Jeff Hawkins: Thousand Brains Theory of Intelligence | Lex Fridman Podcast #25

the following is a conversation with Jeff Hawkins he's the founder of the redwood centre for theoretical neuroscience in 2002 and Numenta in 2005 in this 2004 book titled on intelligence and in the research before and after he and his team have worked to reverse-engineer the neocortex and proposed artificial intelligence architectures approaches and ideas that are inspired by the human brain these ideas include hierarchical temporal memory htm' from 2004 and new work the thousands brains theory of intelligence from 2000 17 18 and 19 Jeff's ideas have been an inspiration to many who have looked for progress beyond the current machine learning approaches but they have also received criticism for lacking a body of empirical evidence supporting the models this is always a challenge when seeking more than small incremental steps forward in AI Jeff was a brilliant mind and many of the ideas he has developed and aggregated from your science are worth understanding and thinking about there are limits to deep learning as it is currently defined forward progress in AI is shrouded in mystery my hope is that conversations like this can help provide an inspiring spark for new ideas this is the artificial intelligence podcast if you enjoy it subscribe on youtube itunes or simply connect with me on twitter at lux friedman spelled fri d and now here's my conversation with Jeff Hawkins are you more interested in understanding the human brain or in creating artificial systems that have many of the same qualities but don't necessarily require that you actually understand the underpinning workings of our mind so there's a clear answer to that question my primary interest is understanding the human brain no question about it but I also firmly believe that we will not be able to create fully intelligent machines until we understand how the human brain works so I don't see those as separate problems I think there's limits so what can be done with machine intelligence if you don't understand the principles by which the brain works and so I actually believe that studying the brain is actually the fast the fastest way to get to machine intelligence and within that let me ask the impossible question how do you not define but at least think about what it means to be intelligent so I didn't try to answer that question first we said let's just talk about how the brain works let's figure out how certain parts of the brain mostly the new your cortex but some other parts to the parts of the very most associated intelligence and let's discover the principles about how they work because intelligence isn't just like some mechanism and it's not just some capabilities it's like okay we don't even have know where to begin on this stuff and so now that we've made a lot of progress on this after we've made a lot of progress on how the neocortex works and we can talk about that I now have a very good idea what's going to be required to make intelligent machines I can tell you today you know some of the things are gonna be necessary I believe to create intelligent machines well so we'll get there we'll get to the neocortex and some of the theories of how the whole thing works and you're saying as we understand more and more about the neocortex about our own human mind we'll be able to start to more specifically define what it means to be intelligent it's not useful to really talk about that until I don't know if it's not useful look there's a long history of AI as you know right and there's been different approaches taken to it and who knows maybe they're all useful right so you know the good old fashioned AI the expert systems current convolution neural networks they all have their utility they all have a value in the world but I would think almost everyone agree that none of them are really intelligent in a set of a deep way that that humans are and so it's it's just the question is how do you get from where those systems were or are today to where a lot of people think we're going to go and just big big gap there a huge gap and I think the quickest way of bridging that gap is to figure out how the brain does that and then we can sit back and look and say oh what do these principles that the brain works on are necessary and which ones or not kula we don't have to build this in and telogen machines aren't going to be built out of you know organic living cells but there's a lot of stuff that goes on the brain it's going to be necessary so let me ask me B before we get into the fun details let me ask me to get depressing or a difficult question do you think it's possible that we will never be able to understand how our brain works that maybe there's aspects to the human mind like we ourselves cannot introspectively get to the core that there's a wall you eventually hit yeah I don't believe that's the case I have never believed that's the case there's not have been a single thing we've ever humans have ever put their minds to so we've said oh we reached the wall we can't go any further it just people keep saying that people used to believe that about life you know Ilan's Vittal right there's like what's the difference in living matter and nonliving matter something special you never understand we no longer think that so there's there's no historical evidence to suggest is the case and I just never even considered that's a possibility I would also say today we understand so much about the neocortex we've made tremendous progress in the last few years that I no longer think of it as an open question the answers are very clear to me and the pieces we know we don't know I are clearly me but the framework is all there and it's like oh okay we're gonna be able to do this this is not a problem anymore it just takes time and effort but there's no mystery a big mystery anymore so then let's get it into it for people like myself we're not very well versed in the human brain except my own can you describe to me at the highest level what are the different parts of the human brain and then zooming in on the neocortex the parts of the neocortex and so on a quick overview yeah sure human brain we can divide it roughly into two parts there's the old parts lots of pieces and then there's a new part the new part is the neocortex it's new because it didn't exist before mammals the only mammals have a neocortex and in humans it's in primates it's very large in the human brain the neocortex occupies about seventy to seventy-five percent of the volume of the brain it's huge and the old parts of the brain are there's lots of pieces there there's a spinal cord and there's the brain stem and the cerebellum and the different parts of the basal ganglia and so on in the old parts of the brain you have the autonomic regulation like breathing and heart rate you have basic behaviors so like walking and running or controlled by the old parts of the brain all the emotional centers of the brain are in the old part of the brains when you feel anger or hungry lust with things like that those are all in the old parts of the brain and and we associate with the neocortex all the things we think about as sort of high-level perception and cognitive functions anything from seeing and hearing and touching things to language to mathematics and engineering and science and so on those are all associative the neocortex and they're certainly correlated our abilities in those regards are correlated with the relative size of our neocortex compared to other mammals so that's like the rough division and you obviously can't understand the new your cortex is completely isolated but you can understand a lot of it with just a few interfaces so the all parts of the brain and so it it gives you a system to study the other remarkable thing about the neocortex compared to the old parts of the brain is the neocortex it's extremely uniform it's not visually or anatomically or it's very sucky I always like to say it's like the size of a dinner napkin about two and a half millimeters thick and it looks remarkably the same everywhere everywhere you look and that children have millimeters is this detailed architecture and it looks remarkably the same everywhere and that's a cross species the mouse versus a cat and a dog and a human or if you look at the old parts of the brain there's lots of little pieces do specific things so it's like the old parts of a brain evolved look this is the part that controls heart rate and this is the part that controls this and this is this the kind of thing and that's this kind of thing and he's evolved for eons a long long time and they have their specific functions and all sudden mammals come along and they got this thing called the neocortex and it got large by just replicating the same thing over and over and over again this is like wow this is incredible so all the evidence we have and this is an idea that was first articulated in a very cogent and beautiful argument by a guy named Vernon mal Castle in 1978 was that the neocortex all works on the same principle so language hearing touch vision engineering all these things are basically underlying or all built in the same computational substrate they're really all the same problem all over the building blocks all look similar yeah and they're not even that low-level we're not talking about like like neurons we're talking about this very complex circuit that exists throughout the neocortex is remarkably similar it is it's like yes did you see variations of it here and there more of the cell uh so that's not all so on but what now encruster argued was it says you know if you take a section on your cortex why is one a visual area and one is a auditory area or why 'since and his answer was it's because one is connected to eyes and one is connected ears literally you mean just its most closest in terms of number of connections to listen sir literally if you took the optic nerve and it attached it to a different part of the neocortex that part would become a visual region this actually this experiment was actually done by Mercosur oh boy and uh in in developing I think it was lemurs I can't remember there was some animal and and there's a lot of evidence to this you know if you take a blind person the person is born blind at Birth they they're born with a visual neocortex it doesn't may not get any input from the eyes because of some congenital defect or something and that region become does something else it picks up another task so and it's it's so it's just it's this very complex thing it's not like oh they're all built on neurons no they're all built in this very complex circuit and and somehow that circuit underlies everything and so this is the it it's called the common cortical algorithm if you will some scientists just find it hard to believe and they decide can't really that's true but the evidence is overwhelming in this case and so a large part of what it means to figure out how the brain creates intelligence and what is intelligence in the brain is to understand what that circuit does if you can figure out what that circuit does as amazing as it is then you can then you then you understand what all these other cognitive functions are so a few words to sort of put neural cortex outside of your book on intelligence you look if you wrote a giant tome a textbook on the neocortex and you look maybe a couple centuries from now how much of what we know now would still be two centuries from now so how close are we in terms of understand I have to speak from my own particular experience here so I run a small research lab here it's like yeah it's like I need other research lab I'm the sort of the principal investigator there was actually two of us and there's a bunch of other people and this is what we do we started the neocortex and we published our results and so on so about three years ago we had a real breakthrough in this in this film just tremendous spectrum we started we've now published I think three papers on it and so I have I have a pretty good understanding of all the pieces and what we're missing I would say that almost all the empirical data we've collected about the brain which is enormous if you don't know the neuroscience literature it's just incredibly big and it's it's the most part all correct its facts and and experimental results and measurements and all kinds of stuff but it none none of that has been really assimilated into a theoretical framework it's it's data without it's in the language of Thomas Kuhns a historian it would be a sort of a pre paradigm science lots of data but no way to fit in together I think almost all of that's correct it's gonna be some mistakes in there and for the most part there aren't really good cogent theories about it how to put it together it's not like we have two or three competing good theories which ones are right and which ones are wrong it's like yeah people just like scratching their heads wrong things you know some people given up on trying to like figure out what the whole thing does in fact is very very few labs that we that we do that focus really on theory and all this unassimilated data and trying to explain it so it's not like we have we've got it wrong it's just that we haven't got it at all so it's really I would say pretty early days in terms of understanding the fundamental theories forces of the way our mind works I don't think so that what I would have said that's true five years ago so I we have some really big breakthroughs on this recently and we started publishing papers on this so look it but so I don't think it's I you know I'm an optimist and from where I sit today most people would disagree with this but from where I sit city from what I know uh it's not super early days anymore we are it's it's you know the way these things go is it's not a linear path right you don't just start accumulating and get better and better better no you okay all the stuff you've collected none of it makes sense all these different things we just turn around and then you're gonna have some breaking points or all sudden oh my god now we got it right so that's how it goes and science and I feel like we passed that little thing about a couple years ago all that big thing a couple years ago so we can talk about that time will tell if I'm right but I feel very confident about it that's my moment to say it on tape like this at least very optimistic so let's before those few years ago let's take take step back to HTM the hierarchical temporal memory theory which you first proposed on intelligence and went through a few different generations can you describe what it is how it evolved through the three generations yes you first put it on paper yeah so one of the things that neuroscientists just sort of missed for many many years and ice and especially people were thinking about theory was the nature of time in the brain brains process information through time the information coming into the brain is constantly changing the patterns from my speech right now if you're listening to it at normal speed we'd be changing on IRA's about every 10 milliseconds or so you'd have it change this constant flow when you look at the world your eyes are moving constantly three to five times a second and the inputs complete completely if I were to touch something like a coffee cup as I move my fingers that input changes so this idea that the brain works on time changing patterns is almost completely or was almost completely missing from a lot of the basic theories like fears of vision and so it's like oh no we're going to put this image in front of you and flash it and say what is it a convolutional neural networks work that way today right you know classify this picture but that's not what visions like vision is this sort of crazy time-based pattern that's going all over the place and was touched and so is hearing so the first part of a hierarchal temporal memory was the temporal part it's it's the same you you won't understand the brain orally understand intelligent machines unless you're dealing with time-based patterns the second thing was the memory component of it was is to say that we aren't just processing input we learn a model of the world that's the memory stands for that model we have to the point of the brain part of the New York white chest it learns a model of the world we have to store things that our experience is in a form that leads to a model the world so we can move around the world we can pick things up and do things and navigate know how it's going on so that's that's what the memory referred to and many people just they were thinking about like certain processes without memory at all it just like processing things and finally the hierarchical component was reflection to that the New York or check so though it's just uniform sheet of cells different parts of it project to other parts which project to other parts and there is this sort of rough hierarchy in terms of them so the hyperbole temporal memory is just saying look we should be thinking about the brain as time-based you know model memory based and hierarchical processing and and that was a placeholder for a bunch of components that we would then plug into that we still believe all those things I just said but we now know so much more that I'm stopping to use the word hierarchal thumper memory yeah because it's it's insufficient to capture the stuff we know so again it's not incorrect but it's I now know more and I would rather describe it more accurately yeah so you're basically we can think of HTM as emphasizing that there's three aspects of intelligence that important to think about whatever the whatever the eventual theory it converges to yeah so in terms of time how do you think of nature of time across different time scales so you mentioned things changing a sensory inputs changing every 10 being myself what about it every few minutes every few yeah Montse well if you think about a neuroscience problem the brain problem neurons themselves can stay active for certain perks of time they parts of the brain with this doctor 4-minute you know so you could hold up a certain perception or an activity for a certain period of time but not most of them don't last that long and so if you think about your thoughts are the activity neurons if you're going to want to involve something that happened a long time ago I'm even just this morning for example the neurons haven't been active throughout that time so you have to store that so if I asked you what did you have for breakfast today that is memory that is you've built into your model of the world now you remember that and that memory is in the in the synapses it's basically in the formation of synapses and so it's it you're sliding into what you know is two different time scales there's time scales of which we are like understanding my language and moving about and seeing things rapidly and over time that's the time scales of activities of neurons but if you want to get longer time scales then it's more memory and we have to invoke those memories to say oh yes well now I can remember what I had for breakfast because I stored that someplace I may forget it tomorrow but I'd stored for afor now so this is memory also need to have so the hierarchical aspect of reality is not just about concepts it's also about time do you think of it that way yeah time is infused in everything it's like you really can't separate it out if I ask you what is the what is your you know how's the brain learning a model of this coffee cup here I have a coffee cup and I'm at the coffee cup I said well time is not an inherent property of this of this of the model I have of this cup whether it's a visual model or attack the model I can sense it through time but if the model self doesn't really much time if I asked you if I said well what is the model of my cell phone my brain has learned a model of the cell phone so if you have a smart phone like this and I said well this has time aspects to it I have expectations when I turn it on what's gonna happen what water how long it's going to take to do certain things if I bring up an app what sequences and so I have instant it's all like melodies in the world you know yeah melody has a sense of time so many things in the world move and act and there's a sense of time related to them some don't but most things do really so it's it's sort of infused throughout the models of the world you build a model of the world you're learning the structure of the objects in the world and you're also learning how those things change through time okay so it's it's it really is just a fourth dimension that's infused deeply and they have to make sure that your models have been intelligence incorporated so like you mentioned the state of neuroscience is deeply empirical a lot of data collection it's uh you know that's that's where it is using meshing Thomas Kuhn right yeah and then you're proposing a theory of intelligence and which is really the next step the really important stuff to take but why why is HTM or what we'll talk about soon the right theory so is it more in this it what is it backed by intuition is it backed by evidence is it backed by a mixture of both is it kind of closer to or string theories in physics where this mathematical components would show that you know what it seems that this it fits together too well for not to be true which is what we're string theory is is that where your fix of all those things although definitely where we are right now it's definitely much more on the empirical side than let's say string theory the way this goes about we're theorists right so we look at all this data and we're trying to come up with some sort of model that explains it basically and there's yeah unlike string theory there's this vast more amounts of empirical data here that I think than most physicists deal with and so our challenge is to sort through that and figure out what kind of constructs would explain this and when we have an idea you come up with a theory of some sort you have lots of ways of testing it first of all I am you know there are hundred years of assimilated unassimilated empirical data from neuroscience so we go back and read papers we said oh did someone find this already with you we can predict x y&z and maybe no one's even talked about it since 1972 or something but we go back and find out we say Oh either it can support the theory or it can invalidate the theory and we said okay we have to start over again oh no it's the poor let's keep going with that one so the way I kind of view it when we do our work we come up we we look at all this empirical data and it's it's what I call is a set of constraints we're not interested in something that's biologically inspired we're trying to figure out how the actual brain works so every piece of empirical data is a constraint on a theory in theory if you have the correct theory it needs to explain every pin right so we have this huge number of constraints on the problem which initially makes it very very difficult if you don't have any constraints you can make up stuff all the day you know here's an answer how you can do this you can do that you can do this but if you consider all biology as a set of constraints all neuroscience instead of constraints and even if you're working on one little part of the neocortex for example there are hundreds and hundreds of constraints these are empirical constraints that it's very very difficult initially to come up with a radical framework for that but when you do and it solves all those constraints at once you have a high confidence that you got something close to correct it's just in mathematically almost impossible not to be so it that's the the curse and the advantage of what we have the curse is we have to solve we have to meet all these constraints which is really hard but when you do meet them then you have a great confidence that you discover something in addition then we work with scientific labs so we'll say oh there's something we can't find we can predict something but we can't find it anywhere in the literature so we will then we have people we collaborated with say that sometimes they'll say you know I have some collected data which I didn't publish but we can go back and look in it and see if we can find that which is much easier than designing in your experiment you know new neuroscience experiments take a long time years so although some people are doing that now too so but between all of these things I think it's reasonable it's actually a very very good approach we we are blessed with the fact that we can test our theories out the ying-yang here because there's so much on a similar data and we can also falsify our theories very easily which we do often it's kind of reminiscent to whenever whenever that was with Copernicus you know when you figure out that the sun's at the center of the the solar system as opposed to earth the pieces just fall into place yeah I think that's the general nature of aha moments is in history Copernicus it could be you could say the same thing about Darwin you could say same thing about you know about the double helix that that people have been working on a problem for so long and I have all this data and they can't make sense of it they can't make sense of it but when the answer comes to you and everything falls into place it's like oh my gosh that's it that's got to be right I asked both Jim Watson and Francis Crick about this I asked him you know when you were working on trying to discover the structure of the double helix and when you came up with the the sort of the structure that ended up being correct but it was sort of a guess you know I wasn't really verified yeah I said did you know that it was right and they both said absolutely so we absolutely knew it was right and it doesn't matter if other people didn't believe it or not we knew it was right they get around the thing agree with it eventually anyway and that's the kind of thing you hear a lot with scientists who who really are studying a difficult problem and I feel that way too about our work if you talk to Kirk or Watson about the the problem you're trying to solve the of finding the DNA of the brain yeah in fact Francis Crick was very interested in this in the latter part of his and in fact I got interested in brains by reading an essay he wrote in 1979 called thinking about the brain and that is when I decided I'm gonna leave my profession of computers and engineering and become a neuroscientist just reading that one essay from Francis Crick I got to meet him later in life I got I spoke at the Salk Institute and he was in the audience and then I had a tea with him afterwards you know he was interested in a different problem and he was he was focused on consciousness yeah and the easy problem right well I I think it's the red herring and and so we weren't really overlapping a lot there Jim Watson who's still alive is is also interested in this problem and he was when he was director of the coast of Harbor laboratories he was really sort of behind moving in the direction of neuroscience there and so he had a personal interest in this field and I have met with him numerous times and in fact the last time was a little bit over a year ago I gave a talk close to me Harbor labs about the progress we were making in in our work and it was a lot of fun because he said well you you wouldn't be coming here unless you had something important to say so I'm gonna go change our talk so he sat in the very front row next to most next to him was the director of the lab was Stillman so these guys are in the front row of this auditorium right so nobody else in the auditorium wants to sit in the front row because Jim Watson is detective and and I gave a talk and I had dinner with Jim afterwards but it's I there's a great picture of my colleague sue Battaglia mahad took where I'm up there sort of like screaming the basics of this new framework we have and Jim Watson is on the edge of his chair he's literally on the edge of his chair like intently staring up at the screen and when he discovered the structure of DNA the first public talk he gave was that Cold Spring Harbor labs so and there's a picture those famous picture Jim Watson standing at the whiteboard was where the overrated thing pointing at something was holding a double helix at this point it actually looks a lot like the picture of me so there was funny I got talking about the brain and there's Jim Watson staring intently I didn't course there was you know whatever sixty years earlier he was standing you know pointing at the double helix and it's one of the great discoveries and and all of you know whatever by all the science all science yeah yeah hey so this is the funny that there's echoes of that in your presentation do you think in terms of evolutionary timeline in history the development of the neocortex was a big leap or is it just a small step so like if we ran the whole thing over again from the from the birth of life on Earth how likely develop the mechanism and you okay well those are two separate questions one it was it a big leap and one was how like it is okay they're not necessarily related maybe correlated we don't really have enough data to make a judgment about that I would say definitely was a big league and leap and I can tell you why I think I don't think it was just another incremental step at that moment I don't really have any idea how likely it is if we look at evolution we have one data point which is earth right life formed on earth billions of years ago whether it was introduced here or it created it here or someone introduced it we don't really know but it was here early it took a long long time to get to multicellular life and then from multi to other started life it took a long long time to get his neocortex and we've only had the New York Texas for a few hundred thousand years so that's like nothing okay so is it likely well certainly isn't something that happened right away on earth and there were multiple steps to get there so I would say it's probably not get something what happened instantaneous on other planets that might have life it might take several billion years on average um is it likely I don't know but you'd have to survive for several billion years to find out probably is it a big leap yeah I think it's it is a qualitative difference than all other evolutionary steps I can try to describe that if you'd like sure you know which way uh yeah I can tell you how pretty much I'll start a little press many of the things that humans are able to do do not have obvious survival advantages precedent yeah you know we create music is that is there a really survival advantage to that maybe maybe not what about mathematics is there a real survival advantage to mathematics it's stretchy you can try to figure these things out right but up but mostly evolutionary history everything had immediate survival advantages too right so I'll tell you a story which I like me may not be true but the story goes as follows organisms have been evolving first since the beginning of life here on earth anything this sort of complexity on to that just sort of complexity and the brain itself is evolved this way in fact there's an old parts and older parts and older older parts of the brain that kind of just keeps calling on new things and we keep adding capabilities and we got for the neocortex initially it had a very clear survival advantage and that it produced better vision and better hearing and better thoughts and maybe a new place so on but what what I think happens is that evolution just kept it took it took a mechanism and this is in our recent theories but it took a mechanism evolved a long time ago for navigating in the world for knowing who you are these are the so called grid cells and place cells of an old part of the brain and it took that mechanism for building maps of the world and knowing we are in those maps and how to navigate those maps and turns it into a sort of a slimmed-down idealized version of it mm-hmm and that ideally this version could now apply to building maps of other things maps of coffee cups and maps the phone's maps of these concepts yes and not just almost exactly and and so you and it just started replicating this stuff right you just think more and more more bits so we went from being sort of dedicated purpose neural hardware to solve certain problems that are important to survival to a general purpose neural hardware that could be applied to all problems and now it's just it's the orbit of survival it's we are now able to apply it to things which we find enjoyment you know but aren't really clearly survival characteristics and that it seems to only have happened in humans to the large extent and so that's what's going on where we sort of have we've sort of escape the gravity of evolutionary pressure in some sense in the neocortex and it now does things which but not that are really interesting discovery models of the universe which may not really help us doesn't matter how is it help of surviving knowing that there might be multiple no there might be you know the age of the universe or what how do you know various stellar things occur it doesn't really help us survive at all but we enjoy it and that's what happened or at least not in the obvious way perhaps it is required if you look at the entire universe in an evolutionary way it's required for us to do interplanetary travel and therefore survive past our own Sun but you know let's not get too but you know evolution works at one time frame it's it's survival if you think of a survival of the phenotype survival of the individual it is that what you're talking about there is spans well beyond that so there's no genetic I'm not transferring any genetic traits to my children that are gonna help them survive better on Mars right it's totally different mechanism let's yeah so let's get into the the new as you've mentioned the idea that I don't know if you have a nice name thousand you call it a thousand brain theory often told I like it so can you talk about the this idea of spatial view of concepts and so on yeah so can I just describe sort of the there's an underlying core discovery which then everything comes from that that's a very simple this is really what happened we were deep into problems about understanding how we build models of stuff in the world and how we make predictions about things and I was holding a coffee cup just like this in my hand and I had my finger was touching the side my index finger and I moved it to the top and I was going to feel the the rim at the top of the cover and I asked myself a very simple question I said well first of all I have to say I know that my brain predicts what its gonna feel before it touches it you can just think about it and imagine it and so we know that the brain is making predictions all the time so the question is what does it take to predict that right and there's a very interesting answer that first of all it says the brain has to know it's touching a coffee cup and I said a model or a coffee cup and needs to know where the finger currently is on the cup relative to the cup because when I make a movement and used to know where it's going to be on the cup after the movement is completed relative to the cup and then it can make a prediction about what's going to sense so this told me that Dean your cortex which is making this prediction needs to know that it's sensing it's touching a cup and it needs to know the location of my finger relative to that cup in a reference frame of the cup it doesn't matter where the cup is relative my body it doesn't matter its orientation none of that matters it's where my finger is relative to the cup which tells me then that the neocortex is has a reference frame that's anchored to the cup because otherwise I wouldn't be able to say the location and I wouldn't be able to predict my new location and then we quickly vary installation instantly you can say well every part of my skin could touch this cup and therefore every part of my skin is making predictions and every part my skin must have a reference frame that it's using to make predictions so the the big idea is that throughout the neocortex there are everything as being is being stored and referenced in reference frames you can think of them like XYZ reference things but they're not like that we know a lot about the neural mechanisms for this but the brain thinks in reference frames and it's an engineer if you're an engineer this is not surprising you'd say if I wanted to build a a CAD model of the coffee cup well I would bring it up in some CAD software and I would assign some reference frame and say this features at this locations and so on but the fact that this the idea that this is occurring through out in your cortex everywhere it was a novel idea and and then zillion things fell into place after that it's doing so now we think about the neocortex as processing information quite differently than we used to do it we used to think about the neural cortex is processing sensory data and extracting features from that sensory data and then extracting features from the features very much like a deep Learning Network does today but that's not how the brain works at all the brain works by assigning everything every input everything to reference frames and there are thousands hundreds and thousands of them active at once in your neocortex it's a surprising thing the thing about but once you sort of internalize this you understand that it explains almost every all the almost all the mysteries we've had about this it's about this structure so one of the consequences of that is that every small part of the neocortex so you have a millimeter square and there's a hundred and fifty thousand of those so it's about 150,000 square millimeters if you take every little square millimeter of the cortex it's got some input coming into it and it's going to have reference frames which assign that input to and each square millimeter can learn complete models of objects so what do I mean by that if I'm touching the coffee cup well if I just touch it in one place I can't learn what this coffee cup is because I'm just feeling one part but if I move it around the cup it touched you to different areas I can build up a complete model the cup because I'm now filling in that three dimensional map which is the coffee cup I can say oh what am I feeling in all these different locations that's the basic idea it's more complicated than that but so through time and we talked about time earlier through time even a single column which is only looking at or a single part of the cortex it's only looking at a small part of the world can build up a complete model of an object and so if you think about the part of the brain which is getting input from all my fingers so there's they're spread across the top and here this is the somatosensory cortex there's columns associated all these from areas of my skin and what we believe is happening is that all of them are building models of this cup every one of them or things not do not all building all not every column every part of the cortex builds models of everything but they're all building models of something and and so you have it so when I when I touch this cup with my hand there are multiple models of the cup being invoked if I look at it with my eyes there again many models of the cup being invoked because each part of the visual system and the brain doesn't process an image that's mr. that's a misleading idea it's just like your fingers touching so different parts of my Radnor of looking at different parts of the cup and thousands and thousands of models of the cup are being invoked at once and they're all voting with each other trying to figure out what's going on so that's why we call it the thousand brains theory of intelligence because there isn't one model of a cop there are thousands of models to this Cup there are thousands of models for your cell phone and about cameras and microphones and so on it's a distributed modeling system which is very different than what people have thought about it so this is a really compelling and interesting idea of f2 first questions - one on the ensemble part of everything coming together you have these thousand brains how do you know which one has done the best job of forming the great question let me try Spain there there's a problem that's known in neuroscience called the sensor fusion problem yes and so is the idea of something like oh the image comes from the eye there's a picture on the retina and it gets projected to than your cortex no by now it's all spread out all over the place and it's kind of squirrely and distorted and pieces are all over this you know it doesn't look like a picture anymore when does it all come back together again right or you might say well yes but I also I also have sounds or touches associated with a couple so I'm seeing the cup and touching the cup how do they get combined together again so this it's called the sensor fusion problem is if all these disparate parts have to be brought together into one model someplace that's the wrong idea the right idea is that you get all these guys voting there's auditory models of the cup there's visual models the cup those tactile models of the cup there's one the individual system there might be ones that are more focused on black and white ones fortunate on color it doesn't really matter there's just thousands and thousands of models of this Cup and they vote they don't actually come together in one spot it just literally think of it this way I imagine you have these columns or like about the size of a little piece of spaghetti okay like a two and a half millimeters tall and about a millimeter in mind they're not physical like but you could think of them that way and each one's trying to guess what this thing is they're touching now they can they can do a pretty good job if they're allowed to move over to us so I could reach my hand into a black box and move my finger around an object and if I touch enough spaces like oh okay I don't know what it is but often we don't do that often I can just reach and grab something with my hand all the once and I get it or if I had to look through the world through a straw so long invoking one little column I can only see part of some things I have to move the straw around but if I open my eyes to see the whole thing at once so what we think is going on it's all these little pieces of spaghetti if you know all these little columns in the cortex or all trying to guess what it is that they're sensing they'll do a better guess if they have time and can move over time so if I move my eyes and with my fingers but if they don't they have a they have a poor guest it's a it's a probabilistic s of what they might be touching now imagine they can post their probability at the top of a little piece of spaghetti each one of them says I think and it's not really a probability decision it's more like a set of possibilities in the brain it doesn't work as a probability distribution it works is more like what we call the Union so you could say and one column says I think it could be a coffee cup sort of can or a water bottle and the other column says I think it could be a coffee cup or you know telephone or camera whatever right and and all these guys are saying what they think might be and there's these long range connections in certain layers in the cortex so there's been some layers in some cell types in each column send their projections across the brain and that's the voting occurs and so there's a simple associative memory mechanism we've described this in a recent paper and we've modeled this that says they can all quickly settle on the only or the one best answer for all of them if there is a single best answer they all vote and say yeah it's got to be the coffee cup and at that point they all know it's a coffee go and at that point everyone acts as if it's the coffee cup they yeah we know it's a coffee even though I've only seen one little piece of this world I know it's coffee cup I'm touching or I'm seeing or whatever and so you can think of all these columns are looking at different parts in different places different sensory and put different locations they're all different but this layer that's doing the voting that's it's solidifies it's just like it crystallizes and says oh we all know what we're doing and so you don't bring these models together in one model you just vote and there's a crystallization of the vote great that's a at least a compelling way to think about about the way you form a model of the world now you talk about a coffee cup do you see this as far as I understand you're proposing this as well that this extends to much more than coffee cups it does or at least the physical world it expands to the world of concepts yeah it does and well first the primary face every evidence for that is that the regions of the neocortex that are associated with language or high-level thought or mathematics or things like that they look like the regions of the new your cortex that process vision hearing and touch there they don't look any different or they look only marginally different and so one would say well if Vernon now Castle who proposed it all that come all the parts of New York or trees doing the same thing if he's right then the parts that during language or mathematics or physics are working on the same principle they must be working on the principle of reference frames so that's a little odd flawed hmm but of course we had no eye we had no prior idea how these things happen so that's let's go with that and we in our recent paper we talked a little bit about that I've been working on it more since I have better ideas about it now I'm sitting here very confident that that's what's happening and I can give you some examples to help you think about that it's not we understand it completely but I understand it better than I've described it in any paper so far so but we did put that idea out there says okay this is it's it's it's it's a good place to start you know and the evidence would suggest this how it's happening and then we can start tackling that problem one piece at a time like what does it mean to do high-level thought what it means a new language how would that fit into a reference frame framework yes so there's a if you could tell me if there's a connection but there's an app called Anki that helps you remember different concepts and they they talk about like a memory palace that helps you remember a completely random concepts by so trying to put them in a physical space in your mind yeah and putting them next to each other the method of loci okay yeah for some reason that seems to work really well yeah no that's a very narrow kind of application of just remembering some facts but that's a very very telling one yes exactly so it seems like you're describing a mechanism why this seems yeah so so basically the way what we think is going on is all things you know all concepts all ideas words everything you know are stored in reference frames and so if you want to remember something you have to basically navigate through a reference frame the same way a rat navigates to a Maeve in the same way my finger rat navigates to this coffee cup you are moving through some space and so what you if you have a random list of things you were asked to remember by assigning him to a reference frame you've already know very well to see your house right an idea the method of loci is you can say okay in my lobby I'm going to put this thing and then and then the bedroom I put this one I go down the hall I put this thing and then you want to recall those facts so we call this things you just walk mentally you walk through your house you're mentally moving through a reference frame that you already had and that tells you there's two things are really important about it tells us the brain prefers to store things in reference frames and that the method of recalling things or thinking if you will is to move mentally through those reference frames you could move physically through some reference frames like I could physically move through the reference name of this coffee cup I can also mentally move to the reference time the coffee cup imagining me touching it but I can also mentally move my house and and so now we can ask yourself or are all concepts toward this way there's some recent research using human subjects in fMRI and I'm gonna apologize for not knowing the name of the scientist that did this but what they did is they they put humans in this fMRI machine which was one of these imaging machines and they they gave the humans tasks to think about Birds so they had different types of birds and beverage it looked big and small and long necks and long legs things like that and what they could tell from the fMRI it was a very clever experiment get to tell when humans were thinking about the birds that the birds that the knowledge of birds was arranged in a reference frame similar to the ones that are used when you navigate in a room that these are called grid cells and there are grid cell like patterns of activity in the new your cortex when they do this so that it's a very clever experiment you know and what it basically says that even when you're thinking about something abstract and you're not really thinking about it as a reference frame it tells us the brain is actually using a reference frame and it's using the same neural mechanisms these grid cells are the basic same neural mechanism that we we propose that grid cells which in the old part of the brain the entire cortex that that mechanism is now similar mechanism is used throughout the neocortex it's the same nature preserve this interesting way of creating reference frames and so now they have empirical evidence that when you think about concepts like birds that you're using reference frames that are built on grid cells so this that's similar to the method of loci but in this case the birds are related so it makes they create their own reference frame which is consistent with bird space and when you think about something you go through that you can make the same example let's take a math mathematics all right let's say you want to prove a conjecture ok what is a conjecture conjecture is a statement you believe to be true but you haven't proven it and so it might be an equation I I want to show that this is equal to that and you have a place you have some places you start with you said well I know this is true and I know this is true and I think that maybe to get to the final proof I need to go through some intermediate results but I believe is happening is literally these equations where these points are assigned to a reference frame a mathematical reference frame and when you do mathematical operations a simple one might be multiply or divide but you might be a little applause transform or something else that is like a movement in the reference frame of the math and so you're literally trying to discover a path from one location to another location in a space of mathematics and if you can get to these intermediate results then you know your map is pretty good and you know you're using the right operations much of what we think about is solving hard problems is designing the correct reference frame for that problem figure out how to organize the information and what behaviors I want to use in that space to get me there yeah so if you dig in an idea of this reference frame whether it's the math you start a set of axioms to try to get to proving the conjecture can you try to describe maybe taking step back how you think of the reference frame in that context is is it the reference frame that the axioms are happy in is it the reference frame that might contain everything is that a changing thing so there it is you any reference frames I mean fact the way the theory the thousand brain theory of intelligence says that every single thing in the world has its own reference frame so every word has its own reference names and we can talk about this the mathematics work out this is no problem for neurons to do this but how many reference changes the coffeeCup have well it's on a table let's say you asked how many reference names could the column in my finger that's touching the coffee cup hat because there are many many copies there many many models of a coffee cup so the coffee there is no walnut model the coffee cup there are many miles of a coffee cup and you could say well how many different things can my finger learn missus it's just the question you want to ask imagine I say every concept every idea everything you've ever know about that you can say I know that thing it has a reference frame associated with him and what we do when we build composite objects we can we sign reference frames to point another reference frame so my coffee cup has multiple components to it it's got a limb it's got a cylinder it's got a handle and those things that have their own reference frames and they're assigned to a master reference frame where we just called this cup and now I have this clementa logo on it well that's something that exists elsewhere in the world it's it's own thing so it has its own reference time so we now have to say how can I sign the new mentor bogel reference frame onto the cylinder or onto the coffee cup so it's all we talked about this in the paper that came out in December this last year the idea of how you can assign reference names to reference names how neurons could do this so well my question is okay even though you mentioned reference frames a lot I almost feel it's really useful to dig into how you think of what a reference frame is I mean I was already helpful for me to understand sure you think of reference frames is something there is a lot of okay so let's just say that we're gonna have some neurons in the brain not many actually 10,000 20,000 are gonna create a whole bunch of reference frames what does it mean right what is the reference in this case first of all these reference names are different than the ones you might have be used to let you know lots of reference in its route for example we know the Cartesian coordinates XYZ that's a type of reference frame we know longitude and latitude that's a different type of reference frame if I look at a printed map you might have Colin a through a Monroe's you know one through twenty that's a different type of reference frame it's a kind of a Cartesian coordinate frame though interesting about the reference frames in the brain and we know this because these have been established through neuroscience studying the anti Rana cortex so I'm not speculating here okay this is known neuroscience in an old part of the brain the way these cells create reference frames they have no origin so what it's more like you have you have a point your appointment in some space and you give it a particular movement you can then tell what the next point should be and you can then tell what the next point would be and so on you can use this to to calculate how to get from one point to another so how do I get from being around my house to my home or how do I get my finger from the side of my cup to the top of the camp how do I get from the the axioms to the conjecture so it's a different type of reference frame and I can if you want I can describe in more detail I can paint a picture how you might want to think about that so really helpful to think it's something you can move through yeah but is there is it is it helpful to think of it as spatial in some sense or is there something definitely spatial its spatial in the mathematical sense we need to mention can it be crazy numbered well that's an interesting question in the old part of the brain the answer I know cortex they studied rats and initially it looks like oh this is just two-dimensional it's like the rat is in some box and the maze or whatever and they know where the rat is using these two-dimensional reference frames and know where it is that's right the maze we saw okay but what about what about bats that's a mammal and they fly in three-dimensional space how do they do that they seem to know where they are right so there's this is a current area of active research and it seems like somehow the rep the neurons in the in tirana cortex I can learn three-dimensional space we just to members of our team along with ela FET from MIT just released a paper this little literally last week it's on by archive where they show that you can if you the way these things work and I'm gonna get unless you want to I won't get into the detail but grid cells can represent any n-dimensional space it there's no it's it's not inherently limited you can think of it this way if you had two-dimensional is the way it works is you add as a bunch of two-dimensional slices that's the way these things work there's a whole bunch of two-dimensional models and you can just you can slice up any n-dimensional space and with two-dimensional projections so and you could all have one dimensional models it does so there's there's nothing inherent about the mathematics about the way the neurons do this which which constrain the dimensionality of the space which I think was important and so obviously I have a three dimensional map of this cup maybe it's even more than that I don't know but it's a clearly three-dimensional map of the cup I don't just have a projection of the cup and but when I think about birds or when I think about mathematics perhaps it's more than three dimensions or who knows so in terms of each individual column building up more and more information over time do you think that mechanism is well understood in your mind you've proposed a lot of architectures there is that a key piece or is it is the big piece the thousand brain theory of intelligence omble at all well I think they're both big I mean clearly the concept as a theorist the concept that's most exciting right we've had a little con it's a high-level concept is this a totally new way of thinking about other new yorker optics work so that is appealing it has all these ramifications and with that as a framework for how the brain works you can make all kinds of predictions and solve all kinds of problems now we're trying to work through many of these details right now okay how do they neurons actually do this well turns out if you think about grid cells and place cells in the old parts of the brain there's a lot of snow and about them but there's still some mysteries there's a lot of debate about exactly the details how these work and what are the signs and we have that still that same level of detail the same level concern what we spend here most of our time doing is trying to make a very good list of the things we don't understand yet that's the key part here what are the constraints it's not like oh this thing seems work we're done no it's like okay it kind of works but these are other things we know what has to do and it's not doing those yet I would say we're well on the way here I'm not done yet there's a lot of trickiness to this system but the basic principles about how different layers in the neocortex are doing much of this we understand but there's some fundamental parts that we don't understand the sums so what would you say is one of the harder open problems or one of them ones that have been bothering you Oh keeping you up at night the most oh well right now this is a detailed thing that wouldn't apply to most people okay yeah please we've talked about as if to predict what you're going to sense on this coffee cup I need to know where my finger is gonna be on the coffee cup that is true but it's insufficient think about my finger touches the edge of the coffee cup my finger can touch it at different orientations right I can rotate my finger around here and that doesn't change ice I can make that prediction and somehow so it's not just the location there's an orientation component of this as well this is known in the old parts of the brain too there's things called head Direction cells which which way the rat is facing it's the same kind of base the idea so my finger were Iraq you know in three dimensions I have a three dimensional orientation and I have a three dimensional location if I was a rat I would have it you might think it was a 2-dimensional location a two dimensional orientation or one dimensional orientation like just which way is it facing so how the the two components work together how it is that I I combine orientation right the orientation my sensor as well as the the location is a tricky problem and I think I've made progress on it though at a bigger version of that so prospective super interesting but super specific yeah it's really good there's a more general version of that do you think context matters the fact that we are in a building in North America that that we in the day and age where we have mugs I mean there's all this extra information that you bring to the table about everything else in the room that's outside of just the coffee cup how does it get yeah so Kanab you think yeah and that is a another really interesting question I'm gonna throw that under the the rubric or the name of attentional problems first of all we have this model I have many many models so there's a and also the question doesn't matter because well it matters for certain things of course it does maybe what we think of that as a coffee cup in another part of the world this commute is something totally different or maybe the our logo which is very benign in this part of the world it means something very different than another part of the world so those things do matter I think the thing the way to think about is the following one way to think about it is we have all these models of the world ok and we have modeled we model everything and as I said earlier it comes snuck it in there our models are actually we we build composite structure so every object is composed of other objects which are composed of other objects and they become members of other objects so this room is chairs and a table and a room and the walls and so on now we can just arrange them in these things a certain way you go that's the new meta conference room so so and what we do is when we go around the world and we experience the world we've I walk into a room for example the first thing I'd like say oh I'm in this room do I recognize the room then I could say oh look there's a there's a table here and I by attending to the table I'm then assigning this table in a context of the room that's on the table there's a coffee cup oh and on the table there's a logo and in the logo this is the word dementia I look in the logo there's a letter e on look it has an unusual Seraph and it doesn't actually but my pretend so the point is you your attention is kind of drilling deep in and out of these nested structures and I can pop back up and I can pop back down I can pop back up and I can pop back down so I when I attend to the coffee cup I haven't lost the context of everything else but but it's sort of nested structure so the attention filters the reference frame information for that particular period of time yes it basically a moment-to-moment you attend the subcomponents and then you can tend to sub components to sub component so you can move up and down you can move up and down then we do that all the time you're not even now that I'm aware of it I'm very conscious of it but scintilla but most people don't don't you think about this you know you don't you just walk in the room and you don't say oh I looked at the chair and I looked at the board and looked at that word on the board and I looked over here what's going on right so what percent of your day are you deeply aware of this and what part can you actually relax and just be Jeff me personally like my personal day yeah unfortunately I'm afflicted with too much of the former I [Laughter] fortunately or unfortunately yeah I don't think it's useful oh I did useful totally useful I think about this stuff almost all the time and I meant one of my primary ways of thinking is when I'm in sleep at night I always wake up in the middle of the night and then I stay awake for at least an hour with my eyes shut in a sort of a half sleep state thinking about these things I come up with answers to problems very often in that sort of half sleeping State I think about on my bike ride I think about on walks I'm just constantly thing about this I have to almost a scheduled time to not think about this stuff because it's very it's mentally taxing are you when you think about the stuffy's are you thinking introspectively like almost gonna step outside yourself and trying to figure out what is your mind doing right I do that all the time but that's not all I do I've constantly observing myself so as soon as I started thinking about grid cells for example and getting into that I started saying oh well grid cells can't mice place a sense in the world you know that's where you know where you are and essentially you know we always have a sense of where we are unless were lost and so I started at night when I got up to go to the bathroom I would start trying to do a complete with my eyes closed all the time and I would test my sense of pretty cells I would I would walk you know five feet and say okay I think I'm here am I really what's my error yeah and then I would count in my error again and see how the errors accumulate so even something as simple as getting up in the middle light or the bathroom I'm testing these theories out it's kind of fun I mean the coffee cup is an example of that too so I think I find that these sort of everyday introspections are actually quite helpful it doesn't mean you can ignore the science I mean I spend hours every day reading ridiculously complex papers that's not nearly as much fun but you have to sort of build up those constraints and the knowledge about the field and who's doing what and what exactly they think is cooperating here and then you can sit back and say okay let's try to piece this all together let's come up with some you know I I'm right in this group here people they know they just I do this all this time I come in with these introspective ideas and say well do you ever thought about this now watch well this all do this together and it's helpful it's not if as long as you don't be all you did was that then you're just making up stuff right but if you're constraining it by the reality of the neuroscience then it's really helpful so let's talk a little bit about deep learning and the successes in the apply space of neural networks the ideas of training model and data and these simple computational units you're on artificial neurons that with backpropagation as the statistical ways of being able to generalize from the training set onto data that similar to that training set so where do you think are the limitations of those approaches what do you think our strengths relative to your major efforts of constructing a theory of human intelligence yeah well I'm not an expert in this field I'm somewhat knowledgeable so odd but I love it is in just your intuition what are you well I have I have a little bit more than intuition but you're going to say like you know one of the things that you asked me do I spend all my time thing about neurons I do that's to the exclusion of thinking about things like convolutional neural networks in you but I try to stay current so look I think it's great the progress they've made it's fantastic and as I mentioned earlier it's very highly useful for many things the models that we have today are actually derived from a lot of neuroscience principles there are distributed processing systems and distributed memory systems and that's how the brain works they use things that we we might call them neurons but they're really not neurons at all so we can just they're not really in terrassa distributed processing systems and and that nature of hierarchy that came also from neuroscience and so there's a lot of things that the learning rules basically not backprop but other you know so have you int I don't know I'd be curious to say they're not in your ons at all he described in which way I mean it's some of it is obvious but I'd be curious if if you have specific ways yeah which you think are the biggest difference yeah we had a paper in 2016 called why neurons of thousands of synapses and it and if you read that paper you don't know what I'm talking about here a real neuron in the brain is a complex thing it let's just start with the synapses on it which is a connection between neurons real neurons can everywhere from five to thirty thousand synapses on the ones near the cell body the ones are too close to the the soma of the cell body those are like the ones who people model in artificial neurons there is a few hundred of those maybe they can affect the cell they can make the cell become active ninety-five percent of the synapses can't do that they're too far away so if you're actually at one of those synapses it just doesn't affect the cell body enough to make any difference any one of them individually anyone emanuelly or even if you do what mass of them what what we but what real neurons do is the following if you activate or they you get 10 to 20 of them active at the same time meaning they're all receiving an input at the same time and those 10 to 20 synapses are forty sensors within a very short distance on the dendrite like 40 microns a very small area so if you activate a bunch of these right next to each other at some distant place what happens is it creates what's called the dendritic spike and then juridic spike travels through the dendrites and can reach the soma or the cell body now when it gets there it changes the voltage which is sort of like gonna make the cell fire but never enough to make the cell fire it's sort of what we call it says we depolarize the cell you raise the voltage a little bit but not enough to do anything it's like well good is that and then it goes back down again so we proposed a theory which I'm very confident in basics are is that what's happening there is those ninety-five percent of those synapses are recognizing dozens to hundreds of unique patterns they can write you know about the 1020 nerve synapses at a time and they're acting like predictions so the neuron actually is a predictive engine on its own it it can fire when it gets enough what they call approximately input from those ones near the cell fire but it can get ready to fire from dozens to hundreds of patterns that it recognizes from the other guys and the advantage of this to the neuron is that when it actually does produce a spike in action potential it does so slightly sooner than it would have otherwise and so what could is slightly sooner well the slightly sooner part is it there's it all the neurons in the the excitatory neurons in the brain are surrounded by these inhibitory neurons and they're very fast the inhibitory neurons it's basket cells and if I get my spike out a little bit sooner than someone else I inhibit all my neighbors around me mm-hmm right and what you end up with is a different representation you end up with a reputation that matches your prediction it's a it's a sparsa representation meaning as fewest known or interactive but it's much more specific and so we showed how networks of these neurons can do very sophisticated temporal prediction basically so so this summarize this real neurons in the brain are time-based prediction engines and and they and there's no concept of this at all in artificial what we call point neurons I don't think you can mail the brain without them I don't even build intelligent it's its theme it's where large part of the time comes from it's it's these are predictive models and the time is in is there's a prior and I'm in a you know a prediction and an action and it's inherent to every neuron the neocortex so so I would say that point neurons sort of model a piece of that and not very well with that either but you know like for example synapses are very unreliable and you cannot assign any precision to them so even one digital position is not possible so the way real neurons work is they don't add these they don't change these weights accurately like artificial neural networks do they basically form new synapses and so what you're trying to always do is is detect the presence of some 10 to 20 active synapses at the same time as opposed and they're almost binary it's like because you can't really represent anything much finer than that so these are the kind of dishes and I think that's actually another essential component because the brain works on sparse patterns and all about all that mechanism is based on sparse patterns and I don't actually think you could build our real brains or machine and tell us about incorporating some of those ideas it's hard to even think about the complex that emerges from the fact that the timing of the firing matters in the brain the fact that you form new new synapses and and the I mean everything you just mentioned in the past okay trust me if you spend time on it you can get your mind around it it's not like it's no longer a mystery to me no but but sorry as a function in a mathematical way it's can you get it they're getting an intuition about what gets it excited what not as easy as there are many other types of neural networks are that are more amenable to pure analysis you know especially very simple networks you know oh I have four neurons and they're doing this can we you know the scribes are mathematically what they're doing type of thing even the complexity of convolutional neural networks today it's sort of a mystery they can't really describe the whole system and so it's different my colleague sue Burton I am on he did a nice paper on this you can get all the stuff on our website if you're interested talking about a little math properties of sparse representations and so we can't what we can do is we can tell mathematically for example why 10 to 20 synapses to recognize a pattern is the correct number it's the right number you'd want to use and by the way that matches biology we can show mathematically some of these concepts about the show why the brain is so robust to noise and error and fallout and so on we can show that mathematically as well as empirically in simulations but the system can't be analyzed completely any complex system can and so that's out of the realm but there is there are mathematical benefits and intuitions that can be derived from mathematics and we try to do that as well most most of our papers have the section about that so I think it's refreshing and useful for me to be talking to you about deep neural networks because your intuition basically says that we can't achieve anything like intelligence with artificial neural networks well not in their current form 9/2 can do it in the ultimate form sure so let me dig into it and see what your thoughts are they're a little bit so I'm not sure if you read this little blog post called bitter lesson by Richard Sutton recently recently he's a reinforcement learning pioneer I'm not sure if you familiar with him his basic idea is that all the stuff we've done in AI in the past 70 years he's one of the old school guys the the biggest lesson learned is that all the tricky things we've done don't you know they benefit in the short term but in the long term what wins out is a simple general method that just relies on Moore's Law on on computation getting faster and faster so this is what he's saying this is what has worked up to now this what has worked up to now they fear trying to build the system if we're talking about he's not concerned about intelligence concern about system that works in terms of making predictions that applied narrow AI problems right that's what there's the discussion is about that you just tried to go as general as possible and wait years or decades for the computation to make it actually do you think that is a criticism or is he saying this is the prescription of what we ought to be doing well it's very difficult he's saying this is what has worked and yes a prescription with the difficult prescription because it says all the fun things you guys are trying to do we are trying to do he's part of the community they're saying it's it's only going to be short-term gains so this all leads up to a question I guess on artificial neural networks and maybe our own biological neural networks is you think if we just scale things up significantly so take these dumb artificial neurons the point here as I like that term if we just have a lot more of them do you think some of the elements that we see in the brain may start emerging no I don't think so we can do bigger problems and of the same type I mean it's been pointed out by many people that today's convolutional no and that works aren't really much different than the ones we had quite a while ago we just they're bigger and train more and we have more label data and so on but I don't think you can get to the kind of things I know the brain can do and that we think about as intelligence by just scaling it up so I that maybe it's a good description of what's happened in the past what's happened recently with the re-emergence of artificial neural networks it may be a good prescription for what's going to happen in the short term but I don't think that's the path I've said that earlier there's an alternate path I should mention to you by the way that we've made sufficient progress on our the whole cortical theory in the last few years that last year we decided to start actively pursuing how do we get these ideas embedded into machine learning well that's it again being led by my colleague just super talked him on and he's more of a machine learning guy am more of a neuroscience guy so this is now our new is I wouldn't say our focus but it is now an equal focus here because we we need to proselytize what we've learned and we need to show how it's beneficial to - to the Machine were earlier so we're putting we have a plan in place right now in fact we just did our first paper on this I can tell you about that but you know one of the reasons I want to talk to you is because I'm trying to get more people in the machine learning the community say I need to learn about this stuff and maybe we should just think about this a bit more about what we've learned about the brain and what are those team Aetna meant - what have they done is that useful for us yeah yeah so there is there elements of all the the cortical Theory the things we've been talking about that may be useful in the short term yes in the short term yes this is the sorry to interrupt the the open question is it it there it certainly feels from my perspective that in the long term some of the ideas we've been talking about will be extremely useful yeah question is whether in the short term well this is a always that what we I would call the entrepreneurs dilemma so you have this long term vision oh we're gonna all be driving electric cars or all kind of computers or or whatever and and you're at some point in time and you say I can see that long-term vision I'm sure it's gonna happen how do I get there without killing myself you know without going out of business right that's the challenge that's the dilemma it's a really difficult thing to do so we're facing that right now so ideally what you'd want to do is find some steps along the way you can get there incremental you don't have to like throw it all out and start over again the first thing that we've done is we focus on the sparse representations so I just just in case you don't know what that means or some of the listeners don't know what that means in the brain if I have like 10,000 neurons what you would see is maybe 2% of them active at a time you don't see 50 percent you know 3 30 percent you might see 2 percent and it's always like that for any set of sensory input it doesn't matter anything just about any part of the brain but which neurons differs which neurons are active yes I take 10,000 neurons that are representing something though it's sitting there in a bullet block together it's a teeny little blocking around 10,000 there right and they're representing a location they're representing a cop they're representing the input for my sensors I don't know it doesn't matter it's representing something the way the representations occur it's always a sparse representation meaning it's a population code so which 200 cells are active tells me what's going on it's not individual cells on it's not important at all it's the population code that matters and when you have sparse population codes then all kinds of beautiful properties come out of them so the brain used the sparse population codes that we've we've written and described these benefits in some of our papers so they give this tremendous robustness to the system student brains are incredibly robust neurons are dying all the time and spasming and synapse is falling apart and you know that all the time and it keeps working so what simatai and Louise one of our other engineers here have done I've shown they're introducing sparseness into accomplished neural networks and other people thinking along these lines but we're going about it in a more principled way I think and we're showing that with you enforced sparseness throughout these convolutional neural networks in both the active the which sort of which neurons are active and the connections between them that you get some very desirable properties so one of the current hot topics in deep learning right now are C's adversarial examples so you know I can give me any deep Learning Network and I can give you a picture that looks perfect and you're gonna call it you know you're gonna say the monkey is you know an airplane that's the problem and DARPA just announced some big thing they're trying to you know have some contest for this but if you if you enforce sparse representations here many of these problems go away they're much more robust and they're not easy to fool so we've already shown some of those results it was just literally in January or February just last month we did that and you can I think it's on bio archive right now or on I cry for you can read about it but so that's like a baby step okay that's taking something from the brain we know we know about sparseness we know why it's important we know what it gives the brain so let's try to enforce that on to this what's your intuition why sparsity leads to robustness because it feels like it would be less robust so why why would you feel the Russell bust you so it just feels like if the fewer neurons are involved the more fragile that represents a there was lots of food I said it's like 200 that's a lot is that a lot is yes so here's an intuition for it this is a bit technical so for you know for engineers pyram machine land people let's be easy but all the listeners maybe not if you're trying to classify something you're trying to divide some very high dimensional space into different pieces a and B and you're trying to create some point where you say all these points in this high dimensional space are a and all these points inside dimensional space or B and if you have points that are close to that line it's not very robust it works for all the points you know about but it's it's not very robust because you just move a little bit and you've crossed over the line when you have sparse representations imagine I pick I have I'm gonna pick 200 cells active out of out of 10,000 okay so I have to nurse cells active now let's say I pick randomly another a different representation 200 the overlap between knows is going to be very small just a few I can pick millions of samples randomly of 200 ons and not one of them will overlap more than just a few so one way to think about is if I want them fool one of these representations to look like one of those other representations I can't move just one cell or two cells or three cells or four cells I have to move a hundred cells and that makes them robust in terms of further so the you mentioned sparsity well maybe the next thing yeah okay so what we have we picked one we don't know if it's going to work well yet so again we're trying to come up incremental ways to moving from brain theory to add pieces to machine learning current machine learning world and one step at a time so the next thing we're going to try to do is sort of incorporate some of the ideas of the thousand brains theory that you have many many models and that are voting now that idea is not new there's mixture models has been around for a long time but the way the brain does is a little and and the way it votes is different and the kind of way it represents and certain is different so we're just starting this work but we're going to try to see if we can sort of incorporate some of the principles of voting or principles of thousand brain theory like lots of simple models that talk to each other in this in a very certain way and can we build more machines the systems that learn faster and and also well mostly are multimodal and robust to multimodal type of issues so the one of the challenges there is you know the machine learning computer vision community has certain sets of benchmarks sets the test would based on which they compete and I would argue especially from your perspective that those benchmarks not that useful for testing the aspects that the brain is good at or intelligent they're not only testing in Georgia it's a very fine yeah and it's been extremely useful for developing specific mathematical models but it's not useful in the long term for creating intelligence so yeah you think you also have a role in proposing better tests yeah this is a very you've identified a very serious problem first of all the tests that they have are the tests that they want not the tests of the other things that we're trying to do right you know what are the so on the second thing is sometimes these two could be competitive to in these tests you have to have huge data sets and huge computing power instead you know and we don't have that here we don't have it as well as other big teams and big companies do so there's numerous issues there you know we come at it you know where our approach to this is all based on in some sense you might argue elegance we're coming at it from like a theoretical base that we think oh my god this so this is a clearly elegant this how brains work this one told uses but the machine learning world has gotten in this phase where they think it doesn't matter doesn't matter what do you think as long as you do you know point one percent better on this benchmark that's what that's all that matters and and that's a problem you know we have to figure out how to get around that that's that's a challenge for us that's it's one of the challenges we have to deal with so I agree you've identified a big issue it's difficult for those reasons but you know what you know part of the reasons I'm talking to here today is I hope I'm gonna get some machine learning people to say read those papers those might be some interesting ideas I'll show you I'm trying to doing this point one percent improvement stuff you know well that's that's why I'm here as well because I think machine learning now as a community is it a place where the next step is uh needs to be orthogonal to what has received success in the past oh you see other leaders saying this machine learning and leaders you know Geoff Hinton with his capsules idea many people have gotten up say you know we're gonna hit road but maybe we should look at the brain you know things like that so hopefully that thinking walk occur organically and then then we're in a nice position for people to come and look at our work and say well welcome you learn from these guys yeah MIT is launching a billion-dollar computing College the center on this idea so it's on this idea of what uh well the idea that you know the humanities psychology neuroscience have to work all together to get to ability s yeah Stanford just did this human-centered a I said yeah I'm a little disappointed in these initiatives because yeah you know they're they're is sort of a human side of it and it could very easily slip into how humans interact with intelligent machine interest which is nothing wrong with that but that's not that is orthogonal to what we're trying to do we're trying to say like what is the essence of intelligence I don't care I think I want to build intelligent machines that aren't emotional that don't smile at you that you know that aren't trying to tuck you in at night yeah there is that pattern that you when you talk about understanding humans is important for understanding intelligence you start slipping into topics of ethics or yeah like you said the interactive elements as opposed to no no no what's the zoom in on the brain study say what the human brain the baby the what's funny what a brain dolls does and then we can decide which parts of we want to recreate in some system but do you have that theory about what the brain does what's the point you know it's just you're gonna be wasting time right just to break you down on the artificial network side maybe you could speak to this on and that biologic and you know aside the process of learning versus the process of inference maybe you can explain to me what is there a difference between you know an artificial neural networks there's a difference between the learning stage and the inference stage do you see the brain is something different one of the one of the big distinctions that people often say I don't know how correct it is is artificial neural networks need a lot of data they're very inefficient learning do you see that as a correct distinction from the biology of the human brain that the human brain is very efficient or is that just something we deceive ourselves no it is efficient obviously we can learn new things almost instantly and so what elements do you think yeah I can talk about that you brought up two issues there so remember I talked early about the constraints we always feel well one of those constraints is the fact that brains are continually learning that's not something we said oh we can add that later that's something that was upfront had to be there from the start made our problems harder but we showed going back to the 2016 paper on sequence memory we showed how that happens how the brains infer and learn at the same time and our models do that and they're not two separate phases or two separate sets at the time I think that's a big big problem in AI at least for many applications not for all so I can talk about that there are some that gets detailed there are some parts of the neocortex in the brain where actually what's going on there's these those ease with these cycles uh they're like cycles of activity in the brain and there's very strong evidence that you're doing more of inference on one part of the phase and more of learning on the other part of the phase so the brain can actually sort of separate different populations of cells or going back and forth like this but in general I would say that's an important problem we have a you know all of our networks that we've come up with do both and it's it they're learning continuous learning networks and you mentioned benchmarks earlier well there are no benchmarks about that exactly so so we you know we have to like you know begin our little soapbox say hey by the way we yeah this is important you know and here's a mechanism for doing that but and you know but until you can prove it to someone in some you know commercial system or something's a little harder so yeah one of the things I had to linger on that is in some ways to learn the concept of a coffee cup you only need this one coffee cup and maybe some time alone in a room with it the first things is I when I was imagine I reach my hand into a black box and I'm reaching I'm trying to touch something yeah I don't know upfront if it's something I already know or if it's a new thing right and I have to I'm doing both at the same time I don't say oh let's see if it's a new thing oh let's see if it's an old thing I don't do that I as I go my brain says oh it's new or it's not new and if it's new I start learning what it is so and it by the way it starts learning from the get-go even if we couldn't recognize it so they're they're not separate problems they're in so that's the flinger the other thing you mentioned was the fast learning um so I was distorting my continuous learning but there's also fast I mean literally I can show you this coffee cup and I say here's a new coffee cup it's got the logo on it take a look at it done you done you can predict what it's going to look like you know in different positions so I can talk about that too yes in the brain the way learning occurs I mentioned this earlier but I mentioned again the way learning occurs I'm imagining a mass section of a dendrite of a neuron and I want to learn I'm gonna learn something new I'm just doesn't matter what it is I'm just gonna learn something new I I need to recognize a new pattern so what I'm gonna do I'm gonna form new synapses new synapses we're gonna rewire the brain on to that section of the dendrite once I've done that everything else that neuron has learned is not affected by it that's because it's isolated to that small section of the dendrite they're not all being added together like a point neuron so if I learn something new on this segment here it doesn't change anything occur anywhere else in that neuron so I can add something without affecting previous learning and I can do it quickly now let's talk we can talk about the quickness how it's done in real neurons you might say well doesn't it take time to form synapses yes it can take maybe an hour to form a new synapse we can form memories quicker than that and I can explain that albums too if you want but it's getting a bit neuroscience II oh that's great but is there an understanding of these every level yes so from the short-term memories in the forming uh well so this idea synaptogenesis the growth of new synapses that's well described as well understood and that's an essential part of learning that is learning that is learning okay you know back you know the going back many many years people you know as what's-his-name the psychologist who proposed heavy hem Donald Hebb he proposed that learning was the modification of the strength of a connection between two neurons people interpreted that as the modification of the strength of a synapse he didn't say that he just said there's a modification between the effect of one neuron another so synaptogenesis is totally consistent with Donald Hebb said but anyway there's these mechanisms that growth a new sense you can go online you can watch a video of a synapse growing in real time it's literally you can see this little finger it's pretty impressive yeah so that's those mechanisms are known now there's another thing that we've speculated and we've written about which is consistent with no neuroscience but it's less proven and this is the idea how do i form a memory really really quickly like instantaneously if it takes an hour to grow synapse like that's not instantaneous so there are there are types of synapses called silent synapses they look like a synapse but they don't do anything they're just sitting there it's like they do a action potential that comes in it doesn't release any neurotransmitter some parts of the brain have more of these and others for example the hippocampus has a lot of them which is where we associate most short to remember with so what we we speculated again in that 2016 paper we proposed that the way we form very quick memories very short-term memories or quick memories is that we convert silence and synapses into axis enough it's going it's like seeing a synapse there's a zero weight in a one way but the long-term memory has to be formed by synaptogenesis so you can remember something really quickly by just flipping a bunch of these guys from silent to active it's not like it's not from point one to point one five it's like doesn't do anything to it releases transmitter and if I do that over a bunch of these I've got a very quick short-term memory so I guess the lesson behind this is that most neural networks today are fully connected every neuron connects every other nerve from layer to layer that's not correct in the brain we don't want that we actually don't want that it's bad if you want a very sparse connectivity so that any neuron connects just some subset of the neurons in the other layer and it does so on a on a dendrite by dendrite segment basis so it's a very sparse elated out type of thing and and that then learning is not adjusting all these ways but learning is just saying okay connect to these 10 cells here right now in that process you know with artificial neural networks it's a very simple process of back propagation that adjusts the ways the process of synaptogenesis synaptogenesis it's even easier it's even easier is even easier that propagation requires something we it really can't happen in brains this back propagation of this error signal it really can't happen people are trying to make it happen and brain fits on a vertebrate this is this is pure heavy and learning well synaptogenesis pure have been learning it's basically saying there's a population of cells over here that are active right now and there's a population of cells over here active right now how do i form connections between those active cells and it's literally saying this guy became active this these 100 neurons here became active before this neuron became active so form connections to those ones that's it there's no propagation of error nothing all the networks we do all models we have work on almost completely on heavy and learning but in in on dendritic segments and multiple synapses at the same time so nonetheless I have turned the question that you already answered and maybe you can answer it again if you look at the history of artificial intelligence where do you think we stand how far are we from solving intelligence you said you were very optimistic yeah can you elaborate on that yeah you know it's just always the the crazy question to ask because you know no one can predict the future absolutely so I'll tell you a story I used to I used to run a different Neuroscience Institute called the red burn neuroscience tattoo and we would we would hold these symposiums we get like 35 scientists from around the world to come together and I used to ask him all the same question I would say well how long do you think it'll be before we understand his and your cortex works and everyone went around the room and they had introduced the name and they have to answer that question so I got the the typical answer was 50 to 100 years some people would say 500 years some people said never I said well your size so you know but it doesn't work like that as I mentioned earlier these are not these are step functions things happen and then bingo they happen you can't predict that I fill I've already passed a step function so if I can do my job correctly over the next five years then meaning I can proselytize these ideas I can convince other people they're right we can show that other people machine learning people should pay attention to these ideas then we're definitely in an under 20 year time frame if I can do those things if I'm not successful in that and this is the last time anyone talks to me and no one reads our papers and you know I'm wrong or something like that then then I don't know but it's it's not 50 years it's it you know it'll it'll you know the same thing about electric cars how quickly are they going to populate the world which probably takes about a 20 year span it'll be something like that but I think if I can do what I said we're starting it and of course there could be other you said step functions it could be everybody gives up on your ideas for 20 years and then all of a sudden somebody picks it up again wait that guy was on to something yeah so that would be a that would be a failure on my part right you know yeah think about Charles Babbage you know Charles Babbage invented the computer back in the eighteen hundreds and everyone forgot about it until you know but he was ahead of his time I don't think you know like as I said I recognize this is part of any entrepreneurs challenge I use it entrepreneur broadly in this case I'm not meaning like I'm building a business trying to sell something I mean I come trying to sell ideas and this is a challenge as to how you get people to pay attention to you how do you get them to give you a positive or negative feedback how do you get the people act differently based on your ideas so you know we'll see how what we do on them so you know that there's a lot of hype behind artificial intelligence currently do you uh as as you look to spread the ideas that are of neocortical theory of the things you're working on do you think there's some possibility we'll hit an AI winter once again it's certainly a possibility no don't worry about yeah well I guess do I worry about it I haven't decided yet if that's good or bad for my mission that's true yeah very true because uh it's almost like you need the the winter to refresh the palate yeah it's so it's like I want here's what you want to have it is you want like the extent that everyone is so thrilled about the current state of machine learning and AI and they don't imagine they need anything else that makes my job harder right if if everything crashed completely and every student left the field and there was no money for anybody to do anything and it became an embarrassment to talk about machine intelligence an AI that wouldn't be good for us either you want you want sort of the soft landing approach right you want enough people the senior people in AI and machine learning say you know we need other approaches we really need other approaches but damn we need two approaches maybe we should look to the brain okay let's look the brain who's got some brain ideas okay let's let's start a little project on the side here trying to do brain idea related stuff that's the ideal outcome we would want so I don't want a total winter and yet I don't want it to be sunny all the time you know so what do you think it takes to build a system with human level intelligence where once demonstrated you would be very impressed so does it have to have a body this have to have the the the c-word we used before consciousness as an entirety as a holistic sense first of all I don't think the goal is to create a machine at his human level intelligence I think it's a false goal it back to Turing I think it was a false statement we want to understand what intelligence is and then we can build intelligent machines of all different scales all different capabilities you know a dog is intelligent I don't need you know that'd be pretty good to have a dog yeah you know but what about something that doesn't look like an animal at all in different spaces so my thinking about this is that we want to define what intelligence says agree upon what makes an intelligence system we can then say ok we're now going to build systems that work on those principles or some subset of them and we can apply them to all different types of problems and the the kind of the idea it's not computing we don't ask if I take a little you know little one ship computer I don't say well that's not a computer because it's not as powerful is this you know big server over here you know no because we know that what the principles are computing are and I can apply those principles to a small problem into a big problem insane intelligence needs to get there we have to say these are the principles I can make a small one a big one I can make them distribute it I can put them on different sensors they don't have to be human like at all now you did bring up a very interesting questions about embodiment does that have to have my body it has to have some concept of movement it has to be able to move through these reference frames I talked about earlier I whether it's physically moving like I need if I'm going to have a a I that understands coffee cups it's gonna have to pick up the coffee cup and touch it and look at it with it with its eyes and hands or something equivalent to that if I have a mathematical AI maybe it needs to move through mathematical spaces I could have a virtual AI that lives in the internet and it's true its movements are traversing links and digging into files but it's got a location that it span is traveling through some space you can't have an AI that just takes some flash thing input and we call it flash different system here's a pattern Thun know its movement moving pattern moving pad and moving pad attention digging building building structure just so I figure out the model the world so some sort of embodiment whether it's physical or not has to be part of it so self-awareness in the way to be able to answer where my bring up self I was two different topics self-awareness or no the very narrow definition of self meaning knowing a sense of self enough to know where am I yeah the space was yeah yeah basically the system the system needs to know its location where each component of the system needs to know where it is in the world at that point in time so self awareness and consciousness do you think one from the perspective neuroscience and your cortex these are interesting topics solvable topics give any ideas of what why the heck it is that we have a subjective experience at all yeah I belong is it useful or is it just a side effect it's interesting to think about I don't think it's useful as a means to figure out how to build intelligent machines it's it's something that systems do and we can talk about what it is that are like well I build the system like this then it would be self-aware or and if I build it like this it wouldn't be self-aware so that's a choice I can have it's not like oh my god itself away I can't turn oh I I heard interview recently with this philosopher from Yale I can't remember his name apologize for that but he was talking about well if these computers are self-aware then it would be a crime to unplug them I'm like oh come on you know I employed myself every night go to sleep what is that a crime you know I plugged myself in again in the morning I am so people get kind of bent out of shape about this I have very different very detailed understanding or opinions about what it means to be conscious and what it means to be self-aware I don't think it's that interesting a problem you've talked about Christoph caulk you know he thinks that's the only problem I didn't actually listen to your interview with him but I know him and I know that's the thing he also thinks intelligence the cautions are disjoint so I mean it's not I don't have to have one or the other so he is I just agree with that I just totally agree with that so where's hear your thoughts the cautions were doesn't emerge from because it is so we then we have to break it down to the two parts okay because consciousness isn't one thing that's part of the problem that term is it means different things to different people and there's different components of it there is a concept of self-awareness okay that it can be very easily explained you have a model of your own body the your cortex models the things in the world and it also models your own body and and then it has a memory it can remember what you've done okay so it can remember what you did this morning can remember what you had for breakfast and so on and so I can say to you okay Lex were you conscious this morning when you know I had your you know bagel and you'd say yes I was conscious now what if I could take your brain and revert all the synapses back to the state they were this morning and then I said to you Lex were you conscious when you ate the bagel you should know and I wasn't hot just actually here's a video of eating the bagel he's saying I wasn't there I have no I that's not possible because I was I must have been unconscious at that time so we can just make this one-to-one correlation between memory of your body's trajectories through the world over some period of time a memory that and the ability to recall that memory is what you would call conscious I was conscious of that it's a self awareness um and and in any system that can recall memorize what it's done recently and bring that back and invoke it again would say yeah I'm aware I remember what I did yeah all right I got it that's an easy one although some people think that's a hard one the more challenging part of consciousness is this one that sometimes you just go by the word of quality um which is you know why does an object seem red or what is pain and why just pain feel like something why do I feel redness so what do I feel a little pain is in no way and then I could say well why does sight seems different than just hearing you know it's the same problem it's really yeah these are all dis neurons and so how is it that why does looking at you feel different than you know I'm hearing you it feels different but this is noise in my head they're all doing the same thing so that's the interesting question the best treatise I've read about this is by guy named Oh Reagan or Regan he wrote a book called why red doesn't sound like a bill it's a little it's not it's not a trade book easy read but it and and it's an interesting question take something like color color really doesn't exist in the world it's not a property of the world property the world that exists is light frequency and that gets turned into we have certain cells in the retina that respond to different frequencies different than others and so when they enter the brain you have a bunch of axons that are firing at different rates and from that we perceive color but there is no color in the brain I mean there's there's no color coming in on those synapses it's just a correlation between some some some axons and some property of frequency and that isn't even color itself frequency doesn't have a color it's just a it's just what it is so then the question is well why does it even appear to have a color at all just as you're describing it there seems to be a connection of these those ideas of reference frames I mean it just feels like consciousness having the subject assigning the feeling of red to the actual color or to the wavelength it's useful for intelligent that's a good way putting it it's useful as a predictive mechanism or useful there's a generalization I did it's a way of grouping things together to say it's useful to have a model like this yeah think about the the the there's a well-known syndrome that people who've lost a limb experience called phantom limbs and what they claim is they can have their arm is removed but they feel their arm that not only feel it they know it's there they it's there I can I know it's there they'll swear to you that it's there and then they can feel pain in the arm and feeling their finger in it they move their they move their non-existent arm behind your back then they feel the pain behind their back so this whole idea that your arm exists is a model of your brain it may or may not really exist and just like but it's useful to have a model of something that sort of correlates to things in the world so you can make predictions about what would happen when those things occur it's a little bit of a fuzzy but I think you're getting quite towards the answer there it's it's useful for the model of to express things certain ways that we can then map them into these reference frames and make predictions about them I need to spend more time on this topic it doesn't bother me do you really need to spend more time yeah yeah it does feel special that we have subjective experience but I'm yet to know why I'm just I'm just personally curious it's not it's not necessary for the work we're doing here I don't think I need to solve that problem to build intelligent machines at all not at all but there is so the the silly notion that you described briefly that doesn't seem so silly does humans is you know if you're successful building intelligent machines it feels wrong to then turn them off because if you're able to build a lot of them it feels wrong to then be able to you know to turn off the Y but just be let's let's break it down a bit as humans why do we fear death there's there's two reasons we fear death well first of all stay when you're dead doesn't matter oh okay you're doing it so why do we fear death we fear death for two reasons one is because we are programmed genetically to fear death that's a that's a survival and propagating the genes thing and we also a program to feel sad when people we know die we don't feel sad for someone we don't know dies it's people dying right now they're always come saying I'm so bad about because I don't know them but I knew them I'd feel really bad so again this these are old brain genetically embedded things that we fear death there's outside of those those uncomfortable feelings there's nothing else to worry about wait a second do you know the denial of death by becquer I don't know you know there's a thought that death is you know our whole conception of our world model kind of assumes immortality and then death is this terror that underlies it all so like well some people's world not mine but okay so what what Becker would say is that you're just living an illusion you've constructed an illusion for yourself because it's such a terrible terror the fact that what is the illusion that deathless about you still not coming to grips with the delusion of what that death is are going to happen it's not going to happen you're a mess you're actually operating you haven't even though you said you've accepted it you haven't really except in Russia guys what do you say so it sounds like it sounds like you disagree with that notion every night I go to bed it's like dying a little deaths and if I didn't wake up it wouldn't matter to me only if I knew that was gonna happen would it be bothers him if I didn't know was gonna happen how would I know know it then I would worry about my wife so imagine imagine I was a loner and I lived in Alaska and and I lived them out there and there's no animals nobody knew I existed I was just eating these roots all the time and nobody knew was there and one day I didn't wake up where what what pain in the world would there exist well so most people that think about this problem would say that you're just deeply enlightened or are completely delusional but I would say I would say that's a very light enlightened way to see the world is that that's the rational rational that's right but the fact is we don't I mean we really don't have an understanding of why the heck it is were born and why we die and what happens after well maybe there isn't a reason maybe there is so I mentioned those big problems too right you know you you interviewed max tegmark you know and there's people like that right I'm missing those big problems as well and in fact when I was young I made a list of the biggest problems I could think of first why is anything exists second why did we have the laws of physics that we have third is life inevitable and why is it here fourth is intelligence inevitable and why is it here I stopped there because I figured if you can make a truly intelligent system will be that will be the quickest way to answer the first three questions I'm serious yeah and and so I said my mission I mean I you asked me earlier my first missions understand the brain but I felt that is the shortest way to get to true machine intelligence and I want to get the true machine tells us because even if it doesn't occur in my lifetime other people will benefit from it because I think it'll occur in my lifetime but you know 20 years it's you never know and but that would be the quickest way for us to you know we can make super mathematicians we can make soup space explorers we can make super physicists brains that do these things and that can run experiments that we can't run we don't have the abilities to manipulate things and so on but we can build intelligent machines that do all those things and with the ultimate goal of finding out the answers to the other questions let me ask you know the depressing and difficult question which is once we achieved that goal do you of creating it over know of understanding intelligence do you think we would be happier more fulfilled as a species the understand intelligent understanding the answers to the big questions understanding intelligence Oh totally totally for more fun place to live you think so oh yeah I mean beside this you know terminator nonsense and and and and just think about you can think about we can talk about the risk of AI if you want I'd love to so let's uh I think world's before better knowing things we're always better than no things do you think it's better better place to work the living that I know that our planet is one of many in the solar system and the soleus is one of many of the calluses I think it's a more I I dread I used to I sometimes think like God what would be like the list three hundred years ago I'd be looking at the sky god I can't understand anything oh my god I'd be like throwing a bit of light going what's going on here well I mean in some sense I agree with you but I'm not exactly sure that I'm also a scientist so I have I share you've used but I'm not we're like rolling down the hill together oh oh what's down the hill I feel for climbing a hill whatever anything cooler getting closer to enlightenment we're climbing we're getting pulled up a hill the way you're putting our polio studies put we're pulling ourselves up the hill by our curiosity yeah Sisyphus is doing the same thing with the rock yeah yeah but okay our happiness decide do you have concerns about you know you talk about sam harris you know a musk of existential threats of intelligence no I'm not worried about exercise there are there are some things we really do need to worry about even today's things we have to worry about we have to worry about privacy and about how impacts false beliefs in the world and and we have real problems that and things to worry about with today's AI and that will continue as we create more intelligent systems there's no question you know the whole issue about you know making intelligent armament and weapons it's something that really we have to think about carefully I don't think of those as existential threats I think those are the kind of threats we always face and we all have to face them here and hope to deal with them the ie we can we could talk about what people think are the existential threats but when I hear people talking about them they all sound hollow to me they're based on ideas they're based on people who really have no idea what intelligence is and and if they knew what intelligence was they wouldn't say those things so those are not experts in the field in at home so yeah so there's two right there's so one is like super intelligence so a system that becomes far far superior in reasoning ability than us humans how is that an existential threat then so there's a lot of ways in which it could be one way as us humans are actually irrational inefficient and get in the way of of not happiness but whatever the objective function is of maximizing that objective function yeah super intelligent paperclip problem things like but so the paperclip problem but with a super intelligent yeah so we already face this threat in some sense they're called bacteria these are organisms in the world that would like to turn everything into bacteria and they're constantly morphing they're constantly changing to evade our protections and in the past they have killed huge swathes of populations of humans on this planet so if you want to worry about something that's going to multiply endlessly we have it and I'm far more worried in that regard I'm far more worried that some scientists in a laboratory will create a super virus or a super bacteria that we cannot control that is a more existential strep putting putting in its halogen thing on top of it actually seems to make it less existential to me it's like it's it limits its power is limits where it can go and limits the number of things that can do in many ways a bacteria is something you can't you can't even see so that's the only one of those problems yes exactly so the the other one just in your intuition about intelligent you think about intelligence as humans do you think of that as something if you look at intelligence on a spectrum from zero to us humans do you think you can scale that to something far superior yeah all the mechanisms with me I want to make another point here that Lex before I get there sure intelligence is the neocortex it is not the entire brain if I the goal is not to be make a human the goal is not to make an emotional system the goal is not to make a system that wants to have sex and reproduce why would I build that if I want to have a system that wants to reproduce enough sex make bacteria make computer viruses those are bad things don't do that just those are really bad don't do those things regulate those but if I just say I want to intelligent system why does it have to have any human like emotions why couldn't I does he even care if it lives why does it even care if it has food it doesn't care about those things it's just you know it's just in a trance thinking about mathematics or it's out there just trying to build the space plant you know for it on Mars it's C we don't that's a choice we make don't make human-like things don't make replicating things don't make things which have emotions just stick to the neocortex so that's that's a view actually that I shared but not everybody shares in the sense that you have faith and optimism about us years of systems humans as builders of systems got to to do not put in stupid not so this is why I mentioned the bacteria one yeah because you might say well some person's gonna do that well some person today could create a bacteria that's resistant to all the non antibacterial agents so we already have that threat we already knows this is going on it's not a new threat so just accept that and then we have to deal with it right yeah so my point has nothing to do with intelligence it intelligence is the separate component that you might apply to a system that wants to reproduce and do stupid things let's not do that and in fact it is a mystery why people haven't done that yeah my my dad is a physicist believes that the reason you so for some nuclear weapons haven't proliferated amongst evil people so one is one belief that I share is that there's not that many evil people in the world that would that that would use Spectre whether it's bacteria and you clear weapons or maybe the future AI systems to do bad so the fraction is small and the second is that it's actually really hard technically yeah so the the intersection between evil and competent is small in terms and otherwise it really annihilate humanity you'd have to have you know sort of the the nuclear winter phenomena which is not one person shooting you know or even ten bombs you'd have to have some automated system that you know detonates a million bombs or whatever many thousands we have extreme evil combined with extreme competence and it's just like only some stupid system that would automatically you know dr. Strangelove type of thing you know I mean look we could have some nuclear bomb go off in some major city in the world like no I think that's actually quite likely even in my lifetime I don't think that's on I like to think and it'd be a tragedy but it won't be an existential threat and it's the same as you know the virus of 1917 whatever it was you know the influenza these bad things can happen and the plague and so on we can't always prevent them we always to always try but we can't but they're not existential threats until we combine all those crazy things together one so on the on the spectrum of intelligence from zero to human do you have a sense of if whether it's possible to create several orders of magnitude or at least double that of human intelligence type on your cortex I think the wrong thing to say double the intelligence you break it down into different components can I make something that's a million times faster than a human brain yes I can do that could I make something that is has a lot more storage than the human brain yes I could more common more copies of comp can I make something that attaches the different sensors than human brain yes I can do that could I make something that's distributed so these people yet we talked early about that important in your cortex voting's well they don't have to be co-located why you know they can be all around the places I could do that too those are the levers I have but is it more intelligent what depends what I train it on what is it doing if it's oh here's the thing so let's say larger neocortex and or whatever size that allows for higher and higher hierarchies yeah to form right we're talking about rains in canto I could could I have something as a super physicist or a super mathematician yes and the question is once you have a super physicist will they be able to understand something do a sense that it'll be orders to make like us compared to ever understand it yeah most people cannot understand general relativity right it's a really hard thing together I mean paint in a fuzzy picture stretchy space you know yeah but the the field equations to do that in the deep intuitions are really really hard and I've tried I unable to do it is to get you know it's easy to get special relativity general that's it man that's too much and so we already live with this to some extent the vast majority of people can't understand actually what the vast majority other people actually know we're just either we don't have the effort to or we can't or it on time are just not smart enough whatever so but we have ways of communicating Einstein has spoken in a way that I can understand he's given me analogies that are useful I can use those analogies from my own work and think about you know concepts that are similar it's not stupid it's not like he's existed some other plane there's no connection to my plane in the world here so that will occur it already has occurred that's from my point that this story is it already has a kirby liveth everyday one could argue that with me crepe machine intelligence that think a million times faster than us that it'll be so far we can't make the connections but you know at the moment everything that seems really really hard to figure out in the world when you actually figure it out it's not that hard you know we can everyone most everyone can understand the multiverses and most everyone can understand quantum physics we can understand these basic things even though hardly any baby people could figure those things out yeah but really understand so only a few people really understand you need to only understand the the projections the sprinkles of the useful my example of Einstein right his general theory of relativity is one thing that very very very few people can get and what if we just said those other few people are also artificial intelligences how bad is that in some sense they right yeah they say already you mean Einstein wasn't a very normal person he had a lot of where the quirks and so the other people who work with him so you know maybe they already were sort of this astral plane of intelligence that we live with it already it's not a problem it's still useful and you know so do you think we are the only intelligent life out there in the universe I would say that intelligent life has and will exist elsewhere in the universe I'll say that there is a question about contemporaneous intelligence life which is hard to even answer when we think about relativity in the the nature of space-time you can't say what exactly is this time someplace else in the world but I think it's it's you know I do worry a lot about the the filter idea which is that perhaps intelligent species don't last very long and so we haven't been around very long you know as a technological species we've been around for almost nothing man you know what 200 years I'm like that and we don't have any data a good data point on whether it's likely they will survive or not so do I think that there have been intelligent life elsewhere in the universe almost certain that of course in the past in the future yes does it survive for a long time I don't know this is another reason I'm excited about our work is our work meaning that general Worlds of AI and I think we can build intelligent machines that outlast us and you know they don't have to be tied to earth they don't have to you know I'm not saying that recreating you know you know aliens I'm just saying well if I asked myself and this might be a good point to end on here if I asked myself you know what's special about our species we're not particularly interesting physically we're not we don't fly we're not good swimmers we're not very fast from that very strong you know it's our brain that's the only thing and we are the only species on this planet it's built the model of the world it extends beyond what we can actually sense we're the only people who know about the far side of the Moon and the other universes and I mean other other galaxies and other stars and and but what happens in the atom there's no what that knowledge doesn't exist anywhere else it's only in our heads cats don't do it dogs into a monkey's don't do it it's just on and that is what we've created that's unique not our genes it's knowledge and if I asked me what is the legacy of humanity what what what should our legacy be it should be knowledge we should preserve our knowledge in a way that it can exist beyond us and I think the best way of doing that in fact you have to do it is to has to go along with intelligent machines to understand that knowledge it's a very broad idea but we should be thinking I call it a state planning for Humanity we should be thinking about what we want to leave behind when as a species we're no longer here and that'll happen sometime it sooner or later it's gonna happen and understanding intelligence and creating intelligence gives us a better chance to prolong it does give us a better chance prolonging life yes it gives us a chance to live on other planets but even beyond that I mean our solar system will disappear one day just give enough time so I don't know I thought we'll ever be able to travel to other things but we could tell the stars but we could send Intel's machines to do that say you have a you have an optimistic a hopeful view of our knowledge of the echoes of human civilization living through the intelligence systems we create Oh totally well I think the telephone systems are created in some sense the the vessel for bring him beyond Earth or making him last beyond humans themselves so how do you feel about that that they won't be human quote-unquote human what does human our species are changing all the time human today is not the same as human just fifty years ago its what does human do we care about our genetics why is that important as I point out our genetics are no more interesting than about two Miam genetics there's no more interesting them you know monkeys genetics what we have what what's unique and what's family better I start is our knowledge art what we've learned about the world and that is the rare thing that's the thing we want to preserve its genes the knowledge the knowledge that's a really good place to end thank you so much for talking you

Channel: Lex Fridman

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Id: -EVqrDlAqYo

Length: 129min 42sec (7782 seconds)

Published: Mon Jul 01 2019

Reddit Comments

[u/JoeyvKoningsbruggen]

The theory sounds a bit like Kurzweil's pattern recognition theory.