Jeff Hawkins: The Thousand Brains Theory of Intelligence | Lex Fridman Podcast #208

the following is a conversation with. jeff hawkins a neuroscientist. seeking to understand the structure. function. and origin of intelligence in the human. brain he previously wrote the seminal. book on the subject. titled on intelligence and recently. a new book called a thousand brains. which presents a new theory of. intelligence. that richard dawkins for example has. been. raving about calling the book quote. brilliant and exhilarating. i can't read those two words and not.

think of him saying it in his british. accent. quick mention of our sponsors codecademy. bio optimizers. expressvpn a-sleep and blinkist check. them out in the description to support. this podcast. as a side note let me say that one small. but powerful idea that jeff hawkins. mentions in his new book. is that if human civilization were to. destroy itself. all of knowledge all our creations will. go with. us he proposes that we should think.

about how to save that knowledge. in a way that long outlives us whether. that's on earth. in orbit around earth or in deep space. and then to send messages that advertise. this backup of human knowledge. to other intelligent alien civilizations. the main message of this advertisement. is not. that we are here but that we were. once here this little difference. somehow was deeply humbling to me that.

we may. with some non-zero likelihood destroy. ourselves and that an alien civilization. thousands or millions of years from now. may come across this knowledge store. and they would only with some low. probability. even notice it not to mention be able to. interpret it. and the deeper question here for me is. what information in all of human. knowledge is even essential. does wikipedia capture it or not at all. this thought experiment. forces me to wonder what are the things. we've accomplished.

and are hoping to still accomplish that. will outlive us. is it things like complex buildings. bridges cars. rockets is it ideas like science physics. and mathematics. is it music and art is it. computers computational systems or even. artificial intelligence systems. i personally can't imagine that aliens. wouldn't already have. all of these things in fact much more. and much better to me the only unique. thing we may have.

is consciousness itself and the actual. subjective experience of suffering. of happiness of hatred of love. if we can record these experiences in. the highest resolution directly from the. human brain. such that aliens will be able to replay. them that. is what we should store and send as a. message. not wikipedia but the extremes of. conscious experiences. the most important of which of course is. love. this is the lex friedman podcast and.

here is my conversation. with jeff hawkins we previously. talked over two years ago do you think. there's still neurons in your brain. that uh remember that conversation that. uh remember me. and got excited like there's a lex. neuron in your brain that just like. finally has a purpose. i do remember our conversation or i have. some memories of it. and i formed additional memories of you. in the meantime. um i wouldn't say there's a neuron or a. neurons in my brain that know you. there are synapses in my brain that have.

formed. that reflect my knowledge of you and the. model i have of you in the world and. whether the exact same synapses were. formed two years ago it's hard to say. because these things come and go all the. time but. we know from one thing to know about. brains is that when you think of things. you often erase the memory and rewrite. it again so. yes but i have a memory of you and i. have that's instantiated in synapses. there's a simpler way to think about it. like so you have we have a model. of the world in your head and that model.

is continually being updated. i updated this morning you offered me. this water you said it was from the. refrigerator. i remember these things and so we and so. the model includes where we live the. places we know the words the. objects in the world it's a monstrous. model and it's constantly being updated. and people are just part of that model. so we're animals or other physical. objects. so our events we've done so um. it's there's no special in my mind. special place for the memories of humans. i mean.

obviously i know you know i know a lot. about my wife. um but and friends uh and so on but. it's not like a special place for humans. over here but we model everything and we. model other people's behaviors too so if. i said you're a. copy of your mind in my mind it's just. because i know how humans. i've learned how humans behave and um. and i've learned some things about you. and that's part of my world model well i. just also mean. the collective intelligence of the human.

species. i wonder if there's something. fundamental to the brain that enables. that so modeling other humans with their. ideas. you're actually jumping into a lot of. big topics like collective intelligence. is a separate topic that a lot of people. like to talk about we can talk about. that. uh but um and so that's interesting like. you know we're not just individuals we. live in society and so on. but from our research point of view and. so again let's just talk. we study the neocortex it's a sheet of.

neural tissue it's about 75. of your brain it runs on this very. repetitive algorithm. it's a very repetitive circuit and so. you can apply that algorithm to lots of. different problems but it's all. underneath it's the same thing we're. just building this model. so from our point of view we wouldn't. look for these special circuit someplace. buried in your brain that might be. related to. other you know understanding of the. humans it's more like. you know how do we build a model of. anything how do we understand anything.

in the world and humans are just. another part of the things we understand. so there's nothing uh there's nothing to. the brain that. knows the emergent phenomena of. collecting the intelligence well i. certainly know about that i've heard the. terms i've read. no but that's right right well okay. right as an idea well i think we have. language which is is. sort of built into our brains and that's. a key part of collective intelligence so. there are some you know prior. assumptions about the world we're going. to live in when we're born we're not. just. a blank slate um and so you know did we.

evolve. to take advantage of those situations. yes but again. we study only part of the brain the. neocortex there's other parts of the. brain are very much. involved in societal interactions and. human emotions and. um and how we interact and even societal. um. issues about you know how we are how we. interact with other people when we. support them when we're greedy and. things like that i mean certainly the. brain. is a great place where to study.

intelligence i wonder if it's the. fundamental. uh atom of intelligence well i would say. it's. it's it's absolutely an essential. component even if you believe in. collective intelligence as. um hey that's where it's all happening. that's what we need to study which i. don't believe that by the way i think. it's really important but i don't think. that is the thing. um but even if you do believe that then. you have to understand how the brain. works in doing that. um it's you know it's more like we are. intelligent and.

we are intelligent individuals and. together we are much more magnified our. intelligence we can do things that we. couldn't do individually. but even as individuals we're pretty. damn smart and. we can model things and understand the. world and interact with it. so um to me if you're going to start. some place you need to start. with the brain then you could say well. how do brains interact with each other. and what is the nature of language and. how do we. share models that i've learned something. about the world how do i share it with. you which is really what you know. sort of communal intelligence is i know.

something you know something. we've had different experiences in the. world i've learned something about. brains maybe i can impart that to you. you've learned something about. you know whatever physics and you can. part that to me. but it also comes down to even just the. epistemological question of. well what is knowledge and how do you. represent it in the brain right. and it's not that's where it's going to. reside right or in our writings. it's obvious that human collaboration. human interaction. is how we build societies right but some.

of the things you. talk about and work on. some of those elements of what makes up. an intelligent. entity is there with a single person oh. absolutely i mean. it'd be we can't deny that the brain is. the core element here in. in uh at least i can't i think it's. obvious the brain is the core element in. all theories of intelligence. uh it's where knowledge is represented. it's where knowledge is created. we interact we share we build upon each. other's work. but uh without a brain you'd have.

nothing you know there would be no. intelligence without brains. and so um so that's where we start. i got into this field because i just was. curious as to who i am. you know how you know how do i think. what's going on in my head when i'm. what i'm thinking what does it mean to. know something you know i can ask what. it means for me to know something. independent of how i learned it from. you or from someone else or from society. so what does it mean for me to know that. i have a model of you in my head what. does it mean to know i know what this. microphone does and how it works. physically even though i can't see it. right now.

how do i know that what does it mean how. the neurons do that at the. fundamental level of neurons and. synapses and so on those are. really fascinating questions and uh i'm. happy to. be just happy to understand those if i. could. so in your um in your new book. you talk about our brain our mind as. being made up of many brains. uh so the book is called the thousand. brains a thousand brain theory of. intelligence what is the key idea of.

this book. uh the book has three sections. and it has sort of maybe three big ideas. so the first section is all about what. we've learned about the neurocortex and. that's the thousand brains theory. just did we complete the picture the. second section is all about ai and the. third section is about the future of. humanity. so the thousand brains theory. the the big idea there if i had to. summarize into one big idea. is that we think of the the brain the.

neocortex is learning this model of the. world. but what we learned is actually there's. tens of thousands of independent. modeling systems going on and so each. what we call a column in the cortex is. about 150. 000 of them is a complete modeling. system so. it's a collective intelligence in your. head in some sense so the thousand. brains theory says. well where do i have knowledge about you. know this coffee cup where is the model. of this. cell phone it's not in one place it's in. thousands of separate models that are. complementary and they communicate.

with each other through voting so this. idea that we have we feel like we're one. person. you know that's our experience we can. explain that but reality there's. lots of these like almost like little. brands like but they're. they're sophisticated modeling systems. about 150 000 of them in each of. the human brain and that's a totally. different way of thinking about. how the neural cortex is structured than. we or anyone else thought of even just. five years ago. so you mentioned you started this. journey. and just looking in the mirror trying to.

understand who you are. so if you have many brains who are you. then. so it's interesting we have a singular. perception right you know we think oh. i'm just. here i'm looking at you but it's it's. composed of all these things like. there's sounds and there's. and there's uh this vision and there's. touch and. all kinds of inputs yeah we have the. singular perception and what the. thousand brain theory says we have these. models that are visual models we have a. lot of models of auditory models models. of toxin models and so on. but they vote and so um they send in the. cortex you can think about these columns.

as that like little grains of. rice 150 000 stacked next to each other. and each one is its own little modeling. system. but they have these long-range. connections that go between them. and we call those voting connections or. voting neurons. um and so the different columns. try to reach the consensus like what am. i looking at okay you know. each one has some ambiguity but they. come to a consensus oh there's a water. bottle i'm looking at. um we are only consciously able to.

perceive the voting. we're not able to perceive anything that. goes on under the hood. so the voting is what we're we're aware. of. the results of the vote yeah the. velocity well it's it's you can imagine. it this way. we were just talking about eye movements. a moment ago so as i'm looking at. something my eyes are moving about three. times a second. and with each movement a completely new. input is coming into the brain it's not. repetitive it's not shifting it around. it's completely new. i'm totally unaware of it i can't. perceive it but yet if i looked at the. neurons in your brain they're going on. and off i don't know.

but the voting neurons are not the. voting neurons are saying you know. we all agree even though i'm looking at. different parts of this is a water. bottle right now. and that's not changing and it's in some. position and. and pose relative to me so i have this. perception of the water bottle about two. feet away from me at a certain pose to. me. um that is not changing that's the only. part i'm aware of i can't be aware of. the fact that the inputs. from the eyes are moving and changing. and all this others happening. so these long range connections are the. part we can be conscious of. the individual activity in each column.

is doesn't go anywhere else it doesn't. get shared anywhere else it doesn't. there's no way to extract it. and talk about it or extract it and even. remember it to say oh. yes i can recall that um so but these. long-range connections are the things. that are accessible to language. and to our you know it's like the. hippocampus or our memories you know. our short-term memory systems and so on. so we're not aware of. 95 or maybe it's even 98 of what's going.

on in your brain. we're only aware of this sort of stable. somewhat stable. voting outcome of all these things that. are going on underneath the hood. so what would you say is the basic. element in the thousand. brains theory of intelligence of. intelligence. like what's the atom of intelligence. when you think about it. is it the individual brains and then. what is a brain. well let's let's can we just talk about. what intelligence is first. and then and then we can talk about the. elements are so in my.

in my book intelligence is the ability. to learn. a model of the world so to build. internal to your head. a model that represents the structure of. everything you know. to know what this is a table and that's. a coffee cup and this is a gooseneck. lamp and all this. to know these things i have to have a. model in my head i just don't look at. them and go what is that. i already have internal representations. of these things in my head. and i had to learn them i wasn't born of. any of that knowledge. you were you know we have some lights in. the room here i you know that's not part.

of my evolutionary heritage right it's. not in my genes. so um we have this incredible model and. the model includes not only what things. look like and feel like but where they. are relative to each other and how they. behave. i've never picked up this water bottle. before but i know that if i took my hand. on that blue thing and i turn it it'll. probably make a funny little sound as. the little plastic things detach. and then it'll rotate and it'll look a. certain way it'll come off how do i know. that right because i have this model in. my head. so the essence of intelligence as our. ability to learn a model and the more. sophisticated our model is.

the smarter we are not that there is a. single intelligence because. you can know about you know a lot about. things that i don't know and i know. about things you don't know. and we can both be very smart but we. both learn the model of the world. through interacting with it. so that is the essence of intelligence. then we can ask ourselves what are the. mechanisms in the brain. that allow us to do that and what are. the mechanisms of learning not just the. neural mechanisms what is the general. process but how we learn a model. so that was a big insight for us it's. like what are the what is the actual. things that how do you learn this stuff.

it turns out you have to learn it. through movement. um you can't learn it just by that's how. we learn we learn through movement we. learn. um so you build up this model by. observing things and touching them and. moving them and. walking around the world and so on so. either you move or the thing moves. somehow yeah you obviously can learn. things just by reading a book something. like that but. think about if i were to say oh here's a. new house yeah i want you to learn. you know what do you do you have to walk. you have to walk from room to the room. you have to open the doors. look around see what's on the left. what's on the right as you do this.

you're building a model in your head. it's just that's what you're doing you. can't just sit there and say i'm going. gonna grock the house. no you know or you could you don't even. want to sit there and read some. description of it right. yeah you literally physically. interactive the same with like a. smartphone if i want to. learn a new app i touch it and i move. things around i see what happens when i. when i do things with it so that's the. basic way we learn in the world and by. the way when you say model. you mean something that can be used for. prediction in the future. it's it's used for prediction and for. behavior. and planning right um and does a pretty.

good job in doing so. yeah here's the way to think about the. model a lot of people get hung up on. this so. um you can imagine an architect making a. model of a house. right so there's a physical model that's. small and why do they do that. well we do that because you can imagine. what it would look like from different. angles you could say okay. look at them here look in there and you. can also say well how how far to get. from. from the garage to the to the swimming. pool or something like that right you. can imagine looking at this you can say. what would be the view from this.

location so we built these physical. models to let you. imagine the future and imagine that. behaviors. now we can take that same model and put. it in a computer so we now. today they'll build models of houses and. a computer and they. and they do that using a set of um. we'll come back to this term in a moment. reference frames but eventually you. assign a reference frame for the house. and you assign different things for the. house in different locations. and then the computer can generate an. image and say okay this is what it looks. like in this direction. the brain is doing something remarkably. similar to this surprising.

um it's using reference frames it's. building these it's similar to a model. in a computer. which has the same benefits of building. a physical model it allows me to say. what would this thing. look like if it was in this orientation. what would likely happen if i push this. button. i've never pushed this button before or. how would i accomplish something i want. to. i want to um convey a new idea i've. learned. how would i do that i can imagine in my. head well i could talk about it. i could write a book i could do some. podcasts. i could um you know maybe tell my.

neighbor. you know and i can imagine the outcomes. of all these things before i do any of. them. that's what the model lets you do it. let's just plan the future and imagine. the. consequences of our actions prediction. you asked about prediction. prediction is not the goal of the model. prediction is an inherent property of it. and it's how the model corrects itself. so prediction is fundamental to. intelligence. it's fundamental to building a model and. the model's intelligent. and let me go back and be very precise.

about this prediction you can think of. prediction two ways one is like. hey what would happen if i did this. that's the type of prediction um. that's a key part of intelligence but. using predictions like oh what's this. this is this water bottle gonna feel. like when i pick it up. you know and that doesn't seem very. intelligent but the way to think one way. to think about intelligence prediction. is. it's a way for us to learn where our. model is wrong. so if i picked up this water bottle and. it felt hot i'd be very surprised. or if i picked up was very light it. would be very i'd be surprised or.

if i turned this top and it didn't i had. to turn the other way i'd be surprised. and so almost might have a prediction. like okay i'm gonna do it i'll drink. some water. i'm okay okay do this there it is i feel. opening right what if i had to turn it. the other way or what if it it split in. two. then i say oh my gosh i i misunderstood. this i didn't have the right model of. this thing. my attention would be drawn to i'll be. looking at it going well how the hell. did that happen. you know why did it open up that way and. i would update my model. by doing it just by looking at it and. playing around with that update and say. this is a new type of water bottle. but you so you're talking about sort of.

uh. complicated things like a water bottle. but this also applies for just. basic vision just like seeing things. it's almost like a precondition of just. perceiving the world is predicting. it's just everything that you see is. first passed through your prediction. everything you. see and feel in fact this this is the. insight i had. uh back in the late 80s uh and excuse me. early 80s. and um another people reach the same.

idea is that. every sensory input you get not just. vision but touch. and hearing you have an expectation. about it. and um a prediction sometimes you can. pick very accurately sometimes you can't. i can't predict what next word is going. to come out of your mouth but as you. start talking about. better and better predictions and if you. talk about some topics i'd be very. surprised. so i have this sort of background. prediction that's going on all the time. for all my senses again the way i think. about that.

is this is how we learn it's it's more. about how we learn. it's the test of our understanding our. predictions are our test. did is this really a water bottle if it. is i shouldn't see. you know a little finger sticking out. the side and if i saw a little finger. stick and i was like what the hell is. going on. you know that's not normal um i mean. that's. fascinating that just let me linger on. this. for a second i it really honestly feels. that prediction is fundamental. to everything uh to the way our mind.

operates. to intelligence so like it's just a. different way to see. intelligence which is like everything. starts at prediction. and prediction requires a model you. can't predict something unless you have. a model of it right but the action is. prediction it's like the. the thing the model does is prediction. and but it also yeah and you but. you can then extend it to things like uh. what would happen. if i took this today i went and did this. what would be like that.

or how you can extend predictions like. oh i want to get a promotion at work. um what action should i take and you can. say if i did this i predict what might. happen if i. spoke to someone i predict what might. happen so it's not just low level. predictions yeah it's all prediction. it's all predictions like this. black box so you can ask basically any. question low level or highlight so we. start off with that observation it's all. it's like this non-stop prediction and i. write about this in the book about. and then we ask how do neurons actually. make predictions. physically like what does the neuron do.

when it makes a prediction and. um what the neural tissue does when it. makes predictions and then we ask what. are the mechanisms by how we build a. model that allows you to make prediction. so we started with prediction as sort of. the fundamental. research agenda if in some sense like. and say well we understand how the brain. makes predictions. we'll understand how it builds these. models and how it learns and that's core. of intelligence so it was like it was. the key that got us in the door. to say that is our research agenda. understand predictions. so in this whole process where does.

intelligence. originate would you say so. it if we look at things that are. much less intelligent to humans and you. start to build up a human the process of. evolution. where is this magic thing that uh. has a prediction model or a model that's. able to predict. that starts to look a lot more like. intelligence is there a place where. richard dawkins wrote an introduction to.

your uh to your book an excellent. introduction. i mean it puts a lot of things into. context. and it's funny just looking at parallels. for your book and. darwin's origin of species so darwin. wrote about the origin. of species so. what is the origin of intelligence well. we have a theory about it and it's just. that it's a theory. theory goes as follows as soon as living. things. started to move they're not just. floating in sea they're not just. a plant you know grounded some place as.

soon as they started the move. there was an advantage to moving. intelligently to moving in certain ways. and there's some very simple things you. can do you know bacteria or. single cell organisms can move towards a. source of gradient of food or something. like that. but an animal that might know where it. is and know where it's been and how to. get back to that place or an animal that. might. say oh there was a source of food. someplace how do i get to it or there. was a. danger how do i get to there was a mate. how do i get to them.

um there was a big evolution advantage. to that so early on there was a pressure. to start. understanding your environment like. where am i. and where have i been and what happened. in those different places. so we still have this neural mechanism. in our brains um it's in in the in the. mammals it's in the. hippocampus and internal cortex these. are older parts of the brain. um and these are very well studied um. we build a map of the of our environment.

so. these neurons in these parts of the. brain know where i am in this room and. where. the door was and things like that so a. lot of other. mammals have this all mammals have this. right and almost. any any animal that knows where it is. and get around must have some mapping. system must have some way of saying. i've learned a map of my environment i. have hummingbirds in my backyard and. they. and they go the same places all the time. they have to they must know where they. are they just know where they are when. they're. they're not just randomly flying around. they know they know particular flowers.

they come back to. so we all have this and it turns out. it's. very tricky to get neurons to do this to. build a map of an environment it's just. and so we now know there's this these. famous studies that's still very active. about. place cells and grid cells and these. other types of cells in the older parts. of the brain. and how they build these maps of the. world it's really clever it's obviously. been under a lot of evolutionary. pressure over a long period of time to. get good at this. so animals not know where they are what.

we think has happened. uh and there's a lot of evidence to. digest this is that that mechanism we. learn to map. like a space is. was repackaged the same type of neurons. was repackaged into a more compact form. and that became the cortical column and. it was. it was in some sense genericized if. that's a word it was turned into a very. specific thing about learning. maps of environments to learning maps of. anything. learning a model of anything not just.

your space but coffee cups and so on. and it got sort of repackaged. into a more compact version a more. universal version. and then replicate it so the reason. we're so flexible is we have a very. generic version of this. mapping algorithm and we have 150 000. copies of it. sounds a lot like the progress of deep. learning. how so uh so take neural networks that. seem to work well for a specific task.

compress them and multiply it. by a lot and then you just stack them on. top of it it's like the story of. transformers and uh yeah. but interesting networks they end up. you're replicating an element but you. still need the entire network to do. anything. right here what what's going on each. individual element is a complete. learning system. this is why i can take a human brain cut. it in half and it still works.

it's it's pretty amazing it's. fundamentally distributed it's. fundamentally distributed complete. modeling systems. so but that's that's our story we like. to tell. i i i would guess it's it's likely. largely right um but you know it's. there's a lot of evidence supporting. that story this evolutionary story. the thing which brought me to this idea. is that the human brain. got big very quickly so that that. led to the proposal a long time ago that.

well there's this common element just. instead of. creating new things it just replicated. something we also are extremely flexible. we can learn things that we had no. history about right and so that tells it. that the. learning algorithm is very generic it's. very kind of universal. because it's it doesn't assume any prior. knowledge about what it's learning. and so you combine those things together. and you say okay well how did that come. about where did that universal algorithm. come from it had to come from something. that wasn't universal it came from.

something that was more specific. and so anyway this led to our hypothesis. that you would find grid cells and place. cell equivalents in the neocortex. and when we first published our first. papers on this theory. we didn't know of evidence for that it. turns out there was some but we didn't. know about it. uh and since then um so then we became. aware of evidence for grid cells in. parts of the neural cortex. and then now there's been new evidence. coming out there's some. interesting papers that came out just. january of this year so our one of our. predictions was. if this evolutionary hypothesis is.

correct we would see grid cell place. cell equivalents cells that work like. them. through every column in the near cortex. and that's starting to be seen. what does it mean that uh why is it. important that they're present. because it tells us well we're asking. about the evolutionary origin of. intelligence right. so our theory is that these columns in. the cortex. are working on the same principles. they're modeling systems and it's hard. to imagine how neurons do this and so we. said. hey it's really hard to imagine how. neurons could learn these models of.

things. we can talk about the details of that if. you want but. let's um but there's this other part of. the brain we know that learns models of. environments so could that mechanism to. learn to model this room be used to. learn a model the water bottle. is it the same mechanism so we said it's. much more likely the brain is using the. same mechanism. which case it would have these. equivalent cell types. so it's basically the whole theory is. built on the idea that. um these columns have reference frames. and they're learning these models.

and these these grid cells create these. reference frames so it's it's basically. the major in some sense the major. predictive. part of this theory is that we will find. these equivalent mechanisms in. each column in the near cortex which. tells us that's that. that that's what they're doing they're. learning these sensory motor models. of the world so just we're pretty. confident. that would happen but now we're seeing. the evidence so the evolutionary process. nature does a lot of copy and paste and. see what happens yeah. yeah there's no direction to it but but.

um it just found out like hey if i. took this these elements and and made. more of them what happens and let's hook. them up to the eyes and let's look up. the ears and. and um and that seems to work pretty. well yeah like for us. again just to take a quick step back to. our. conversation of collective intelligence. do you sometimes. see that as just another copy and paste. aspect is copying pasting these uh. brains and. humans and making a lot of them and then.

creating. uh social structures that then almost. operates as a single brain. uh i wouldn't have said it but you said. it sounded pretty good. so to you the brain is fundamental is uh. is like uh. is its own thing right i mean our goal. is to understand how the neural cortex. works. we can argue how essential that is to. understand a human brain because it's. not the entire human brain. you can argue how essential that is to. understanding human intelligence. you can argue how essential it is to um.

to uh you know a sort of communal. intelligence. um i i'm not i didn't our goal was to. understand the neocortex yeah so what is. the neural cortex and where does it fit. in um the various aspects of what the. brain does. like how important is it to you well. obviously. again we i mentioned again in the. beginning it's it's it's. about 70 to 75 of the volume of a human. brain. so it's you know it dominates our brain. in terms of size not in terms of number. of neurons but. in terms of size size isn't everything.

jeff. i know but it's it's nothing it's. nothing. it's not that we know that all. high-level vision. hearing and touch happens in the air. context we know that all language. occurs and is understood in the. neurocortex whether that's spoken. language written language sign language. with. language of mathematics language of. physics music. math you know we know that all. high-level planning and thinking occurs. in the new york cortex. if i were to say you know what part of. your brain designed a computer. and understands programming and and.

creates music it's all the neural cortex. so then that's kind of undeniable fact. uh if but then there's other parts of. our brain are important too. right our emotional states uh our body. regulating our body. um so the way i like to look at it is. you know could you can you understand. the neocortex about the rest of the. brain. and some people say you can't and i. think absolutely you can. it's not that they're not interacting. but you can understand them can you. understand the neocortex without.

understanding the emotions of fear yes. you can you can understand how the. system works it's just a modeling system. i make the analogy in the book that it's. it's like a map of the world. and how that map is used depends on. who's using it. so how our map of our world in our. neocortex. how we how we manifest as a human. depends on the rest of our brain what. are our motivations you know what are my. desires am i a nice guy or not a nice. guy. am i a cheater or a you know or not a. cheater um. uh you know how important different.

things are in my life. so um so but the. new projects can be understood on its. own um and and i say that. as a neuroscientist i know there's all. these interactions and i want to. say i don't know them and we don't think. about them but from a layperson's point. of view you can say. it's a modeling system i don't tend to. think too much about the communal aspect. of intelligence which you brought a. number of times already. um so that's not really been my concern. i just wonder if there's a continuum. from the origin of the universe like.

this com pockets of complexities that. form. yeah living organisms i wonder if if. we're just. if you look at humans we feel like we're. at the top. but i wonder if there's like just where. everybody probably every living type. pocket of complexity. is probably thinks they're the uh pardon. the french. they're the yeah they're they're. they're at the top of the parent well. if they're thinking um well then then. what is thinking what the all right.

in this sense the whole point is in. their. sense of the world they their sense. is that they're at the top of it i think. what is it turtle. but you're you're you're bringing up you. know the the problems of complexity and. complexity theory. are you know it's a huge interesting. problem in science. um and you know i think we've made. surprisingly little progress in. understanding complex systems. right in general um and so you know the.

santa fe institute was founded to to. study this and and even the scientists. there will say it's really hard we. haven't really been able to figure out. exactly you know that science isn't. really congealed yet we're still trying. to figure out the basic elements of that. science. uh what you know where does complexity. come from and what is it and how you. define it whether it's. dna creating bodies or phenotypes or if. it's. individuals creating societies or ants. and you know. markets and so on it's it's a very. complex thing i'm not a complexity. theorist. person right um and i i think.

they ask well the brain itself is a. complex system so. can we understand that um i think we've. made a lot of progress understanding how. the brain works. so but i haven't brought it out to like. oh well where are we on the complexity. spectrum. you know it's like um that's a great. question. i'd prefer for that answer to be we're. not special. it seems like if we're honest most. likely we're not special so if there is. a spectrum.

we're probably not in some kind of. significant place there's one thing we. could say that we are special. and and again only here on earth i'm not. saying i'm bad. is that if we think about knowledge. what we know um we clearly. human brains have um the only brains. that have a certain types of knowledge. we're the only brains on. on this earth to understand uh what the. earth is how old it is. that the universe is a picture as a. whole the only organisms understand dna. and the origins of you know of species.

uh no other species on on this planet. has that. knowledge so if we think about i like to. think about. you know one of the endeavors of. humanity is to. understand the universe as much as we. can um. i think our species is further along in. that undeniably. um whether our theories are right or. wrong we can debate but at least we have. theories you know we. we know that what the sun is and how. it's fusion is and how. what black holes are and you know we.

know. general theory relativity and no other. animal has any of this knowledge. so in that sense that we're special uh. are we special in terms of. the the hierarchy of complexity in in. the universe probably not. can we look at a neuron yeah you say. that prediction. happens in the neuron what does that. mean so neuron traditionally seen as the. basic element. of the the brain so we i mentioned this. earlier. that prediction was our research agenda.

yeah we said okay. um how does the brain make a prediction. like i i'm about to grab this water. bottle. and my brain is predicting what i'm. going to feel um on all my parts of my. fingers if i felt something really odd. on any part here i notice it. so my brain is predicting what it's. going to feel as i grab this thing. so what is that how does that manifest. itself in neural tissue right we got. brains made of neurons and there's. chemicals and there's neurons and. there's spikes and the connect you know. where where is the prediction going on.

and one argument could be that well when. i'm predicting something. um a neuron must be firing in advance. it's like okay this neuron represents. what you're going to feel and it's. firing it's sending a spike. and certainly that happens to some. extent but our predictions are so. ubiquitous. that we're making so many of them which. we're totally unaware of just the vast. majority we have no idea that you're. doing this. um that it wasn't really. we were trying to figure how could this. be where where is these where are these.

happening. right and i won't walk you through the. whole story unless you. insist upon it but we came to the. realization. that most of your predictions are. occurring. inside individual neurons especially. these the most common are in the. pyramidal cells. and there are there's a property of. neurons. we everyone knows or most people know. that a neuron is a cell and it has this. spike. called an action potential and it sends. information. but we now know that there's these. spikes internal to the neuron. they're called dendritic spikes they.

travel along the branches of the neuron. and they don't leave the neuron they're. just internal only. there's far more dendritic spikes than. there are action potentials. far more they're happening all the time. and. what we came to understand that those. dendritic spikes the ones that are. occurring are actually a form of. prediction. they're telling the neuron the neuron is. saying i expect. that i might become active shortly and. that internal. so the internal spike is a way of saying. you're going to you might be generating.

external spikes soon. i predicted you're going to become. active and and we we've we've. we wrote a paper in 2016 which explained. and how this. manifests itself in neural tissue and. how it is that this all works together. but the vast ma we think it's there's a. lot of evidence supporting it. um so we that's where we think that most. of these predictions are internal that's. why you can't. be per their internal neuron you can't. perceive them. from understanding the the prediction. mechanism of a single neuron.

do you think there's deep insights to be. gained about the prediction. capabilities of the mini brains within. the bigger brain and the brain oh yeah. yeah yeah. so having a prediction side of the. individual neuron is not that useful. you know what so what um the way it. manifests itself. in neural tissue is that. when a neuron a neuron emits these. spikes or a very singular type event. if a neuron is predicting that it's. going to be active it makes it spike. very a little bit sooner just a few.

milliseconds sooner than it would have. otherwise it's like. i give the analogy in the book there's. like a sprinter on a on a starting. blocks in. a race and if someone says get ready set. you get up and you're ready to go. and then when your race starts you get a. little bit earlier start so that it's. that. that ready set is like the prediction. and the neuron's like ready to go. quicker. and what happens is when you have a. whole bunch of neurons together. and they're all getting these inputs the. ones that are in the predictive state. the ones that are. anticipating to become active if they do. become active they they happen sooner.

they disable everything else and it. leads to different representations in. the brain so. you have to it's not isolated just to. the neuron the prediction occurs within. the neuron. but the network behavior changes so what. happens under different predictions. different inputs have different. representations so how i. what i predict um it's going to be. different under different contexts. you know what my input will be is. different under different context so. this is this is a. key level theory how this works so the. theory of the thousand brains.

if you were to count the number of. brains how would you do it. the thousand main theory says that. basically every cortical column. in the in your neurocortex is a complete. modeling system. and that when i ask where do i have a. model of something like a coffee cup. it's not in one of those models it's in. thousands of those models there's. thousands of models of coffee cups. that's what the thousand brains there's. a voting mechanism then there's a voting. mechanism which leads which. is the thing you're which you're. conscious of which leads to your. singular perception.

um that's why you perceive something so. that's the thousand brains theory. the details how we got to that theory. um are complicated it wasn't you just. thought of it one day. and one of those details is we had to. ask how does a a model make predictions. and we've talked about just these. predictive neurons. that's part of this theory it's like. saying oh it's a detail but. it was like a crack in the doors like. how are we going to figure out how these. neurons build do this. you know what is going on here so we. just looked at prediction as like. well we know that's ubiquitous we know.

that every part of the cortex is making. predictions. therefore whatever the predictive system. is it's going to be everywhere. we know there's a gazillion predictions. happening at once so let's see if we can. start teasing apart. you know ask questions about you know. how could neurons be making these. predictions and that. sort of built up to now what we have the. thousand brains theory. which is complex you know it's just some. i can state it simply but we just didn't. think of it. we had to get there step by step very it. took years. uh to get there and where does uh.

reference frames fit in so yeah. okay so again a reference frame i. mentioned. um earlier about the you know a model of. a house and i said if you're going to. build a model of a house. in a computer they have a reference. frame and you can then reference them. like. cartesian coordinates like x y and z. axes. so i can say oh i'm going to design a. house i can say well the. the front door is at this location xyz. and the roof is at this location xyz and. so on. that's a type of reference frame so it. turns out.

for you to make a prediction and then i. walk you through the thought experiment. in the book where i was. predicting what my finger was going to. feel when i touched the coffee cup. it was a ceramic coffee cup but this one. will do um. and what i realized is that to make a. prediction with my finger's going to. feel like it's just going to feel. different than this which would feel. different if i touch the hole or the. thing on the bottom. make that prediction the cortex needs to. know where the finger is the tip of the. finger. relative to the coffee cup and exactly. relative to the coffee cup.

and to do that i have to have a. reference frame for the coffee up it has. to have a way of representing the. location of my finger. to the coffin up and then we realize of. course every part of your skin has to. have a reference frame relative things. to touch and then we. did the same thing with vision but so. the idea that a reference frame. is necessary to make a prediction when. you're touching something or when you're. seeing something. and you're moving your eyes you're. moving your fingers it's just a. requirement. to know what to predict if i have a if i. have a structure i'm going to make a. prediction i have to. i have to know where it is i'm looking. or touching it.

so then we say well how do neurons make. reference frames it's not obvious. you know xyz coordinates don't exist in. the brain it's just not the way it works. so that's when we looked at the older. part of the brain the hippocampus and. the antorano cortex. where we knew that in that part of the. brain. there's a reference frame for a room or. reference name for environment remember. i talked earlier about how you could. know make a map of this room. so we said oh um that they are. implementing reference frames there so. we knew that reference frames needed to.

exist in every cortical column. and so that was a deductive thing we. just deduced it. has to go so you take the old. mammalian ability to know where you are. in a particular space. and you start applying that to higher. and higher levels yeah you first you. apply it to physical like where your. finger is. so here's what i think about it the old. part of the brain says where's my body. in this room. yeah the new part of the brain says. where's my finger. relative to this this object yeah where.

is. the a section of my retina relative to. this object like where where is. i'm looking at one little corner where. is that relative to this patch of my. retina yeah. um and then we take the same thing and. apply it to concepts. mathematics physics you know humanity. whatever you want to think eventually. you're pondering your own mortality. well whatever but the point is when we. think about the world when we have. knowledge about the world how is that. knowledge organized. lex where do you where is it in your. head the answer is it's in reference.

frames. so the way i learn the structure of this. water bottle. where the features are relative to each. other when i think about history or. democracy or mathematics. the same basic underlying structures. happening there's reference frames for. where the knowledge. that you're assigning things to so in. the book i go through examples like. mathematics. and language and politics but. the evidence is very clear in the. neuroscience the same mechanism that we. use to model this coffee cup we're going. to use to model. high level thoughts your your your.

demise of the humanity whatever you want. to think about. it's interesting to think about how. different are the representations of. those. higher dimensional concepts. higher level concepts how different the. representation there is in terms of. reference frames. versus spatial but interesting thing. it's it's. it's a different application but it's. the exact same mechanism. but isn't there some aspect to uh. higher level concepts that they seem to. be hierarchical.

like they just seem to integrate a lot. of information into so. is our physical objects so take this. water bottle. uh i'm not particular to this brand but. this is a fiji water bottle. and it has um a logo and i use this. example in my book. our company's coffee cup has a logo on. it but. this object is hierarchical it is. it's got like a cylinder and a cap but. then has this logo on it and the logo. has a word the word has letters the. letters of different features.

and so i don't have to remember i don't. think about this so i said oh there's a. fiji logo on this water bottle i don't. have to go through and say. oh what is the fiji logo it's the f and. i and the j and i and there's a hibiscus. flower and. and uh oh it has the pest you know the. stamen on it i don't have to do that i. just incorporate all of that. in some sort of hierarchical. representation i say um. you know put this logo on this water. bottle yeah and. and and then the logo has a word and the. word has letters. all hierarchical just all that stuff is. big it's amazing that the brain. instantly just does all that yeah the.

idea that there's there's water it's. liquid and the idea that you can. uh drink it when you're thirsty the idea. that there's brands. yeah and then there's like all of that. information is. instantly like built into the whole. thing. once you proceed so i wanted to get back. to your point about hierarchical. representation. the world itself is hierarchical right. and i can take this microphone in front. of me i know inside there's going to be. some electronics i know there's going to. be some wires and i know there's going. to be a little. diaphragm that moves back and forth i.

don't see that but i know it. so everything in the world is. hierarchical you just go into room it's. composed of other components the kitchen. has a refrigerator you know. the refrigerator has a door the door has. a hinge the hinge has screws and pin. yeah i mean. so anyway the the the modeling system. that exists in every cortical column. learns the hierarchical structure of. objects. so it's a very sophisticated modeling. system in this grain of rice it's hard. to imagine but this grain of ice can do. really sophisticated things it's got a. hundred thousand neurons in it.

it's very sophisticated so that same. mechanism that can model a water bottle. or a coffee cup can model conceptual. objects as well. it's if that's the beauty of this. discovery that this guy vernon mount. castle made many many years ago which is. that. there's there's a single cortical. algorithm underlying everything we're. doing. so so common sense concepts and higher. level concepts are all. represented in the same way they're set. in the same mechanisms yeah. it's a little bit like computers right.

all computers are universal turing. machines. even the little teeny one that's in my. toaster. and the big one that's you know running. some cloud server or someplace. um they're all running on the same. principle they can apply different. things. so the brain is all built on the same. principle it's all about. learning these models structured models. using movement. and reference frames and it can be. applied to. something as simple as a water bottle in. a coffee cup and it can be just thinking. like what's the future of humanity and. you know why do you have a hedgehog on.

your. on your desk i don't know nobody knows. i think it's hedgehog that's right it's. a hedgehog in the fog. it's a russian reference does it give. you any. inclination or hope about how difficult. it is to engineer. common sense reasoning so how. complicated this. is this whole process so looking at the. brain. is this a marvel of engineering or is it. pretty dumb stuff stacked on top of each. other over and over.

can it be both can it be both right. i don't know if it can be both because. uh. if it's an incredible engineering job. that means it's. v so evolution did a lot of work it. uh yeah but then but then it just copied. that. right so as i said earlier the figuring. out how to model something. like a space is really hard and. evolution had to go through a lot of. trick and these these these cells i was. talking about these grid cells and place.

cells they're really complicated this is. not simple stuff. this neural tissue works on these really. unexpected weird mechanisms um. but it did it it figured it out but but. now you can just make lots of copies of. it. but then finding yeah so it's a very. interesting idea that's a lot of copies. of a basic mini brain. but the question is how difficult it is. to find that mini brain that you can. copy and paste. uh effectively okay today we.

know enough to build this i'm sitting. here with. you know i know the steps we have to go. there's still some engineering problems. to solve. but we know enough and this is not like. oh this is an interesting idea. we have to go think about it for another. few decades no we actually understand in. pretty well details. so not all the details but most of them. so it's complicated but it is an. engineering problem. so in my company we are working on that. we are basically. a road map how we do this um it's not.

going to take decades. it's better a few years um. [Music]. optimistically but i think that's. possible um. it's you know complex things if you. understand them you can build them. so in which domain do you think it's. best to. build them are we talking about robotics. like. uh entities that operate in the physical. world that are able to interact with. that world are we talking about entities. that operate in the digital world. are we talking about something more like.

uh. more specific like is done in the uh. machine learning community where you. look at natural language. or computer vision where do you think is. easiest it's the first it's the first. two more than the third one i would say. um again again let's just use computers. as an analogy. um the pioneers of computing people like. john van noyman and. um turing they created this thing you. know we now call the universal turing. machine which is the computer right. did they know how it was going to be.

applied where it was going to be used. you know could they. envision any of the future no they just. said this is like a really interesting. computational idea about algorithms. and how you can implement them in in a. machine. and we're doing something similar to. that today like we are. we are building this sort of universal. learning principle. that can be applied to many many. different things. but the the robotics piece of that okay. the interactive okay all right let's. be specific you can think of this.

cortical column as this what we call a. sensory motor learning system it has the. idea that there's a sensor. and then it's moving that sensor can be. physical it could be like my finger. and it's moving in the world it could. like my eye and it's physically moving. it can also be virtual so it could be. um an example would be i could have a. system that lives in the internet. that that actually samples information. on the internet and moves by following. links. that's that's a sensory motor system so. something that echoes the the process of.

a finger moving along a. car but in a very very loose sense it's. it's like. again learning is inherently about the. subbing the structure in the world and. discover the structure of the world you. have to move through the world. even if it's a virtual world even if. it's a conceptual world. you have to move through it you don't it. doesn't exist in one. it has some structure to it so. here's here's a couple of predictions. that getting what you're talking about. in humans the same algorithm is does. robotics right it moves my arms my eyes.

my body. right um and so. in my in the future to me robotics and. ai will merge. they're not going to be separate fields. because they're going to the the. the algorithms to really controlling. robots are going to be the same. algorithms we have in our brand the. brain at these sensory motor algorithms. i today we're not there but i think. that's going to happen. and and then so but not all ai systems. will have b robotics. you can have systems that have very. different types of embodiments some will.

have physical movements. some will have non-physical movements. it's a very generic. learning system again it's like. computers the turing machine is it's. like. it doesn't say how it's supposed to be. implemented it doesn't tell how big it. is doesn't tell you what you apply it to. but it's an interesting it's a. computational principle. cortical column equivalent is a. computational principle is about. learning. it's about how you learn and it can be. applied to a gazillion things this is. what i think this is. i think this impact of ai is going to be. as large if not larger than computing. has been in the last century by far.

because it's it's getting at a. fundamental thing it's not a vision. system or a learning system it's a. it's not a vision system or a hearing. system it is a learning system it's a. fundamental principle how you learn the. structure in the world. how you can gain knowledge and be. intelligent and that's what the thousand. brain says what's going on and we have a. particular implementation in our head. but doesn't have to be like that at all. do you think there's going to be some. kind of impact. okay let me ask it another way what do. uh increasingly intelligent ai systems.

do with us humans in the following way. like how hard is the human in the loop. problem. how hard is it to to interact. the finger on the coffee cup equivalent. of having a conversation with a human. being. so how hard is it to fit into our little. human world. uh i don't i think it's a lot of. engineering problems i don't think it's. a fundamental problem i could ask you. the same question how hard is for. computers to fit into a human world. right that i mean that's essentially.

what i'm asking like how. um much are we uh elitist are we as. humans. like we try to keep out uh systems. i don't know i i sure i think i'm not. sure that's the right question. let's let's look at computers as an. analogy computers are million times. faster than us they do things we can't. understand most people have no idea. what's going on when they use computers. right how do we integrate them in our. society. um well they're that we don't think of.

them as their own entities. they're not living things um we don't. afford them rights. um we uh we rely on them. our survival as a seven billion people. or something like that. is relying on computers now um don't you. think that's a fundamental problem. that we see them as something we can't. we don't give rights to. so computers so yeah computers so uh. robots computers intelligence systems it. feels like for them to operate. successfully.

they would need to have a lot of the. elements. that we would start having to think. about like. should this entity have rights i i don't. think so i. i think it's tempting to think that way. personally i don't think anyone hardly. anyone thinks that for computers today. no one says oh this thing. needs a right i shouldn't be able to. turn it off or you know if i throw it in. the trash can. you know and hit it with a sledgehammer. i might perform a criminal act no no one. thinks that. um and now we think about intelligent.

machines. which is where you're going um. and and all of a sudden like well now we. can't do that. i think the basic problem we have here. is that people think intelligent. machines will be like us. they're going to have the same emotions. as we do the same feelings as we do. what if i can build an intelligent. machine that have absolutely could care. less about whether it was on or off or. destroyed or not. it just doesn't care it's just like a. map it's just a modeling system. it has no desires to live nothing. is it possible to create a system that.

can model the world deeply and not care. about whether it lives or dies. absolutely no question about it. to me that's not 100 percent obvious. it's obvious to me so okay. we can debate it if you want yeah where. does your where does your desire to live. come from. it's an old evolutionary design. i mean we could argue does it really. matter if we live or not objectively no. right we're all going to die eventually. um.

but evolution makes us want to live. evolution makes us want to fight to live. evolutionists want to care and love one. another and to care for our children. and our relatives and our family and and. so on. and those are all good things but they. come about not because we're smart. because we're animals that grew up you. know the the hummingbird in my backyard. cares about its offspring. you know the every living thing in some. sense cares about you know. surviving but when we talk about. creating intelligent machines we're not.

creating. life we're not creating evolving. creatures we're not creating. living things we're just creating a. machine that can learn. really sophisticated stuff and that. machine it may even be able to talk to. us. but it doesn't it's not going to have a. desire to live. unless somehow we put it into that. system well. there's learning right the the thing is. but you don't learn to like want to live. that's built into you it's wow people. like ernest becker argue. so okay uh there's the fact the.

finiteness of life. the way we think about it is something. we learn. uh perhaps so okay yeah and some people. decide they don't want to live. and some people decide you know you can. but the desire to live is built in dna. right. but i think what i'm trying to get to is. uh in order to accomplish goals. it's useful to have the urgency of. mortality is what the stoics talked. about. is meditating in your mortality yeah it. might be a very useful. thing to do to die and have the urgency.

of death. and to realize that to uh conceive. yourself as an entity. that operates in this world that. eventually will no longer be a part of. this world and actually conceive of. yourself as a conscious entity might be. very useful. for you to be a system that makes sense. of the world. otherwise you might get lazy well okay. we're going to build these machines. right and so we're talking about. building. ais what but we're we're building the uh.

uh the the the equivalent of the. cortical columns the uh the neocortex. the neocortex and the the question is. where do they. arrive at because we're not hard-coding. everything in. where uh well well in terms of if you. build the neocortex equivalent. it will not have any of these desires or. emotional states now you can. argue that that neocortex won't be. useful. unless i give it some agency unless i. give it some desire unless i give it. some motivation otherwise you'll be as.

lazy and do nothing. right you could argue that um but on its. own. it's not going to do those things it's. just not it's not going to sit there and. say. i understand the world therefore i care. to live no it's not going to do that. it's just going to say i understand the. world. why is that obvious to you why why why. don't do you think it's. okay let me ask it this way do you think. it's possible. it will at least assign to itself. agency and perceive. itself in this world as being a.

conscious entity. as a useful way to operate in the world. and and to make sense of the world. i think intelligent machine could be. conscious but that doesn't not. again imply any of these um these. desires and goals and. and that you're worried about it we can. i have a. we can talk about what it means for the. machine to be conscious and by the way. not worry about but get excited about. it's not necessarily that we should. worry about it so i think there's a. legitimate. problem or not problem a question asked. if you build this modeling system what's.

it gonna model. yes right what's it what's its desire. what is it what's its goal what are we. applying it to. right so that's an interesting question. um. one thing if it and it depends on the. application. it's not something that inherent to the. modeling system it's something we apply. to the modeling system in a particular. way. so if i wanted to make a really smart. car it would have to know. about driving in cars and what's. important in driving in cars. it's not going to figure that out on its. own it's not going to sit there and say.

you know i've understood the world. and i've decided you know no no no we. have to tell it we're going to have to. say like. so i imagine i make this car really. smart it learns about your driving. habits it learns about the world. and it's just you know is it one day. going to wake up and say. you know what i'm tired of driving and. doing what you want. i think i have better ideas about how to. spend my time well. okay no it's not going to do that part. of me is playing a little bit of devil's. advocate but part of me. is also trying to think through this. because.

i've studied cars quite a bit and i. studied pedestrians and cyclists quite a. bit. and there's part of me that thinks. that there needs to be. more intelligence than we realize in. order to drive successfully. that game theory of human interaction. seems to require some deep understanding. of. of human nature that. okay when a pedestrian crosses the.

street there's some sense. they they look at a car usually and then. they look away. there's some sense in which they say i. believe that you're not going to murder. me. you don't have the guts to murder me. this is the little dance of pedestrian. car interaction. yeah is saying i'm going to look away. and i'm going to put my. life in your hands because i think. you're human you're not gonna kill me. and then the car in order to. successfully operate in. like manhattan streets has to say no no.

no. i am going to kill you like a little bit. there's a little bit of this weird. inkling of. mutual murder yeah yeah and that's a. dance and then somehow. successfully operate through do you. think you were born of that did you. learn that social interaction. uh i think it might have a lot of the. same elements that you're talking about. which is we're leveraging. things we were born with and applying. them in the context. that uh all right i would i would answer. that i would have said that that kind of.

interaction. is learned because you know people in. different cultures have different. interactions like that. if you cross the street in different. cities and different around the world. they have different ways of interacting. i would say that's learned and i would. say an intelligent system could learn. that too. but that does not lead and the. intelligent system can understand. humans it could understand that you know. just like i can study an animal and. learn something about that animal. you know i could study apes and learn. something about their culture and so on. i'd have to be an ape to know that um i.

may not be. completely but i can understand. something so intel's machine can model. that that's this part of the world is. this part of the interactions. the question we're trying to get at will. the intelligent machine. have its own personal agency that's. beyond you know what we assign to it or. it's its own personal. you know goals or will it evolve and. create these things. my confidence comes from understanding. the mechanisms. i'm talking about creating this is not. hand wave stuff. it's down in the details we i'm going to. build it and i know what it's going to.

look like and i know it's. going to behave i know what the kind of. things it could do and the kind of. things it can't do. just like when i build a computer i know. it's not going to on its own decide to. put another. register inside of it it can't do that. no way. no matter what your software does it. can't add a register to the computer. um so in this way when we build ai. systems. we have to make choices about the the. the under the how we embed them so i. talked about this in the book i said you. know. it's a brain intelligence system is not. just the neocortex equivalent.

you have to have that but it has to have. some kind of embodiment physical a. virtual. it has to have some sort of goals it has. to have some sort of uh ideas about. dangers about things it shouldn't do. like you know like we. we build in safeguards into systems uh. we have them in our bodies we have put. them into cars. right you know my car follows my. directions until the day it sees. i'm about to hit something and it. ignores my directions and puts the. brakes on. so we can build those things in so. that's a very interesting problem.

um how to build those in i think my. my my differing opinion about the risks. of ai for most people. is that people assume that somehow those. things will just appear automatically. it'll evolve and intelligence itself. begets that stuff or requires it but. it's not intelligence. of the neural cortex equipment doesn't. require this the new cartridge equipment. just says. i'm a learning system tell me what you. want me to learn and i'll tell you. ask me questions i'll tell you the. answers but. in that again it's again like a map it.

doesn't a map has no. intent about things but you can use it. um. to solve problems okay so the building. engineering the neural cortex in itself. is just creating. an intelligent prediction system. modeling system. sorry modeling system yeah uh you can. use it to then make predictions and then. but you can also put it inside a thing. that's actually acting in this world. you have to put it inside something it's. again think of the map analogy right map.

on its own doesn't do anything right. it's just inert it's just it can learn. but it's just so we have to embed it. somehow in something to do something. so so what's your intuition here you had. a conversation. with sam harris recently that was uh. sort of um. you've had a bit of a disagreement and. you're sticking on this point. you know elon musk stuart russell. kind of have us worry existential. threats of ai what's your intuition why.

if we engineer an increasingly. intelligent neural cortex type of system. in the computer why that shouldn't be a. thing that we. it was interesting we used the word. intuition and sam harris used the word. intuition too. and and when he used that intuition that. word i immediately stopped and said oh. that's the. problem he's using intuition i'm not. speaking about my intuition. yes i'm speaking about something i. understand something i'm going to build. something i am building. something i understand completely or at. least well enough to know what it's all.

i'm guessing i know what this thing's. going to do and. i think most people who are worried they. have trouble. separating out they don't have they. don't have the um knowledge or the. understanding. about like what is intelligence how is. it manifest in the brain how is it. separate from these other functions in. the brain. and so they imagine it's going to be. human-like or animal-like it's going to. have it's going to have the same sort of. drives and emotions we have but there's. no reason for that. that's just because there's there's. unknown if you're if the unknown is like.

oh my god you know i don't know what. this is going to do we have to be. careful it could be like us but really. smarter. i'm saying no it won't be like us it'll. be really smart but it won't be like us. at all. and um and but i i'm coming from that. not because i just. guessing i'm not intuitive using. intuition i'm basically like okay i. understand this thing works this is what. it does. let me explain it to you okay but uh to. push back. so i also disagree with the the. intuitions that sam has. but but i also disagree with what you. just said.

which you know what's a good uh analogy. so if you look at the. twitter algorithm in in the early days. just recommender systems you can. understand how recommender systems work. what you can't understand in the early. days is when you apply that recommender. system at scale. to thousands and millions of people how. that can change societies. yeah so the question is yes. you're just saying this is how an. engineer in neurocortex. works but the ques like when you have a.

very useful. uh tic toc type of service that goes. viral when your neural cortex. goes viral and then millions of people. start using it cannot destroy the world. no. uh well first of all this is back one. thing i want to say is that uh. ai is a dangerous technology i don't i'm. not denying that all technology is. dangerous well an ai. maybe particularly so yeah okay so um. am i worried about it yeah i'm totally. worried about it the the thing where. the narrow component we're talking about. now is the existential risk of ai.

right so i want to make that distinction. because i think ai can be applied poorly. it can be applied in ways that you know. people are going to. understand the consequences of it. these are all potentially very bad. things. but they're not the ai system creating. this existential risk on its own. and that's the only place i disagree. with other people right so. so i i think the existential risk thing. is um. humans are really damn good at surviving. so to kill off the human race.

it'd be very very difficult you can even. yes but you can even i'll go further i. don't think ai systems are ever going to. try to i don't think ar systems are ever. going to like say. i'm going to ignore you i'm going to do. what i think is best. i don't think that's going to happen at. least not in the way. i'm talking about it so you the twitter. recommendation algorithm this. interesting example. let's let's use computer as an analogy. again right. i build a computer it's a universal.

computing machine i can't predict what. people are going to use it for they can. build all kinds of things they can. they can even create computer viruses. it's you know all kinds of stuff. so there's some unknown about its. utility about where it's going to go. but on the other hand i pointed out that. once i build a computer. it's not going to fundamentally change. how it computes. it's like i use the example of a. register which is a part internal part. of a computer. um you know i say it can't just say. because computers don't evolve. they don't replicate they don't evolve. they don't you know the physical.

manifestation of the computer itself. is not gonna there's certain things it. can't do right so we can. break into things like things that are. possible to happen we can't predict. and things are just impossible to happen. unless we go out of our way to make them. happen they're not going to happen. unless somebody makes them happen yeah. so there's there's a bunch of things to. say one. is the physical aspect which you're. absolutely right. we have to build a thing for it to. operate in the physical world. and you can just stop building them uh. you know the moment they're not doing.

the thing you want them to do or just. change the design. or change the design the question is i. mean there's it's possible in the. physical world. this is probably longer term is you. automate the building. it makes it makes a lot of sense to. automate the building there's a lot of. factories that are doing more and more. and more. automation to go from raw resources to. the final product. it's possible to imagine that it's. obviously much more efficient to keep. to create a factory that's creating. robots that do something you know do.

something extremely useful for society. it could be uh. personal assistance it could be uh it. could be it could be your toaster but a. toaster that's much. has deeper knowledge of your culinary. preferences yeah. and and that could uh well i think now. you've hit on the right thing the real. thing we need to be worried about lex. is self-replication right that is the. thing that we're in the physical world. yeah or even the virtual world. self-replication. because self-replication is dangerous. it's probably more likely to be killed. by a virus you know or a human.

engineered virus. anybody can create you know this the. technology is getting so almost anybody. but not anybody but. a lot of people could create a. human-engineered virus that could wipe. out humanity. that is really dangerous no intelligence. required just self-replication. so um so we need to be careful about. that. so when i think about you know ai. i'm not thinking about robots building. robots don't do that don't build a. you know just well that's because you're. interested in creating intelligence.

it seems like self-replication. is a good way to make a lot of money. well fine but. so is you know maybe editing viruses is. a good way to i don't know. the point is if as a society when we. want to look at existential risks. the existential risks we face that that. we can control. almost all evolve around. self-replication yes. the question is i don't see a good uh. way to make a lot of money by. engineering viruses and deploying them.

in the world. there could be there will be. applications that are useful but let's. separate out let's. separate out i mean you don't need to. you only need some you know terrorists. who wants to do it because it doesn't. take a lot of money to make viruses. um let's just separate out what's risky. and what's not risky. i'm arguing that the intelligence side. of this equation is not risky it's not. risky it's not risky at all. it's the self-replication side of the. equation is risky and. i'm not dismissing that i'm scared as. hell it's like the paperclip. maximizer thing yeah those are often.

like. talked about in the same conversation um. i think you're right like creating. ultra-intelligent super-intelligent. systems is not necessarily coupled with. the self-replicating arbitrarily. self-replicating systems. yeah and you don't get evolution unless. you're self-replicating yeah. and so i think that's the gist of this. argument that people have trouble. separating those two out they just think. oh yeah intelligence is like us. and look how look at the damage we've. done to this planet like how we've.

you know destroyed all these other. species yeah well we replicate we're. eight billion of us are seven million of. us now so um. i think the idea is that the the more. intelligent. we're able to build systems the more. tempting. it becomes from a capitalist perspective. of creating products the more tempting. it becomes to create. self uh reproduction systems all right. so let's say that's true so does that. mean we don't build intelligent systems. no that means we regulate. we we understand the risks uh we.

regulate them. yeah uh you know look there's a lot of. things we could do a society which have. some sort of financial benefit to. someone. which could do a lot of harm and we have. to learn how to regulate those things. we have to learn how to deal with those. things i will argue this i would say the. opposite. i would say having intelligent machines. at our disposal. will actually help us in the end more. because it'll help us understand these. risks better and help us mitigate these. risk riders there might be ways of. saying oh. well how do we solve climate change. problems you know how do we do this or. how do we do that.

that just like computers are dangerous. in the hands of the wrong people but. they've been so great for so many other. things we live with those dangers. and i think we have to do the same with. intelligent machines we just but we have. to be constantly vigilant about. this idea of a bad actors doing bad. things with them and b. um don't ever ever create a. self-replicating system. um and by the way i don't even know if. you could create a self-replicating. system. that uses a factory that's really. dangerous you know.

nature's way of self-replicating is so. amazing. um you know it doesn't require anything. it just me know. the thing and resources and it goes. right yeah. if i said to you you know what we have. to build uh. our goal is to build a factory that can. make that builds new factories. and it has to end to end supply chain. it has to mine the resources get the. energy. i mean that's really hard it's you know. no one's doing that in the next. you know 100 years i've been extremely.

impressed. by the efforts of elon musk and tesla to. try to do exactly that. not not from raw resource well he. actually i think. states the goal is to go from raw. resource to the. uh the final car in one factory yeah. that's that's the main goal of course. it's not currently possible. but they're taking huge leaps well he's. not the only one to do that. this has been a goal for many uh. industries for a long long time. um it's difficult to do well a lot of. people what they do is instead they have.

like. a million suppliers and then they like. there's. everybody's men they all co-locate them. and. they tie the systems together it's it's. a fundamentally distributed even. i think that's that also is not getting. at the issue i was just talking about um. which is self-replication it's. um i mean self-replication means there's. no entity involved other than the entity. that's replicating. um right and so if there's humans in. this in the loop.

that's not really self-replicating right. it's unless. somehow we're duped but. it's also i i don't necessarily. agree with you because you've kind of. mentioned that ai will not say no to us. i i just think they will yeah yeah so. like. uh i think it's a useful feature to. build in i'm just trying to like. uh put myself in the mind of engineers. to sometimes say no. you know if you you yeah well i gave an.

example earlier right i get an example. of my car. yeah right my car turns the wheel and. and. applies the accelerator and the brake as. i say until. it decides there's something dangerous. yes and then it doesn't do that. yeah now that was something. it didn't decide to do is something we. programmed into the car. uh and so good it was a good idea right. the question again isn't like if we. create an intelligent system will it. ever.

ignore our commands of course it will on. sometimes is it going to do it because. it came. up came up with its own goals that serve. its purposes. and it doesn't care about our purposes. no i don't think that's going to happen. okay so let me ask you about these uh. super intelligent cortical systems that. we engineer. and us humans. do you think uh with these entities. operating out there in the world. what does the future most promising. future look like is it. us merging with them.

or is it us like how do we keep. us humans around when you have. increasingly intelligent beings is it. uh one of the dreams is to upload our. minds in the digital space. so can we just give our minds. to these uh systems yeah so they can. operate on them. is there some kind of more interesting. merger or is there more more. in the third part of my book i talked. about all these scenarios and let me. just walk through them. sure um the uploading the mind one yes.

extremely really difficult to do like. like we have no idea how to do this even. remotely right now um so it would be a. very long way away. but i make the argument you wouldn't. like the result. um and you wouldn't be pleased with the. result it's really not what you think. it's going to be. um imagine i could upload your brain. into into a computer right now and now. the computer's sitting there going hey. i'm over here. great get rid of that old bio person i. don't need them you're still sitting. here. yeah what are you gonna do no no that's. not me i'm here right yeah are you gonna.

feel satisfied. that then you but people imagine look. i'm on my deathbed and. i'm about to you know expire and i push. the button and now i'm uploaded but. think about it a little differently and. and so i don't think it's going to be a. thing because people. by the time we're able to do this if. ever. because you have to replicate the entire. body not just the brain it's. it's really it's i walk through the. issues it's really substantial. um do you have a sense of what makes us. us. is there is there a shortcut to what can.

only save a certain part. that makes us truly ours no but i think. that machine would feel like it's you. too. right right if you people just like i. have a child i have a child right i have. two daughters. they're independent people i created. them well partly yeah. and um uh i don't. just because they're somewhat like me i. don't feel i'm them and they don't feel. like i'm me so if you split it apart you. have two people so. we can come back to what what makes what. consciousness we want we can talk about. that. but we don't have a remote consciousness.

i'm not sitting there going oh i'm. conscious of that you know i mean that. system over there so let's say let's. let's stay on our topic okay so. one was uploading a brand yep ain't. gonna happen in a hundred years. maybe a thousand but i don't think. people are gonna wanna do it. the merging your mind with uh. you know the neural link thing right. like again. really really difficult it's it's one. thing to make progress to control a. prosthetic arm it's another to have like. a billion. or several billion you know things and.

understanding what those signals mean. like it's the one thing they're like. okay i can learn to think some patterns. to make something happen. it's quite another thing to have a. system a computer which actually knows. exactly which cells it's talking to and. how it's talking to them and interacting. in a way like that. very very difficult we're not getting. anywhere closer to that. um interesting can i uh can i ask a. question here. what so for me what makes that merger. very difficult. practically in the next 10 20 50 years.

is like literally the biology side of it. which is like. it's just hard to do that kind of. surgery in a safe way. but your intuition is even the machine. learning. part of it where the machine has to. learn what the heck it's talking to. that's even hard. i think it's even harder and it's not. it's it's easy to do when you're talking. about hundreds of signals. it's it's a totally different thing to. say you're talking about billions of. signals. so you don't think it's the raw it's a. machine learning problem you don't think. it could be learned.

well i'm just saying no i think you'd. have to have detailed knowledge. you'd have to know exactly what the. types of neurons you're connecting to. i mean in the brain there's these. they're neurons that do all different. types of things it's not like a neural. network it's a very complex organism. system up here we talked about the grid. cells or the place cells you know you. have to know what kind of cells you're. talking to and what they're doing and. how their timing works and all. all this stuff which you can't today. there's no way of doing that right. but i think it's i think it's a i think. the problem you're right that the. biological. aspect of like who wants to have surgery. and have this stuff inserted in your.

brain. that's a problem but this is when we. solve that problem. i think the the information coding. aspect is much worse. i think that's much more it's not like. what they're doing today today it's. simple machine learning stuff. because you're doing simple things but. if you want to merge your brain like i'm. thinking. on the internet i'm merge my brain with. the machine and we're both doing. i that's a totally different issue. that's interesting i i tend to think if. okay if you have a super clean signal. from a bunch of neurons at the start you.

don't know what those neurons are. i think that's much easier than. the getting of the clean signal i think. if you. think about today's machine learning. that's what you would conclude. right i'm thinking about what's going on. in the brain and i don't reach that. conclusion so we'll have to see. sure but i don't think even even then i. think there's kind of a sad. future like you know do i do i have to. like. plug my brain into a computer i'm still. a biological organism i assume i'm still. going to die.

so what what have i achieved right you. know what have i achieved to do some. sort of oh. i i disagree we don't know what those. are but it seems like there could be a. lot of different applications. it's like virtual reality is to expand. your brain's capability to uh. to to like to read wikipedia yeah but. but fine but but you're still a. biological organization yes yes you know. you're still. you're still mortal you're still all. right so what are you accomplishing. you're making your life in this. short period of time better right just. like uh. having the internet made our life better.

yeah yeah okay so i. i think that's of of if i think about. all the possible gains we can have here. that's a marginal one. it's an individual hey i'm better you. know i'm smarter. um but you know fine i'm not against it. i just don't think it's earth-changing. i but so this is the true of the. internet. when each of us individuals are smarter. we get a chance to then share our. smartness. we get smarter and smarter together as. like as a collective this is kind of. like this ant colony. but why don't i just create an.

intelligent machine that doesn't have. any of this biological nonsense. this is all the same it's it's. everything except. don't burden it with my brain yeah right. it. has a brain it is smart it's like my. child but it's much much smarter than me. so i have a choice between doing some. implant doing some hybrid weird you know. biological thing that bleeding and all. these problems. and limited by my brain or creating a. system which is super smart that i can. talk to. um that helps me understand the world. they can read the inter you know read. wikipedia and talk to me.

i i guess my uh the open questions there. are. what does the manifestation of super. intelligence look like so. like what are we going to you talked. about why do i want to merge with ai. like what. what's the actual marginal benefit here. if i. if we have a super intelligent system. yeah how will it make our life better. so let's let's that's a great question. but let's break it down to little pieces. all right. on the one hand it can make our life.

better in lots of simple ways. you mentioned like a care robot or. something that helps me do things it. cooks i don't know what it does right. little things like that we have soup. better smarter cars we can have. you know better agents and aids helping. us in our work environment and things. like that to me that's like the easy. stuff the. simple stuff in the beginning um. and so in the same way that computers. made our lives better in. ways many many ways i will have those. kind of things. to me the really exciting thing about ai.

is. sort of its transcendent transcendent. quality in terms of humanity. we're still biological organisms we're. still stuck here on earth. it's going to be hard for us to live. anywhere else i don't think. you and i are going to want to live on. mars anytime soon. and um and we're flawed. you know we may end up destroying. ourselves it's. totally possible uh we if not completely. we could destroy our civilizations. you know it's let's face the fact we. have issues here.

but we can create intelligent machines. that can help us in various ways for. example one example i gave. another sounds a little sci-fi but i. believe this if we really wanted to live. on mars. we'd have to have intelligent systems. that go there and build the habitat for. us. not humans humans are never going to do. this it's just too hard. um but could we have a thousand or ten. thousand you know. engineer workers up there doing this. stuff building things terraforming mars. sure. maybe we can move to mars but then if we. want to if we want to go around the. universe. should i send my children around the.

universe or should i send some. intelligent machine which is like a. child. that represents me and understands our. needs here on earth. that could travel through space so it's. sort of. it in some sense intelligence allows us. to transcend our. the limitations of our biology uh. with and and don't think of it as a. negative thing it's in some sense. my children transcend my the my biology. too. because they they live beyond me yeah um. and we impart. they represent me and they also have. their own knowledge and i can impart.

knowledge to them so intelligent. machines will be like that too. but not limited like us but the question. is um. there's so many ways that transcendence. can happen. and the merger with ai and humans is one. of those ways so. you said intelligent basically beings or. systems. propagating throughout the universe. representing us humans. they represent us humans in the sense. they represent our knowledge and our. history. not us individually right.

right but i mean the question is is it. just the database. with uh with the really damn good uh. model. no they're conscious conscious just like. us okay. but just different they're different. just like my children are different. they're like me but they're different um. these are more different. i guess maybe i've already i kind of. i take a very broad view of our life. here on. on earth i say you know why are we. living here are we just living because. we live.

is are we surviving because we can. survive are we fighting just because. we want to just keep going what's the. point of it yeah right. so to me the point if i ask myself. what's the point of life is. what transcends that ephemeral sort of. biological experience. is to me this is my answer is. the acquisition of knowledge to. understand more about the universe. and to explore and that's partly to. learn more. right i don't view it as.

a terrible thing if the ultimate. outcome of humanity is we create systems. that are intelligent. that are our offspring but are not like. us at all and we stay. we stay here and live on earth as long. as we can which won't be forever. but as long as we can and. but that would be a great thing to do. it's not a it's not like a. negative thing well would uh. you'd be okay then if uh the human.

species vanishes but our knowledge is. preserved. and keeps being expanded by intelligent. systems. i want our knowledge to be preserved and. expanded. yeah am i okay with humans dying no i. don't want that to happen. but if if if it does happen what if we. we were sitting here and this is uh. we're the last two people on earth we're. saying lex we blew it. it's all over right yeah wouldn't i feel. better if i. knew that our knowledge was preserved. and that we had agents.

represent that knew about that that were. trans you know they were that left earth. i would want that it's better than not. having that you know i make the analogy. of like you know the dinosaurs the poor. dinosaurs they live for you know tens of. millions of years. they raised their kids they you know. they they fought to survive they were. hungry they. they they did everything we do and then. they're all gone. yeah like you know and and if we didn't. discover their bones. nobody would ever know that they ever. existed right. do we want to be like that i don't want. to be like that but there's a sad aspect.

to it and. it's kind of it's jarring to think about. that. it's possible that a human-like. intelligent civilization has previously. existed on earth. oh yeah the reason i say this is like it. is. jarring to think that we would not if. they weren't extinct we wouldn't be able. to find evidence of them. after a sufficient amount after a. sufficient amount of time. of course there's like look basically. humans like if we destroy ourselves now. human civilization destroy ourselves now. after a sufficient amount of time we.

would not be. we'd find the evidence of the dinosaurs. we would not find evidence. of those humans yeah that's kind of an. odd thing to think about although. i'm not sure if we have enough knowledge. about. species going back for billions of years. that we could we could. we might be able to eliminate that. possibility but it's an interesting. question. of course this is a similar question to. you know there were lots of intelligent. species throughout the without our. galaxy. that have all disappeared yeah that's. super sad that. um there exactly that.

there may have been much more. intelligent alien civilizations in our. galaxy. that are no longer there yeah um you. actually talked about this. um that humans might destroy ourselves. yeah. and how we might preserve our knowledge. yeah and advertise that knowledge. to other advertisers a funny word to use. from a pr person there's no financial. gain in this.

uh you know like make it like from a. tourism perspective make it interesting. can you describe how well there's a. couple things i broke it down to the two. parts. actually three parts one is um. you know there's a lot of things we know. that what if what if we were to what if. we ended. up our civilization collapsed yeah i'm. not talking tomorrow yeah we could be a. thousand years from now alex you know we. don't really know but. but historically would be likely at some. point time flies when you're having fun. yeah that's a good way to put it um.

you know could we and then then. intelligent life evolved again on this. planet. wouldn't they want to know a lot about. us and what we knew when they wouldn't. be able to ask us. questions so one very simple thing i. said how would we archive what we know. that was a very simple idea i said you. know what that wouldn't be that hard but. a few satellites. you know going around this the sun and. we upload wikipedia every day and. um that kind of thing uh so you know we. can end up killing ourselves well it's. up there and the next intelligence piece. will find it and learn something that. would be they would like that they would. appreciate that.

so that's one thing the next thing i. said well what if you know. how to outside of our solar system we. have the seti program we're looking for. these intelligent signals from everybody. and if you do a little bit of math which. i did in the book uh and you say well. what if intelligent. species only live for 10 000 years. before you know technologically. intelligent species like ones are really. able to do this. we're just starting to be able to do um. well the chances are we wouldn't be able. to see any of them because they would. have all been disappeared by. now um they would they've lived for 10.

000 years and now they're gone. and so we're not going to find these. signals being sent from these people. because. i said what kind of signal could you. create that would last a million years. or a billion years. that someone would say damn it someone. smart lived there. we know that that would be a. life-changing event for us to figure. that out well what we're looking for. today in the study program isn't that. we're looking for very. coded signals in some sense and so i. asked myself what would be a different. type of signal one could create. i've always thought about this. throughout my life and in the book i. gave one.

one possible suggestion which was um. we now detect planets going around other. other suns. other stars excuse me and we do that by. seeing this the. the slight dimming of the light as the. planets move in front of them that's how. we detect uh planets elsewhere in our. galaxy. um what if we created something like. that that just rotated around our. around the sun and it blocked out a. little bit of light in a particular. pattern that someone said hey. that's not a planet that is a sign that.

someone was once there you can say. what if it's beating up pi you know. three point whatever. um so the idea of a distance you can. from a distance. broadly broadcast takes no continue. activation on our part this is the key. right. no one has to be seen here running a. computer and supplying it with power. it just goes on so we go it's continues. and and i argue that part of the study. program should be looking for signals. like that. and to look for signals like that you. ought to figure out what the how would.

we create a signal. like what would we create that would be. like that that would persist for. millions of years. that would be broadcast broadly you. could see from a distance that was. unequivocal it came from an. uh by an intelligent species and so i. gave that one example. um because they don't know what i know. of actually and then and then finally. right if if our. ultimately our solar system will die at. some point in time. you know how do we go beyond that and i. think. it's possible if at all possible we'll.

have to create intelligent machines that. travel throughout this throughout the. the solar system or throughout the. galaxy and i don't think that's going to. be humans. i don't think it's going to be. biological organisms so these are just. things to think about you know like. what's the. you know like i don't i don't want to be. like the dinosaurs i don't want to just. live and okay that was it we're done you. know. well there is a kind of presumption that. we're going to live forever which. uh i i think it is a bit sad. to imagine that the message we send as. as you talk about. is that we were once here.

instead of we are here well it could be. we are still here. uh but it's more of a it's more of an. insurance policy in case we're not here. you know well i don't know but there's. something. i think about we as humans don't often. think about this but. it's like like whenever i um. [Music]. record a video i've done this a couple. times in my life i've recorded a video. for my future self just for personal. just for fun and it's always just.

fascinating. to think about that preserving yourself. for future civilizations for me it was. preserving myself for future. me but that's a little that's a little. fun example. of archival these podcasts are are. preserving you and i. in a way yeah for future uh hopefully. well after we're gone but you don't. often we're sitting here talking about. this.

you are not thinking about the fact that. you and i are going to die. and there will be like 10 years after. somebody watching this. and we're still alive you know in some. sense i do. i'm here because i want to talk about. ideas right and these ideas. transcend me and they transcend this. time. in on our planet um. we're talking here about ideas that. could be around a thousand years from. now or a million years from now. i when i wrote my book i had an audience.

of mine. and one of the clearest audiences was. aliens. no were people reading this 100 years. from now yes. i said to myself how do i make this book. relevant to summer reading this 100. years from now what would they want to. know that we were thinking back then. what would make it. like that was an interesting it's still. an interesting book. i'm not sure i can achieve that but that. was how i thought about it because these. ideas like especially in the third part. of the book the ones we're just talking. about. you know these crazy it sounds like. crazy ideas about you know storing our.

knowledge and. and you know merging our brains of. computers and sending. you know our machine down to space is. not going to happen in my lifetime. um and they may not have been happening. the next 100 years it may not happen for. a thousand years who knows. uh but we have the unique opportunity. right now. we you me and other people like this um. to. sort of at least propose the agenda. um that might impact the future like. that that's a fascinating way to think. uh both like writing or creating.

try to make try to create ideas. try to create things that uh hold up in. time yeah. you know understanding how the brain. works we're gonna figure that at once. that's it it's gonna be figured out once. and after that. that's the answer and people will people. will study that. thousands of years now we still we still. you know venerate newton and einstein. and um and you know because. because ideas are exciting even well.

into the future. well the interesting thing is like big. ideas even if they're wrong. are still useful like. yeah especially if they're not. completely wrong like. right newton's laws are not wrong. they're just einsteins they're. better um well it's. so yeah i mean but we're talking with. newton and einstein we're talking about. physics i wonder. if we'll ever achieve that kind of. clarity but understanding um. like complex systems and the this.

particular manifestation of complex. systems which is the. human brain i'm totally optimistic we. can do that i mean. we're making progress at it i don't see. any reasons why we can't. completely i mean completely understand. in the sense um. you know we don't really completely. understand what all the molecules in. this water bottle are doing. but you know we have laws that sort of. capture it pretty good um. and uh so we'll have that kind of. understanding i mean it's not like. you're gonna have to know what every. neuron in your brain is doing um but. enough to uh.

first of all to build it and second of. all to do you know. do what physics does which is like have. uh concrete experiments where we can. validate we're we're this is happening. right now like. it's not this is not some future thing. um. you know i'm very optimistic about. because i know about art our work and. what we're doing. i have to prove it to people um but um. i i consider myself a rational person. and. um you know until fairly recently i.

wouldn't have said that but right now. i'm where i'm sitting right now i'm. saying you know we can this is going to. happen. there's no big obstacles to it um. we finally have a framework for. understanding what's going on in the. cortex and. um and that's liberating it's it's like. oh it's happening. so i i can't see why we wouldn't be able. to understand it i just can't. okay oh so i mean on that topic let me. ask you to play devil's advocate. is it possible for you to imagine. luck look a hundred years from now and.

looking at your book. uh in which ways might your ideas be. wrong oh i worry about this all the time. um. yeah it's still useful yeah yeah. i think there's you know um well. i can i can best relate it to like. things i'm worried about right now. so we talk about this voting idea right. it's happening there's no question. that's happening.

but it could be far more. [Music]. there's there's enough things i don't. know about it that it might be. working in ways differently i'm thinking. about the kind of what's voting who's. voting. you know where are representations i. talked about you have a thousand models. of a coffee cup. like that that could turn out to be. wrong. because it may be maybe there are a. thousand. models that are sub models but not. really a single model the coffee cup. um i mean there's things these are all. sort of on the edges.

things that i i present as like oh it's. so simple and clean well it's not that. it's always going to be more complex. and um and there's. parts of the theory which i don't. understand the complexity well. so i think i think the idea is brain is. a distributed modeling system is. not controversial at all right that's. not that's well understood by many. people. the question then is are each quarter of. a column an independent modeling system. right um i could be wrong about that. um i don't think so but i worry about it.

my intuition not even thinking why you. could be wrong. is the same intuition i have about any. sort of physicist. like strength theory that we as humans. desire for a clean explanation. and uh 100 years from now. uh intelligent systems might look back. at us and. laugh at how we try to get rid of the. whole mess. by having simple explanation when the. reality is it's.

it's way messier and in fact it's. impossible to understand you can only. build it it's like this idea of complex. systems and. cellular automata yeah you can only. launch the thing you cannot understand. it. yeah i think that you know the history. of science suggests that's. not likely to occur the history of. science suggests that. look as a theorist and we're theorists. you look for simple explanations right. fully knowing that whatever simple. explanation you're going to come up with. is not going to be completely correct i.

mean it can't be. i mean it's just it's just more. complexity but that's the role of. theorists play they they sort of they. give you a framework. on which you now can talk about a. problem and figure out. okay now we can start dig in more. details the best frameworks stick around. while the details change. you know again you know the classic. example is newton and einstein right you. know. um newton's theories are still used. they're still. valuable they're still practical they're.

not like wrong it's just they've been. refined yeah but that's. in physics it's not obvious by the way. it's not obvious for physics either. that the universe should be such that. it's amenable to these. simple but so far it appears to be as. far as we can tell. um yeah i mean but as far as we could. tell. and but it's also an open question. whether the brain is amenable to such. clean theories that's the uh not the. brain but intelligence. well i i i don't know i would take. intelligence out of it just say you know.

um well okay. um the evidence we have suggests that. the human brain. is a at the one time extremely messy and. complex but there's some parts that are. very regular and structured. that's why we started the neocortex it's. extremely regular in its structure. yeah and unbelievably so and then i. mentioned earlier the other thing is. it's it's universal abilities it is so. flexible to learn so many things we. don't.

we haven't figured out what it can't. learn yet we don't know but we haven't. figured out yet but to learn things that. it never was evolved to learn. so those give us hope um that's why i. went into this field because i said you. know. this regular structure it's doing this. amazing number of things there's got to. be some underlying principles that are. that are common and other other. scientists have come up with the same. conclusions. um and so it's promising it's promising. and um and that's and whether the. theories. play out exactly this way or not that is.

the role that theorists play. and so far it's worked out well even. though you know maybe. you know we don't understand all the. laws of physics but so far it's been. pretty damn useful the ones we have. are our theories are pretty bit useful. you mentioned. that uh we should not necessarily be at. least to the degree that we are. worried about the existential risks of. artificial intelligence. relative to uh human. risks from human nature being.

existential risk. what aspect of human nature worries you. the most in terms of the survival of the. human species. i mean i'm disappointed in humanity as. humans i mean all of us i'm one so i'm. at disappointed myself too. it's kind of a sad state there's two. things that disappoint me. one is how it's difficult for us to. separate our rational. component of ourselves from our.

evolutionary heritage which is. you know not always pretty you know. rape is a is an evolutionary good. strategy. for reproduction murder can be at times. too. you know making other people miserable. at times. is a good strategy for reproduction it's. just and it's just and. and so now that we know that and yet we. have this sort of you know we and i can. have this very rational discussion. talking about you know. intelligence and brains and life and so. on so. it seems like it's so hard it's just a.

big transition to get. humans all humans to to to make the. transition from be like let's. pay no attention to all that ugly stuff. over here. let's just focus on the instances what's. unique about humanity is our knowledge. and our. intellect but the fact that we're. striving is in itself amazing right. the fact that we're able to overcome. that part and. it seems like we are more and more. becoming successful and overcoming that. that is the optimistic view and i agree. with you.

yeah but i worry about it i'm not saying. i'm worrying about i think maybe that. was your question i still worry about it. yes. um you know we could be end tomorrow. because some terrorists could get. nuclear bombs and you know blow us all. up who knows. right the other thing i think i'm. disappointed is. uh and it's just i understand it it's i. guess you can't really be disappointed. it's just a fact. is that we're so prone to false beliefs. that we you know we have a model in our. head. the things we can interact with directly. physical objects. people that model is pretty good and we.

can test it all the time. right i touch something i look at it i. talk to you see my model's correct. but so much of what we know is stuff i. can't directly. interact with i can't i don't know. because someone told me about it yeah. and so. so we're prone inherently prone to. having. false beliefs because if i'm told. something how am i going to know it's. right or wrong right. and so then we have the scientific. process which says. we are inherently flawed so the only way. we can. get closer to the truth is by looking.

for. um contrary evidence yeah um like this. uh conspiracy theory this this theory. that scientists keep telling me about. that the earth is round. uh as far as i can tell when i look out. it looks pretty flat. yeah so yeah there is there's a tension. but it's also um. um i tend to believe that we haven't. figured out most of this thing. right most of nature around us is a.

mystery. and so it um but that doesn't work does. that worry you i mean it's like oh. that's that's like a pleasure more to. figure out. right yeah that's exciting but i'm. saying like there's going to be a lot of. quote unquote wrong ideas i mean i've. been. thinking a lot about engineering systems. like social networks and so on. and i've been worried about censorship. and thinking through all that kind of. stuff because there's a lot of wrong. ideas there's a lot of dangerous ideas. but then i also read a history.

read history and see when you censor. ideas that are wrong. now this could be a small-scale. censorship. like a young grad student who comes up. who like raises their hand and says some. crazy idea. yes a form of censorship could be i. shouldn't use the word censorship but. i think you may uh just like like. de-incentivize them from no no no this. is the way it's been. yeah yeah you're you're a foolish kid. don't do it yeah yeah yeah foolish uh. so in some sense uh. those wrong ideas most of the time end.

up being. wrong but sometimes i agree with you so. i don't like the word censorship. um at the very end of the book i i. ended up with a sort of a a plea or a. recommended course of action and. the best way i could i know how to deal. with this issue that you bring up. is if everybody understood as part of. your. upbringing in life something about how. your brain works. that it builds a model of the world uh.

how it works you know how basically. builds that model the world and that the. model. is not the real world it's just a model. and it's never going to reflect the. entire world and it can be wrong and. it's. easy to be wrong and here's all the ways. you can get the wrong model in your head. right it's not prescribed what's right. or wrong. just understand that process if we all. understood the process. then i got together and you say i. disagree with you jeff and i said lex i. disagree with you that. at least we understand that we're both.

trying to model something. we both have different information which. leads to our different models. and therefore i shouldn't hold it. against you and you shouldn't hold it. against me. and we can at least agree that well what. can we look for in in. its common ground to test our our. beliefs as opposed to so much. uh as our we raise our kids on dogma. which is this is a fact and this is a. fact and these people are bad. and and you know where ever if everyone. knew just. to to be skeptical of every belief.

and why and how their brains do that i. think we might have a better world. do you think the human mind is able to. comprehend. reality so you talk about sort of this. creating models that are better and. better how close do you think we get to. uh to reality there's so the wildest. ideas is like donald hoffman saying. we're very far away from reality. uh do you think we're getting close to. reality well i guess it depends on what. you define reality. uh we are getting we have a model of the.

world that's very useful. right for basic well for our survival. and. our the pleasure whatever right. um so that's useful um i mean it's. really useful oh we can build planes we. can build computers we can do these. things right. uh i don't think i i don't know the. answer to that question um. i think that's part of the question. we're trying to figure out right like. you know obviously if you end up with a. theory of everything that. really is a theory of everything and all.

of a sudden everything comes into play. and there's no room for something else. then you might feel like we have a good. model of the world yeah but we if we. have a theory of everything and somehow. first of all you'll never be able to. really conclusively say it's a theory of. everything but say somehow. we are very damn sure it's the theory of. everything we understand. what happened at the big bang and how. just the entirety of the physical. process i'm still not sure that. gives us an understanding of uh the next. many layers of the hierarchy yeah.

abstractions that form well also what if. string theory turns out to be true. and then you say well we have no reality. no modeling what's going on in those. other dimensions that are wrapped into. it on each other. you're right or or the multiverse you. know. i i honestly don't know how for us. for human interaction for ideas of. intelligence how it helps us to. understand that we're made up of. vibrating strings that are. like tend to the whatever times smaller. than us. yeah i don't you know you could probably.

build better weapons and better rockets. but you're not going to be able to. understand intelligence i guess i guess. maybe better computers no you won't be. able. i think it's just more purely knowledge. you might lead to a better. understanding of the of the beginning of. the universe. right it might lead to a better. understanding of uh. i don't know i guess i think the. acquisition of knowledge has always been. one where. you you pursue it for its own pleasure. um and you don't always know what is.

going to make a difference. yeah you're pleasantly surprised by the. the weird things you find. do you think uh for the for the. neocortex in general. do you think there's a lot of innovation. to be done on the machine. side you know you use the computer as a. metaphor. quite a bit is there a different types. of computer that would help us build i. mean what are the intelligences like the. manifestations of intelligent machines. yeah or is it oh no it's going to be. totally. crazy uh we have no idea how this is.

going to look out yet. but you can already see this today we of. course remodel these things on. traditional computers and now now gpus. are really popular with. with you know neural networks and so on. um. but there are companies coming up with. fundamentally new physical substrates. that are just really cool i don't know. if they're going to work or not. but i think there'll be decades of. innovation here. yeah totally do you think the final. thing will.

be messy like our biology is messy or do. you think. um it's it's the it's the old bird. versus airplane question. or do you think we could just uh. build airplanes yeah that. that fly way better than birds in the. same way we can build. uh uh electrical and. yeah yeah you know can i can i can i. refund the bird thing a bit because i. think it's interesting. people ability misunderstand this the. wright brothers.

um the problem they were trying to solve. was controlled flight. how to turn an airplane not how to. propel an airplane. they weren't worried about that. interesting yeah they already had at. that time. there was already wing shapes which they. had from studying birds there was. already gliders that carry people. the problem is if you put a rudder on. the back of a glider and you turn it the. plane falls out of the sky. so the problem was how do you control. flight and. they studied birds and they actually had. birds in captivity they watched birds in. wind tunnels. they observed in the wild and they.

discovered the secret was the birds. twist their wings. when they turn and so that's what they. did on the wright brothers flyer they. had these. sticks you would twist the wing and that. was that was their innovation. not their propeller and today airplanes. still twist their wings we don't twist. the entire wing we just just the tail. end of it. the the the flaps which is the same. thing so today's airplanes fly on the. same principles as. birds which is observed by so everyone. get that analogy wrong. but let's step back from that right once. you understand.

the principles of flight you can choose. how to implement them yeah. no one's going to use bones and feathers. and muscles. um but they do have wings and uh we. don't flap them we have propellers so. when we have the principles. of of computation that goes on to. modeling the world in the brain we. understand those principles very clearly. we have choices on how to implement them. and some of them will be biologically. like and some won't. and um but i do think there's going to. be a huge amount of innovation here. just think about the innovation we're in.

the computers they had to invent the the. transistor they invented the the silicon. ship. they had the invent you know then this. software i mean. zillions of things they had to do memory. systems um we're gonna do. it's gonna be similar well it's. interesting that the deep. learning um the effectiveness of deep. learning for. a specific task is driving a lot of. innovation in the hardware. which may have effects for uh. actually allowing us to discover. intelligent systems that operate very.

differently. or that's much bigger than deep learning. yeah interesting so. ultimately it's good to have an. application that's making our life. better now. because the the the capitalist process. if you can make money. yeah yeah that works i mean the other. way i mean. neil degrasse tyson writes about this is. the other way we fund science of course. is through military. so like yeah uh conquest so here here's. an interesting thing we're doing on this. regard. so we've decided we we used to have a. series these biological principles and. we can see how to build these.

intelligent machines. but we've decided to apply some of these. principles to today's machine learning. techniques. so uh one of the we didn't talk about. this principle one is uh. sparsity in the brain um most of the. neurons are active at any point in time. as far as and the connectivity is sparse. and that's different than deep learning. networks. um so we've already shown that we can. speed up existing deep learning networks. uh anywhere from 10 to a factor of 100. i mean literally 100 and make it more. robust. at the same time so this is commercially.

very very valuable. um and so you know if we can prove this. actually in the larger systems that are. commercially applied today. there's a big commercial desire to do. this well. sparsity is something that doesn't run. really well on existing hardware it. doesn't really run really well. on gpus um and on cpus. and so that would be a way of sort of. bringing more and. more brain principles into the existing.

system on a on a commercially valuable. basis. another thing we can think we can do is. we're going to use the dendrites. models of we i talked earlier about the. the prediction occurring inside of. neuron that that basic property can be. applied to. existing neural networks and allow them. to learn continuously which something. they don't do today. and so yeah well. we wouldn't model this spikes but the. idea that you have that. neuro today's neural networks have. something called the point neuron which.

is a very simple model of a neuron. and uh by adding dendrites to them with. just one more level of complexity. that's in biological systems you can. solve problems in continuous learning. um and rapid learning so we're trying to. take. we're trying to bring the existing field. and we'll see if we can do it we're. trying to bring the existing field of. machine learning. commercially along with us you brought. up this idea of keeping you know paying. for it. commercially along with us as we move. towards the ultimate goal of a true ai. system.

even small innovations on neural. networks are really really exciting. yeah because it seems like such a. trivial model. of the brain and applying different. insights. that just even like you said continuous. uh learning. or uh making it more asynchronous. or maybe making more dynamic or. like uh incentivizing making it fast. even just from robots. and making it somehow much better.

incentivizing sparsity uh somehow yeah. uh well if you can make things 100 times. faster then there's plenty of incentive. people people spending millions of. dollars you know just training some of. these networks now these. uh these transformer networks let me ask. you a big question. how for young people uh listening to. this today. in high school and college what advice. would you give them. in terms of uh which career path to take. and um maybe just about life in general.

well in my case um. i didn't start life with any kind of. goals i was when i was going to college. i was like oh what did i say well maybe. i'll do. electrical engineering stuff you know um. it wasn't like you know today you see. some of these young kids are so. motivated they're going to change the. world i was like. you know whatever and um. but then i did fall in love with. something besides my wife but i fell in. love with this like oh my god it would. be so cool to understand how the brain.

works. and then i i said to myself that's the. most important thing i could work on. i i can't imagine anything more. important because if we understand how. brains work you'd build telescope. machines and they could figure out. all the other big questions of the world. right so and then i said. i want to understand how i work so i. fell in love with this idea and i became. passionate about it. and this is you know a trope people say. this but it was. it's true because i was passionate about. it. i was able to put up almost so much crap. you know you know i was i was in that.

you know i was like. person said you can't do this i was i. was a graduate student at berkeley when. they said you can't study this problem. you know no one's gonna solve this or. you can't get funded for it you know. then i went to do you know mobile. computing and it was like people say you. can't do that you can't build a cell. phone. you know so but all along i kept being. motivated because i wanted to work on. this problem i said i want to understand. the brain works and if i got myself male. i got one lifetime i'm gonna figure it. out. do the best i can so by having that. because you know these it's really as.

you point out lex it's really hard to. do these things people it's just there's. so many downers along the way. so many ways obstacles are getting your. way yeah i'm sitting here happy all the. time but trust me. it's not always like that that's i guess. the the happiness that the. the passion is a prerequisite for. surviving the whole. yeah i think so i think that's right um. and. so i i don't want to sit to someone and. say you know you need to find a passion. and do it no maybe you don't. but if you do find something you're. passionate about. then then you can follow it as far as.

your passion will. let you put up with it do you remember. how you found it this is. how the spark happened. why specifically for me yeah like. because you said it's. such an interesting so like almost like. later in life by later i mean like not. in. when you were five yeah you you didn't. really know. and then all of a sudden you fell in. love with that yeah yeah there was there. was. there's two separate events that. compounded one another one. when i was probably a teenager might. have been 17 or 18. i made a list of the most interesting.

problems i could think of. first was why does the universe exist it. seems like not existing is more likely. yeah the second one was well given. exists why does it behave the way it. does. you know it's laws of physics y is equal. to m c squared not m c cubed you know. attention question i don't know. third one was like what's the origin of. life um. and the fourth one was what's. intelligence and i stopped there. i said well that's probably the most. interesting one and i put that aside. um as a teenager but then when i was 22.

and i was reading the um no it was. excuse me i was 70 it was 1979 excuse me. 1979. i was reading uh so i was at that time i. was 22. i was reading uh the september issue of. scientific american which is all about. the brain. and then the final essay was by francis. crick. who of dna fame and he had. taken his interest to studying the brain. now and he said you know. there's something wrong here he says we. got all this data.

oh this fact this is 1979 all these. facts about the brain tons and tons of. facts about the brain. do we need more facts or do we just need. to think about a way of rearranging the. facts we have maybe we're just not. thinking about the problem correctly. you know because he says this shouldn't. be. it shouldn't be like this you know so i. read that and i said wow. i said i don't have to become like an. experimental neuroscientist. i could just look at all those facts and.

try to. and become a theoretician and try to. figure it out. and i said that i felt like it was. something i would be good at. i said i wouldn't be a good. experimentalist i don't have the. patience for it. but i'm a good thinker and i love. puzzles. and this is like the biggest puzzle in. the world it's the biggest puzzle of all. time. and i got all the puzzle pieces in front. of me damn that was exciting. and there's something obviously you. can't convert it towards it just kind of. sparked. this passion and i have that a few times. in my life just something.

um yeah just just like you uh. it grabs you yeah i thought it was. something that was both important. that i could make a contribution to yeah. and so all of a sudden it felt like. oh it gave me purpose in life yeah you. know i honestly don't think it has to be. as big as one of those four questions. no no i think you can find those things. in in the smallest. oh absolutely i'm with uh david foster. wallace said like the key to life is to. be unborable i'm. i think i think it's very possible to. find that.

intensity of joy in the smallest. absolutely i'm just you asked me my. story. yeah yeah i'm actually speaking to the. audience yeah it doesn't have to be. those four. you happen to get excited by one of the. bigger questions of. in the universe but uh but that even the. smallest things and watching the. olympics now. just uh just giving yourself life uh. giving your life over to the study and. the mastery of a particular sport is. fascinating. and and uh if if it sparks.

joy and passion you're able to in the. case of the olympics. basically suffer for like a couple of. decades to achieve i mean you can find. joint passion just being a parent i mean. yeah yeah the the parenting one is funny. so i always. uh not always but for a long time wanted. kids and. get married and stuff and especially. that has to do with the fact that. i've seen a lot of people that i respect. get a whole other level of joy from kids. and you know at first is like.

your thinking is well like i don't have. enough time in the day. right if i have this passion which is. true. yes but like if i want to solve. intelligence how is this. kids situation gonna help me but then. you realize. that uh. you know like you said the things that. sparks joy and it's very possible that. kids can provide even a greater or. deeper more meaningful. joy than those bigger questions yeah.

when they they enrich each other and. that that seemed like um. obviously when i was younger it's. probably a counter-intuitive notion. because there's only so many hours in. the day. but then life is finite and you have to. pick the things that give. give you joy yeah but you know also. you understand you you can be patient. too i mean it's finite. but we do have you know whatever 50. years or so it's not so long yeah. so so in my case you know in my case i. had to give up on my dream of the. neuroscience. because i i was a graduate student at.

berkeley and they told me i couldn't do. this and i couldn't get funded and. you know and and so i went back in. and went back in the computing industry. for a number of years i thought it would. be four but it turned out to be more. but i said but i said i'll come back you. know i definitely i'm definitely gonna. come back i know i'm gonna do this. computer stuff for a while but i'm. definitely coming back everyone knows. that. and it's they moved like raising kids. well yeah you still you have to spend a. lot of time with your kids it's fun. enjoyable um but that doesn't mean. you have to give up on other dreams it. just means that you may have to wait a. week or two.

to work on that next idea well. you talked about the the the darker side. of me disappointing sides of human. nature that we're. hoping to overcome so that we don't. destroy ourselves i tend to. put a lot of value in um the broad. general concept of love. of uh the human capacity to um. of compassion towards each other of just. kindness whatever that longing of like. just the human.

human to human connection yeah it. connects back to our initial discussion. i tend to see a lot of value in this. collective intelligence aspect. i think some of the magic of human. civilization happens when there's. uh a party is not as fun when it you're. alone yeah. i totally agree with you on these issues. uh do you think. from a neurocortex perspective. uh what role does love play in the human. condition uh well those are two separate. things from a new project. i don't think it doesn't impact our. thinking about human uh about the.

neocortex. from a human condition point of view i. think it's core um. i mean we get so much pleasure out of. loving people. and helping people um. so you know i can i'll rack it up to old. brain stuff and maybe you can throw it. under the. the bust of evolution if you want um. that's fine. um uh it doesn't impact how i think. about how we model the world. but from a humanity point of view i. think it's essential well i tend to.

give it to the new brain and also i tend. to think that some of aspects. of that need to be engineered into ai. systems. both in their ability to have compassion. for other humans. and their ability to. maximize love in the world between. humans. so i'm more thinking about the social. network so like. whenever there's a deep integration. between ai systems and humans. so specific applications where it's uh.

ai. and humans i think that's something that. often not talked about in terms of um. metrics over which you try to maximize. uh like which metric to maximize in a. system. it seems like one of the most powerful. things in societies. is the capacity to work it's fascinating. i think it's. it's a great way of thinking about it. you know i have.

i have been thinking more of these. fundamental mechanisms in the brain as. opposed to. the social interaction between the. interaction between humans and ai. systems in the future which is. and i think if you think about that. you're absolutely right um. but that's that's a complex system i can. have intelligent systems that don't have. that component but they're not. interacting with people you know they're. just running something or building a. building someplace or something i don't. know um. but if you think about interacting with. humans yeah it's it's gonna. and then but it has to be engineered in. there i don't think it's gonna appear on. its own.

uh that's a good question i yeah well we. could. in terms of uh uh from a reinforcement. learning perspective. whether the darker sides of human nature. or the better angels of our nature. uh win out yeah statistically speaking i. don't know. i tend to be optimistic and hope that. love wins out in the end. um you've done a lot of incredible stuff. and your book is uh.

driving towards this fourth question. that you started with. on the nature of intelligence what do. you hope your. legacy for people reading a hundred. years from now. how do you hope they remember your work. how do you hope they remember this book. well i think as an entrepreneur or. scientist or. any human who's trying to accomplish. some things i have a view that. really all you can do is accelerate the. inevitable.

um yeah it's like you know if we didn't. figure out if we didn't study the brain. someone else would study the brain if. you know if elon just. didn't make electric cars someone else. would do it eventually and if you know. if thomas anderson didn't invent a light. bulb we wouldn't be using candles today. so what you can do as an individual is. you can accelerate. something that's beneficial and make it. happen sooner than whatever that's. that's really it that's all you can do. um you can't create a new reality that. it wasn't gonna happen. um so from that perspective.

um i would hope that our work not just. me but our work. in general um people would look back and. said. hey they really helped make this better. future happen sooner. um they you know they helped us. understand the nature of false beliefs. sooner than we met up they made it. now we're so happy that we have these. intelligent machines doing these things. helping us. that that maybe that solved the climate. change problem and. they made it happen sooner so i think. that's the best. i would hope for some would say those.

guys just. moved the needle forward a little bit in. time well. i do it it feels like the progress of. human civilization is not. is uh there's a lot of trajectories and. if you have individuals that accelerate. towards one direction that helps steer. human civilization so. i think in this long stretch of time all. all trajectories will be traveled but i. think it's nice. for this particular civilization on.

earth to travel down one that's not yeah. well i think you're right i mean look we. have the. take the whole period of you know world. war ii nazism or something like that. well that was a bad sidestep right. went over there for a while but you know. there is the optimistic view about life. that. um that ultimately it does converge in a. positive way. it progresses ultimately even if we have. years of. darkness um so yeah so i think you can. perhaps. that's accelerating the positive it. could also mean eliminating some bad.

missteps along the way too um. but but i i'm an optimistic in that way. i was like you know despite we talked. about the end of civilization. you know i i think we're gonna live for. a long time i hope we are. um i think our society in the future is. gonna be better we're gonna have less. discord we're gonna have less people. killing each other you know we'll solve. you know we'll make the. they'll live in some sort of way that's. compatible with the carrying capacity of. the earth. um i'm optimistic these things will. happen. and all we can do is try to get there. sooner and at the very least if we do.

destroy ourselves we'll have a few. satellites. i will uh that will tell alien. civilization that we. were once or maybe our future you know. future inhabitants of earth you know. imagine you know. the planet of the apes scenario you know. we kill ourselves in a you know million. years from now or billion years from now. there's another species on the planet. curious creatures were once here yeah um. jeff. thank you so much for your work and um. thank you so much for talking to me once. again. well it's great i love what you do i. love your podcast you have the most.

interesting people. me aside so. it's a real service i think you do for. uh a very broader sense for humanity i. think. thanks jeff all right pleasure thanks. for listening to this conversation with. jeff hawkins. and thank you to codeacademy bio. optimizers. expressvpn asleep and blinkist. check them out in the description to. support this podcast. and now let me leave you with some words. from albert camus.

an intellectual is someone whose mind. watches. itself i like this because i'm happy to. be both haves. the watcher and the watched can they be. brought together. this is a practical question we must try. to answer. thank you for listening and hope to see. you next time. you.