor and give this to a neuroscientist and the neuroscientist does ablation studies and looks at the structure that the universal scientist finds is the neuroscientist able to reverse engineer this microprocessor which is dramatically simpler than the nervous system of course and it turns out they can't but it is to convert according who is a neuroscientist that is the methods of Neuroscience might be um for some more deep reason because they're not functionalist enough able to discover how information processing and nervous system works yet which means that the theoretical tools of neuroscientists might not yet be ready even to understand what brains are doing and I think that when we as non-neuroscientists look at brains and the textbooks of liver science that take what I learned at University I find from my current perspective that there are some shortcomings in this description that if I want to sit down as a computer scientist and discover the space of possible solutions in nervous systems and uh for functional approximation and thus get the machine to search through it and so on would be insufficient and this starts out with the idea that a neuron is a specialist which at the way in which we abstract the neurons since the perceptron is that the new one is some very simple um a gate or something element like in a circuit that does something in a mechanized automatic algorithmic way and I don't think that's really true a neuron is not a specialized switch a neuron is a single celled animal right it's quite complicated it wants something it wants to survive it is able to learn by itself it is adaptive it can do a lot of things right it's almost like a amoeba that links up with other amoebas and rows into this static structure depending on its environment to perform a particular task and this is quite different as a perspective it means that your neuron is not just some kind of integrator or some kind of weighted sum of real numbers of activations that come in the neuron is really a reinforcement learning agent with a little bit of memory and a little bit of ability to look into the future not very far but a little bit and it can implements a number of adaptive functions to deal with its environment and reap rewards that ultimately allow the neuron to survive in the brain and not be strapped by the organism or killed by it because it's not doing the right thing the second misunderstanding I think Beyond neurons are not just specialized switches is that it's only neurons there is probably no fundamental difference between neurons and other cells that is every cell can do information processing in conjunction with other styles if it's in a multicellular animal it probably needs to be somewhat multicellular because if it's a single celled animal it's maybe evolving in such a way that it's as adversarial to its environment it does not benefit to if it computes information together with others but if it lives together with others in a high degree of organization like the cells in our body are doing and you co-evolve them then the cells can all send many types of messages via chemicals over the cellular membranes to neighboring cells they can interpret those messages and they can learn how to respond to these messages which means they can learn to perform arbitrary computation so what's the difference between a neuron and another cell if they can do the same thing well the specialization of neurons is that they have extremely long accents so which they can send information coded as electrochemical impulses very quickly over long distances I think that new ones might be best understood as Telegraph cells at a very special cells that have evolved only in animals or mostly in animals and that are very expensive to run because they need a lot of energy to send information so quickly and the benefit is that they can move an animal very quickly so nature so it can eat plants or other animals to get that energy so basically the animal gets more energy that it could get from photosynthesis and uh to uh as a benefit it's able to afford to have this expensive nervous system and uh decode that the new ones are using is probably different from the quote from the chemical codes that the cells are using for the messages it's like a morse code probably something like a telegraph system so it can send information so quickly and initially um the it seems that to me that neurons might have evolved to move scatter the muscles at the limit of physics so it can from centralized coordinated in the nervous system from the central nervous system send information so quickly that the whole organism is coordinated much much faster at much water time spents than plants are and the other thing is once you can move very quickly you also need to proceed very quickly so it's also driving sensory input and the evaluation of sensory input and decision making and learning so it's basically duplicating the information processing that existed in the body and uh is creating something like a second information processor in the body that is running at much much faster time scales than the normal cellular information processing that will also exist in large longer plans right so my perspective is that uh just by looking at means and motive uh the possibilities of what evolution can do and the capabilities of what an individual cell has I suspect that every long-lived organism with many cells is basically going to function like a very very slow brain and uh there are almost no limits what this slow brain can do if it lives long enough but it's not going to do this at the same time frame so animals will be able to out think plants just because they are so fast and run circles around them and the third misunderstanding is that we think that Consciousness is extremely rare that Consciousness may be only existing in humans and forms only very late in the evolution of intelligent systems but it turns out we don't get conscious after the PhD we seem to be conscious before we can track a finger and if that is the case maybe self-reflexive attention is a requirement if you want to learn Beyond happier learning if you don't want to just look at core activation between status there's a learning private line which is probably sufficient to map your body surface and so on but you want to have a coherent model of reality maybe you need to start out with some kind of core that organizes everything into coherence and what we find confusing is about Consciousness that is that we don't seem to need it for many things the Sleepwalker can do many of the things that normally require Consciousness and this is confusing philosophers to no end but maybe Consciousness is in some sense like government in a society and I don't mean government is some kind of abstract principle but as a real-time interaction that is making Society um coherent and a society can do everything without government if you would be shutting down the government today it would be days before we know this and uh years before we crashed right but uh to get to the state in which society is today with streets and infrastructure and educational system and so on you need to have a government you're not going to bootstrap a group of people into an organization without having some kind of hierarchical organization that makes this hoop of people coherent in their actions and create some Next Level agent out of them and to do this the government needs to start out with some local coherence with some with the making itself coherent and then imposing some kind of organization on the environment that is branching out and scales over all the individual agents and if you just put individual people together for long enough then different forms of government will emerge in some kind of evolutionary competition between them and eventually one of them will take over and organize this group of people in such a way and the idea that the same thing Could Happen among the neurons that they're basically different forms of organization that start out from small course and then move as activation patterns that are agnostic of the individual units that they're run on but it impose the same language on all of them similar capabilities on all of them so the locus of action can move around between them um this idea that the brain organization could evolve like this is similar to Gary edelman's idea of neural Darwinism that basically our brain organization our mental organization is not hard-coded in the genome as a blueprint but uh what the genome contains is the conditions to start this Evolution between different forms of organization and then Rick The Evolution a particular way so it converges quickly there are some ideas that you could take from biology into Technical Systems and uh first of all I think that the system needs to be real time it needs to be coupled to the environment and needs to go into resonance with whatever environment it is coupled to and it needs to regulate the interaction with that environment until it at the level of coupling the temporal resolution that it has is able to track reality around it and that's something that our machine learning models are not doing yet and not real time uh even something like a stable diffusion is trained individual images that are not happening in real time they're not happening in the right order there are uh just 800 million disconnected images or which the system is trying to find structure and this would not work for a logical organisms I don't think that we could converge from this amount of data instead what we get is a verb that changes continuously by small degrees and these small changes are make sure that every frame is related to the last frame and we can learn universal laws in which these frames are related there are laws of conservation of information and without this conservation of information where we learn the transitions between adjacent frames I don't think that we would be able to learn from the universe so one thing is we change the Paradigm from image to video or other streaming data that has information preservation and in the beginning this might look more difficult to us isn't it harder to learn from video than it is to learn from single pictures Well if you what what you want to learn is the fact that you are living in the universe with moving objects that happen in three space and so on it's actually much easier to learn this for a video because it contains way more constraints in this way they're much more obvious the next thing is the way in which our brain is modeling these things seems to be made from lots of small periodic loops small interlocking periodic loops and it uh first of all it has to be Loops because the brain is relatively slow information translation or transmission in the brain is so slow that it takes um appreciable fraction of a second to just for a signal to cross the entire cortex and if you want to create simultaneity between these uh different parts of the brain you're not going to get there it's provided in our computers and our CPUs and so on and our gpus we make everything simultaneous by exploiting uh the speed of the signal Transmission in our CPUs and gpus it also means that we cannot increase the frequency at which we run them arbitrarily or we cannot make the CPUs and gpus arbitrarily large because it just takes so much time for a signal to cross the for over the entire circuit right if you uh The Next Step could be to use photonics so we can go to the speed of light and make this system slightly faster and slightly larger again while having coherence over the entire system but our biological systems don't have a chance of doing that they must live with the fact that it takes very long for signals to get there and the way to deal with that is to make sure that you are okay if you are out of sync you just need to be in the same phase basically you go at the same frequency and different parts of the brain and you make sure that the signal eventually gets there but it's okay if it's from the previous cycle or two cycles ago or as several Cycles ago as long as the content of the different brand areas that only changes gradually right if that happens if you're able to integrate over that you can use predictive algorithms and so on and can synchronize the whole thing so basically this idea of slow oscillators is something that we could translate into digital systems um the next thing is this emergent management and neural Darwinism so instead of having a particular circuit that uh require is required to be like this uh let's evolve this uh computational operators that we need um and the next one is that many of the functions that the brain discovering are only discovered once and this this tool for simple functions like addition integration multiplication simple computational Primitives rotation that are being used for many many mathematical Primitives that we require to describe the geometry of sound of images of thought and these are this basic arithmetic this basic library of computational functions at the moment to train this into a neural network takes a very long time right despite the neural network being built over addition and multiplication it's not easy for a neural network to learn arithmetic it's possible to do it but it takes enormous amount of training data and in every context well locally the neural network is performing the same arithmetic over and over again it has to retrain this these functions into into a different region of the network again and uh basically getting all these uh different computational Primitives bootstrapped into the neural network is something that is hard and it has an interesting effect you can for instance train a neural network on audio input or and then use it to discover structure and vision and it's going to be much faster because the audio input already prepares the neural network to learn many of The computational Primitives basic arithmetic in many parts of the network that can then adapt for a new task and the way in which our brain is doing this is probably that it learns some useful functions and then these neurons have a way to uh to exchange these functions possibly via RNA and uh so basically the functions that are being computed are to some degree agnostic to the individual neuron they are somehow substrate independent they just migrate to so a different Paradigm might be instead of using local functions over the neighborhood of every neural and every neuron is learning its own set of functions you learn a set of global functions and every neuron is deciding which ones of those to use and it's also something that has almost never been tried in Ai and that what I'd like uh I like to get seen uh another thing is that the main focus is on reward what you try to do in the brain is to do the most useful thing with fix it fixed resources and this means you have to assess the global reward that the organism is getting out of the contributions of all the neurons and then you need to uh distribute this reward among all the neurons that contribute to the result this is similar to what you do in a corporation it's an economic problem right the cooperation tries to do the most valuable thing but it can do with all the employees that it has and to do this it needs to get rewards to all the individual employees and the rewards are not given in such the way that every employee gets a different amount of money and the one that has the biggest contribution to the bottom line by the others are also important gets much much more there is some degree of this but it's mostly depending on the negotiation power of individual employees on the market if every uh if there was no fungibility and you would need to train all your employees it would make sense to give them all the same amount of money right and in the liberal capitalism it doesn't make sense to give a good uh retail worker the same amount of salary then it has to give money to a good manager but if you need retail workers you will have to employ one and the reason why you retail workers are less than managers is mostly because there are many more retail workers competing for positions than there are managers competing for positions so there the the supply and demand regulates labor market in such a way but this is not true in the biological system every neural basically is going to consume the same amount of resources just for existing and being ready to do something so uh our reward here is different it's not being accumulated in the bank account of the neuron instead this is just the signal that tells the organism that this new one is still going to get fat because it's useful in what it does and the neuron needs to get feedback similar to the feedback that you get from your colleagues that's the actual report that tells you you're doing the right thing right so it needs to be some kind of communicative reward some messages that are not directly food but that are more anticipated reward that are like money only without accumulation and so there's basically going to be a reward driven language and uh who is Distributing all the rewards in the brain well all the cells are all the neurons mostly but also the other cells maybe glial cells and so on the contribute in the distribution of the reports and so what's happening in the biological system is that it evolves a reward language there are two types of signals that are being sent around one is the results of the computation which was read by other neurons based on what kind of activation they're interested in filtering out of the environment and the other one is going to be signals that uh amount to reward and punishment that basically tell other ones whether they should do more or less of a certain computation and uh so basically reading of information is going to be pulled you you draw information from the environment and uh reward is going to be push because you should not be able to escape a negative reward punishment and so on right and uh I think we could approximate this using a new paradigm in a number of experiments that I would like to do in this regard so basically a neuron has an internal State Vector that contains um the slight history of the neurons over the last few activations that is read and the type of the neuron and so on and it has a selector function the selector function is basically defining the receptive field of the neuron and the receptor field of the neuron can be just the environment of the new one interpreted as a certain topologies so basically it looks at its neighborhood as if it was a space with a certain number of dimensions and uh how can this be the neoquatics is two-dimensional or two and a half dimensional but it has a number of layers that a number of layers being very small and it's a large 2D area subdivided to the different regions well it turns out that you can interpret a 2d area as something that is in higher dimension in the same way as you can take the linear a one-dimensional address space of your computer and interpret it as a two-dimensional map or as a three-dimensional space or is something that happens in eight dimensions and can perform operations on it and this is what the selector function does the selector function is basically interpreting the environment of the individual cell as a space that contains information in a certain arrangement and it doesn't need to be a regular space it can be a manifold it can be something that is very selective it can be something that only uses five neighbors and these neighbors can even be physically very distant and so this neuron could be a juncture or some kind of Hub that sends information locally into the network and so on so you're going to have some neurons that have a topology that allow long distance connectivity and others that uh performs uh local maps in 2D or 3D and perform functions on them and this next to the selector function you have the modifier function so the modifier functions tell the individual neuron how it should change its state based on its own State and the activation that it reads in the environment and by use having some history in its own State it's able to respond to a spatial temporal activation distribution its environment and if we uh use this idea that the urine can use Global functions it means that we can arrange many neurons densely enough and some kind of lattice and these functions can arrange themselves as they need to be arranged and they can shift around as they have to and duplicate themselves if they have to yes I understand the interest of uh time yes I'm basically done okay for reminding me so uh this uh selector function modifier function Paradigm allows us to come up with a new way of describing a function approximation Beyond deep learning and at the moment uh the search space in my experiments is way too large so uh basically there are too many ways in which this could be implemented for my current perspective to get this converge to a good solution the alternative is I can just handcraft a solution but it might not be optimal but it's something that I would like to definitely look more into in the future and uh not just me I suspect that many people are currently discovering such ideas and um we'll be working on in the future and uh while it's not clear that this can provide a viable alternative to deep learning that is much faster and converging faster and more efficiently than present deep Learning Systems this is something that I believe is closer to what biologic biology has discovered and that scales are very reliably over many many classes of algorithms okay that's it from me all right um we would like to have a quick one to two minute uh break right now so that the panelists can set set up their presentation we will uh we'll do Michael Levin's presentation next so in the meantime uh yosha let me ask you some questions that uh got popular that got posted while you're talking um so a question from uh Nikolai like how neurons are small organisms that operate in the emerging super organism that is a human which future AGI architecture need to be made up of made up of many smaller AIS so there is no centralized control in the brain in the sense that all the centralized control is emergent over all the organizations between the neurons there is no dedicated CPU that is able to process every neuron and updated state every state update happens locally in the individual cells but this is an engineering constraint that doesn't exist in the Technical Systems and it's not clear yet to me to which degree we need to uh have local control to make it happen at the moment I did machine learning algorithms that we are using by uh treating the individual nodes in a network just as memory and the updates are done by a centralized algorithm that is updating all this memory right and you have either have a CPU that is reading and writing or you have lots of local CPUs in the GPU that is doing this with multiple pipelines in parallel and there are biologically inspired chips that are mostly experimental at the moment like Intel zohi that are using many many very small simple CPUs that are doing this and it's not clear if the best solution is to have a CPU for every memory cell it's probably not the case so there are some things that you can do in the Technical Systems like informing conformance to a centralized algorithms to centralize specifications that the technical system is going to implement and in a biological system uh this is just not feasible because there is no such centralized Authority no engineer who can make nature behave by itself but if you want to think about how to build a mind from a biological perspective we have to think about how to build something that wants to grow into a mind and we can take some of these ideas it's not clear that we need to make this with completely local control only maybe it's more efficient to have a mixture of some local control and a lot of global control where we already know what the control is going to be I think Mike is ready yes excellent Michael the floor is now yours great uh okay well that was extremely interesting so let me see what what I can add here um I've got some uh I've got some slides so here we go uh hopefully hopefully you can see that so uh what I would like to talk about is of course there's this idea that biology should be an inspiration for AI and uh what I would like to do is to deconstruct uh some of the biology that people typically think about in these contexts and uh ditch a lot of things that are very common binary categories and and a focus on brains a focus on neurons a focus on humans I want to step away from all of that and uh rebuild a different framework that I think has uh many many implications for um for for AI so so the first thing I want to talk about is this idea of of an of a typical human so there's this kind of classic idea that we we know what a human is and it certainly works for practical purposes in in society but the idea is is uh very sort of pre-scientific and and it's still many people are still even scientists are often still caught up in this the idea that okay so we have these humans and they are discrete natural kind and they're different from other animals and so there's this you know here's Adam um naming naming the other animals and so there's the discrete this species and and so on but uh if we take developmental biology and evolution and uh synthetic biology and bioengineering if we take these things seriously then what we find out is that actually there are no such natural kinds because all of this both on the evolutionary time scale and the developmental time scale and now in terms of the technological time scale there are uh very gradual very small very slow changes that go all the way back from from what people think of as a typical human and and their intelligence all the way back to very different types of organisms and developmental biology and of course Evolution too offers absolutely no place to put a sharp line and say this creature was not pick an adjective intelligent cognitive conscious whatever you like pick an adjective to say this creature was knotted but it had some Offspring and The Offspring now are right that's that just doesn't that doesn't exist because because all of these changes are very slow and very continuous and so we there were changes uh uh during Evolution there are we all Start Life as a single cell in the future there will be all kinds of changes to our to our bodies with biological and engineered kinds of devices and so all of these are continua of really uh novel uh types of embodiments and we you know we build we we build um certain kinds of conceptual metaphors uh that uh try to distinguish different categories here but but these are these are uh discrete tools the phenomena themselves are deeply continuous and multi-scale and to give you just a simple idea and then we'll enlarge on this is this so this is a this is a this caterpillar is a kind of soft-bodied robot that lives in a two-dimensional world that crawls around on leaves it likes to chew plants and it has this brain that's very very suitable for this purpose what it needs to do is turn into this creature which is completely different it lives in the three-dimensional world that doesn't care about the leaves at all at once nectar and it flies and it does various things and so during this process there is a metamorphosis where not on an evolutionary time scale but during the lifetime of the individual the brain is basically dissociated and rebuilt into a new architecture and by the way there are evidence there's um there are data that memories persist so if you train the caterpillar the butterfly or moth still remembers the original information but you can sort of think about um you know what's it never mind the question of what's it like to be a butterfly what's it like to be a caterpillar changing into a butterfly right that that you know that process of of of slow um but but but drastic change in your in your embodiment and so from here we can just remembering that we are all made of parts that can uh modify during our lifetime we can ask some interesting questions for example um you look at a brain and we're sort of conditioned to expect that it's obvious that a brain contains one human uh worth of of intelligence but this is just because we're used to that in terms of our interactions if I showed you a brain and you didn't know what what this was and asked you how many different cells are in there you would actually have we have no ability to to answer that question we have no ability no no way to to ask how much and I think I think Joshua got to some some of this you know how how much of this real estate is necessary for for one human's worth of of of of of uh performance we have no idea how much is actually in there um and actually very interestingly uh the same uh issue occurs in embryonic development so we all begin as a cellular blastoderm so this is a sheet a two-dimensional sheet of cells and that she turns into an embryo now what does that mean first of all can we guess in advance how many embryos are going to come from that sheet actually we cannot and I'll show you why uh and it's not genetics and then there's the question of what are we actually counting when we count an embryo and there's 50 000 cells let's say here what is it that we're counting when we say there's an embryo what are we actually counting when we say there's a single human inhabitant in this in this bunch of tissue so one of the things that you can do in embryogenesis is you take this this blastoderm and you take a little needle and you put some some some uh some um kind of uh scratches into this blastoderm and then they heal but before they heal what will happen is that each of these regions uh being isolated from the other regions uh decides to organize an embryo because uh because the they don't know for a while anyway that the other regions are there then when it heals up it becomes conjoined twins and you can do this very easily in chicken and Duck and other other embryos but humans work exactly the same way and so then there will be multiple embryos within the same blastoderm and then there will be some disputed zones here there's some cells that aren't quite sure which one they belong to but this this deep idea of of individuation of taking some kind of a um a continuous in fact it's even worse than continuous because it's multi-scale substrate and uh having itself organized into discrete what so in the case of embryos which you have are discrete groups of cells that are that are trying to uh follow anatomical goals they're trying to achieve particular walks in anatomical space they're going to construct the right number of fingers the right number of eyes that you know whatever whatever it is the same thing in in cognitive development you know there are issues or there are there are disorders of individuation that you see in split brain patients and dissociations and so on so this question of how many are in there is is is deeply deeply interesting and it gets to the bottom of what it means to be a a coherent agent When You're Made Of parts and I think I think um Alan Turing although as far as I can tell he didn't write directly about this I think he was well aware of this issue because of course he was interested in intelligence and and and um you know generic embodiment and so on but he was also interested in morphogenesis he wrote this paper on uh on biological morphogenesis and I think he understood that these are deeply and profoundly the same problem the problem of morphogenesis and the problem of the Mind are the same problem because of this because of this emphasis on on uh on on emerging as a coherent entity from multiple parts so people often talk about well uh ants and termites are some kind of collective intelligence and we can argue about you know what that means but but but we are really uh you know a unified a centralized intelligence we're not like bird flocks or ant colonies but actually of course all uh biological systems are made of parts and so we too are a kind of collective intelligence what's interesting is the scaling interface is what is it that allows these these individual subunits to work together and present to other intelligences to themselves by the way and to the environment and a a picture of a coherent agent so this is this is the journey that we all took we began Life as a as a piece of physics so basically as a quiescent or a site so it's a blob of chemicals not doing terribly much and then through this incredibly just just magical process of embryonic development that we we arrive at something like this which is a which is a complex organism with metacognitive capacity that's going to make statements about how we're not just machines and we're not you know if we're different than physics and all that but this whole this whole process is extremely smooth and gradual it happens second by second there is no lightning flash at which point physics becomes mined it's a gradual process and we can talk about um face Transitions and such but it's really you know there's really not that much evidence for any of that it's it's a very continuous process so this is the kind of thing so I think um you know alluded to this a few times this is the sort of thing I mean not exactly this this is a lacrum area it's a it's a free living organism but here's a single cell right this is what we are made of these guys uh there's no brain here's there's no nervous system um this the single single cell creature in real time uh using uh all of the intelligence of its chemical networks and we can talk about this I mean a quite literally chemical networks can learn and they can do inference and many other things uh even though it's a single organism is handling all of its uh single cell agendas in in its environment so it's also metabolically um physiologically anatomically it's uh it's you know it's doing what it needs to do so we are made of extremely competent uh Parts um here's another example this is a this whole thing you'll see you'll see this um I'm gonna pause it whoops I'm gonna pause this this whole thing right here this is a this is called faizaram polycephalum it's a slime mold the whole thing is one cell okay and what it's what what I'm showing you here is that it's sitting in this environment these are three glass discs these are very very light uh there's no there's no chemicals there's no food there's just glass inner glass there's one glass disc here and what it's going to do is it's going to uh for the first uh of you know few hours it's going to just generically grow in all directions here what it's doing during this process is it's tugging on its substrate and feeling the vibrations it gets back and it can sense the stress The Strain angle of the objects in its environment and then we'll eventually reliably grow out to the heavier Mass but during this so that'll happen at this point but during this time is when it's processing that information and learning from its environment and then boom now the behavior begins right so single cells are very competent even even um microbial single cells and so what we have to understand is that biology so so here's a principle that I think is really important for future AI biology is deeply nested that is not not merely structurally I mean that's obvious we're made of organs tissues and so on but each layer is competent it solves problems in its own space all of these things from from molecular networks all the way up to whole organs and and Beyond are solving specific problems in specific spaces so we are really interested in my group we're really interested in creating a framework that allows us to relate to really a very diverse intelligence so you know of course familiar creatures um all kinds of weird Biologicals Colonial organisms swarms of course new and I'll show you some in a couple minutes new new engineered creatures artificial intelligences and maybe at some point exobiological uh you know truly alien agents we need to be able to deal with all of this it's not enough to deal with but you know crows and and monkeys and then maybe octopus you know that's that's way too narrow and so of course this is a an idea that has been has been addressed before so here's Weiner and and and and colleagues trying to come up with a very sort of cybernetic way to classify different degrees of behavior all the way from passive mechanical Behavior up to complex cognition in a in a way that abstracts from its uh familiar embodiments right so there's no talk of brains or neurons or anything like that this is this is very very sort of functionalist and um one thing about us as as humans is that we are very primed to to recognize intelligence of of in in the three-dimensional space so basically medium-sized objects moving at medium speeds through three-dimensional space when we see it we know what agency looks like we know what intelligence looks like but we are really bad at and this is why we must get better at it recognizing intelligence in other types of problem spaces so imagine if you had a direct feeling of all of your blood chemistry if you were able to feel your blood chem industry the way that you can see objects in three-dimensional space you would be very it would be very obvious that your kidneys your liver and so on are have a degree of intelligence and they're doing amazing things in their problem spaces so we study how individual cells navigate the space of gene expression uh however the physiology and morphos space the space of um patterns this is what I'm talking going to talk about today and just for a few minutes here here's an example uh here's an example of cell solving an entirely novel problem in uh genetic space so here's a planarian this is a flatworm they regenerate parts of their body when amputated what we did was we exposed planaria to a solution of barium barium blocks all of their potassium channels the the the the cells and the neurons are really unhappy their heads explode literally just explode um over the next week or so they rebuild keeping them in the barium they rebuild a brand new head the new head doesn't care about barium at all so we asked a simple question how can that be what what is the new head doing that the original head couldn't do and we found out that there's actually very few genes that this system up and down regulated to be able to do its business in the presence of barium the kicker is planaria never get exposed to Barium in the in the uh you know in the real world there is no ecological precedent for this so just imagine you're you know you're a cell you've got you've got you know I don't know tens of thousands of possible genes you've got a disaster a physiological disaster you don't have time to try every combination it's there is no time to uh you know try everything and then whoever survives survives these cells don't turn over that fast you have to solve this novel problem possibly by generalizing because you've never seen barium before but you have seen epilepsy before and barium excitability might look a little bit like epilepsy and so maybe you can do some of the same things so this idea of solving novel problems in physiological space is one one example of what what what biology can do but here's another example so this is how we all start as a as a kind of a collection of early cells but this is a cross-section through a human torso now look at the incredible order here right all the the tissues the organs everything is in the right place the right size and shape and relative to each other where does that come from uh you might be tempted to say DNA but of course we can read genomes now and what's in the DNA isn't any of that what's in the DNA is the the sequence of the micro level sort of Hardware that every cell gets to have the proteins that's what the DNA specifies so you really you still need to understand the physiology by which these cells compute what to do here and then there are lots of questions you know as regenerative medicine workers we try to figure out what do we say to these cells to rebuild pieces that are missing uh and as Engineers we want to know what's what's actually possible what can you reprogram this can you make them do something else so the amazing thing about development is that while it is incredibly reliable and robust and in fact hides all of its intelligence from us when we see acorns giving rice to oak trees and frog eggs make frogs we sort of assume well what else is it going to do like that's obvious right that's how it has to be but that's only what happens in the default condition what we find out is that for example if you take an early embryo and cut it in half you don't get two half bodies you get two perfectly normal monozygotic Twins and in fact more generally the process of development can navigate this anatomical space in a way to reach the same goal from different starting positions despite really drastic perturbations by taking different paths it's not just a hardwired set of emerges this is not about emergence of course complex things emerge from Simple Rules this isn't that at all this is the ability of the system to get to its goal despite really really radical changes um so here's one change here's another change as an adult some organisms like the salamander you uh they regenerate their eyes their limbs their jaws their tails you can you can make Cuts anywhere you like along here and these cells will very rapidly grow and and undergo morphogenesis and then they will stop when do they stop they stop when a correct salamander limb has formed doesn't matter where you cut it it will only grow exactly the right amount and it will stop when exactly the right thing is formed so you've got some sort of error minimization scheme going on here it knows exactly what it looks like it knows what the target state is and in fact this is something that that we discovered that so this is a tadpole here are some eyes here's the brain the gut the nostrils these tadpoles have to become frogs in order to become frogs they have to rearrange their face the Jaws have to move the nostrils after everything has to move we find and so you might imagine that this is some sort of hardwired uh set of uh uh emergent outcomes where every organ gets displaced to its appropriate distance and Direction so we made what's called Picasso tadpoles basically we we scrambled everything so that everything's in the wrong place the eyes are off to the side of the head the Jaws are on the other side everything is just scrambled because we have this hypothesis that this is more intelligent than than people gave it credit for sure enough these what what these guys do is every every structure moves in novel paths and keeps moving no matter where it started from until it gets to be a pretty a pretty normal looking fraud so what the genetics gives you is not a piece of Hardware that does the same thing all the time it gives you a machine that can recognize unexpected changes and take corrective action as needed to get to the same goal the most amazing part of this is that in doing this and this is this is an example of top-down causation which is how it's why it's really important to understand this how high level goals filter down to the sort of implementation machine scenery is that what you see is that this is this is an example from from the um kidney tubule of a newt if you take it in cross-section normally there's I don't know eight or nine cells that that work together to make the Lumen of that tubule but one thing you can do is you can make these so you can force these cells to be gigantic and when you do this uh when you make them larger fewer cells will do this forming exactly the same Lumen diameter until you make the cell so large that a single cell will wrap around itself to give you whoops to give you the same structure what's amazing about that is that these are completely different molecular mechanisms this is cell to cell communication this is cytoskeletal bending so in the service of a high level goal meaning make this large-scale anatomical structure different molecular mechanisms get activated okay um and this is this is this is very unusual this this idea is very unusual in biology biology biologists tend to think about things emerging from from molecules not going the other way but it has certain parallels in in computer science where the algorithm makes the electrons dance in an important way right in a functionally important way and so um what we've been doing is I'm trying to build models that go these sort of full stack models that go all the way up from from molecular uh kinds of activities that set um the the uh ion channels and other things in the membrane two we we specifically I don't have too much time today but but we specifically study bioelectrics we study how all cells not just neurons all cells use electrical signaling to form computational networks and so we study how uh the what the tissue level electrical patterns look like um and then what the organ level patterns look like and then how that becomes uh literally an algorithmic set of steps that determines things like how many heads a flatworm is going to have and during this process we want to know we want to know a few things um we're going to know how does the cognitive light cone and what I mean by that is simply the spatial temporal size the scale of the largest goal that that particular system can conceive of pursuing right so if you're a bacterium your cognitive light cone is very tiny because really all you care about is the local sugar concentration with about you know maybe 10 minutes uh forward and back but if you're a human you can have gigantic goals that exceed your lifespan it could be planetary scalables and then of course every every kind of creature in between so so we Define this kind of cognitively cone based around the types of goals that A system can pursue and so we need to understand during this process how do the goals enlarge how do they shift into different spaces so individual cells care about things in metabolic space and physiological space and transcriptional space those are their goals collectives of sales care about very much larger goals such as the shape of your hand and the fact that you have to have to have exactly 5 five fingers and then of course this question of where do these goals come from in the first place we'll address that momentarily so about the only piece of bioelectricity I'm going to show you because because yosha brought up this idea of counter factual um memories is simply this we treat the behavior of these cells and tissues as a collective intelligence literally the group of cells is a collective intelligence that tries to solve problems in anatomical space and because we have semi understanding now of what the medium is of that collective intelligence not shockingly just like in the brain it's bioelectric why because that's how the brain learned its tricks um you already heard in Yoshi's absolutely right there are very um uh very uh difficult uh task to try to distinguish what makes a neuron different from other cells because even bacteria from the time of microbial biofilms have already been using all of the same tricks as as the brain as the brain uses this this electrical Network stuff is ancient and so what we are able to do is read and write write the memories of this collective intelligence and so we use a specific technique that reads the electrical gradients this is just like neural decoding as the neuroscientists try to do in the brain so here there's a particular pattern that says if injured you're going to make one head we can rewrite that and we can create a worm here he is where the pattern says no actually a correct worm should have two heads and if you go ahead and cut that animal they will go ahead and make two heads this is not Photoshop these are real real two-headed polaria but the cool thing about this pattern is this is not a reading of this animal this is a reading of this perfectly normal anatomically one-headed genetic you know transcriptionally one-headed animal so this is a kind of counterfactual memory it's a representation of a state that it's what you are going to do in the future if you get injured if you don't get injured it stays latent it never comes up so uh and we have lots more data on this we can actually get make it make heads of other species of worms and and many other things the idea is that a single single body can store one of two different representations of what the goal state is going to be if they get injured and then they build to that goal state so this should sound very familiar this is this is both the nervous system works this way and of course reprogrammable devices work this way the same Hardware can hold on to multiple computational goal States now now what's really let's let's go back to where where we started with this which is this this notion of of scaling up from from components so so here's your here's your single cell um what evolution has done is allowed these cells to merge into networks that are able to able to store much larger goal States so so this guy only cares about his own physiology and and his own metabolics this collection of cells is is uh is very competent in reaching a particular region of anatomical amorphous space that looks like this the goal is huge it's centimeters in size and if it's deviated from that it will do its best to come back to even even with the you know kind of drastic interventions but that process has a failure mode that failure mode is known as cancer what happens this is this is human glioblastoma cells if individual cells get disconnected from this electrical and and other signals as well from this network that binds them towards a common uh journey in that space that common goal they revert back to their evolutionarily ancient self what is the goal of a single cell well it's to become two cells and to go wherever life is good that's metastasis and so you can see how what happens with these cancer cells is they're not any more selfish than uh than any other cell they're just their selves are smaller and and we've talked you know I talked to roboticists and and folks like that to with this idea that why don't robots get cancer right the reason that our current technology isn't prone to this is because we do not have a multi-scale architecture where the components have their own goals that we we have we have some some fairly dumb components typically and then we hope that the collective that has some kind of you know is doing some kind of computation but the parts are not trying to do anything biology isn't like that every component will do interesting things if freed from its neighbors and I'll show you that but of course you know biomedically we can we can sort of take that this this kind of weird way of looking at things and ask can we can we simply enlarge the um enlarge the the boundary of the self enlarge the border between self and outside world and so you can do that we have techniques to do that where when we inject a particular um human oncogenes and these tadpoles to make make tumors and you can already see this is voltage Imaging you can see that uh these cells are already starting to defect as far as they're concerned the rest of the animal is just outside environment so that's something else yosha mentioned is this idea of being in conflict or not with your environment it's it's never obvious to a new agent what the environment is every cell is some other cell's external environment and so normally all of these cells believe that the water out here is the external environment but once you disconnect them using using these oncogenes then as far as the cells are concerned all of this stuff is external environment they don't care what happens to that they're going to do their best they're going to live their best life they're going to dump entropy into the into the environment and of course that's maladaptive for the organism but one thing you can do is you can force using specific techniques including optogenetics and some other things you can force these cells to remain in electrical in in the correct electrical state with their neighbors and if you do that even though the oncogen this is the same animal here even though the oncogene is very strong there's no tumor because uh the problem is that because the hardware problem isn't isn't really fundamental it's the software that's fundamental it's are these cells working on a large goal like making a nice liver and muscle and skin and whatever or are they individual cells working on individual goals so we spend a lot of time thinking about these kinds of things how do we what what are the mechanisms of course and but also algorithms policies for connecting up little tiny uh homeostats these cells that like to keep certain States into much larger networks that then have these interesting properties that of course people in the connectionist world have been studying for a really long time so you know in painting our painting all this kind of stuff so so we can we can talk about our efforts to sort of understand how the goals scale they scale from these really humble metabolic kinds of goals of individual cells right these these homeostatic Loops into anatomical homeostasis eventually behavioral homeostasis and behavioral clever emotion through three-dimensional space and eventually linguistic space and who knows what else so um just for the last couple of minutes I just want to show you um one thing which is simply which is simply this uh in studying these uh these kind of Novel perturbations and asking what what our cells actually capable of what you know what other what other modes are there uh we asked the following the following thing and I have to do a disclosure here because Josh bongard and I are co-founders of this thing called fauna systems it's a biorobotics uh kind of uh company so so what we did in this all the biology was done by Doug Blackiston in my lab and uh there was a lot of computer science here done by Sam kriegman and Josh's lab um what we decided to do was to liberate cells from the normal environment and give them a chance to reboot their multicellularity how much creativity is there what else can they what else can they do and specifically um and I think somebody on the chat asked this before where do these goals come from so that's what we wanted to understand a completely novel creature that's never existed before what goals do they have where do their goals come from okay and so I'm going to just show you a couple of examples so um so what what we did here is uh we took an early frog embryo and so and so what doug does is he uh takes all of these cells up here which are skin they're basically uh determined to be skinned and he dissociates them and and puts them into a little a little depression here now there are many things that they could have done after that they could die they could spread out and sort of walk away from each other they could form a flat two-dimensional monolayer the way that cell culture does instead what happens is this um oh and this is time lapse of course so so overnight these these guys will get together and they will coalesce into this interesting little little thing here um and what is it well we call this a xenobot xenopus lavis is the name of the frog and it's a biobot so xanabot uh what what it's doing is it's using the little hairs on its surface these hairs are normally there to spread mucus down the body of the Frog what they've done is repurpose those hairs for swimming so here it goes it's chugging along um you can see that they can go in circles they can sort of Patrol back and forth like this they can have group behaviors this this one's going on kind of a long journey these are interacting together these are having to have an arrest um here's what here's what it does in a maze so so you can see it swims along it's gonna take a turn here without having to bump into this outside wall so it takes a turn and then at this point for some internal reason uh we have no idea about it decides to turn around and go back where it came from okay so there's all sorts of you know primitive kinds of Dynamics just keep in mind even though these things have uh this is calcium signaling you see it's the kind of thing you see when you do brain Imaging um there are no neurons here this is just skin this whole this whole thing is is just skin cells but there are doing a lot of you know calcium readout is a great readout of computation and and you know could they be saying something to each other of course we don't know this is still a very much ongoing subject of Investigation but one of the one of the amazing um things that uh that Doug and Sam discovered is that their computational models of these guys make predictions that differently shaped Bots are going to rearrange their environment in different ways so so they did a lot of simulations and so then we tried it and and we just did it in Vivo and here's what we found so so here are the Bots uh the white stuff here is their cells they're loose skin cells that we sprinkled into the dish and what they're basically doing because we made it impossible for them to reproduce in the normal froggy fashion they are basically implementing Von Neumann's dream they are constructing uh other well what they do is they run around and they sort of collect these skin cells into little piles then they then they kind of Polish the piles and these piles because they're working with an agent material where they're not working with passive particles of working with cells what do these cells like to do they like to become the the xenobot and so of course they create the next generation of xenobot which then matures and guess what it goes it does the same thing and you get the Next Generation and so on so this is kinematic self-replication okay and they'll make multiple generations of this so so here's here a couple of uh interesting uh corollaries to this and I'm almost done uh the exact same genome so here's the specification of a micro level Hardware this is what every cell gets to have can do one of two things under normal circumstances it will do this it has this developmental sequence that it makes these tadpoles that do various things but under other circumstances it makes this this is a xenobot this is a developmental sequence this is I think a month old or something xenobot you know where did the shape come from right and they have a different Behavior with this thing called kinematic self-replication so here's a few interesting things number one um typically when you talk about why a certain creature has certain capacities if everybody leans on Evolution while for eons uh it was selected to do this or that well there's never been any xenobots there's never been any selective pressure to be a good xenobot this is completely emergent they do this uh they they form this this coherent uh kind of system with new behaviors both anatomically and behave and uh with motility uh basically overnight this is this has never been selected for specifically um they're completely new in the in the biosphere as far as we know no other living creature does kinematic self-replication that's the first thing the second thing is that uh how did we engineer these I mean um there are no trans genes here so the if you sequence this all you see is normal xenopus latest there's nothing wrong with the genome it's wild type because there are no Nano materials uh you know some of them um some of them Doug can make some uh some some modifications uh to them surgically according to the AI that that Josh and Sam built but basically the way way we engineer these is not by adding anything it's by liberating them from the influence of the other cells so normally if you just look at the normal path of this of this biological system you would say you would say what do the skin cells like to do well they like to be in fact all they can be is to be the outside two-dimensional layer of keeping out the bacteria it's very boring passive life they just sort of sit there and keep out the bacteria but that's only what happens when they're basically bullied into it by the other cells it's it's Behavior shaping it's instructive interactions from the other cells that tell them to sit quietly and be the outer layer in the absence of all that stuff liberated from all that they have a completely different default lifestyle and this is it which you would not see without you know without this without this thing so and and we don't know what else you know certainly we're studying right now all kinds of Behavioral capacities do they learn do they anticipate all sorts of things I'm not making any claims yet about that so but but but this idea of of of what evolution I think really does and and then we can talk about why I think I think we have now some ideas about why it doesn't produce solutions to specific problems it produces generic problem-solving machines and so the big thing that every living system has to do and I think these are you know if I had to make a list these are these are some things that I think I think are required for the kind of thing we want first of all I think it's really important uh that your parts have agendas it's not enough to have dumb parts and try to engineer an agenda for the organ for the for the whole system you have to have a marketplace where every layer is uh competing cooperating and doing its attempting to do its own thing um you of course risk failure modes you risk Parts trying to go off on their own that's one of the one of the trade-offs but but overall it becomes I think of an incredibly powerful architecture uh they have to emerge spontaneously that is um real agents don't know where their boundaries are if you are a new embryo coming into the world you don't know how many cells you're going to to have because we might remove half of them and you still have to make a good embryo you don't know how big your cells are because we might make gigantic cells or smaller cells you don't know exactly how many uh well how many chromosomes you're going to have because we can make um all kinds of weird weird chimeras and so on um you have to be able to surviving life has to be able to play the hand it's dealt from scratch you you really can't take past experience too seriously you have to improvise on the Fly this is what this is what biology does so it does not have nobody says this is the Border this is where you are and then everything else is the outside world it has to guess and it has to make a self model and it has to make a world model then there are the energy constraints uh typical AIS as far as I know have all their energy needs met they can do whatever whatever they they want they don't have to worry about it uh organisms evolved under very stringent energy and time constraints which means that they cannot afford to be some kind of uh uh you know a laplacian demon paying attention to all the micro states of the world they have to do a lot of core screening and they have to uh kind of bundle uh all sorts they have to generalize all sorts of things that go on into models of Agents doing things of selves doing things right that's the only way you can you have the time to compute what you should do next and if you get good at that eventually you turn that on yourself and you start telling stories about meaning making internal models of yourself doing things and this becomes this idea why do we all you know innately believe in free will because from the time that we were single cells we had to tell stories about agents doing things and making choices otherwise we just wouldn't wouldn't survive without bad ability and then there's some other things like the shared stress and the you know the sharing the the scaling of stress via sharing it among parts and so on we can talk about that and and the idea that they it's open-ended the living things select their own problem space and explore it and so on so um this is you know this is uh I'm just going to stop here but this is what what I tell people is that because of this because biology is so incredibly interoperative because none of the parts make any assumptions about what's going to happen they uh they do their best in whatever environment they happen to be in every combination of evolved material some sort of uh engineered material and software is potentially a viable agent so high brots uh cyborgs biorobots all of this there's this huge option space of new creatures of of new bodies and new minds everything when Darwin said endless forms most beautiful you know sort of impressed with a variety of of living beings all of that stuff is a tiny dot it's a tiny Corner everything on Earth is a tiny corner of the space of possible beings it's truly immense and all of these things and we're going to be surrounded by these things some of this already exists as some hybrids and cyborgs already exist but there's going to be an incredible variety of them that we are going to be living with this has major implications for ethics for example because up until now we were all of our ethical Frameworks about how to relate to other beings really boil down to two things do they look like us and did they come from do they have the same origin story as us and so this is uh you know even even today in in bioethics um sessions at conferences people said well does it look like a human brain then we have to then we have to worry about but the reality is that this is these categories are uh they're not going to survive the next couple of decades we cannot uh gauge anything about um the potential uh intelligence in terms of the type of cognition and what space they're working in by by looking at where they come from the family tree because they're not going to be on our family tree and we have to have completely different Frameworks for this and and the kinds of AIS that we're talking about now are only one part of this they're going to be we're going to be facing the exact same problem uh of dealing with without with the software AIS in in biology so um if anybody's interested uh in these things there are lots of lots of papers where we go into this and I just want to thank the students and post stocks that did all the work that I showed you and of course again the disclosure so I will and there thank you so much Michael that was wonderful um I don't think that I need to introduce Kristoff he uh already had the pleasure of having you on a previous panel and uh Kristoff is currently a professor at Etihad the ini in Zurich and he is a first generation cyber nutrition in a way he's a physicist who is using very uh broad perspective on understanding intelligent systems and without further Ado Christo please the stage is yours yeah I haven't prepared anything I didn't know um I was expected to prepare anything so I am at Liberty to respond to uh some of the things you have the two of you have said let me start with a um with a point uh Joshua you made about the brain which was it is a noisy uh it is a noisy entity um it is not a digital device not on the basic uh basic level so if you want to have billions of uh entities synapses or uh uh neurons to interact in any useful sense you need attractive Dynamics so there must be certain states of the thing that have the property of being stable under noise of uh of uh having a tractor Dynamics and we know that um the the brain is of course essentially a network so in each moment of time a subset of the neurons fire and this subset must be stable and for a short moment metastable if you want to call it that way you want to go through a trajectory of stable States and that means um individual fibers in the individual interactions between neurons must be embedded in alternate alternative Pathways which uh run to the same effect so um a a signal emanating from a a single neuron going off on different Pathways many of these signals must come together again and coincide in space meaning on the same neuron and in time and this is a selection Criterion for the kind of activity States and the underlying connectivity states that make those States stable and I would like to submit the idea that the brain is um totally dominated by um those appropriately shaped connectivity patterns that have this property these connectivity patterns emerge through a process of self-interaction you know you have self-interaction also on the on the small on the slow times scale of Individual Services adapting and individual synapses finding out where they can find coincidences of signals you know each axonal branch has a small Choice a small sphere of a search space where it can end up in plasticity and all the uh the endpoints of axons are searching around in order to find meeting places where they have a high likelihood of coinciding with other the signals of other branches and this process of self-interruption of network sub organization single moves out from the space of all combinatorially possible connectivity patterns a very very small subset let me remind you of the fact that the brain the whole organism the brain is constructed on the basis of one gigabyte of genetic information in order to describe the connectivity pattern of the brain it's an easy calculation you need a petabyte 10 to the 15 bytes of information which is a million gigabytes as you well know so the the genes can only select from the space of all connections a very small can only be able to select from that space a very small subset and I think it is very important to know more about this subset of um of self supporting activity States and connectivity States so in order to put that in action let me remind you that your brain is in every moment of waking time representing the situation in which you are immersed you have a representation of your actual environment if you if you open up your eyes and this representation is so good that you usually equate it with the reality out there you are not aware of of any differences between this uh reconstructed this model of the outside world and the the outside world and you are so confident that it is the reality and not just an imagination because you continuously do experiments you move around so that the perspective of the of the world Chambers um and you test whether your representation is stays in tune over time with the sensory information you do experiments you touch objects and um you you you experiment continuously uh with the uh with the environment in order to make sure that your representation is in tune with it is consistent with it is um rendering uh the reality uh of course what you are representing is only a small sector of what is out there your attention is always picking out only part of it but what you are picking out is um for you for all intensive purposes reality I find it amazing that our models uh our Theories of Intelligence of brain function uh make so little of this very fundamental fact of our individual life now um according to what I said I have uh being explicit about the data structure which is used to create this reality to to represent to a model this reality the data structures of course everybody believes firing neurons but I would like to change your perspective in saying don't look at the neurons each neuron by itself is it doesn't have any uh significant meaning it is the environment the neural environment the firing environment in which the neuron fires which is the important thing you have to look at quite a number of co-firing neurons in order to be able to make sense of it when you look at a TV screen and you can see only one pixel there's no way you can connect that with any meaning the pixel is is something real so to speak but it doesn't tell you anything it is it has uh no significance in order to understand anything on a TV screen you need to see quite a patch of it in order to understand anything of the data structure of of a brain you need to see hundreds probably thousands of neurons at a time and a given neuron can take part in quite a number such but not an infinite number and quite a number of such activity patterns and uh which I would like to call fragments and so I think the perspective on to the nervous system has to be changed very fundamentally in order to see it as a data structure that is up to the job of representing reality now um I I would like one of the last statements uh you Michael made was the um the range of things of intelligent things of organized things that can be generated you said is infinite I would rather like to emphasize the opposite I've recently read an interesting book by an author named Morris a book that was totally focused on the phenomenon in looking at evolutionary phenomenon of convergence a lens eye has been invented 12 times or something like that the facet eye has been invented again and again the lifestyle of a wolf pack has been invented again and again the lifestyle of social insects or certain animals you social animals has been invented again and again so the space of all possible organic patterns that makes sense that have inner coherence where the parts support each other in order to create something that is stable and and significant it is stable in itself and is coherent with the environment the space of those shapes and structures is limited and here is a great uh opportunity for Theory to come forward to understand what to expect from biology the last thing I want to say is that what is missing completely missing so far almost completely missing so far from our um from our artificial intelligence from our machine learning is the equivalent of biological behavior of Behavioral Goods of course an animal has the fundamental goal of of self-preservation of the own structure but evolution has built into the individual species a number of sub goals like feed yourself and avoid danger and so on and find Social contacts sub goals which make up your life and the intelligence in the eyes of many people is just the ability to pursue those goals in a changing context that is a slightly different definition from Joel kyosha yours was [Music] Computing functions the biological goals is the the the the the the the the president of biological intelligence is of course to pursue those uh goals and I think in the present situation um around things like GTP and so on it is becoming quickly clear that those beasts are not intelligent in our sense because what they do doesn't make sense in the light of the goals that we all recognize as such and it would be I I think they will soon be an important drive towards um installing in such systems uh the equivalent of the sense of responsibility the sense of the consequences and utterance that is made may have downed and down the line for ethical for legal reasons but also I feel although I don't have a very strong argument in that favor I have the feeling that in order for an entity to be truly intelligent it needs to have a set of set goals with which it can which it can pursue in its environment uh last remark I want to make is about Consciousness I think um in my view the definition of the conscious state is of your own mind of your own brain is a state in which you concentrate on one topic your whole brain is concentrated on one topic and all the different Sub modalities in your brain are in tune with each other are in mutual understanding so if there any change happens in any part of the system all the other agency other modalities can immediately respond to to that so Consciousness is um not the icing on the cake I don't think it makes sense to talk of zombies um uh Consciousness is a condition for a system to be a functional and if you go down in the letter of um of evolution to simple animals I don't think you can find a point uh that is a point Michael also made you can find a point where Consciousness disappears but Consciousness just loses volume you lose language when you go from from humans to to animals and you use the imagination of distant future when you go to animals and and so going further and further down the evolutionary leather um the volume of Consciousness gets less but I would have a hard time um yeah I I think when talking about the fly it has its own uh level of Consciousness so When approaching a wall it it it it senses the uh the impending approach and reacts accordingly so the whole organism is able to react to signals so that is my um view on Consciousness thank you thank you so much Kristoff so uh we're on to the discussion and Q a question um I would like to start by asking a question of Michael Levin so Michael you you were talking about how a lot of the problem solving within uh the organism is actually done at the local scale and there's also the interest the interesting remark that was made by one of participants in the chat that you mentioned that the planaria tale would have become the Hat had not been bullied by the rest of the organism so what do you think are the communication protocols that are necessary to enable different types of intelligence and is it the case that humans for example have cancer because we don't have enough intelligence at the slower lower local organismic level or is it because we pursue the higher scale goal and some kind of trade-off has to be made um yeah great great questions a few things uh first of all it is it is definitely not local so one of the key things about all of this stuff is that larger systems being collect make decisions in spaces that are uh much larger than than their parts and so here's a very simple simple example you have a planarian it's got a head and a tail you cut it in half these two cells on either side of the cut these guys will have to make a new head these guys will have to make a new tail but they were sitting right next to each other before you separated them with a scalpel you cannot locally decide whether you're a head or a tail it's a decision that has to take into accountable do we already have a head do we have a tail which way is the wound facing this is a global decision it cannot be made locally all of this stuff uh is like that and it uses uh the exact same the scheme that bacterial biofilms use to decide when different parts of the thing should eat so that everybody has a turn and the exact same thing the exact same set of mechanisms that brains use to try to synthesize the activity of individual neurons into some sort of global goal for the rat or human or whatever it's it's an electrical Network it has certain properties only a few of which we understand but it is absolutely not local what what these uh what what what this bioelectricity is very good at is at uh implementing uh of integrated information across across space and time to uh to make decisions in in new spaces and and that's and maybe I think I forgot what was the second part of your I lost track uh the second part uh is so do humans so you remind that humans have cancer but uh other some other animals don't and some other animals are in fact Immortal so what is it that uh what is it in your opinion that doesn't allow human organs to solve the problem of immortality does it have something to do with higher level goals or is it just a lack of intelligence yeah um I think that uh well so so there's two there's there's kind of a a simple answer and then there's a more interesting answer the simple answer that people usually give is simply that uh you all by so so so evolution of course doesn't really optimize for long life happiness intelligence it doesn't optimize for any of that it optimizes for biomass that's it and so right and so the end the simple answer is we don't need to be uh Immortal and uh and and and cancer resistant because it's perfectly possible to be a human and have lots of Offspring and still get cancer and die after your reproductive years that's it that's the that's the standard um answer that that it's actually there's just not a lot of pressure for for humans to to do anything different now I think the more interesting answer is this um there are most organisms do get get cancer and do age there are a few that that are resistant let's look at the planaria one really interesting thing about planaria is that many of them reproduce by tearing themselves in half and regenerating now one interesting thing that that the the implications of that are unlike for us for us if you get a mutation in your body during your lifetime it doesn't get passed on to your Offspring right so because because of the weissman's barrier and sexual reproduction in planaria that do this every cell that doesn't die from that mutation contributes copies of itself to the next body right because they they have to they have to repopulate and they have to regenerate the new worm so planaria accumulate mutations like crazy so over over 400 million years that they've been around their genomes are a complete mess uh they basically look like a tumor they're mixoploid every every cell might have a different number of chromosomes it's a disaster now this is this is really a scandal because because uh nowhere in a typical sort of um you know biology curriculum will you hear that the animal with the worst genome is by the way Immortal cancer resistant and highly regenerative right they have the best Anatomy what's going on we're told we're told that our genomes are are you know that's where your body information is right how can this be so this has been bugging me for a really long time this this disconnect and I think we finally have an idea of of what's going on we we just like two days ago just published a paper on on some simulations that talk about this I'll just give you a very simple example uh if one thing you have to do is you have to model not just the G not just the genotype and the phenotype meaning the genome and then the thing that gets evaluated in these evolutionary simulations but you have to model the morphogenetic process in in between those two the morphogenetic process has certain competencies for example um some of them I showed you there are many more so for example if I if if there's some mutation that that puts your mouth off to the side the mouth is perfectly competent until I come back where it needs to be if it's a mutation that causes you to fall apart as an early embryo act you'll just be a bunch of twins you know multiples if um if we we took once we took some eyes of Doug blackest and did this too he took eyes and put them uh put them instead on the animal's tail they can see perfectly well out of those eyes no problem no period of adaptation needed it's all good the nerves come find the spinal cord it's all good so that that all of those kinds of things abilities to make up for these kinds of issues we call uh developmental competencies now one thing that happens is that when you have an animal with with a little bit of Developmental competency you come up for selection and it turns out you're very good right but why are you good selection cannot tell whether you have a great genome or you're good because you're highly competent and you fixed all the things your genome actually was pretty sloppy about so that means it's harder for evolution to see the good genomes it can't do all as much work in perfecting The genome but what it can do is is crank up the competencies right so when you do that then of course that makes the problem worse because the more competent you are the less it's possible to find a good the best genomes and so there's this positive feedback loop this ratchet and there are some other things that sort of work against it but I think what happened is that and this is this is very much a hypothesis still I think what happened is that planaria went all the way meaning that in in that lineage probably because they reproduce this way it doesn't make any sense to assume that your genome is any good and the only architecture that survives is where the algorithm is so good that we're going to make a perfect world no matter what happens to the genome this means aging uh you know carcinogenic mutation Nations the algorithm meaning meaning the the the Machinery that that maintains that that goal State and physiological and anatomical space is so good that it can pretty much ignore a lot of uh issues in the hardware most of us aren't like that salamanders are sort of in the middle so salamanders are highly regenerative but they age and die and so I think what's what what you know salamander sort of went part of the way there and they can fix certain things but not enough to to really like keep it going forever uh mammals probably stopped even earlier than that but I actually uh I actually don't think any of this is is fundamental I mean we're working on on regeneration in mammals now I do think someday we will all you know sort of regenerate like planaria I think I think it is going to be possible but I do think that Evolution makes these trade-offs that uh that we you know there's they're just easier ways to to be a human I think um so question for everyone um so in terms of communication protocols uh to what extent is intelligence simply the ability to organize the cells and what are the conditions necessary for that to occur and to what extent is intelligence is some internal competence competency of the cell or neuron or whatever computational unit we're talking about Joshua would you like to start oh okay I'm currently um thinking about um the question of um whether it's possible to make something that is as long-lived as the planaria that doesn't look like a blob right there seems to be some correlation between the structural coherence of the organism and the detail and the solution that it has and uh the degree of fidelity that is expected from interpreting The Operators defined in its genome and um more specifically um I wonder what how we can formalize the idea that Michael bought up earlier of multi-scale organization and such a way that it leads to coherence what is the Criterion that makes a single agent coherent in itself and leads to this coherence on a particular level arguably our own mind is some kind of society of agents and the organism has lots of local agents every organ is an agent in a way every cell is an agent but there is also a globally coherent agent and that is different from having a multiple twins uh coexisting next to each other and forming a sample operative Chimera but uh that leads to some Global element on the other hand Kristoff has pointed this out did if you think about Consciousness it seems to relate to a unified experience and this unified experience of all sensory data is what makes it specific what is interesting about Consciousness is that I normally don't have multiple conscious experiences unified in one perspective how is this Unity being realized or more generally speaking can we come up with this kind of formal Criterion that defines how everything has a place in the greater whole and uh the condition needs to be measurable and lead to globally coherent behavior on the next level of organization if we take this and to account and if you look at Michael's diagram of biology brought up in the context of Ethics all the different agents that all seem to be centered about individual humans it turns out that individual humans are not the main agents in in the human sphere right organizations of humans are much more powerful than individual human beings and while the individual human beings Implement these organizations for the most part we gradually transition more of that to machines than we are building it seems to me that there is uh different levels of organization that transcend the individual organisms this multi-scale organization doesn't stop as humans and the next scales are getting more and more agency also I don't think that humans are all that important right it seems to be that humans are very specific thing that has a very specific role all our cousin species are dead hominids are not long-lived species and uh it seems to be that the reason why Gaia brought us up is that we fulfill our job which is to burn all the fossil fuels as quickly as possible this is what we're here for then we burn ourselves out if we manage to teach the rocks how to sink in the meantime that's a stretch goal but after we are gone there will be more intelligent species and we are very specific one right we are this type of monkey that is not going to get his hand out of the Caldwell trap if that's fossil fuel inside and that's somewhat predictable if you look at the way in which we work because we are very smart and intelligent on very short time skills but we are not globally coherent we don't find ourselves in This Global coherent god-like organization and if we succeed in building the next level of intelligence maybe this next level organization some kind of very fast tightly integrated globally coherent mind is going to be emerging and maybe humans will play a very small part in whatever is going to come afterwards but it's not about us right life about on Earth is not about us life on Earth is about the cell and overall it's about fighting back entropy it's about sustaining yourself or maintaining complexity so uh my question would be to um Kristoff and uh to um Michael can we come up with the Criterion that uh determines coherence yeah yeah I think um the important thing is uh as I said that a coherent form has a kind of stability it is made up out of continuous variables which are prone to to noise and for the whole thing to um to to to have a lasting existence is the different signals that converge and run Point have to have to agree with each other they have to stabilize each other the same way as a crystal um is forms a rigid body in that the individual forces between atoms which are also acting of course in a liquid but are not able to to form something like a stable shape in a liquid but in the crystal they have fallen into a configuration in which in each individual interaction each individual Force gets um it gets support by other indirect Pathways and so I think just as in the space of all mathematics those pieces of mathematics that have been found are singular points um that admit no change if you if you have come up with the idea of a group then the rest of the whole story thousands of pages in in in in mathematical journals follows by force from the definition of what a a group is a a finite group of finite number of elements and the same way the shapes that dominate life have this inherent self-consistency that is the different uh chains of forces that interact support each other and and the rest of mind I apologize uh Crucify My apologies I think we're almost on top of the hour and Michael has to go at 11. so um or well in in one minute Michael any any last comments from you before we let you run I'm sorry this is this is uh extremely interesting uh so thank you so much for having me here this was this was amazing um thanks for coming I really wanted to introduce you to uh Christoph and have one more conversation with you uh after our uh lucky podcast um right and so I'm very very happy that uh you could come and hope to see you again soon and uh stay in touch I really like many of your ideas um in my space off um self-organizing intelligent systems and uh it's very uh lucky that we could have you here today uh Kristoff uh do you have some more time to stay on yes I do perfect thank you very much everybody I've got to run I'm happy to uh great meeting you and I'm finally continued by all the examples that you have shown it's it's uh it's it's just great cool thank you so much see you all later bye-bye thank you Michael thank you bye bye so we are going to continue for a little bit more um if it's fine with you Tanya you have time right yes perfect yes of course uh so Kristoff you mentioned that uh you think that the genome is that contributes to the brain is the gigabyte but that's the whole of the genome right and so it's uh the part that codes for the brain is going to be a small fraction of that yeah I wonder how much information is remarkable that the genome hasn't expanded from mouth to man so making a larger brain doesn't need more genes yes of course if you take the genome and you drop it into physics it's not going to form a cell so uh and every cell that exists is the result except for the first one of uh the server application of another cell it's all cells in some sense depend on the existence of a cell before the cell is not empty and I wonder what the glamorgo of complexity of the cell itself is how complex is this Machinery that is being copied and copied all over maybe that's much larger than a gigabyte uh and of course the principles of that organization um embodied in the cell lead to the search for um more coherent organization on the next level right so the cell is already an agent a self-replicator a turing machine um and a neck entropy extractor and the self-replication is the main feed of the cell to make that happen robustly in physics and this is what enables everything else I wonder how much of that we need yeah I could imagine that the um that the grunt of the genetic information needed for a single cell already and then um you just you may just add I I don't know how much on top of that in in the form of the uh of the pre-existing cell you know each cell is is the daughter of another cell and so there is some information in the arrangement at least in the arrangement molecular arrangement of the cell that needs to be counted as information although I have a hard time seeing that that will be in a significant way more than a gigabyte you know at the present time there are groups that try to create artificial cells and as they would learn the hard time that a lot needs to be in place to make a cell tick you know the different kinds of membranes the membranes shape themselves that's very important they shape themselves into into the Golgi apparatus rinse and things like that and so there is information in the pre-existing cell but quantitatively I don't think it is going to be uh more than a gigabyte prepare to build an intelligent system say artificial visual cortex how would you go about it and uh what would be the main differences to the existing approaches well I think um you know when you look at um with your own brain with your own eyes at a moving body You can predict the next Split Second and compare it to to to the signals that come in so we have this Machinery in our visual system that is able to do the differential geometry take taking into account the shape of the surface the the play of light on the surface the the movement um and so what I would create is a um a sequence of an array of Rea [Music] um like V1 the primary visual cortex and Mt which is concentrated on motion maybe uh V3 concentrated on color and so on in a number of sub modalities which each um are two and a half D entities um they they refer to the two-dimensional way we we perceive the world and the um with added internal spaces of um a quality spaces like color a three-dimensional space and the texture I don't know what maybe a 40 dimensional space depth a one-dimensional space motion a two-dimensional space and so on and so they reflect the um the retinal image on the one hand um in in these different modalities and they uh another set of of such Rea build up the invariant static scene as as such couple them by um by projection patterns which have to be dynamic because if you roll your eyes the the image moves and in order to connect the moving images to the static representation you need Dynamic protection patterns and these are very important in in themselves in the um the deforming projection of the moving retinal image of a rotating object the Performing the the the deforming projection of that onto a static version of that thing tells you about the the shape of the object and and so I think what what you need is um a a huge array of local feature uh on local texture local um modality descriptions uh all linked together with Dynamic patterns such that the whole thing is a self-supporting attractor space and describes the external world in in in detail as far as you concentrate on the on it of course it's a um I I think it's uh quite an amount of work that is uh necessary I once um tried to put together a company where I believed I need I would need uh something like six or eight intelligent co-workers that together create this structure you know the first feat to convince investors would be to let the system look at moving objects and build up an instant model of that moving object in its 3D form uh in in instant uh replica of that um with the ability to handle it uh to uh to uh to connect to a self-motion to connect to manipulative motion I think uh you know the uh complete uh the abysmal failure of the car industry to come up with level five autonomy has very much to do with the inability to represent the traffic scene in this sense and so my idea was I would get investors would be ready to invest in that direction um however I found out that this whole perspective of mine is so much sailing Against The Wind that I wouldn't even find the co-workers to help me create it let alone uh the investors I suspect that part of the difficulty to create self-driving cars to do this the way in which the model is being generated which means that a deep learning currently relies on building classifiers for individual things and there is no end-to-end train system for a deep learning that is self-driving in this sense and at the same time reliable right if you want to create reliable behavior that is a rule based that where you basically have a set of traffic laws and safety measures and precautions that are built into the system that drive all the behavior the object that this system is going to relate to are crafted by hand so the self-driving car exists in a handcrafted software build where all the objects are being defined by a developer whereas the world that we are living in is an open world and when we see new phenomena we are able to integrate them into this model and when they start having cars see something new that it hasn't seen before that the developer didn't expect like a bicycle painted on the outside of a truck this might lead to confusions for the classifiers yeah yeah you made the very important observation that kids learn yeah on on the basis of very few examples compared to do deep learning they learn moreover in a very simple environment in in their Nursery with uh fairy uh tiles and and interacting with a few people and uh playing with with objects and then they walk out into the world and understand traffic situations you don't hand them the key to the car yet because they don't have the sense of responsibility they they can't foresee the long-term effects of their actions so you you only rent them drive when they are 18 but they understand traffic scenes very well when they are six or or ten so all of this is driven by learning by inter Direction with a simple environment in generalization from there yeah but of course in 99 point something percent of all the cases the self-driving car is good enough it's mostly the long tail of cases that leads to uh situations where the system is producing undesirable Behavior uh I was talking uh a couple years ago that whenever a journalist writes that uh there will never be self-driving cars police is stopping a test lab is the sleeping driver safely on their way home and uh so in many ways self-driving cars exist and they are almost good enough in the sense that they are better than a really really bad driver but uh they're just not uh working to the degree of uh Perfection of a very competent driver yeah it's very mean to ask them to be so perfect much more perfect than humans they are as you said they are if all cars were self-driving traffic would be much more safer than now but the public takes it very badly if an accident happens that could have been prevented yeah we're also in an interesting situation where the public is mostly the media and the media is at a moment in the U.S very much seeing itself in competition with the tech industry because there are competing with the same advertising revenue for the most part and so uh it's at the moment very difficult to find articles that are optimistic and positive about technological developments uh in the media I find so this creates a very unique situation that a very even useful developments are delayed that could save lives um because they're being seen in competition with existing economic and social structures which also creates enormous pressure on AI models like chat GPT I think that jet CBD is a tremendous achievement my kids have been playing with it my daughter has been creating a story of a horse uh that she got to know on the way home from school and then created very variants by modifying The Prompt until she had the story that she liked and then she turned it into a poem was very casual rhymes my son used it to explain this that the system would explain to him how to implement a platform of a game and he was explaining him to how to structure the project and then he was asking how to make an event Loop in Pious and it printed out to his code and explain the source code he spent several hours copying the code into triplet and getting it to work so uh to me this our systems where you have a little bit of human interloop to make it coherent for a particular task and it's amazing what the thing can already do and it seems to me what's missing to get the system to work is to A system that makes it coherent basically you can decompose the Mind into perceptual systems that can in some sense to image guided and audio guided diffusion to coalesce to an internal state that is able to reproduce these sensory data and then confabulation to build alternatives for Solutions Alternatives of what could be alternatives for the future and then the third component which doesn't exist yet which is proving from first principles what works basically rejecting those Generations that don't work and then learning those that worked and building up the system in the way that is continuously learning it also seems to me that many people cannot change their opinion in real time and you have talk to a person that has a strong opinion about something that Roots deeply into their mind you can present them with arguments but you have to talk to them the next day if you want to see any changes which seems that seem to be parts of mental organization at least in some people require offline retraining there's limits to what we can do in online learning some balancing needs to be done offline while we are decoupling the system from the environment and producing data augmentation and restructuring I I wonder how much of that retraining will also be built into the system so either artificial systems will have to sleep into dream yeah yeah before you take an important uh decision you have to sleep over it and give your subconscious mind uh the opportunity to work on making the ideas more consistent than you are able to make them under conscious control yeah very much so um so I I think you rightly said these achievements of GPT 3 GTP and so on are extremely impressive it's very difficult to see where the limit is I agree with you the um the Transformers have a new uh very new architectural feature which is the online computation on the computation on the Fly of connections and of um of of these representation vectors they are computed on the Fly that's all very promising but these systems don't have any insight into real world geometric mechanic and so on representations of what they talk about and they are lambasted mainly for that reason that they don't know what it means something is dead they just know how people talk about it but um they they don't know the significance of it or the geometric arrangement of something and and so that is of course due to lack of instant lack of interaction you know they cannot play with with uh Toy objects as kids do and and and cannot get the the corresponding insights but I still think that what is missing what is sort of needs to be improved is the data structure of representation of of themes and of realities and uh um I I don't think these vectors that I use these days are up to the job I I think that the embedding spaces are not necessarily ever presented in full right if you think about the embedding space as a manifold with 30 000 dimensions and a lot of resolution trying to expand this space in storage in memory is not going to be feasible for the most part so instead what is required is the language that allows to a sparsely and efficiently construct representations in that embedding space the embedding space is the mathematical construct that is uh basically every Dimension is a function that describes a feature and that feature has parameters right absolutely basically every Dimension is a parameter in that feature yeah yeah yeah you did right of course so uh you know I once uh applied for funds to do face recognition and the idea was um to collect data images which varied in all those Dimensions the identity of the person the illumination the opposed the expression um the the texture and so on and I didn't get the money and I'm very glad I didn't get the money because this 15-dimensional space or so cannot be filled with examples that's totally impossible there's too much space in in high dimensional spaces and that's the point you want to make I I suppose so one has to find a way of representing only sub Dimensions low dimensional projections of that and a means of pasting them together together as an equivalent of the high dimensional thing it turns out that when we conceptualize an object it's chunked and a chunk is basically a node this that is composed of features that Define the nature of that tank and you have these seven plus minus two features usually it's less so typically it's more like five which means that if you can define an object by five features you have a local function a locally five-dimensional space maybe sometimes it's nine-dimensional but it's not much more right and these few Dimensions allow us to construct a family of operators that would allow us in up to these two Dimensions construct uh all the 30 000 other dimensions or millions of other dimensions depending on how we look at this function space right so so the the essential Point here is that a high dimensional thing gets projected down in our brain onto low-dimensional representations plus the ability to glue them together and this growing together I don't see in the present uh neurotechnology I think that it happens on a level that we normally don't look at it happens in the activation traces in the network so it's not in the weights of the between the links and it's also not when the synaptic connections between neurons it is in the content of the traces that are moving through this right so the uh neurons and the nodes in the neural network are uh providing the computational Machinery to modulate these patterns according to the content of the patterns and it's the content of the activation wave that is determining how the activation wave is being processed something like a distributed computational pipeline I'm involved in a multi-month or probably multi-year intensive discussion with a colleague at Amy and Zurich Institute of New York Matthew cook and he doesn't want to hear of dynamic mappings he says anything like schemata and role fillers that's all nonsense he believes and he claims all you need is um uh components which he calls them slips of paper into which uh yeah the various features are written into slips of paper can overlap in in the subset of the features and so that that makes clear how they belong together um components Each of which is a small set of um of of of entities and they they overlap in in subsets of these entities and that way they can cover a complex situation I I defy him again and again to create a that way a system that can do something like invariant object recognition um or uh you know the application of a syntactical rule like subject verb object to an arbitrary set of components of appropriate components of course and now two nouns and the verb and uh which is of course very important the the cognitive scientists insist on that on the ability to impose a an abstract pattern onto concrete elements and I think for that you have to have variables that make clear this abstract node belongs to this concrete node this uh you know if you want to represent the sentence John loves Mary you have to make clear that this subject is linked to John and the object is linked to Mary because you can also have the sentence Mary loves John and then the uh they are called in a different way you need variables to make that distinction what are those variables that that's what I call the glue that glues together the abstract form and the concrete elements that make it up for instance or the um the texture you know the computer graphics people have a very good ontological theory of visual scenes they can create them in a very convincing way and they create them out of part descriptions like shape only or Texture only or illumination only or motion applied to a shape and have a way of putting them together and for that you need to have variables that may clear this textural element belongs onto this form on this point on that form and what are those variables if you try to find the minimal set of Link types that we need to or I mean one minimal set written optimize for the smallest one to perform all the semantic representations that we wanted to have and uh the model that we used this was inspired by Aristotle and his uh four kausai which basically means you have a formalis and efficients and um basically these four cases describe platonomic links part of and being composed of and being caused by and leading two so basically you have lateral links that give you a causal ordering and you have compositional links that allow you to compose a script or a task of subtasks and in this way you can describe arbitrary scripts and these arbitrary scripts can express arbitrary functions when you combine them with low level operators that can then for instance perform some basic operations on the network sends data in the environment in the network itself and so on um you I mentioned earlier on that I think of intelligence is the ability to construct a path to a space of computable functions so intelligence is not the ability to compute the function every computer again computer function without being intelligent the trick is to discover that function in the first place and to discover this function we basic have three perspectives on how to do this the first one is to converge to it that is what deep learning does you have a space of possible functions in the net space of possible functions you make it large enough you would start out with some random function and then you modify that function along many dimensions nudge it many billions of times or trillions of times until it gets close to what you wanted to do and this uh follow you follow a gradient so for this it needs to be differentiable and this algorithm of stochastic gradient descent using back propagation is still the Workhorse of all machine learning at this point and a second approach is to do hierarchical pattern matching so you look for operators that you've already learned a small library of efficient operators that you have evolved and evolution is all you need from this perspective that lets you get through the situation that you want based on the configuration that you have so this operators are basically looking for Activation patterns that they match on and they'll change the activation pattern into the next one and in this way you can perform arbitrary functions and the way this is also the way in our computers our Computing functions and the third one is construction and construction requires uh some degree of memory and uh because you need to be able to retrace your steps and you need a way to justify the steps that you're making and when to retract retract them and our Consciousness seems to be strongly involved in such a construction process where we have a stream of Consciousness that allows me oh I tried this thought before and this didn't work so I now retrace it retract it modify it and I think this one should work for the following reasons and then I see the outcome and say no it didn't work so this reason was not wrong so I tried the next thing and this is something that is difficult to achieve just this pattern matching or this gradient descent so this constructive discovery of solutions um seems to be crucial and uh while it seems to me that our deep learning models are not very good at constructing they are very much able to emulate what it would be like to be constructing right so well gpt3 or church GPT are not conscious they are able to create a story about something that is conscious while they're unable to reason they create a story about the Reasoner and draw on the inferences from that reasoning and the more closely you describe the reasoning that's reason step by step and so on uh the better the results can become and so it's very difficult to determine the difference between a system that pretends to perform a certain thing and it actually does it right if you can pretend it well enough you're actually doing it yeah yeah so the same way as you pretend to be conscious and I know the only conscious being in the world is myself when did you figure that out we know we know we're NPCs how did I figure that out [Laughter] okay yeah so first off I enjoyed this very much we are at the end of my timeline but for now and I hope that we get to continue the conversation soon in uh Zurich and yeah I'm looking forward to talking more to you and um okay I'm very glad that you could make it today yeah it was a great pleasure I I just had to take an earlier train from from Frankfurt to to Berlin because I had to be there only tomorrow morning I'm sitting here in the in a room in the so-called hamnock House of the the Guesthouse of the prank society which is very convenient okay have a nice day and Tanya thank you so much for organizing this and setting everything up and supporting our discussion and I'm glad you would also like to thank our audience for uh paying attention asking questions we didn't uh get uh to discuss all of them but I'm very glad that we could make this event happen thank you Kristoff see you soon thank you everyone bye bye now

Channel: Cognitive AI

Views: 56,152

Keywords:

Id: p-OYPRhqRCg

Length: 149min 14sec (8954 seconds)

Published: Tue Jan 10 2023

Reddit Comments

[u/NateThaGreatApe]

Thanks to u/der_spotter for announcing and u/tolgaakman for linking!

[u/AlrightyAlmighty]

Good stuff. Levin and Joscha are great together