Dileep George: Brain-Inspired AI | Lex Fridman Podcast #115

the following is a conversation with the leap george a researcher at the intersection of neuroscience and artificial intelligence co-founder of vicarius with scott phoenix and formerly co-founder of numenta with jeff hawkins who's been on this podcast and donna dubinsky from his early work on hierarchical temporal memory to recursive cortical networks to today the leaps always sought to engineer intelligence that is closely inspired by the human brain as a side note i think we understand very little about the fundamental principles underlying the function of the human brain but the little we do know gives hints that may be more useful for engineering intelligence than any idea in mathematics computer science physics and scientific fields outside of biology and so the brain is a kind of existence proof that says it's possible keep at it i should also say that brain-inspired ai is often over-hyped and used as fodder just as quantum computing for uh marketing speak but i'm not afraid of exploring these sometimes over-hyped areas since where there's smoke there's sometimes fire quick summary of the ads three sponsors babel raycon earbuds and masterclass please consider supporting this podcast by clicking the special links in the description to get the discount it really is the best way to support this podcast if you enjoy this thing subscribe on youtube review 5 stars on apple podcast support on patreon i'll connect with me on twitter at lex friedman as usual i'll do a few minutes of ads now and never any ads in the middle that can break the flow of the conversation this show is sponsored by babel an app and website that gets you speaking in a new language within weeks go to babel.com and use codelex to get three months free they offer 14 languages including spanish french italian german and yes russian daily lessons are 10 to 15 minutes super easy effective designed by over 100 language experts let me read a few lines from the russian poem by alexander block that 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podcast and now here's my conversation with the leap george do you think we need to understand the brain in order to build it yes if you want to build the brain we definitely need to understand how it works so blue brain or henry markham's project is trying to build a brain without understanding it like just trying to uh put details of the brain from neuroscience experiments into a giant simulation by putting more and more neurons more and more details but that is not going to work because when it doesn't perform as uh what you expect it to do then what do you do you do you just keep adding more details how do you debug it so it's a so unless you understand unless you have a theory about how the system is supposed to work how the pieces are supposed to fit together what they're going to contribute you can't you can't build it at the functional level understand so can you actually linger on and describe the blue brain project it's kind of fascinating uh principle an idea to try to simulate the brain as we're talking about the human brain right right human brains and rad brains or cat brains have lots in common that the cortex the neocortex structure is very similar so initially they were trying to just simulate a cat brain uh and uh to understand the nature of evil they understand the nature of evil or uh as it happens in most of these simulations uh you you easily get one thing out which is oscillations you know yeah if you if you simulate a large number of neurons they oscillate and you can adjust the parameters and say that oh selections match the rhythm that we see in the brain etc but uh oh i see so like uh so the idea is uh is the simulation at the level of uh individual neurons yeah so the blue brain project the original idea as proposed was um you you put very detailed bio physical neurons uh bios physical models of neurons and you interconnect them according to the statistics of connections that we have found from real neuroscience experiments and then uh turn it on and uh see what happens uh and and these neural models are you know incredibly complicated in themselves right because these neurons are modeled using uh this idea called hodgkin-huxley models which are about how signals propagate in a cable and there are active dendrites all those phenomena which those phenomena themselves we don't understand that well uh and then uh we put in connectivity which is part guess work part you know observed and of course if you do not have any theory about how it is supposed to work uh we you know we just have to take whatever comes out of it as okay this is something interesting but in your sense like these models of the way signal travels along or like with the axons and all the basic models that's they're too crude oh well actually they are pretty detailed and pretty sophisticated and they do replicate the neural dynamics if you take a single neuron and you you try to uh turn on the different channels the calcium channels and uh the different receptors uh and see what the effect of uh turning on or off those channels are in the neurons spike output people have built pretty sophisticated models of that and and they are i i would say um you know in the regime of correct well see the correctness that's interesting because you've mentioned in several levels uh the correctness is measured by looking at some kind of aggregate statistics it would be more of the the spiking dynamics in dynamics yeah and and yeah these models because they are they are going to the level of mechanism right so they are basically looking at uh okay what what is the effect of turning on an ion channel uh and um and you can you can model that using electric circuits in and then so they are model so it is not just a uh function fitting it is people are looking at the mechanism underlying it and uh putting that in terms of electric circuit theory signal propagation theory and and modeling that and so those models are sophisticated but getting a single neurons model 99 right does not still tell you how to you know it would be the analog of getting a transistor model right and now trying to build a microprocessor um and if you if you just uh observe you know if you did not understand how a microprocessor works uh but you say oh i have i now can model one transistor well and now i will just try to interconnect uh the transistors according to whatever i could you know guess from the experiments and try to simulate it um then it is very unlikely that you will produce a functioning microprocessor um you want to you know when you want to uh produce a functioning microprocessor you want to understand boolean logic how does how do the the gates work all those things and then you know understand how do those gates get implemented using transistors yeah there's actually i remember this reminds me this is a paper maybe you're familiar with it i remember going through in a reading group that approaches a microprocessor from a perspective a neuroscientist i think it it basically it uses all the tools that we have of neuroscience to try to understand like as if we just aliens showed up to study computers uh yeah and and to see if if those tools could be used to get any kind of sense of how the microprocessor works i think the final the takeaway from the at least this initials uh exploration is that we're screwed there's no way that the tools of neuroscience would be able to get us to anything like not even boolean logic i mean it's just a any aspect of the architecture of the uh function of the processes involved uh the the clocks the the timing all that you can't figure that out from the tools of neuroscience yes i'm very familiar with this this particular paper i think it was uh called um can uh a neuroscientist understand a microprocessor yeah something like that following the methodology in that paper even an electrical engineer would not understand microprocessors so i could so i could so i i don't think it is that bad in the sense of saying um neuroscientists do find valuable things uh by observing the brain they they do find good insights um but those insight cannot be put together just as a simulation you have to you have to investigate what are the computational underpinnings pinnings of those findings how do all of them fit together from an information processing perspective you have to you have to somebody has to uh painstakingly put those things together and build hypothesis um so i don't want to this all of neuroscience is saying oh they are not finding anything no that you know that that paper almost went to that level of uh uh neuroscientists will never understand uh no that that's not true i think they do find lots of useful things but it has to be put together in a computational framework yeah i mean but you know just the ai systems will be listening to this podcast a hundred years from now and it will probably there's some nonzero probability they'll find your words laughable it's like i remember humans thought they understood something about the brain they're totally clueless there's a sense about neuroscience that we may be in the very very early days of understanding uh the brain but i mean that's one perspective in your perspective how far are we into understanding uh any aspect of the brain so the the the dynamics of the individual neuron communication to the how when they in in a collective sense how they're able to store information transfer information how the intelligence then emerges all that kind of stuff where are we on that timeline yeah so you know timelines are very very hard to predict and you can of course be wrong uh and it can be wrong in on either side uh you know we know that uh now when we look back uh the first flight was in 1903. uh in 1900 there was a new york times article on flying machines that do not fly and and you know humans might not fly for another hundred years that was what that article uh stated and uh so but no they they flew three years after that so it is you know it's very hard to um so well and on that point one of the wright brothers uh i think two years before uh said that uh like he said like some number like 50 years he he has become convinced that it's it's uh it's impossible even during their experimentation yeah yeah yeah i mean that's a tribute to when that's like the entrepreneurial battle of like depression of going through just like thinking this is impossible right but there yeah there's something even the person that's in it is not able to see uh estimate correctly exactly but i can i can tell from the point of you know objectively what are the things that we know about the brain and how that can be used to build ai models which can then go back and inform how the brain works so my way of understanding the brain would be to basically say look at the insights neuroscientists have found understand that from a computational angle information processing angle build models using that and then building the that model which which functions which is a functional model which is which is doing the task that we want the model to do it is not just trying to model a phenomena in the brain it is it is trying to do what the brain is trying to do on on the whole functional level and building that model will help you fill in the missing pieces that you know biology just gives you the hints and building the model you know fills in the rest of the the pieces of the puzzle and then you can go and connect that back to biology and say okay now it makes sense that this part of the brain is uh doing this or this layer in the cortical circuit is doing this uh and and and then continue this iteratively because now that will inform new experiments in neuroscience and of course you know building the model and verifying that in the real world will you will also tell you more about does the model actually work uh and you can refine the model find better ways of putting these neuroscience insights together so so i would say it is it is you know it so neuroscientists alone just from experimentation will not be able to build a model of the of the brain uh or a functional model of the brain so we you know there there's uh lots of efforts which are very impressive efforts in collecting more and more connectivity data from the brain yeah you know how how are the micro circuits of the brain connected with each other those are beautiful by the way those are beautiful uh and at the same time those those do not itself um by themselves convey the story of how does it work yeah uh and and somebody has to understand okay why are they connected like that and what what are those things doing uh and and we do that by building models in ai using hints from neuroscience and and repeat the cycle so what aspect of the brain are useful in this whole endeavor which by the way i should say you're you're both the neuroscientists and and ai person i guess the dream is to both understand the brain and to build agi systems so you're it's like an engineer's perspective of trying to understand the brain so what aspects of the brain uh functioning speaking like you said you find interesting yeah quite a lot of things all right so one is um you know if you look at the visual cortex um uh and and you know the visual cortex is is a large part of the brain uh i forget this exact fraction but it is it's a it's a huge part of our brain area is uh occupied by just just vision um so vision visual cortex is not just a feed-forward cascade of neurons um uh there are a lot more feedback connections in the brain compared to the feed-forward connections and and it is surprising to the level of detail neuroscientists have actually studied this if you if you go into neuroscience literature and poke around and ask you know have they studied what will be the effect of poking a neuron in level i.t uh in level v one and uh um have they studied that uh and you will say yes they have studied that so every every possible combination i mean it's it's a it's not a random exploration at all it's very hypothesis driven right they are very uh experimental neuroscientists are very very systematic in how they probe the brain uh because experiments are very costly to conduct they take a lot of preparation they they need a lot of control so they they are very hypothesis driven in how they probe the brain and um often what i find is that when we have a question in um in ai uh about have has anybody probably probed how lateral connections in the brain works and when you go and read the literature yes people have probed it and people have probed it very systematically and and they have hypothesis about how those lateral connections are supposedly contributing to visual processing uh but of course they haven't built very very functional detail models of it by the way how do you know studies start to interrupt that do they do they stimulate like a neuron in one particular area of the visual cortex and then see how the travel of the signal travels kind of thing fascinating very very fascinating experiments right you know so i can i can give you one example i was impressed with um this is uh so before going to that let me like let me give you a a you know a overview of how the the layers in the cortex are organized right uh visual cortex is organized into roughly four hierarchical levels okay so uh v one v two v four i t and in v one of v three uh well yeah there's another pathway okay okay so there's this this is this i'm talking about just the object recognition pathway right okay and then um in v1 itself um so it's there is a very detailed micro circuit in v1 itself there is there is organization within a level itself uh the cortical sheet is organized into uh you know multiple layers and there are columnar structure and and this this layer wise and column structure is repeated in v1 v2 v4 uh it all of them right and and the connections between these layers within a level with you know in v1 itself there are six layers roughly and the connections between them there is a particular structure to them uh and um now so one example of an experiment uh uh people did is when i when you present a stimulus uh which is um let's say requires um separating the foreground from the background of an object so it is it's a textured triangle on a textured background and you can check does the surface settle first or does the contour settle first cerro settle in the sense that the so when you find finally form the percept of the of the triangle you understand where the contours of the triangle are and you also know where the inside of the triangle is right that's when you form the final percept uh now you can ask what is the dynamics of forming that final percept um do the do the neurons um first find the edges and converge on where the edges are and then they find the inner surfaces or does it go the other way the other way around um so so what's the answer uh in this case it it turns out that it first settles on the edges it it converges on the edge hypothesis first and then the the surfaces are filled in from the edges to the inside that's fascinating uh and and the detail to which you can study this it's it's amazing that you can actually not only find um the temporal dynamics of when this happens uh and then you can also find which layer in the you know in v1 which layer is encoding uh the edges which layer is encoding the surfaces and which layer is encoding the feedback which there is encoding the feed forward and what what's the combination of them that produces the final person um and these kinds of experiments stand out when you try to explain illusions uh one one example of a favorite illusion of mine is the kanetsa triangle i don't know that you are familiar with this one so this is um uh this is an example where it's a triangle uh but you know the corners of the only the corners of the triangle are shown in the stimuli the stimulus so they look like kind of pac-man oh the black pac-man exactly yeah and then you start to see your visual system hallucinates the edges yeah um and you can you know you when you look at it you will see a faint edge right and you can go inside the brain and look you know do actually neurons signal the presence of this edge and and if this signal how do they do it because they are not receiving anything from the input in the the input is black for those neurons right uh so how do they signal it when does the signaling happen you know does it you know so so if a real contour is present in the input then the signa the neurons immediately signal okay there is a there is an edge here when when it is an illusory edge um it is clearly not in the input it is coming from the context so those neurons fire later and and you can say that okay these are it's the feedback connections that is causing them to fire uh and and they happen later and you can find the dynamics of them so so these studies are pretty impressive and and very detailed so by the way just uh just take a step back you said uh that there may be more feedback connections and feed forward connections yeah uh first of all it's just just for like a machine learning folks yeah i mean that for that's crazy that there's all these feedback connections i mean we often think about i think thanks to deep learning you start to think about um the human brain as a kind of feed forward mechanism right so what the heck are these feedback connections yeah what's their what's the dynamics well what are we supposed to think about them yeah so this is this fits into a very beautiful picture about how the brain works right um so the the beautiful picture of how the brain works is that our brain is building a model of the world uh i know so our visual system is building a model of how objects behave in the world and and we are constantly projecting that model back onto the world so what we are seeing is not just a feed forward thing that just gets interpreted in in a few word party we are constantly projecting our expectations onto the world and and what the final percept is a combination of what we project onto the world uh combined with what the actual sensory input is almost like trying to calculate the difference and then trying to interpret the difference yeah it's it's um i wouldn't put just calculating the difference it's more like what is the best explanation for the input stimulus based on the model of the world i have got it got it and that's where all the illusions come in and that's but that's that's an incredibly efficient so uh efficient process so the feedback mechanism it just helps you constantly uh yeah so hallucinate how the world should be based on your world model and then just looking at uh if there's novelty uh like trying to explain it like that hence that's why movement we detect movement really well there's all these kinds of things and that this is like at all different levels of the cortex you're saying this happens at the lowest level or the highest level yes yeah in fact feedback connections are more prevalent in everywhere in the cortex and and um so one way to think about it and there's a lot of evidence for this is inference um so you know so basically if you have a model of the world and when when some evidence comes in what you are doing is inference right you are trying to now explain this evidence using your model of the world yep and this inference includes projecting your model onto the evidence and taking the evidence back into the model and and doing an iterative procedure and this iterative procedure is what happens using the feed forward feedback propagation and feedback affects what you see in the world and you know it also affects feed forward propagation and examples are everywhere we we see these kinds of things everywhere the idea that there can be multiple competing hypotheses in our model trying to explain the same evidence and then you have to kind of make them compete and one hypothesis will explain away the other hypothesis through this competition process wait what so you have competing models of the world that tried to explain what do you mean by explain away so this is a classic example in uh uh graphical models probabilistic models um so if you what are those um okay um i think it's useful to mention because we'll talk about them more yeah yeah so neural networks are one class of machine learning models um you know you have distributed set of nodes which are called the neurons you know each one is doing a dot product and you can you can approximate any function using this a multi-level network of neurons so that's a class of models which are used for useful for function approximation there is another class of models in machine learning called probabilistic graphical models and you can think of them as each node in that model is variable which is which is talking about something you know it can be a variable representing is is an edge present in the input or not and at the top of the uh network a node can be uh representing is there an object present in the world or not and and then so it can it is it is another way of encoding knowledge and uh um and then you once you encode the knowledge you can uh do inference in the right way you know how what is the best way to uh you know explain some sort of evidence using this model that you encoded you know so when you encode the model you are encoding the relationship between these different variables how is the edge connected to my the model of the object how is the surface connected to the model of the object and then of course this is a very distributed complicated model and inference is how do you explain a piece of evidence when a set of stimulus comes in if somebody tells me there is a 50 probability that there is an edge here in this part of the model how does that affect my belief on whether i should think that there should be is the square present in the image so so this is the process of inference so one example of inference is having this experience of effect between multiple causes so uh graphical models can be used to represent causality in the world um so let's say um you know uh your uh alarm at home can be uh triggered by a burglar getting into your house uh or it can be triggered by an earthquake both both can be causes of the alarm going off so now you you're right you know you're in your office you heard burglar alarm going off you are heading uh home thinking that there's a burglar got it but while driving home if you hear on the radio that there was an earthquake in the vicinity now your hype you know uh strength of evidence for a burglar getting into their house is diminished because now that that piece of evidence is explained by the earthquake being present so if you if you think about these two causes explaining at lower level uh variable which is alarm now what we are seeing is that increasing the evidence for some cause ex you know there is evidence coming in from below for alarm being present and initially it was flowing to a burglar being present but now since somebody some this there the side evidence for this other cause it explains away this evidence and it evidence will now flow to the other course this is you know two competing causal uh things trying to explain the same evidence and the brain has a similar kind of mechanism for doing so that's kind of interesting and that how's that all encoded in the brain like where's the storage of information are we talking just maybe to get it a little bit more specific is it in the hardware of the actual connections is it in uh chemical communication is it electrical communication do we do we know so this is you know a paper that we are bringing out soon which one this is the cortical micro circuits paper that i sent you a draft of of course this is uh a lot of it is still hypothesis one hypothesis is that a you can think of a cortical column as encoding a a concept a concept you know think of it as say an example of a concept is um is an edge present or not or is is an object present or not okay so it can you can think of it as a binary variable a binary random variable the presence of an edge or not or the presence of an object or not so each cortical column can be thought of as representing that one concept one variable and then the connections between these cortical columns are basically encoding the relationship between these random variables and then there are connections within the cortical column there are each cortical column is implemented using multiple layers of neurons with very very very rich um structure there you know there are thousands of neurons in a cortical column but but that structure is similar across the different cortical columns yeah correct and also these cortical columns collect connect to a substructure called thalamus in the uh you know so all all cortical columns pass through this substructure so our hypothesis is that yeah the connections between the cortical columns implement this uh you know that's where the knowledge is stored about you know how these different connects concepts connect to each other and then the the neurons inside this cortical column and in thalamus in combination implement this uh actual computations needed for inference which includes explaining a way and competing between the different uh hypotheses um and it is all very so what is amazing is that uh neuroscientists have actually done experiments to the tune of showing these things they might not be putting it in the overall inference framework but they will show things like if i poke this higher level neuron it will inhibit through this complicated loop through the thalamus it will inhibit this other column uh so they will do such experiments but do they use terminology of concepts for example so so you're i mean is it uh is it something where it's easy to anthropomorphize and think about concepts like you start moving into logic based kind of reasoning systems so um i would just think of concepts in that kind of way or is it is it a lot messier a lot more gray area you know even even more gray even more messy than the artificial neural network kinds of abstractions the easiest way to think of it as a variable right it's a binary variable which is showing the presence or absence of something so but i guess what i'm asking is is that something that we're supposed to think of something that's human interpretable of that something it doesn't need to be it doesn't need to be human interpretable there's no need for it to be human interpretable uh but it's it's almost like um you you will be able to find some interpretation of it uh because it is connected to the other things yes that you know and the the point is it's useful somehow yeah it's useful as an entity in the graph that in connecting to the other entities that are let's call them concepts right okay so uh by the way what's are these the cortical micro circuits correct these are the cortical micro circuits you know that's what neuroscientists use to talk about the circuits in in uh within a level of the cortex so you can think of you know let's think of a neural network in artificial neural network terms you know people talk about the architecture of the you know so how many how many layers they build uh you know what is the fan in fan out etc that is the macro architecture so and then within a layer of the neural network you can you know the cortical neural network is much more structured with you know within a level there's a lot more intricate structure there uh but even um even within an artificial neural network you can think of in feature detection plus pooling as one one level and so that is kind of a micro circuit uh it's much more uh complex in the real brain uh and and so within a level whatever is that circuitry within a column of the cortex and between the layers of the cortex that's the micro circuitry i love that terminology uh machine learning people don't use the circuit terminology right but they should it's a nice so okay uh okay so that's uh that that's the the cortical micro circuit so what's interesting about what can we say what is the paper that you're working on propose about the ideas around these cortical micro circuits so this is a fully functional model for the micro circuits of the visual cortex so the the paper focuses and your idea in our discussions now is focusing on vision yeah the uh visual cortex okay yeah this is a model this is a full model it says this is how vision works but this is this is a model of science yeah hypothesis okay so let me let me step back a bit um so we looked at neuroscience for insights on how to build a vision model right and and and we synthesized all those insights into a computational model this is called the recursive vertical network model that we we used for breaking captchas and and we are using the same model for robotic picking and uh tracking of objects and that again is the vision system that's the best computer vision system that's the computer mission takes in images and outputs what on one side it outputs the class of the image and also segments the image uh and you can also ask it further queries where is the edge of the object where is the interior of the object so so it's a model that you build to answer multiple questions so you are not trying to build a model for just classification or just segmentation etc so it's a it's a it's a joint model that can do multiple things um and um so so that's the model that we built using insights from neuroscience and some of those insights are what is the role of feedback connections what is the role of lateral connections uh so all those things went into the model the model actually uses feedback connections all these ideas from you know from your science yeah so what what what the heck is a recursive cortical network like what what are the architecture approaches interesting aspects here which is essentially a brain inspired approach to computer vision yeah so there are multiple layers to this question i can go from the very very top and then zoom in okay so one important thing constraint that went into the model is that you should not think vision think of vision as something in isolation we should not think perception as something as a preprocessor for cognition perception and cognition are interconnected and so you should not think of one problem in separation from the other problem um and so that means if you finally want to have a system that understand concepts uh about the world and can learn in a very conceptual model of the world and can reason and connect to language all of those things you need to you need to have think all the way through and make sure that your perception system is compatible with your cognition system and language system and all of them and one aspect of that is top-down controllability um what does that mean so that means you know so so think of it you know you can close your eyes and think about the details of one object right i can i can zoom in further and further i can you know so so think of the bottle in front of me right and and now you can think about okay what the cap of that bottle looks uh i know we can think about what's the texture on that bottle of the of the cap you know you can think about you know what will happen if uh something hits that uh so you can you can you can manipulate your visual knowledge in uh cognition driven ways yes uh and so this top-down controllability uh and being able to simulate scenarios in the world so you're not just a passive uh player in this perception game you you can you can control it you gotta you you have imagination correct so so so basically you know basically having a generating network yeah which is a model and and it is not just some arbitrary generated network it has to be it has to be built in a way that it is controllable top-down it is it is not just trying to generate a whole picture at once uh you know it's not trying to generate photorealistic things of the world you you know you don't have good photorealistic models of the world human brains do not have if i if i for example ask you the question uh what is the color of the letter e in the google logo you have no idea right now yeah although you have seen it millions of times hundreds of times so yeah so it's not our model is not photorealistic but but it is but it has other properties that we can manipulate it uh in the uh and you can think about filling in a different color in that logo you can think about expanding the the letter e yeah you know you can see what in so you can imagine the consequence of you know actions that you have never performed so so these are the kind of characteristics the genetic model need to have so this is one constraint that went into our model like you know so this is when you read the just the perception side of the paper it is not obvious that this was a constraint into the inter that went into the model this top-down controllability of the generating model uh so what what does the top-down controllability in a model look like it's a really interesting concept fascinating concept what is that is that the recursive recursiveness gives you that or how do you how do you do it um quite a few things it's like what what does the model factor or factorize you know what are the what is the model representing us different pieces in the puzzle like you know so so in the rcn uh network it it thinks of the world you know what i say the background of an image is modeled separately from the foreground of the image so the objects are separate from the background they're different entities so there's a kind of segmentation that's built in fundamentally that's why and and then even that object is composed of parts and also and another one is the the shape of the object uh is differently modeled from the texture of the object got it so there's like these um i've been you know who francois charles is yeah he's so there's uh he developed this like iq test type of thing for arc challenge for and uh it's kind of cool that there's um these concepts priors that he defines that you bring to the table in order to be able to reason about basic shapes and things in the iq test right so here you're making it quite explicit that here here are the things that you should be there these are like distinct things that you should be able to uh model and yes keep in mind that you you can derive this from much more general principles it doesn't you don't need to explicitly put it as oh objects versus foreground versus background uh the surface versus structure now these are these are derivable from more fundamental principles of how you know what's the property of continuity of natural signals what's the property of continuity of natural signals yeah by the way that sounds very poetic but yeah uh so you're saying that's a there's some low-level properties from which emerges the idea that shapes should be different than like uh there should be a parts of an object there should be i mean exactly kind of like friends of water i mean there's objectness there's all these things that it's kind of crazy that we're humans uh i guess evolved to have because it's useful for us to perceive the world correct yeah correct and it derives mostly from the properties of natural signals and yeah and so um natural signals so natural signals are the kind of things we'll perceive in the in the natural world i don't know i don't i don't know why that sounds so beautiful natural signals yeah as opposed to a qr code right which is an artificial signal that we created humans are not very good at classifying qr codes we are very good at saying something is a cat or a dog but not very good at you know the classification computers are very good at classifying qr codes so our visual system is tuned for natural signals and there are fundamental assumptions in the architecture that are derived from natural signals properties i wonder when you take a hallucinogenic drugs does that go into natural or is that closer to the qr code it's still natural yeah because it's it is still operating using your brains by the way on that on that topic i i mean i haven't been following i think they're becoming legalized at certain i can't wait until they become legalized to the degree that you like vision science futures could study it yeah just like through through medical chemical ways modify there could be ethical concerns but modif that's another way to study the brain is to be be able to chemically modify it there's probably um probably very long a way to figure out how to do it ethically yeah but i i think there are studies on that already yeah i think so uh because it's not unethical to give uh it to rats oh that's true that's true [Laughter] there's a lot of drugged up rats out there okay yeah cool sorry sorry so okay so there's uh so there's these uh low-level uh things from natural signals that uh that that from which these properties will emerge yes uh but it is still a very hard problem on how to encode that again so you don't you know there is no uh so uh you mentioned um the the the priors uh francho wanted to encode in uh in the abstract reasoning challenge but it is not straightforward how to encode those priors um so so some of those uh challenges like you know the object completion challenges are things that we purely use our visual system to do it is uh it looks like abstract reasoning but it is purely an output of the the vision system for example completing the corners of that condenser triangle completing the lines of that cancer triangle it's a purely a visual system property there is no abstract reasoning involved it it uses all these priors but it is stored in our visual system in a particular way that is amenable to inference and and and that is one of the things that we tackled in the you know so basically saying okay these are the prior knowledge uh which which will be derived from the world but then how is that prior knowledge represented in the model such that inference when when some piece of evidence comes in can be done very efficiently and in a very distributed way um because it is very there are so many ways of representing knowledge which is not amenable to very quick inference in a quick lookups and so that's one um core part of what we tackled in uh the rcn model um uh how do you encode visual knowledge to uh do very quick inference and yeah can you maybe comment on uh so folks listening to this in general may be familiar with different kinds of architectures of neural networks what what are we talking about with rcn uh what are what does the architecture look like what are different components is it close to neural networks is it far away from neural networks what does it look like yeah so so you can uh think of the delta between the model and a convolutional neural network if people are familiar with convolutional neural networks so convolutional neural networks have this feed-forward processing cascade which is called uh feature detectors and pooling and that is repeated in the in the hierarchy in a multi-level uh system um and if you if you want an intuitive idea of what what is happening feature detectors are uh you know detecting interesting co-occurrences in the input it can be a line a corner a an eye or a piece of texture etc and the pooling neurons are doing some local transformation of that and making it invariant to local transformations so this is what the structure of convolutional neural network is um recursive cortical network has a similar structure when you look at just the feed forward pathway but in addition to that it is also structured in a way that it is generating so that again it can run it backward and combine the forward with the backward another aspect that it has is it has lateral connections these lateral connections um which is between so if you have an edge here and an edge here it has connections between these edges it is not just feed forward connections it is um something between these edges which is the nodes are presenting these edges which is to enforce compatibility between them so otherwise what will happen is the constraints it's a constraint it's basically if you if you do just feature detection followed by pooling then your your transformations in different parts of the visual field are not coordinated uh and so you can you will create a jagged when you when you generate from the model you will create jagged um things and uncoordinated transformations so these lateral connections are enforcing the the transformations is the whole thing still differentiable uh no okay no it's not it's not trade using uh backprop okay that's really important so uh so there's this feed forward there's feedback mechanisms there's some interesting connectivity things it's still layered like uh yes there are multiple levels multiple layers okay very very interesting uh and yeah okay so the interconnection between um adjacent the connections across service constraints that like keep the thing stable got it okay so what else uh and then there is this idea of doing inference a neural network does not do inference on the fly so an example of why this inference is important is you know so one of the first applications of that we showed in the paper was to crack uh text-based captchas what are captures by the way by the way one of the most awesome like the people don't use this term anymore is human computation i think uh i love this term the guy who created captures i think came up with this term yeah i love it anyway uh yeah uh what what are captures so captchas are those strings that you fill in uh when you're you know when if you're opening a new account in google they show you a picture you know usually it used to be a set of garbage letters uh that you have to kind of figure out what what what is that string of characters and type in and the reason cap just exist is because you know google or twitter do not want automatic creation of accounts you can use a computer to create millions of accounts uh and uh use that for in nefarious purposes uh so you want to make sure that to the extent possible the interaction that your their system is having is with a human so it's a it's called a human interaction proof a captcha is a human interaction proof um so so this is a captchas are by design things that are easy for humans to solve but hard for computers hard for robots yeah so and text-based captchas where was the one which is prevalent and around 2014 because at that time text-based voice captures were hard for computers to crack even now they are actually in the sense of an arbitrary text based capture will be unsolvable even now but with the techniques that we have developed it can be you know you can quickly develop a mechanism that solves the captcha they've probably gotten a lot harder too the people they've been getting clever and clever generating these text characters yeah right so okay so that was one of the things you've tested on is these kinds of captures in 2014 15. got that kind of stuff right right so what uh what i mean why by the way why captchas yeah yeah even now i would say captcha is a very very good challenge problem uh if you want to understand how human perception works and if you want to build uh systems that work like the human brain uh and i wouldn't say captcha is a solved problem we have cracked the fundamental defense of captures but it is not solved in the way that humans solve it um so i can give an example i can um take a five-year-old child who has just learned characters uh and uh show them any new capture that we create they will be able to solve it uh i can show you pretty much any new capture from any new website you'll be able to solve it without getting any training examples from that particular style of captcha you're assuming i'm human yeah yes yeah that's right so if you are human and if you otherwise i will be able to figure that out using this one but uh so this whole podcast is just a touring test a long turing test anyway i'm sorry so yeah so human humans can figure it out with very few examples or no training examples like no training examples from that particular style of capture and and so you can you know so uh even now this is unreachable for the current deep learning system so basically there is no i don't think a system exists where you can basically say train on whatever you want and then now say hey i will show you a new captcha which i did not show you in in the in the training setup will the system be able to solve it um it still doesn't exist so that is the magic of human perception yeah and doug have starter uh put this uh very beautifully in one of his uh talks the the central problem in ai is what is the letter a if you can if you can build a system that reliably can detect all the variations of the letter a you don't even need to go to the v and the c yeah you don't even know the b and c or the strings of characters and uh so that that is the spirit at which you know with which we uh tackle that what does it mean by that i mean is it uh like without training examples try to figure out the fundamental uh elements that make up the letter a in all of its forms in all of its forms it can be a can be made with two humans standing leaning against each other holding the hands yeah and uh it can be made of leaves it can be yeah you might have to understand uh everything about this world in order to understand letter a yeah exactly so it's common sense reasoning essentially yeah right so so to finally to really solve finally to say that you have solved captcha uh you have to solve the whole problem yeah okay so what how does this kind of the rcn architecture help us to get a do a better job of that kind of yeah so uh as i mentioned one of the important things was being able to do inference being able to dynamically do in france can you can you uh can you uh clarify what you mean because you said like neural networks don't do inference yeah so what do you mean by inference in this context then so okay so in captures what they do to confuse people is to make these characters crowd together yes okay and when you make the characters crowd together what happens is that you will now start seeing combinations of characters as some other new character or or an existing character so you would you would put an r and n together it will start looking like an m uh and and so locally they are you know there is very strong evidence for it being uh some uh incorrect character but globally the only explanation that fits together is something that is different from what you can find locally yes so so so this is inference you are basically taking uh local evidence and putting it in the global context and often coming to a conclusion locally which is conflicting with the local information so actually so you mean inference like uh in the way it's used when you talk about reasoning for example uh as opposed to like inference which is this when you know with artificial neural networks which is a single pass through the network okay so like you're basically doing some basic forms of reasoning like integration of like uh how local things fit into the the global picture and and things like explaining away coming into this one because you are you are uh explaining that piece of evidence uh as something else uh because globally that's the only thing that makes sense um so now yeah you can amortize this inference by you know in a neural network if you want to do this what you you can you can brute force it you can just show it all combinations of things that you want to you want to your reasoning to work over and you can you know like just train the help out of that neural network and it will look like it is doing uh you know inference on the fly but it is it is really just doing amortized inference it is because you you have shown it a lot of these combinations during training time um so what you want to do is be able to do dynamic inference rather than just being able to show all those combinations in the training time and that's something we emphasized in the model what does it mean dynamic in france is that that has to do with the feedback thing yes like what what is dynamic i i'm trying to visualize what dynamic influence would be in this case like what is it doing with the input it's showing the input the first time yeah and is is like what's changing over temporally over what's the dynamics of this inference process so you can think of it as you have um at the top of the model the characters that you are trained on yeah they are the causes they you are trying to explain the pixels using the characters as the causes the you know the characters are the things that cause the pixels yeah so there's this causality thing so the reason you mentioned causality i guess is because there's a temporal aspect of this whole thing in this particular case the temporal aspect is not important it is more like when if if i turn the character on the the pixels will turn on yeah it will be after there's a little bit but okay so that is the causality in the sense of like a logic causality like hence inference okay the dynamics is that uh even though locally it will look like okay this is an a and and locally just when i look at just that patch of the image it looks like an a but when i look at it in the context of all the other courses it might no am is not the something that makes sense so that is something you have to kind of you know recursively figure out yeah so okay so uh and uh this thing performed pretty well on the captchas correct and um i mean is there some kind of interesting intuition you can provide why did well like what did it look like is there visualizations that could be human interpretable to us humans yes yeah so the good thing about the model is that it is extremely um so it is not just doing a classification right it is it is it is it is providing a full explanation for the scene so when when it when it um operates on a scene it is coming at back and saying look this is the part is the a and these are the pixels that turned on uh these are the pixels in the input that tells makes me think that it is an a and also these are the portions i hallucinated it you know it it provides a complete explanation of that form and then it's again these are the contours these are this is the interior and this is in front of this other object so that that's the kind of um explanation it uh the the inference network provides so so that that is useful and interpretable um and uh um then the kind of errors it makes are also i don't want to um read too much into it but the kind of errors the network makes are very similar to the kinds of errors humans would make in a similar situation so there's something about the structure that's uh feels reminiscent of the way humans visual system works well i mean uh how hard-coded is this to the capture problem this idea uh not really hardcoded because it's the uh the assumptions as i mentioned are general right it is more um and and those themselves can be applied in many situations which are natural signals um so it's it's the foreground versus background factorization and the factorization of the surfaces versus the contours so these are all generally applicable assumptions in our vision so why why capture why attack the capture problem which is quite unique in the computer vision context versus like the traditional benchmarks of imagenet and all those kinds of image classification or even segmentation tests all that kind of stuff do you feel like that's uh i mean what what's your thinking about those kinds of benchmarks in um in this in this context i mean those benchmarks are useful for deep learning kind of algorithms where you you know so the settings uh that deep learning works in are here is my huge training set and here is my test set so the the training set is almost uh you know 100 x 1000 x bigger than uh the test set in many many cases uh what we wanted to do was invert that the training set is way smaller than the the test set yes uh and uh uh and you know uh captcha is a problem that is by definition hard for computers and it has these good properties of strong generalization strong out of training distribution generalization if you are interested in studying that and putting having your model have that property then it's it's a good data set to tackle so is there have you attempted to which i think i believe there's quite a growing body of work on looking at mnist and imagenet without training so it's like taking like the basic challenge is how what tiny fraction of the training set can we take in order to do a reasonable job of the classification task have you explored that angle in these classic benchmarks yes so so we did do mnist so um you know so it's not just capture we uh so there was uh also uh uh versions of multiple versions of mnist including the the standard version which where we inverted the problem which is basically saying rather than train on 60 000 uh training data uh you know how uh quickly can you get uh to high level accuracy with very little training data was is there some performance do you remember like how well how well did it do how many examples did he need yeah i i you know i remember that it was you know uh on the order of uh tens or hundreds of examples to get into uh 95 accuracy and it was it was definitely better than the systems other systems out there at that time at that time yeah yeah they're really pushing i think that's a really interesting space actually uh i think there's an actual name for mnist that uh like there's different names the different sizes of training sets i mean people are like attacking this problem i think it's super interesting yeah it's funny how like that mnist will probably be with us all the way to agi yes it's the data set that just sticks by it is it's a clean simple uh data set to uh to study the fundamentals of learning with just like captures it's interesting not enough people i don't know maybe you can correct me but i feel like captures don't show up as often in papers as they probably should that's correct yeah because you know um usually these things have a momentum uh you know once once uh something gets established as a standard benchmark yeah there is a there is a uh there is a dynamics of how graduate students operate and how the academ academy system works that uh pushes people to track that uh benchmark so yeah yeah so nobody wants to think outside the box okay okay so good performance on the captures what else is there interesting um on the rcn side before we talk about the cortical microscope yeah so the same model so the the the important part of the model was that it trains very quickly with very little training data and it's you know quite robust to out of distribution uh perturbations um and and we are using that uh very uh fruitfully in uh advocates in many of the robotic stocks we are solving so you know well let me ask you this kind of touchy question i have to i i've spoken with uh your friend colleague jeff hawkins too i mean he's uh i have to kind of ask there is a bit of whenever you have brain inspired stuff yeah and you make big claims yeah uh big sexy claims yeah there's a you know uh there's critics i mean machine learning subreddit don't get me started on those people uh their heart i mean criticism is good but they're a bit they're a bit over the top um there is quite a bit of sort of skepticism and criticism you know is this work really as good as it promises to be yeah what do you have thoughts on that kind of skepticism do you have comments on the kind of criticism you might have received uh about you know is this approach legit is this is this a promising approach yeah or at least as promising as it seems to be you know advertised as yeah i can comment on it um so you know our arson paper is published in science which i would argue is is a very high quality journal very hard to uh publish in and use you know usually it is indicative of the of the quality of the work and um i can i can i am very very certain that the ideas that we brought together in that paper uh in terms of the importance of feedback connections uh recursive inference lateral connections uh coming to best explanation of the scene as the problem to solve trying to solve um recognition segmentation uh all jointly in a way that is compatible with higher level cognition top-down attention all those ideas that we brought together into something you know coherent and workable in the in the world and solving a challenge tackling a challenging problem i think that will that will stay and that that contribution i stand by right now uh i can i can tell you a story which is funny in the in the context of this right um so if you read the abstract of the paper and you know the argument we are putting in you know we are putting in look current deep learning systems take a lot of training data uh they don't use these insights and here is our new model which is not a deep neural network it's a graphical model it does inference this is what how the paper is right now once the paper was accepted and everything um it went to the press department in in science you know to play as science office we we didn't do any press release when it was published it was he went to the press department what did the what was the press release that they wrote up a new deep learning model solves captchas and uh so so you can see where was you know what was being hyped uh in that uh thing right so so it's like um there is the there is a dynamic in the uh in the community of you know so uh um that's especially happens when there are lots of new people coming into the field and they get attracted to one thing and some people are trying to think different uh compared to that so there's there is some uh i think skepticism is science is important and it is um you know very much uh required but it's also it's not uh skepticism usually it's mostly bandwagon effect that is happening rather than well but that's not even that i mean i'll tell you what they react to which is like i'm sensitive to as well if you if you look at just companies open ai deep mind yeah um vicarious i mean it just there's uh there's a little bit of a race to the top and hype right right it's it's like it doesn't pay off to be humble so like uh and and the press is just uh irresponsible often they they just i mean don't get me started on the state of journalism today like it seems like the people who write articles about these things they literally have not even spent an hour on the wikipedia article about what is neural networks like yeah they haven't like invested just even the language to laziness it's like uh robots beat humans like they they write this kind of stuff that just uh and then and then of course the researchers are quite sensitive to that because it gets a lot of attention they're like why did this work get so much attention uh you know that's that's over the top and people get really sensitive you know the same kind of criticism with uh opening i did work with the rubik's cube with the robot that people criticized uh same with gpt two and three they criticize uh same thing with the deep minds with alpha zero i mean yeah i i'm sensitive to it but and of course with your work you mentioned deep learning but there's something super sexy to the public about brain inspired i mean that immediately grabs people's imagination not even like neural networks but like really brain inspired like like brain like neural networks that seems really compelling to people and um to me as well to to the world as a narrative and so uh people hook up hook on to that and uh sometimes you uh the skepticism engine turns on in the research community and they're skeptical but i think putting aside the ideas of the actual performance on captures or performance in any data set i mean to me all these data sets are useless anyway it's nice to have them but in the grand scheme of things they're silly toy examples the point is is their intuition about the the ideas just like you mentioned bringing the ideas together in a unique way is there something there is there some value there and this is going to stand the test of time yes and that's the hope that's the hope i'm my confidence there is very high i you know i don't treat brain inspect as a marketing term uh you know i am looking into the details of biology and i'm puzzling over uh those things and i am i am grappling with those things and so this it is not a marketing term at all it you know you can use it as a marketing term and and people often use it and you can get combined with them and when when people don't understand how we are approaching the problem it is it is easy to be uh misunderstood and you know think of it as you know purely uh marketing but that's not the way uh we are so you really i mean as a scientist you believe that if we kind of just stick to really understanding the brain that's going to that's the right like you should constantly meditate on the how does the brain do this because that's going to be really helpful for engineering intelligent systems yes you need to so i think it is it's one input and it is it is helpful but you you should know when to deviate from it too um so an example is convolutional neural networks right uh convolution is not an operation brain in implements uh the visual cortex is not convolutional visual cortex has local receptive fields local connectivity but the you know the um there is there is no translation in in variance in the um uh the network weights um in in the visual cortex that is a a computational trick which is a very good engineering trick that we use for sharing the training between the different uh nodes um so and and that trick will be with us for some time it will go away when we have um robots with eyes and heads that move uh and so then that trick will go away it will not be uh useful at that time so so the brain doesn't so the brain doesn't have translational invariance it has the focal point like it has a thing it focuses on correct it does it has a phobia and and because of the phobia um the receptive fields are not like the copying of the weights like the the weights in the center are very different from the weights in the periphery yes at the periphery i mean i did this uh actually wrote a paper and just gotten the chance to really study peripheral peripheral vision which is a fascinating thing very under understood thing of what the br you know at the every level the brain does with the periphery it does some funky stuff yeah so it's uh it's another kind of trick than uh convolutional like it does it it uh it's a you know convolutional convolution in neural networks is a trick to for efficiency is efficiency trick and the brain does a whole another kind of thing yeah yes got it so so you need to understand the principles or processing so that you can still apply engineering tricks yeah when where you want to do you don't want to be slavishly making all the things of the brain um and and so yeah so it should be one input and i think it is extremely helpful uh but you it should be the point of really understanding so that you know when to deviate from it so okay that's really cool that that's work from a few years ago so you'd uh you did work in jumento with jeff hawkins yeah uh with uh hierarchical temporal memory how is your just if you could give a brief history how is your view of the way the models of the brain changed over the past few years leading up to to now is there some interesting aspects where there is an adjustment to your understanding of the brain or is it all just building on top of each other in terms of the higher level ideas especially the ones jeff wrote about in the book if you if you blur out right you know yeah on intelligence right on intelligence if you if you blur out the details and and if you just zoom out and at the higher level idea uh things are i would say consistent with what he wrote about but but many things will be consistent with that because it is it's a blur you know when you when you you know deep learning systems are also you know multi-level hierarchical all of those things right so so at the but um in terms of the detail a lot of things are different uh and and and those details matter a lot um so so one point of difference i had with jeff uh uh was uh how to approach you know how much of biological possibility and realism do you want in the learning algorithms um so uh when i was there uh this was you know almost 10 years ago now so yeah you're having fun i don't know i don't know what just thinks now but 10 years ago uh the difference was that i did not want to be so constrained on saying uh my learning algorithms won't need to be biologically possible based on some filter of biological possibility available at that time to me that is a dangerous cut to make because we are you know discovering more and more things about the brain all the time new biophysical mechanisms new channels uh are being discovered all the time so i don't want to upfront kill off an uh a learning algorithm just because we don't really understand the full uh the full uh biophysics or whatever of how the brain learns exactly exactly well let me ask a sergeant like what's our what's your sense what's our best understanding of how the brain learns so things like back propagation credit assignment so so many of these algorithms have learning algorithms have things in common right it is a back propagation is one way of credit assignment there is another algorithm called expectation maximization which is you know another weight adjustment algorithm but is it your sense the brain does something like this has to there is no way around it in the sense of saying that you do have to adjust the the connections so yeah and you're saying credit assignment you have to reward the connections that were useful and making a correct prediction and not yeah i guess what up but yeah it doesn't have to be differentiable i mean yeah it doesn't have to be differentiable yeah yeah but you have to have a you know you have a model that you start with you where you have data comes in and you have to have a way of adjusting the model such that it better fits the data yeah so that that is all of learning right and some of them can be using backprop to do that some of it can be using uh you know very local uh graph changes to do that um that can you know many of these learning algorithms have similar update properties locally in terms of what the neurons need to do locally i wonder if small differences in learning algorithms can have huge differences in the actual effect so the dynamics of i mean uh sort of the reverse like spiking like the uh if if credit assignment is like a a lightning versus like a rainstorm or something like whether whether there's a like a looping local type of situation with the credit assignment yeah whether there is uh like regularization like how um how it injects robustness into the whole thing like whether it's chemical or electrical or mechanical yeah uh all those kinds of things like that i feel like it that yeah i feel like those differences could be essential right it could be it's just that you don't know enough to on the learning side you don't know enough to say that is definitely not the way the brain does it got it so you don't want to be stuck to it right so that yeah so you you've been open-minded on that side of that correct on the infrastructure on the recognition side i am much more uh i'm able to be constrained because it's much easier to do experiments because you know it's like okay here's the stimulus you know how many steps did it get to take the answer i can trace it back i can i can understand the speed of that computation etc much more readily on the infant side got it and then you can't do good experiments on the learning side correct so that let's let's go right into the cortical micro circuits right back so what uh what are these ideas beyond recursive cortical network that uh you're looking at now so we have made a you know pass through or you know multiple of the steps that we you know i say as i mentioned earlier you know we were looking at perception from the angle of cognition right it was not just perception for perception's sake how do you how do you connect it to cognition uh how do you learn concepts and how do you learn abstract reasoning uh similar to some of the things francois uh uh talked about right um so um so we have uh taken one pass through it basically saying what is the basic cognitive architecture that you need to have which has a perceptual system which has a system that learns dynamics of the world and then has something like a routine program learning system on top of it to learn concepts so we have we've built one the you know the version 0.1 of that system uh this was another uh science robotics paper uh it is it's the title of that paper was you know something like cognitive programs how do you build cognitive programs and and the application there was on manipulation robotics it was um so think of it like this suppose you uh wanted to tell a new person uh that you met you don't know the language or that person uses you want to communicate to that person uh to achieve some task right so i want to say hey um you need to pick up all the the red cups from the kitchen counter and put it here right how do you communicate that right you can show pictures you can basically say look this is the starting state the the things are here this is the ending state and and what does the person need to understand from that the person need to understand what conceptually happened in those pictures from the input to the output right so um so we are looking at pre-verbal conceptual understanding without language how do you how do you have a set of concepts that you can manipulate in your head uh and from this in a set of images of input and output can you infer what is happening in those images got it with concepts that are pre-language okay so what does it mean for a concept to be pre-language like yeah why why so why why is language uh so important here so i i want to make a distinction between concepts that are just learned from text by just just feeding brute force text uh you can you can start extracting things like okay uh cow is likely to be on grass so those kinds of things you can extract purely from text um uh but that's kind of a simple association uh thing rather than a concept as an abstraction of something that happens in the real world you know in a grounded way that i can i can simulate it in my mind and connect it back to the real world and you think kind of the visual uh the visual world concepts in the visual world are somehow lower level than just the language the lower level kind of makes it feel like okay that's like unimportant like it's more like uh i would say uh the concepts in the visual and the motor system and you know the uh the concept learning system which if you cut off the language part just uh just what we learned by interacting with the world and abstractions from that that is a prerequisite for any real language understanding so you're uh so you disagree with chomsky because he says language is at the bottom of everything no i i yeah i disagree with chomsky completely from from universal grammar to yeah so that was a paper in science beyond the recursive cortical network uh what what other interesting problems are there the open problems and brain inspired uh approaches that you're thinking about i mean everything is over right like you know no no no problem is uh solved solved all right uh first uh i think of perception as kind of the the pro the first thing that you have to build but the last thing that you will be actually solved so because if you do not build perception system in the right way you cannot build concept system in the right way so so you have to build a perception system however wrong that might be you have to still build that and learn concepts from there and then you know keep it rating um and and finally perception will get solved fully when perception cognition language all those things work together finally so what uh i'm not so great we've talked a lot about perception but then maybe on the concept side and like common sense or just general reasoning side is there some some intuition you can draw from the brain about how we can do that so i have i have this uh classic example i give um so suppose i give you a few sentences and then ask you a question following that sentence this is a natural language processing problem right so so here it goes i'm telling you uh sally pounded a nail on the ceiling okay oh that's a sentence now i am asking you a question was the nail horizontal or vertical vertical okay how did you answer that uh well i imagined sally it was kind of hard to imagine what the hell she was doing but uh but i imagined i had a visual of the whole situation exactly exactly so so here you know i i post a question in natural language the answer to that question was you you got the answer from actually simulating the scene now i can go more and more detail about okay was sally stan standing on something while doing this you know could could she have been uh standing on a light bulb to do this you know i could i could ask more and more questions about this and i can ask make you simulate the synonym scene in more and more detail right where is all that knowledge that you are accessing stored it is not in your language system it is not it was not just by reading text you got that knowledge it is stored from the everyday experiences that you have had from and and by the by the age of five you you have pretty much all of this right and it is stored in your visual system motor system in a way such that it can be accessed through language i got it i mean right so your the language is just almost services the query into the whole visual cortex and it does the whole feedback thing but i mean it is all reasoning kind of connected to the perception system in some way you can do a lot of it you know you can still um do a lot of it by quick associations without having to go into the depth and and most of the time you will be right right you can just do quick associations but i can easily create tricky situations for you where that quick association is wrong and you have to actually run the simulation so the figuring out the how these concepts connect you have a good idea of how to do that that's exactly what that does one of the problems that we are working on and and and and the uh the way we are approaching that is basically saying okay you need to so the the uh the takeaway is that language is simulation control and your perceptual plus uh motor system is building a simulation of the world and so so that's basically the way we are approaching it and the first thing that we built was a controllable perceptual system and we built a schema networks which was a controllable dynamic system then we built a concept learning system that puts all these things together into programs are subtractions that you can run and simulate and now we are taking the step of connecting into language and uh and uh it will be very simple examples initially it will not be the gpt three like examples but it will be grounded simulation based language and for like the the querying would be like question answering kind of thing correct correct and it will be in some simple world initially on you know uh i but it will be about okay can the system connect the language and uh ground it in the right way and run the right simulations to come up with the answer and the goal is to try to do things that for example gpg3 couldn't do got it speaking of which if we could uh talk about gpt3 a little bit i think it's an interesting thought-provoking set of ideas that open ai is pushing forward i think it's good for us to talk about the limits and the possibilities in neural networks so in general what are your thoughts about this recently released very large 175 billion parameter language model so i have i haven't uh directly evaluated it yet from what i have seen on twitter and you know other people evaluating it it looks very intriguing you know i am i am very intrigued by some of the properties it is displaying and and of course the text generation uh part of that was already evident in gpt2 you know that it can generate cochrane text over uh uh long distances that was uh but of course the weaknesses are also pretty visible in saying that okay it is not really carrying a world state around um and you know sometimes you get sentences like i went up the hill to reach the valley or the thing now there are some you know completely incompatible statements or when you're traveling from one place to the other it doesn't take into account the time of travel things like that so those things i think will happen less than gpt 3 because it is trained on even more data and so and it has it can do even more longer distance uh uh coherence um but it will still have the fundamental limitations that it doesn't have a world model uh and it can't run simulations in its head to find whether something is true in the world or not do you think within so it's taking a huge amount of text from the internet and forming a compressed representation do you think in that could could emerge something that's an approximation of a world model which essentially could be used for reasoning and it's a it's a it's a i'm not talking about gpt three i'm talking about gpt four five and gpt 10. yeah i mean they will look more impressive than gpg3 so you can if you take that to the extreme then uh a markov chain of just first order and if you if you go to um i'm taking the other extreme if you read shannon's book right uh he has a model of english text which is based on faster mark of chains second order markov chains third markov chain sentencing that okay the markov chains look better than uh faster markov chains right so does that mean a faster markov chain has a model of the world yes it does uh so yes in that level uh when you go higher order models or more uh sophisticated structure in the model like the transformer networks have yes they have a model of the text world um but that is not a model of uh the world it's it's a model of the text world and it will have in interesting uh properties and it will be useful but just scaling it up is not going to give us a gi or natural language understanding or meaning well the the question is uh whether being forced to compress a very large amount of text yeah forces you to construct things that are very much like because the ideas of concepts and meaning is a spectrum yeah uh so in order to form that kind of compression maybe it will uh be forced to figure out abstractions which look awfully a lot like the kind of things that we think about as uh as concepts as world models as common sense is that possible no i don't think it is possible because the information is not there well the information is uh is there behind the text right now unless somebody has written down all the details about how everything works in the world to the the absurd amounts like okay it is easier to walk forward than backward uh that you have to open the door to go out of the thing uh doctors wear underwear you know unless all these things somebody has written down somewhere or you know somehow the program found it to be useful for compression from some other text uh the information is not there so that's an argument that like text is a lot lower fidelity than the you know the experience of our physical world like right so you can use a thousand words like that kind of thing well in this case pictures aren't really so the the richest aspect of the physical world isn't even just pictures it's the uh it's the interactivity of the world yeah it's being able to um yeah interact it's almost like it's almost like if you could interact so i i i disagree well maybe i agree with you that picture's worth a thousand words but a thousand it's still yeah you could say you could capture it with the gpt x so i wonder if there's some interactive element where a system could live in text world where it could uh be part of the chat be part of you know talking to people it's it's interesting i mean fundamentally so you're making a statement about the limitation of text okay let's so let's say we have a text corpus that includes basically every experience we could possibly have i mean just a very large corpus of text and also interactive components i guess the question is whether the neural network architecture these very simple transformers but if they had like hundreds of trillions or whatever comes after a trillion parameters whether that could store the information needed that's architecturally do you have like do you have thoughts about the limitation on that side of things with neural networks i mean so transformer is you know still a feed forward neural network this uh uh it's it has a very uh interesting architecture which is good for uh text modeling and probably some aspects of uh video modeling but it is still a feed forward architecture and you believe in the feedback mechanism the recursion oh and and also because you know causality you know being able to do counterfactual reasoning being able to do you know intervention so which is uh uh um actions in the world uh so all those things uh require different kinds of models to be built uh i i don't think uh transformers uh captures that uh family it is very good at statistical modeling of text uh yeah and and it will become better and better with more data uh bigger models but that is only going to get so far you know finally when you in so i had this joke on uh twitter saying that hey this is a model that has read all of quantum mechanics and theory of relativity and we are asking it to do text completion or you know we are actually asking you to solve simple puzzles that you know when when you have agi if you if you you know that's not what you ask a system to do if you just you know we ask we'll ask the system to do experiments you know what should uh and and come up with hypothesis and uh you know revise the hypothesis based on evidence from experiments all those things right those are the things that we want the system to do when we have a gi not solved with simple puzzles so like impressive demo somebody generating a red button in html right uh which are all useful like you know there's no not dissing the the usefulness of it yeah so i get by the way i'm i mean playing a little bit of a devil's advocate uh so calm down internet uh so i just i'm curious almost in which ways will a dumb but large neural network will surprise us yeah so like i'm it's kind of your i completely agree with your intuition it's just that i don't want to dogmatically like 100 percent put all the chips there right it's we've been surprised so much even the current gpt 2 and 3 are so surprising yeah uh the self-play mechanisms of alpha zero are really surprising and i the reinforcement the fact that reinforcement learning works at all to me is really surprising the fact that neural networks work at all is quite surprising given how non-linear the space is the fact it's able to find local minima that are at all reasonable it's very surprising so it uh i i wonder sometimes whether us humans just want it to not the for agi not to be such a dumb thing so i just because exactly what you're saying is like the ideas of concepts and be able to reason with those concepts and and connect those concepts in uh like hierarchical ways and then to be able to have uh world models i mean just everything we're describing in human language in this poetic way seems to make sense that that is what intelligence and reasoning are like i i wonder if at the core of it it could be much dumber uh well finally it is still connections and messages passing over them right right so that way it's done so i guess the recursion the the feedback mechanism that does seem to be a fundamental kind of thing um yeah yeah the idea of concepts also memory correct like having an episodic memory yeah yeah that seems to be an important thing so how do we get memory so yeah we have another piece of work that which came out recently on how do you form episodic memories and and form abstractions from them uh and we haven't figured out a you know all the connections of that to the overall cognitive architecture but um well yeah what are your ideas about how you could have episodic memory so at least it's very clear that there you need to have two kinds of memory right that that's very very clear right because there are things that happen uh as statistical patterns in the world uh but then there is the the one timeline of things that happen only once in your life right uh uh and this day is not going to happen ever again and and so and that needs to be stored as a as a you know just a stream of uh string right this is this is my experience and then then the question is about how do you take that experience and connect it to the statistical part of it how do you now say that okay i experienced this thing now i want to be careful about similar situations uh and so so you need to be able to index that similarity using your other giant status you know the the model of the world that you have learned although the situation came from the episode you need to be able to index the other one so uh the episodic memory being implemented as an indexing over the other uh model that you're building so the memories remain and they uh they they're an index into this like the statistical thing that you formed yeah statistical causal structural model that you built over over time so so it's basically the idea is that uh the hippocampus is uh just storing or sequencing uh in a set of pointers that happens over time and then whenever you want to reconstitute that memory and evaluate the different uh aspects of it whether it was good bad do i need to encounter the situation again you need the cortex to reinstantiate to replay that memory so how do you find that memory like which direction is the important direction both directions are units again bi-directional so i guess how do you retrieve the memory so this is again hypothesis right yeah we're making this work so when you uh when you come to a new situation right uh your your cortex is doing inference uh over in the new situation and then of course hippocampus is connected to different parts of the cortex um and and you have this deja vu situation right okay i have seen this thing before and uh and then in the hippocampus you can have an index of okay this is when it happened as a timeline uh and and and then then you can use the hippocampus to drive the the similar timelines to say now i am i am rather than being driven by my current input stimuli i am going back in time and rewinding my experience for applying it but putting back into the cortex and then putting it back into the cortex of course affects what you're going to see next in your current situation got it yeah so that's that's the whole thing having a world model and then yeah uh connecting to the perception yeah it does seem to be that that's what's happening it'd be on the neural network side it's um it's interesting to think of how we actually do that yeah yeah to have a knowledge base yes it is possible that you can put many of these structures into uh neural networks and we will find ways of combining properties of neural networks and graphical models so uh i mean it's already started happening yes uh graph neural networks are kind of emerge between them and there will be more of that thing so but to me it is the direction is pretty cl i mean looking at biology and the histo history of uh uh evolutionary history of intelligence it is pretty clear that okay what does need is more structure in the models and modeling of the world and supporting dynamic inference well let me ask you uh there's a guy named elon musk there's a company called neurolink and there's a general field called brain computing interfaces yeah um it's kind of uh interface between your two loves yes the brain and the intelligence uh so there's like very direct applications of brain computer interfaces for people with different conditions more in the short term yeah but there's also these sci-fi futuristic kinds of ideas of ai systems being able to communicate in a high bandwidth way with the brain bi-directional yeah uh what are your thoughts about uh neural link and bci in general as a possibility so i think bca is a cool research area and in fact um when i got interested in brains initially when you know so i was enrolled at stanford and when i got interested in brains it was it was through a brain uh computer interface talk that krishna gave that's when i even started thinking about the problem so uh so it is definitely a fascinating research area and it is the applications are enormous right um so you know there is a science fiction scenario of you know brains directly communicating let's you know let's keep that aside for the time being uh even just the the intermediate milestones that pursuing which are very reasonable as far as i can see uh being able to control an external limb using uh uh in a direct connection from the brain and being able to write things into the brain uh so so those are all uh good steps to take and they have enormous applications you know people losing limbs being able to control prosthetics quadriplegics being able to control something so and therapeutics and you know i also know about another company working in the space called paradromix they're doing you know it's based on a different uh electrode array but trying to attack some of the same problems so i think it's a very also surgery correct surgically implanted electrons yeah um so yeah i think of it as a very very promising field especially when it is helping people overcome uh some limitations now at some point of course it will advance the level of being able to communicate uh how hard is that problem do you think like so so okay let's say we magically solve what i think is a really hard problem of doing all of this safely yeah so so like being able to uh connect electrodes and not just thousands but like millions to the right i i think it's very very hard because you also do not know what the what will happen to the brain with that right in the sense of how does the brain adapt to something like that and it's you know as we're learning it's the brain is quite uh in terms of neuroplasticity it's pretty malleable so it's going to adjust so the machine learning side the computer side is going to adjust and then the brain is going to adjust exactly and then what what soup does this landers the kind of hallucinations you might get from this that might be pretty intense yeah yeah just connecting to all of wikipedia it's interesting whether we need to be able to figure out the basic protocol of the brain's communication schemes in order to get them to the machine and the brain to talk because another possibility is the brain actually just adjusts to whatever the heck the computer is doing exactly that's the way i think that i find that to be a more promising way it's basically saying you know okay attach electrodes to some part of the cortex okay and make sure maybe if it is done from birth the brain will adapt it says that you know that part is not damaged it was not used for anything these electrodes are attached there right and now you you train that part of the brain to do this high bandwidth communication between something else right and and uh if you do it like that either then it is brain adapting to and of course your external system is the sciences that it is adaptable you know just like we you know design computers or mouse keyboard all of them to be uh interacting with humans so of course that feedback system is designed to be uh human compatible but um now it is not trying to record from the all of the brain and uh you know now you know two systems trying to adapt to each other it's the brain adapting into one way it's passing the brain is connected to like the internet it's connected yeah just imagine just connecting it to twitter and just just just taking that stream of information um yeah but again if we take a step back i don't know what your intuition is i feel like that is not as hard of a problem as the doing it safely there's there's a huge barrier to surgery right because because the biological system it's it's a mush of like weird stuff correct so that the surgery part of it biology part of it the the long term repercussions part of it again i don't know what else will uh you know we we often find uh after a long time uh in biology that okay that idea was wrong right you know so people used to cut off this the gland called the thymus or something and then they found that oh no that actually causes cancer and then there's a subtle like millions of variables involved but this whole process the nice thing and just like again with elon just like colonizing mars seems like a ridiculously difficult idea but in the process of doing it we might learn a lot about the biology of the neurobiology of the brain the neuroscience side of things it's like if you want to learn something do the most difficult version of it yeah and see what you learn the intermediate steps that they are taking sounded all very reasonable to me yeah yeah it's great well but like everything with elon is the timeline seems insanely fast so that's that's the only awful question uh well one we've been talking about cognition a little bit so like reasoning we haven't mentioned the other c word which is consciousness uh do you ever think about that one do is that useful at all uh in this whole context of what it takes to create an intelligent reasoning being or is that completely outside of uh your uh like the engineering perspective uh it is not outside the realm but it doesn't on a day-to-day way uh you know basis inform what we do but it's more so in in many ways the company name is connected to this uh idea of consciousness what's what's the company name vicarious you know so vacation is the company name and uh and so what does victorious mean right it's um uh at the first level it is about modeling the world and uh and it is internalizing the external actions so so you interact with the world and learn a lot about the world and now after having learned a lot about the world you can run those things in your mind without actually having to uh act in the world so you can run uh things vicariously just in your in your in your brain and similarly you can experience another person's thoughts by you know having a model of how that person works and uh and running their you know putting yourself in some other person's shoes so that is being vicarious now it's the same modeling apparatus that you're using to model the external world or some other person's thoughts you can turn it to yourself you can up you know if that same modeling thing is applied to your own modeling apparatus then that is what gives rise to consciousness i think well that's more like self-awareness there's the heart problem of consciousness which is like when the model becomes when when the model feels like something when this whole process is like it act it's like you really are in it you feel like an entity in this world not just you know that you're an entity but it feels like something to be that entity it um it you know and thereby we attribute this you know then it starts to be wherein something that has consciousness can suffer you start to have these kinds of things that we can reason about that yes much much heavier it seems like there's much greater cost of your your decisions and like mortality is tied up into that like the fact that these things end right first of all i end at some point and then other things end and you know that that somehow seems to be at least for us humans a deep motivator yes and that you know that that idea of motivation in general we talk about goals and ai but right the goals aren't quite the same thing as like the our mortality it feels like it feels like first of all humans don't have a goal and they just kind of create goals at different levels they like make up goals because we're terrified by the mystery of the thing that that gets us all so we we make these goals up so we're like a go generation machine as opposed to a machine which optimizes the trajectory towards a singular goal so it feels like that's an important part of uh cognition that whole mortality thing well it is it is a part of human uh cognition uh but there is no uh reason for uh that mortality to come to the equation for a uh artificial system because we can uh copy the artificial system the the the problem with humans is that we cut i can't clone you i can't like you know i can i can close even if i clone usb uh you know the hardware your experience uh that was stored in your brain uh your uh episodic memory all those will not be captured in the in the new clone um so um but that's not the same with an ai system right so but it's also possible that the the thing that you mentioned with that with us humans is actually fundament of fundamental importance for intelligence so like the fact that you can copy an ass system yeah means that that ai system is not yet an um agi so like there it could so if you look at existence proof yeah if we reason yeah based on existence proof would you could say that it doesn't feel like death is a fundamental property of an intelligence system but we don't yet give me an example of an immortal intelligent being we don't have those it could it's very possible that you know that's that is a fundamental property of intelligence is a thing that has a deadline for itself so you can think of it like this so suppose you invent a way to freeze people uh for a long time it's not dying right yeah uh so so you can be frozen and woken up uh thousands of years from now uh so it's no fear of death so well no the you're still it's it's not it's not about time it's about the knowledge that it's temporary and the that aspect of it the finiteness of it i think um creates a kind of urgency correct for us for humans yeah for humans yes uh and that that is part of our drives uh but um and that's why i'm not too worried about ai uh you know uh having motivations to kill all humans and uh those kinds of things why just wait you know so so why do you need to do that yeah i've never heard that before that's a good it's a good point because yeah just murder seems like a lot of work we'll just wait wait it out they'll probably hurt themselves let me ask you um people often kind of wonder world-class researchers such as yourself what kind of books technical fiction philosophical were had an impact on you in your life and maybe ones you could prob possibly recommend that others read maybe if you have three books that pop in the mind yeah so i definitely liked uh judy apple's book uh probabilistic reasoning and intelligence systems it's um it's a very deep technical book but what i liked is that so there are many uh places where you can learn about probabilistic graphical models from but throughout this book judea pulls kind of sprinkles his philosophical observations and and he thinks about us to how the brain thinks and attentions and resources all those things so so that whole thing makes it more interesting to read uh he emphasizes the importance of causality so that was in his later book so this was the first book probabilistic reasoning in interlinked systems he mentions causality but he hadn't really sunk his teeth into like you know how do you actually formalize that yeah and uh the second book causality so two thousand uh the one in two thousand that one is really hard so i wouldn't recommend that uh uh yes so that looks at the like the mathematical like his model of uh calculus do calculus yeah it was pretty dense mathematically right yeah right uh the book of why is definitely more enjoyable oh for sure yeah um so yeah so i would i would recommend probabilistic reasoning in intelligent systems another book i liked uh was uh one from doug huff starter uh this is a long time ago though here's a book he had a book i think called it was called the mind's eye it was um uh probably half starter and daniel dennett together uh yeah so and i actually was uh i i bought that book so much i haven't read it yet but i uh i couldn't get an electronic version of it which is annoying because i'm you read everything on kindle okay uh you had to actually purchase the physical it's like one of the only physical books i have because yeah anyway there's a lot of people recommended it highly so yeah and the third one uh i would definitely recommend reading is um uh this is not a technical book it is history it's called it's the name of the book i think is bishop's voice it's about wright brothers and uh and their their their path and how it was uh it's there are multiple books on this topic and all of them are great it's um uh fascinating how a flight was uh you know treated as an unsolvable problem and and and also you know what aspects did people emphasize uh you know people thought oh it is all about the just powerful engines you know just need to have powerful lightweight engines uh and um so you know some people thought of it as how far can we just throw the thing you know just throw it yeah catapult yeah so so it is it's a very fascinating and even after they uh made the invention of people not believing it and uh uh the social aspect of it this is the social aspect it's a different you know very important do you uh draw any parallels between you know birds fly so there's the natural approach to uh to flight and then there's the engineered approach do you um do you see the same kind of thing with the brain and are trying to engineer intelligence yeah it's it's a good analogy to have uh of course all analogies have their you know uh yeah so for sure so people in uh you know ai often uh use airplanes as an example of hey we didn't learn anything from birds look right there yeah but the the funny thing is that uh and and the saying is uh airplanes don't flap wings yeah right this is what they say the funny thing and the ironic thing is that that you don't need to flap to fly is something right brothers found by observing birds yeah so they have in their notebook you know in some of these books they show their notebook drawings right they they make detailed notes about buzzards uh just soaring over the thermals and they basically say look flapping is not the important propulsion is not the important problem to solve here we want to solve control uh and uh once you solve control propagation will fall into place all of these are people you know they re realize this by observing birds beautiful part put that's actually brilliant uh because people do use that knowledge a lot i'm gonna have to remember that one do you have a advice for people interested in artificial intelligence like young folks today i talk to undergraduate students all the time uh interested in neuroscience interesting in understanding how the brain works is there advice you would give them about their career maybe about their life in general sure i think every you know every piece of advice should be taken with a pinch of salt of course because you know each person is different their motivations are different but i can i can definitely say if your goal is to understand the brain from the angle of wanting to build one you know then uh being an experimental neuroscientist might not be the way to go about it um uh it might a better way to pursue it might be through computer science electrical engineering machine learning and ai and of course you have to study up the neuroscience but that you can do on your own um if you are more uh attracted by finding something intriguing about discovering something intriguing about the brain then of course it is uh better to be an experimentalist so find that motivation what are you intrigued by and of course find your strengths too some people are very good experimentalists uh and and they enjoy doing that and essentially to see which department if you're if you're picking in terms of like your education path whether to um uh to go with like in mit it's branding computer uh no uh bcs yeah brandon cognitive sciences yeah uh or or the cs side of things right and actually uh the brain folks the neuroscience folks are more and more now embracing of uh you know learning tensorflow by torch right there they they see the power of uh trying to engineer ideas uh that uh that they get from the brain into and then explore how those could be used to uh to create intelligent systems so that might be the right department actually to uh so this was a question in uh uh you know one of the redwood neuroscience institute workshops or that jeff hawkins organized almost 10 years ago this question was put to a panel right what what should be the undergrad major you should take if you want to understand the brain and uh and the majority opinion that one was electrical engineering interesting uh because i mean i'm a doubly undergrad so i got lucky in that way but it i i think it does have some of the right ingredients because you learn about circuits you you learn about how you can construct circuits to uh you know approach you know do functions uh you learn about microprocessors um you learn information theory you learn signal processing uh you learn continuous math so um so in that way it's it's a good step to if you want to go to computer science or neuroscience you can it's a good step the downside you're more likely to be forced to use matlab [Laughter] one of the interesting things about i mean this is changing the world is changing but uh like certain departments lagged on the programming side of things on developing good uh good habits as a software engineering but i think that's more and more changing and and students can take that into their own hands like learn to program i feel like everybody should learn to program because it uh like everyone in the sciences because it empowers it puts the data at your fingertips so you can organize it you can find all kinds of things in the data and then you can also for the appropriate sciences build systems that like based on that so like then engineer intelligence systems uh we already talked about mortality so we hit no a ridiculous point but let me ask you the uh you know one of the things about intelligence is it's goal driven and you study the brain so the question is like what's the goal that the brain is operating under what's what's the meaning of it all for us humans in your view what's the meaning of life the meaning of life is whatever you construct out of it it's completely open it's open yeah so there's no there's nothing uh uh like you mentioned you like constraints what's uh it's it's wide open is there is there some useful aspect that you think about in terms of like the openness of it and just the basic mechanisms of generating goals uh and studying cognition in the brain that you think about or is it just about because everything we've talked about kind of the perception system is to understand the environment that's like to be able to like not die exactly like not fall over and like be able to uh you don't think we need to um think about anything bigger than that yeah i think so because it's it's basically being able to understand the machinery of the world uh such that you can push you whatever goals you want right so the machinery of the world is is really ultimately what we should be uh striving to understand the rest is just the rest is just whatever the heck you want to do or whatever whatever is culturally popular i think that's i that's beautifully put i don't think there's a better way to end it delete i'm so honored that you you show up here and waste your time with me it's been an awesome conversation thanks so much for talking today oh thank you so much this was this was so much more fun than i expected thank you thanks for listening to this conversation with the league george and thank you to our sponsors babel raycon earbuds and masterclass please consider supporting this podcast by going to babel.com and use codelex going to buy raycon.com lex and signing up at masterclass.com lex click the links get the discount it really is the best way to support this podcast if you enjoy this thing subscribe on youtube review five star snap a podcast support it on patreon i'll connect with me on twitter alex friedman spelled yes without the e just f-r-i-d-m-a-n and now let me leave you with some words from marcus aurelius you have power over your mind not outside events realize this and you will find strength thank you for listening and hope to see you next time you