Don’t Believe AI Hype, This is Where it’s Actually Headed | Oxford’s Michael Wooldridge | AI History

The Singularity is [ __ ] why is that the history of AI still has lessons to teach us there's another project called psych that attempted to store all of the

knowledge that we have as a civilization into this kind of logical structure what I want is not moral AI I think it's moral human beings what were once

philosophical questions have now become experimental science one of the great ironies of mathematical history that computers get invented as a byproduct

the golden age as you described it 1956 1974 tell us about this first boom of ai ai actually didn't have a good reputation at all AI was viewed as kind

of homeopathic medicine neural networks were regarded as a dead field oh wow well this is we're really at The Cutting Edge now we're I mean this is a big

research question is about Oxford's Michael walridge is a veteran AI researcher and a pioneer of agent-based AI in this interview walridge is going to take us through the

entire hundred-year history of AI development from Turing all the way to contemporary llms now you might wonder why should we bother studying the history of a technology right technology should be about the state-of-the-art

techniques it should be about the latest developments what's the point of revisiting the old and outdated it turns out there's at least two important reasons to study this

history and the first is that it'll help you better anticipate its future w is extremely skeptical that this iteration of AI will lead to the singularity partially because he's lived through so

many similar cycles of AI hype whenever there's a breakthrough you always get apocalyptic predictions pushed by religious zealots which sets up unreasonable expectations that

eventually sets the field back studying this history of boom and bust will help you see through the hype and grasp at where the technology is really going now the second and even more

valuable reason to study this history is that it contains Overlook techniques that could Inspire Innovations today now even for me someone who started coding when I was 15 who studied CS in college

and then went to build a tech company walridge uncovered entire paradigms of AI that I haven't even heard of wri's claim is that these paths not taken in

AI right the alleged failures weren't wrong they were just too early and so this 100-year history is a treasure Trove of ideas that we should look back to today for inspiration my name is John

Jan B I'm a fellow at the cosmos Institute where we study the intersection of philosophy and AI if you want to follow along this episode via transcript the link is in the

description without further Ado Michael waldridge this is the provocative chapter title uh in part of your book The Road to conscious machines The

Singularity is [ __ ] why is that there is this uh narrative out there and it's a very popular narrative and it's very compelling which is that at some point machines are going to become as

intelligent as human beings and then they can apply their intelligence to making themselves even smarter the story is that it all spirals out of our control and of course this is the plot

of quite a lot of science fiction movies notably Terminator I love those movies just as much as anybody does um but it it's it's deeply implausible and I

became frustrated with that narrative for all sorts of reasons one of which is that that narrative whenever it comes up in sort of serious debate about where AI

is going and what the risks are you know there are real risks associated with AI it tends to suck all the oxygen out of the room in in in the the phrase that my colleague used and it tends to dominate

the conversation and distract us from things that we should really be talking about right in fact there's a discipline that's come out of this called existential risk right it's kind of the worrying about the ter Terminator situation and figuring out how can we

perhaps better align these super intelligent agents to to human interests um and if you look at not just the narrative but actually the funding and what the smartest people are devoting

their time into thinking in in not only companies but policy groups X risk existential risk is is the dominant share of the entire market so to speak why do you think this narrative has

gained such a such a big uh big following I think it's the low probability but very very highrisk argument that I mean I think most people accept that this is not tremendously plausible

but if it did happen it would be the worst thing ever and so very very very high risk and when you multiply that probability by the risk then it's the argument is that it's something that you

should that you should start to think about but um this when the success of large language models became apparent and chat GPT was released and everybody

got very excited about this last year the kind of debate around this sort of reached sort of slightly hysterical levels um and it became slightly unhinged at some point my sense is the

debate is calmed down a little bit and is being more focused on on the actualities of where we are and what the risks are right I think that's quite a charitable reading I think of the psychology right it's a rational

calculus there's small probability but there's a large sort of cost I study religious history and when I talk to people in the exis world the psychology

kind of reminds me of uh the the Christian apocalyptic that that there's these people throughout Christian history that are like Now's the Time you know this happened most recently probably when we were uh going through

the Millennium right 1999 and it's this psychological drive that wants to grab at something total and eschatological in a way to orient

the entire world so so people I guess what I'm trying to highlighting is maybe you can see some of the psychology and climate risk as well it's not to say that these things aren't true right it's it's not to say that the world isn't

ending in Christianity the climate isn't changing or there is no exis it's that the reason that people seem attracted to this narrative is almost a Rel religious phenomena I think that's right and I

think it's appeals to something almost Primal and kind of human nature I mean it's most fundamental level it's the idea that you create something you have a child and they turn on you you know that kind of the Ultimate Nightmare for

parents you know you give birth you nurture something you you create something exactly so uh or you know and this that narrative that story is very

very resonant and for example you go back to the the original science fiction text Frankenstein that literally is the plot of Frankenstein you use science to create life to to give life to something

to create something and then it turns on you and you've lost control of that thing so it's a very very resonant idea I think and so very easy for people to latch onto right you know it's easy for

us to critique the psychology here but but what's what what do you think is wrong or what do you think people Mis about the argument itself that once we have super intelligent or or at least uh

on par with human level uh machine intelligence that they can recursively improve upon themselves what what do you think people are missing when they give too much weight to that that argument the frustrating part is the Skynet part

of the argument you know the kind of the Terminator thing that suddenly this will spiral out of control in ways that we just can't control in an incredibly short um an incredibly short period of time if you look under the hood of of

how these things work and how many patches are required to hold AI together um it just it just doesn't seem terribly plausible more concretely there are

basically two arguments for how existential risk might come around and the first is the famous paperclip argument which I'm sure you're familiar with you know so you build a highly intelligent machine and you ask it to

build as many paper clips as possible and it follows your instructions in ways that you didn't anticipate right for example enslaving all of humanity to build of humanity and turning them to

the production of paper clips uh you know until it turns everything into paper clips that's the uh that's the paperclip argument and there is some strength to that argument in the sense

that AI can go wrong in those ways but for it to uh to to hurt us it has to be empowered to hurt us we have to give it the keys we have to give it control assum there's no guard rails and there's

no guard rails and again that just doesn't seem terribly plausible that that we would do that I mean it would be a dumb thing for us to do to hand over the nukes to an AI so that's the first

argument about how AI might become an existential threat the second argument is just that we build very very intelligent machines which develop their

own goals which aren't aligned with ours now this is much more nebulous we don't know how that might happen um uh it's and and so it's slightly harder to address but we really aren't at the

moment anywhere near that and I don't see even with very very powerful AI that we have have now the road map from where we go to that so some your understanding

uh the first case is when the AI is executing our goals but not ingesting the kind of uh assumptions and implicit values the humans are imputing whereas

the second one is a have developed their own goals um and there it seems even more far-fetched because llms as as powerful as they are they don't seem to be have any semblance of agency right

and that's fundamentally what it would require so me and my friends have actually come up with this half joking term called existential risk risk which is the risk upon a society that focuses

too much on existential risk and away from other other risks that we could actually be facing due to AI today if you can wave a magical wand and and

swing The Narrative of AI away from X risk what are the actual problems uh and conversations that we should be having right now we are heading into a world

where basically within a decade two decades think at the most um pretty much everything we read uh and see on social media and the internet is going to be AI

generated and we're not going to know what's real and what isn't real in that world and there are many many risks associated with that that Society just fragments because there is no Common

Core of beliefs anymore that we're all obsessed with some particular issue and that social media and the Internet is just driving us around that one particular issue because AI is programmed to pick up on the issues that

you care about and to feed you stories emphasizing those risks and so on where I was particularly concerned is going into elections in the US the UK I was really worried that what we were going

to be see was social media drowning in AI generated fake news we didn't see that as it happens at least not on the scale that I feared it might occur um

but nevertheless I wouldn't take my eye off that as a risk I think that's a very very real risk um that uh that uh autocratic States uh control media just

use AI to generate stories endless stories fake news stories that populist politicians do the same thing and so on and that we just drown in fake news till we no longer know how to tell what's

real and what isn't and don't trust anything as a consequence given all these problems let me read you uh a quote from your book do we need laws maybe even International treaties to

control the development of AI in the same way that we do for nuclear power I find the idea of introducing general laws to govern the use of AI rather imp plausible it seems a bit like trying to

introduce legislation to govern the use of mathematics what do you think should be the role of government and policy if any in the mitigation of these risks so what

I'm concerned about is some some sort of naive attempt to create a neural network law you know thou shal not use neural networks or something like that um and that's what seems to me to be

implausible because neural networks under the hood are just a bit of mathematics actually not terribly complex mathematics there's a lot of it but it's not terribly complex and so

regulating that well where do you draw the line I mean is is a bit of basic statistics you know the kind of thing that you would routinely do is that AI um you neural networks are quite a lot of linear algebra do we Outlaw linear

algebra when we when we write programs so trying to regulate technology by pointing at neural networks and saying you know we should not use these is is is problematic in a way that I think

pointing at nuclear weapons you know and nuclear fishing devices is not we can easily identify a nuclear fishing device I don't think there's much debate about

that uh or the use of chemical weapons and so on outling the use of uh chlorine gas or whatever in in weapons you know

these things are fairly easy and robust to uh robustly identifiable AI isn't and it's a really gray area about whether

something actually is AI or isn't AI so my preference would be that we focused on the uses of technology and I think the one that I pick up on the book is

surveillance Technologies if somebody's using surveillance technology on me I don't care whether it's a neural network or a logic program or guy sitting in a in a cabin watching exactly what I care

about is somebody is using surveillance technology on me and that's where um uh the outlawing should happen and so what I would prefer when we look at regulation rather than aiming for some

general neural network law is to look for specific sectors law Health uh defense and security uh all of those

different areas Finance um uh all of those different areas education and so on and think about what are the issues that AI raises there about the use of the technology and legislate around

those right and maybe we can tie this into your critique of X risk as well because I think a lot of the impulse or the intuition behind this these like neural net laws as you described is kind

of preemptive like oh my God like if we don't set up the right controls like it's going to spiral out of control and then we're going to have Skynet whereas you're saying be because we should be more uh moderate in our expectations of

what it can and cannot do including the harm it can do we can just regulate this like anything in any technology or any physical industry and infrastructure that we had in the specific use cases that's what I would that's what I prefer

to see right so far we we've talked about the forward-looking view I I want to spend the rest of the interview really diving into your book and talk about the the historical view of of AI

but let's begin with a question that I imagine many people in technical disciplines would would have which is why should we care about the history of

AI because the history of AI still has lessons to teach us and one of the big lessons that it teaches us is that it's very easy to get OV excited and to read too much into what you're seeing in Ai

and people have done that on multiple occasions in the past um now I think with the current wave of AI I think there is real substance here I think we are at a breakthrough moment uh but I'm

not convinced that we're at the end of the road in AI or that the Transformer is the magic ingredient um a few years ago people were saying deep learning alone is the magic ingredient for AI now

it's the Transformer architecture and so on I don't think either of those things are the magic ingredient I think there are some ingredients that we don't yet know about and I think your point about

there's things for even serious AI researchers to learn from the history of AI can be made even stronger it's not just the negative of oh you know uh calm your calm your excitement but let me

draw an analogy um in philosophy there's an idea that a lot of moral intuitions good moral intuitions are lost through Paradigm shifts so we gain things in this whole Christian worldview but we

moved away from the Roman world and the Pagan world and there's things to be rescued from that world that have been forgotten and my my training early training was in stem stem usually

doesn't study historical stuff right you usually just study the latest physical theories but there is a view of even stem Innovation Thomas [ __ ] being the

biggest proponent as being these Paradigm shifts that there are things that are important that are lost in previous paradigms and I think we're going to see this today and so the positive pitch I would say to give even

to Serious technical researchers to the history of AI is that there are methods and ways of thinking about programming artificial intelligence in general that

have been overlooked in our current Paradigm that perhaps might be rescued and is Perhaps Perhaps what we need to get us to the to the next Frontier I think that's right I think we are to use

Coon's phrase we are at a paradigm shift moment there's preg GPT and post GPT it's been boiling up for a decade or more really start I mean things became clear that things were happening in New

networks around about 2005 with the Advent of deep learning so here are the key history Point 2005 Advent of deep learning 2012 people realized gpus could

be used for training neural networks and all of a sudden you got 10 times more bang for your for your buck uh by using gpus in terms of training neural networks and so you can multiply what

you do and stuff gets really overheated then then in 2017 there's a Transformer architecture 2020 there's gpt3 those are the kind of moments but we are in a

paradigm shift right now and in Computing I genuinely believe that the world is Shifting now from a kind of an era where we were very interested in

coding exact and optimal algorithms and thinking what is the right algorithm for solving this problem now it's give us the data we'll just throw it at machine learning and let machine learning sort

it out do we care about exactly how it's doing it not necessarily it's just going to give us the answer but it's so many cases it turns out that the answer it's

giving us is an incredibly useful one even though we sacrifice something and what we sacrifice is kind of guarantees of correctness and optimality but

nevertheless it just turns into such a powerful tool so the the the the paradigm shift is towards a kind of datadriven world totally and and um so I

did my uh computer science degree 2016 to 2020 and uh if you had asked me the history of AI I would have given exactly what you told me and no more but reading

your book it extends greater another at least half century right and in fact there's a whole other dimension of AI about explicit programming what you call symbolic AI that's been completely

overlooked that was the dominant Paradigm over ml but now when we think AI we think ML and so there are perhaps intuitions that we can rescue in the next hour for even the leading technical

researchers but let's go to the very beginning with Alan Turing tell us about how he laned the groundwork not just for AI but for computer science as a field with his turing machine yeah so Alan

churing for all that he's now one of the most famous mathematicians I think in history there's still a huge part of the churing story that people don't really understand and what everybody knows touring for is the codebreaking work he

did at Bletchley Park which was an incredibly important part in the in the Allied victories and that's led to the Hollywood movie and so on I mean it's an entertaining movie but but hopelessly

inaccurate um but anyway go back to the 1930s he's doing his PhD um in uh in Cambridge and there's one of the big mathematical problems of the age the en

shidong problem it's called the translates as the decision problem and roughly speaking what the decision problem asks is can you automate

mathematics can you reduce mathematics to just a procedure that you follow that is take away all human Insight uh just to a procedure and it was one of the

defining problems of the early part of the 20th century and with incredible precociousness I think churing set himself the task of attacking the idun's

problem and solved it very very quickly but to solve it he invented a kind of mathematical machine a machine that follows instructions and at the

beginning it was just a mathematical abstraction but his work on codebreaking machines in the second world war leads him and a bunch of other people to realize that actually you could build

these touring machines and a touring machine basically with a few practical tweaks is the modern digital computer that's all a computer that is a machine for following instructions so it's kind

of one of the great ironies of mathematical history that computers get invented as a byproduct I mean he wasn't setting out to invent machines that could do things he was setting out to

solve the idun's problem and he had to invent computers in order to do that but he did that after the war he goes and works on the first computers right and

those computers those very early incredibly crude computers there's there's there's less than a handful in the whole world but they're capable of what seem like incredible intellectual

Feats they can do huge quantities of mathematics very quickly and very accurately much more quickly and accurately than any human being could do and people start to think are these

machines intelligent and that puts the idea of AI in the air and what I think is amazing about that period is we went from the beginning of the 1950s where there were probably two or three

computers in the whole world ridiculously crude by today's standards by the end of that decade we'd gone to having machines that could do the

rudiments of planning problem solving playing a decent game of chess or Checkers you know from having nothing whatsoever to machines that could do those things extraordinary progress in

just a decade there are giants intellectual Giants where even their kind of uh secondary thoughts end up spawning entire disciplines like Newton calculus for example what I want to

emphasize about the turing machine especially for our non-technical audience is that it's what you can tell it to do is very rudimentary it's

literally unambiguous explicit instructions go like if a then B move pointer from E to Z right like write

this value at this Place read this value from that place extremely rudimentary procedures and I want to emphasize again for our non-technical audience that is

still the foundation of our current computers today it's not like we've designed a fundamental new paradigm but that's what's fascinating yeah is that

we can get from those again deterministic simple explicit procedures to the kind of emergent behavior from of

of chat gbt and why this is so exciting for me as a philosopher was I remember in my undergrad uh uh I was debating with a Conan scholar uh you know the continence you know famously or

infamously you ask believe in Free Will and the conent scholar said well no deterministic system can come up with the kind of not even theoretical reasoning but the common sense reasoning

you and I can do and you know I was I was in ML Class I remember very very fondly 4771 and I was learning how to use

explicitly only deterministic systems to come up with the kind of common sensical natural language manipulation that humans do and and and this is just one

example of how as you said philosophy is becoming experimental science yeah no and I completely agree with you I think it's one way of framing the AI question

is can intelligence be reduced down to those incredibly simple instructions explicit instruction that computers and if you've never done any programming it's quite hard to

imagine how dumb simple instruct this is why computer programmers are paid a lot of money right because you know you require a special mindset to be able to think

down at that level and to understand how machines operate but yeah uh and and it is remarkable that what we see in um you know large language models state of the

rart AI uh these kind of very dazzling capabilities ultimately are just reducing down to those very very simple instructions but my golly there's a lot of those simple instructions in order to

do what they're doing I can't help but compare our current moment today with two watershed moments in maternity the cernic revolution humans don't think you're too

special in a universe Darwin humans don't think you're so special in an animal human it feels as if our last Bastion our our last sacred

ability a sacred thing that we have in our possession our intelligence has been reduced down to Binary bits yeah I don't think we're all the

way there yet I think uh you know I think the fundamental nature of of human beings I mean one of the fundamental components of human beings is that we

have experiences we experience the world um that's you know nobody really understands what Consciousness is but roughly speaking people agree agree that that that ability to experience things

from a personal perspective and that your personal perspective is private and unique to you and I can imagine what you're experiencing but it really is

private and unique to you uh and you know are we at the point of getting that from machines I think no definitely not and and how we might do that is is very opaque to me if it was an interesting

thing to do at all right um and this is a perfect segue to talk about the other thing about ching he's famous for which is the Turing test touring test so so tell us about why he formulated this test and how it impacted the development

of AI so in the 1950s then we have the first digital computers the first computers that operate according to the structures that we recognize today and they came out of touring Envision for the touring machines and then people

realizing that actually we could build machines that look like this so we've got the early 1950s we've got the first digital computers um and this starts a debate a

kind of public debate about AI even though it isn't given that that that that term and touring gets frustrated because people dogmatically insist that

computers will never be able to do X where X is creativity or emotion or whatever uh and he and crucially machines will never be able to understand something in the same way

that a human being is so he invents the touring test and the very famous touring test is beautiful in its Simplicity uh and it's a test for or indistinguishability so the touring test

goes as follows roughly speaking you have uh a human judge who's interacting with something via as he described a teletype but you know imagine a computer screen where you're just typing uh you

know typing whatever you want they could be questions but they could just be whatever you want and you're getting responses through that screen and you don't know whether the thing on the

other end that's producing those responses is a human being or a computer program and maturing test says if you cannot reliably tell the difference that

is if this machine can effectively pass itself off as a human being then stop arguing about it there's no point in arguing about it because you cannot distinguish between what the machine is

doing or what a human does by any reasonable test so there's two ways to interpret the Turning test philosophically one way is to reduce metaphysics to phenomenology and this is

to say look the metaphysical question of does it understand something is it really thinking is totally collapsible to the phenomenological the empirical question can we distinguish the

outputs but or it could be making an epistemic point which is to say the metaphysical question doesn't really matter let's just focus on the empirical question which reading do you think

Turing tur is given there yeah not clear I think which which reading I suspect the former I think he just thought was I suspect the former I think he probably just would thought we should there's no

point in having the debate after this point I mean do do you agree with that premise I mean surely surely surely not right I mean what this reminds me of actually is the behavioralist the

psychological school and the behavioralist even in the most charitable interpretation think that everything about a human uh uh can be known through their behaviors and

interactions with the external environment that's the most terrible reading the least charitable reading is and and there's literally passages they almost literally say this the mind

doesn't exist the Consciousness doesn't really I I don't even know like what that could even mean for it to be plausible but but but that is almost kind of the mistake that I see Turing

making here so but I think what this illustrates to us is that the touring test beautiful as it is it's not a terribly interesting test for intelligence in human beings I me I

think what churing said is it was kind of just for strated with the debate and said there's no point in having this argument I mean if it is just doing something uh that that is

indistinguishable then why are we even debating after that point uh and in a purely practical sense whether it's really um really understanding in a way that human beings are is kind of

irrelevant at that point there are distinctions between strong and weak Ai and the idea of strong AI is that we what what what we have what we're aiming for or what we have is machines that

really understand and experience and so on in the same way that a human being or animal does um the weak version of AI is no they don't really understand but they

can simulate those things I'm not terribly interested in strong AI except after a couple of glasses of wine in a in a in a in a chat with colleagues and

I don't know very many AI researchers that really are interested in strong AI the goals of AI are much more pragmatic by the way I think for all practical intents and purposes we passed the

touring test at some point in the last few years um uh but what that illustrates is I think is just actually The Limited value the touring test has

as a real test for uh for intelligence right for me the only reason the consciousness of a uh a Computing

machine uh has or does not have um the only real concern for me is is whether we have to treat them as moral agents right if you think that a a machine might be suffering it doesn't want you to turn it off we might have to give

some weight to that but that seems to be like the only possible reason why someone would be interested in in strong versus weak right yeah I think people some people just think it's a it's an

interesting thing yeah um yeah uh the idea of you AI as moral agents I'm worried about this um there is a uh

there is a body of work which is all about trying to equip AI with kind of ethical and moral reasoning um and I understand why people want to do that so that we have machines that make choices

that we would want them to make what worries me about that is that it allows people to try to abdicate their moral and ethical responsibilities wasn't my fault it was the machine's fault you

know um but we can't hold a machine to account for its actions in the way that that we can hold a human being to account um and I say I'm really concerned particularly in like the

military sphere that what we're going to hear is wasn't me it wasn't me you know it wasn't our fault it was the AI did it the AI chose the Target that school and fire the missile it was our fault at all

and so I think what I want is not moral AI I think it's moral human beings and it's the people that build and deploy the AI where the responsibility and the ethical considerations have to sit and

they are the ones that we need to hold to account for the actions of the machines that they deploy I see so that's Turing and that's the beginning of the field of not just artificial intelligence but computer science as a

whole I want to move on to the actual history of the implementations and I'll begin with a quote from your book historically AI has adopted one of two main approaches to this problem put

crudely the first possibility involves trying to model the mind the alternative is to model the brain to model the mind is what we've been talking about as symbolic AI to give it explicit

instructions of what to do to to model the the processes that we rationally consciously go through in our heads to model the brain that's machine learning that's the neural Nets that's to model

the architecture the physical architecture of the brain even if we don't have that much insight into what is actually going on let's talk about symbolic AI first let's talk about modeling the Mind first the golden age

as you described it 1956 1974 tell us about this first boom of AI yeah by the end of the 1950s we've got machines that can show the rudiments

of intelligence that can that can plan that can do mathematics which to be frank you know would be above the typical level of the people on the street you know here in Oxford you know you have chances of getting somebody who

could uh who could tell you what goal back's conjecture was or something like that um would would be limited so you've got machines that can do mathematics that can that can solve problems play

games and so there is this real excitement that you know actually we're going to be very quickly making progress towards something like full general

intelligence and it's called the Golden Age because you know we went from having nothing to having machines that could do those things um and there was a period

where you know where you know the the the the modus operandi for a for a PhD student was in in AI was well let's think of some task that requires intelligence in in humans and just build

a machine to do those things and turned out to do crude versions of those things were turned out not to be that that hard but there was this massive optimism for

that reason that progress was just going to be swift people thought within decades that they were going to be at the end of the road in AI we'd have full general intelligence AI Hae is not a new

phenomenon aiap is very much not a new phenomenon and uh and uh by the early 1970s it becomes clear really that progress is stalled and there are lots

of reasons why progress stalled one of the reasons that progress stalled is people were looking at artificial versions of problems rather than real problems that is they were looking at some problem in the real world like a

robotics problem and then coming up with a simplified simulation of that problem in a computer they were able to solve it in the simple simulated version but that

simulated version didn't address any of the problems that were there in the real world problem um so classically in robotics people would do simulations of

robots in warehouses and you'd look at a screen and you'd see a simulated robot carrying packages around and it looks very compelling you know great okay so show me the system in the real world but

robots carrying a package round in a in a warehouse in the real world is nothing like the simulated version and so those simulated versions they were called

microw worlds uh and again a standard motus operandi for a PhD student is come up with a microw world for your particular problem uh whatever it was show that you could build a program that

could solve it in that microw world but actually you've you've abstracted away everything that's difficult about the real problem in the real world and you know and and research funders would say

fine show us this then in a real uh Warehouse they wouldn't be able you described the general philosophy and strategy in this period of the golden age as divide and conquer so this is

splitting out what we conceive our mental faculties to be and then trying to build I mean mostly search algorithms in different variations to satisfy that

so one example would be the the towers of uh towers of B exactly where you have to move the the the discs on top of one another to get it to the right shape and

those are the traveling St salesman right you give given given a list of cities and try to find the the optimal algorithm there and those are the type of problems that people seem to be working

on there was a common ceiling that people were hitting across these problems um and that had to do with NP completeness so can you give our our lay non-technical audience a rough idea of

what this means so a lot of approaches to AI in uh in the early days involved something called search and search just means if you're given a particular

problem just look through all possible candidate Solutions so uh we mentioned the idea of the traveling salesman problem the the traveling salesman problem you're given a particular map and that the salesman so to speak has to

visit a whole bunch of cities on this map and return to return to base can the salesman do that on a certain budget of fuel um that's the traveling salesman problem and so one way to approach that

is just to look through all the possible candidate Solutions the problem is that the number of candidate Solutions in that case just grows astronomically um

so for example if there are something like 70 cities there would be more possible candidate Solutions than there are atoms in the universe you will never have a computer that could exhaustively

look through all of those candidate Solutions and it was assumed in the early days that we would be able to fix that problem uh it's called combinatorial explosion uh we would be

able to fix that problem somehow that we would find some techniques to be a ble to do it by the early' 70s there was an emerging theory of what's called

computational complexity which is which is all about understanding the intrinsic complexity of certain tasks and for the traveling salesman problem it belongs to

a class of computational problem that's called NP complete now what that means roughly speaking is that we don't have any efficient way to do it there is no more efficient way than looking through

all of the candidate Solutions in order to find one um which means that in practice uh there's a huge barrier with that kind of problem if we want to try and solve them

um but then it be began to appear that actually a whole bunch of problems everywhere we looked in AI we found okay this problem's actually NP complete problems in computer vision are NP

complete endless problems in reasoning and problem solving are NP complete or even worse there's this big hierarchy of complexity uh complexity classes as

they're called where things can be even harder than NP complete problems and then problems that are even harder than that and and so on and we found everywhere we looked we were

encountering these problems with AI and we hit a wall and the wall was this barrier of uh of combinatorial explosion all these problems have the same

character in principle you can solve them just by looking through all of the candidate solutions to try to find the right one one that works but in practice it's impossible to do that right by the

mid '70s because of the hype as well as the the series of technical problems that the AI field ran into it went into its first

but not certain certainly not only winter and what that means is just funding dried up interest dried up people were sometimes portrayed as charlatans in the AI field and the

public just grew very suspicious of AI claims you yourself have worked through many of these Winters boom and bus Cycles is it almost better to work in in

a winter because you get the people who are actually serious about about AI so uh for most of the time that I've been studying AI it was a relatively quiet

existence and the nice thing about that was I just got on with my thing there was very few people working in the same area and as a researcher actually that's quite a nice thing uh as a researcher

having you know a big space to yourself is actually really quite sort of refreshing you can just explore the territory so when the field becomes became popular we found huge numbers of people flooding into it now the nice

thing about that is huge numbers of very talented people but as a researcher what you're finding is you're no longer the only person that's looking at your problem you're surrounded by extremely

capable people all working on exactly the same problem and so you know it's changed the character of doing AI research really really quite a lot you're not the only person at the cace you know there's a whole bunch of people

there chipping away at the same place along with you but you have to remember actually just go back two decades and AI actually didn't have a good reputation

at all I mean in science AI was viewed as kind of homeopathic medicine neural networks were regarded as a dead field a dead end and I can remember colleagues

saying you know why are you working in AI you know this is uh this this is not a field that's going to be good for your career it's just extraordinary how much

that's changed so in the 80s we came out of the first AI winter and this new wave this new paradigm of AI was called expert systems tell us about how the

philosophy in this second wave of AI was different from the Golden Age so the big idea in the second wave AI is that intelligence is primarily a problem of knowledge and so if you want to build a

machine that can do something for you translate from French to English or to play chess or whatever then the key problem is to figure out what knowledge the human beings use when they do that

task and give that knowledge to a machine uh that was the big idea knowledge knowledge is the key to intelligence um and the AI is primarily

a problem of giving machines the right knowledge and a technology emerged rule-based systems as they were called which made it possible to give knowledge

about particular problems to machines classic example was the M system which was an expert in diagnosing blood diseases in human beings and the the way

M was developed um was that uh uh the developers talked to human experts Physicians experts in blood diseases and they ask them how do you go about diagnosing causes of blood diseases and

they would say well the first thing I do is take somebody's temperature and then if the temperature is above this range I would do this experiment and so on and that knowledge about how humans solve

that problem is coded in the form of discrete chunks of what are called rules if a human has a temperature greater than this and uh this particular blood test comes up negative and so on then

they have Lassa fever with probability 0.7 that would be an example of a rule all of those rules were coded given to the machine and then you interact with the machine the machine asks questions

like does the patient have a temperature have you done this test what's the outcome of this test and so on and in the end it tells you I think your patient has Lasser fever or something

like that I see um but not only were new systems developed as a a CS student myself I was uh very surprised to to hear about a new paradigm of of uh

programming that I that I haven't heard of before um so when you're go to school today you're taught two types declarative that's when you specify to the machine what you want right SQL a

lot of database languages give me all the apples that are green and in that are from 2024 uh and then there's imperative languages right and this is Java this is C++ this is you telling the

machine if this then do that uh uh this is what video games for example are made of there's another Paradigm that I just learned about in your book called logical the logic Paradigm so let me let

me give you a quote the war plan planning system written by David Warren in 1974 which could solve planning problems including the blocks world so

this is a simulated uh Factory uh search problem we described in the Golden Age including the bloxs world and far beyond that required just a 100 lines

of prologue code prologue is The Logical prog programming language writing the same system in a language like python imperative would be likely to require

thousands of lines of code and months of effort the Temptation with logic programming is if I just give you the fundamental truths that I know logical

deduction can elegantly take it all the way yeah and that's that's a beautiful idea which beguiled an enormous number of AI researchers so the idea of logic

programming takes symbolic Ai and knowledge-based AI one step further and it says that okay if we want to build machines that have knowledge the way that we give them that knowledge is by

expressing that in the form of logic we give them these these logical these logical descriptions of the world and this is Aristotle essentially right if if Socrates is a man all men are mortal

Socrates is Mortal this is Aristotelian logic 101 exactly so we give it but we give it all of all of the knowledge about a particular problem whether it's diagnosing blood diseases or solving

planning problems and so on we express that in a logical form and then inbuilt logical reasoners will sort out the details for us they will they will do the logical reasoning and so the idea

there was in logic based AI that intelligence is primarily a problem of deduction of logical reasoning and it's a beautiful and elegant idea and the war plan program you say with 15 lines of

code it's just it's a ridiculously short program problem is it's just not very efficient firstly actually and it turned out to be in many cases hopelessly inefficient for lots of problems uh but

also it just turned out that again you know if you want to do robotics prologue is not the language you need for doing robotics it's just completely unsuitable for that but it

would be impossible to imagine expressing all the knowledge that chat GPT has been exposed to to man manually Express all that in the form of logical

expressions and give that to it just wouldn't work that didn't stop one particular project you described M which was very limited to specifically to doctors and hospitals and the idea was

again let's just dump all the the the the first principles the primary facts we know about the world logical deduction is going to figure out if Socrates is a man then Socrates is Mortal there's another project called

psych cyc that attempted to store all of the knowledge that we have as a civilization

into this kind of logical structure yeah so the psych project has a somewhat mixed place in uh in the history of of AI so the vision this was the vision of

Doug Leonard Leonard was a really brilliant researcher who really dazzled people in the early 70s with uh with his

work um and uh he became convinced that that the really big problem of AI the problem of building machines which are as fully capable of human beings is simply a problem of knowledge and he

said there's no shortcut to this we're just going to have to give the machine all this knowledge so uh he convinced some funders to support his work and at one point they had kind of warehouses

full of people busy encoding all of human knowledge in these forms of rules if this and this and this then this uh uh with the idea that of eventually this

would be as capable as as a human being and and lennet was uh was very very optimistic about his project he said you know within a couple of years I remember reading this in the uh beginning of the

90s he said within a couple of years Psych is going to be smart enough that we'll it'll just be able to write its own rules and we won't need to we'll just give it textbooks kind of like the

reflexive yeah exactly that um now the the happy part of the psych story is that the knowledge graphs that are used

by search engines and uh behind a lot behind the scenes in a lot of search now they trace their intellectual history to these very very what I call very large

knowledge bases but the the psych was just ridiculed at the time as being just ludicrously over ambitious it never delivered anything at the scale that was

anticipated for it it found some applications but relatively Niche applications and it never delivered anything at the scale that Lenard hoped

for it and so it was kind of often it was the ridicule it was ridicule but it was held up as that that one project which summarized everything that went wrong about symbolic AI let me give you

what I think to be the funniest quote from your book psych's main role in AI history is an extreme example of AI hype which very publicly failed to live up to the Grand predictions that were made for

it the founder of Psych Doug lenn's role in AI has been mythologized in a piece of computing folklore a mik micr lenit so the joke goes is the scientific unit

for measuring how bogus something is why a microl lenit because nothing could be as bogus as a whole L it yeah and I think this is what you were trying to

get at about why it's important to study the history of AI to get the proper perspective with every new paradigm the Golden Age s we're so close to using search to solve everything with the

expert systems oh like Psych is going to be able to to basically reflect L recursively write its own rules but even when you when you go back like every technology whether it's the printing press people immediately wanted to write

the encyclopedias right that captured all the knowledge in the world this is the drive that you see in today's AI today's AI yeah um so I think uh going going back to leonnard and psych I mean

that's kind of that that quote's kind of slightly cruel I think I mean the joke is a slightly cruel quote but the truth is I mean I think it would have a much happier place in AI history if it hadn't

seen so many just inflated claims that were that were implausible at the time and that just weren't delivered uh if it had had slightly more measured objectives it would have held up much

better as an exercise in large scale knowledge based development and I say you know there are in the DNA of the knowledge graph behind the scenes of of Google search and so on there was a

little bit of of of Psych there it was all for not it was it for nothing but it was just these overinflated claims like you know it's going to start reading and writing its own rules and so on but

there is a striking I think analogy that you pick up on which is the way that large language models are trained which is that we just expose them to every bit of Digital Data that we can get our

hands on the difference is that in the site case human beings were interpreting all of that and writing coding down The rules in the computer language with

large language models none of that goes on it is just presented to the model and in some sense and I'm waving my hand madly at this point in some sense it it

finds order in that and how it does that actually we don't really understand as we were talking about earlier we talked about the Golden Age with search we talked about expert systems in the late

80s Rodney Brooks in as a reaction almost to to the overe exaggeration of of the of these expert systems started a new paradigm called behavioral AI uh and

some of that philosophy is behind iroot the company that he founded tell us about what the philosophy in this Paradigm was so Brooks questioned the

fundamental principles on which AI had been working since the 1950s for 30 years and those principles were that uh intelligence uh can be solved through a

process of symbolic reasoning that we give the machine the knowledge it needs to solve a problem and that those are the key components of intelligence Brooks said actually I I just don't

think that's how intelligence Works in human beings and he came up with an alternative Theory this kind of Behavioral Theory and roughly speaking what he said is we are a mass of

conflicting behaviors that some of which are genetically hardwired into us through through evolutionary processes some of which we learn throughout our

lives but we're just a mass of these behaviors and somehow uh human uh human intelligence arises from the interaction

of those behaviors so he said let's start out by building layer by layer those behaviors and he was also um extremely unhappy with the idea of

Intelligence being manifested in disembodied systems he said that's not real intelligence human intelligence is something in the world we do things in the world and he was deeply critical of

any version of AI that wasn't capable of dealing with the world I mean he was a roboticist he wanted to build robotics that could do things so he built an architecture a framework for doing this

where you would start with the most fundamental behaviors the most basic behaviors imaginable and in robotics famously the most fundamental behavior that you learn on day one of any

robotics course is obstacle avoidance um because your robot crashing into things is expensive and you'll be on the end of lawsuits and all sorts of things like that so you you start out by building

your very first layer is obstacle avoidance and then imagine a robot that's going to go around this room picking up trash the next level of in Behavior might be exploring right just

exploring around the room to try to find the trash and the next level of behavior might be if you see trash you pick it up you gradually build up layer and layer and layer you then have to think about

how those behaviors interact with one another and what takes what takes precedence so obstacle avoidance for example tends to take precedence over everything you know if you're if it's a

question of Destruction versus survival you know you always you know you want to choose surv Ral and the really cool thing is he was able to build robots that could do some quite impressive

tasks in the real world as a way towards intelligence uh it hit problems that were not dissimilar in spirit to the

kind of problems that people had encountered when in symbolic Ai and combinatorial explosion just we after a relatively small number of behaviors it

starts to be very hard to organize those behaviors and think about the way that those behaviors are going to interact with one another and so it kind of reached a limit at some point by by

the mid 90s I think but what Brooks did is he was able to build successful robotic systems famously the Rumba robots uh are built using a version of

right his ideas and I think uh that is the best example of all the ideas that he's talking about right so so the the the vacuum robots essentially uh that go around in your house they're embodied they're Rob robots they're not just a

software but importantly if you look at the programming behind the robots it's not like a top- down search and go through the entire space it's kind of like go straight if there's an obstacle

to take a random number turn this amount of degrees and map out the space it's it's very reactive right and so philosophically I think the helpful contrast between the first two paradigms

the Golden Age and the expert systems is that those are kind of like top down systematic right I'm gonna have one search and I'm gonna search through all the combinatorial possibilities or or I'm going en code all of human knowledge

and this one thing and behavioral is the opposite yeah it's highly reactive it's to say if you meet this situation then go do this almost like a look lookup

table and and at the time people picked up on analogies with behavioral theories of psychology and Skinner um you know who did used to do all the experiments

and training dogs and whatever by giving them stimuluses and rewards and and punishments and so on and uh and behavioral Psychology was kind of somewhat discredited largely discredited

I think at the time as a as a general theory um uh of human behavior and it people picked up on exactly those critiques and pointed exactly those critiques at at Brooks's behavioral AI

right but that's not just I think what was interesting for me was it was one of the relatively few attempts to really go back to the basics of what AI is and say

how what is our fundamental guiding princip we do this and really there been relatively few of those there's symbolic AI there's behavioral AI there's the new AI of machine learning and deep learning

and so on which is kind of datadriven AI right the last Paradigm in this symbolic world that I want to talk about is the early '90s agent-based Ai and my understanding here is that it's an

attempted synthesis of the intuition of uh the expert systems and the golden age that we want our machines to be proactive that there's a goal directed

but in combination with the reaction uh of of the behavioral AI in addition to a third idea of of Aging being social in

nature so tell us a bit more about this agent based P so we're on home territory for me this is what I've worked on basically my whole career so um one way

to think about this is changing our relationship to computer software on Microsoft Word everything that happens because you make it happen you select something from a menu or click on an

icon but there's only one agent in that interaction and it's you and you are just telling the machine very much like you know you're giving detailed low-level instructions to Microsoft Word somewhat like programming it right in

the same kind of style and the idea that emerged uh in the end of the 1980s beginning of the 1990s and which I worked on was to change the relationship

of software so that the software becomes an agent that's acting on your behalf that's cooperating with you working with you on the task that you set it um so

it's not just the dumb recipient of instructions but it's actually now an active participant working with you uh and potentially with other agents so to put it another way um the the

manifestation of the agent dream that we see most obviously now is in Siri and Alexa and Cortana they literally they they are they are direct descendants of that idea and actually uh Siri emerged

from work on Agents from people that I knew working in the same community in in the 1990s um so if um if we have Siri the

idea of Siri is that it is uh actively working with us on a problem rather than just being told what to do uh but that might involve interacting with other

Series so if I want to arrange a meeting with you why would I call you why would my Siri call you why doesn't my Siri just talk directly to your Siri that is the idea of what's called multi-agent

systems and that's that's kind of what's Driven most of my work for the last 35 years I see well what I found fascinating about the agent Paradigm is that it almost it's agnostic to and it cuts across the symbolic modeling the

the mind and modeling the brain because how it is proactive or how it is reactive or how it interacts with other agents that's you abstracted away from the type of questions you're thinking

about yeah so what does multi-agent systems have to offer to the current Paradigm of foundational models now oh wow well this is we're really at The Cutting Edge now we're I mean this is a

big research question is about we have large language models and they are not sort of full general intelligence but they are nevertheless very capable how

do we actually deploy those in our agents do they just handle the natural language part the conversational part or could we actually leverage them to do problem solving or things like that now

we've already talked about the idea you know can large language models solve problems and I'm a bit of a skeptic at the moment about the extent to which they can do that but how exactly do we

leverage this technology in the best way possible uh is is right at The Cutting Edge of research right now that's exactly what people are thinking about so what Drew you to this agent Paradigm

and especially this multi-agent Paradigm oh well so as an undergraduate in the 1980s uh I was fascinated with AI uh but I also became fascinated with computer

networks and you have to remember at the time computer networks were not common you know the the the the predecessor of the internet the arpanet um developed by the advanced research projects agency in

the US essentially military research funding agency had you know a very incomplete International network with just a few nodes connected in the UK but

I got the opportunity to work on the UK's extension of that called Janet The Joint academic Network and I had a kind of moment of Revelation at which point I

realized this is going to be the future networks they're just going to be everywhere we're going to everybody is going to be using computer networks it was obvious to me that you were going to hook up to the network through your phone or something like it and this was

going to be everywhere and so I had those two ideas in my head I had I'm really interested in AI I know that networks are going to be the future this is uh this is obvious it's going to be

the future put those together and think about a network of AIS AIS talking to one another and that's how I got interested literally that's how I got

interested in in the idea just having what happens if we got two AI systems that are capable of communicating well how are they going to communicate what's the language what are the rules of the protocols that they're going to use and

that's what kicked off my interest by the way having realized that networks were the future I completely failed to anticipate the worldwide web or Amazon

or any of that I look I look at the missed opportunities in my life um for for doing transformational work I totally got that networks were going to be the future but I still didn't understand exactly what that future was

going to look like it sounds like you were expecting us to go directly to multi-agent like I have my AI bargaining on behalf of me exactly whereas first we

went through this multi almost symbolic phase where you know what is Amazon if what is Google if not a big advanced search algorithm but now do do you think we're heading into a multi-agent world

in the sense that I'm going to have my own AI agent to to act on behal of me that's make a lot of this uh early internet stuff obsolete I think it is inevitable one way or another I don't I

think absolutely the history of computing tells us that this surely must all the lessons that we we learn from the history of computing point to the

future of AI being not just one big isolated system but multiple AI systems interacting with one another because that's how Computing the history of computing has gone so I absolutely

believe that the problem is I don't know exactly what that's going to look like and that's what I'm trying to figure out now that's what my current research is trying to figure out um there's another way in which multi-agent systems I

imagine are are currently being deployed even llms not multiple llms talking to each other but how you split work within one llm right so the the rough intuition

is you know maybe uh it's better to train actually three hidden LMS one's good with math one's good with intuition one's good with creativity or language

and then you have a a a a job sort of processing unit that gives uh the different llms different tasks to to process that that also is a type of multi-agent work yeah absolutely and

those are exactly that those kind of architectural questions how do llms fit what does that architecture look like that's again we're right at The Cutting Edge that's some people looking at those questions right now and trying to figure

out what the right way to organize all that stuff is right and so what are some of the biggest questions uh in the field right now what enormous numbers of people in the AI Community are grappling

with is is trying to get to grips with the capabilities of large language models to really map out what these models can reliably do and what they

can't reliably do and exactly what capabilities they really do have versus those that they don't actually have and it turns out this is really quite difficult one of the reasons it's quite

difficult is that um they've because they've essentially been exposed to all the digital content in the world it's quite tough to come up with things that you're confident they've really

fundamentally never seen before um but this is uh a really exciting area of science it's one of the one of the key areas I think one of the most important areas of science right now and it goes

back to this thing that you know AI has just become this experimental science in the way that it wasn't previously and this is part of that picture trying to map out these capabilities and it's also

really frustrating because these models frankly behave in slightly weird ways you think you've got some principle or some rule one day and then you just change your prompt slightly in ways that

seem innocuous to you and you get a completely different answer the next day and it's uh okay so what went on there what how why did it change but mapping that out uh is is is genuinely very

fascinating at the moment right um so I want to move on to the last part of our conversation which is I focused most of our time talking about the history on the symbolic AI side because that I I feel like it's almost a forgotten

history at this point because when we think AI we think ML and not the symbolic explicit programming side um I just want to trace out and round out this history for for our viewers because

what was fascinating to me was that AI people didn't use to associate AI with ml in fact machine learning it seemed from your book grew as a separate field

starting in the 40s right this idea of can we recreate the brain structure with uh electric neurons with computation and then the big Milestones Connection

connectionism in the 1980s this is when we figured out back propagation basically a way to add more layers to to actually simulate to train these networks deep learning even more layers

and more scale in 2000s and eventually Transformers Foundation models in the 2020s what I find so poetic about this this entire history now that Mo comes full

circle is what didn't work was rationally trying to explicitly tell computers what to do what did work or what is working

now let me say is by imitating the biological structures of the human the human brain yeah it is a remarkable uh

it's a remarkable uh change in fortunes for neural networks which I say 20 25 years ago was really regarded as kind of homeopathic medicine was in some sense

not s taken very very seriously part partly because of the scale that would be required to build large neural networks and it didn't seem plausible 25 years ago that we would have computers

that could process neural networks with 200 billion parameters or 500 billion parameters and yet that's that became possible because of the computer power that we have available now and it turns

out that these these systems can be incredibly capable so it really is a remarkable remarkable story I think one point in the book I say you know if you if you think that science is about

orderly progress from ignorance to truth absolutely is not it's messy false turns um almost kind of like ideological

Crusades I mean and it really is ideology religious this rounds in a full circle the apocalyptic uh mentality of the of the ex risk people I want to talk

about this new generation of foundation models and I'll I'll begin with a quote from your book again large language models of which gpt3 is perhaps the best known are the most prominent example of

current Foundation models while Foundation models have demonstrated impressive capabilities in certain tasks because they are inherently disembodied they are not the end of the road in

artificial intelligence so in the past three years the jump from Deep learning to what we have right now Foundation models is the Transformer architecture

and increasing scale a lot and a lot of of data some people seem to think that the architecture is already there we've

solved it with the Transformer we have what we need to go to AGI all we need is more scale what do you think is wrong about that argument so firstly let me say what

we've seen in the last few years in terms of Transformer architectures and that that which were released by Google a Google lab I believe in 2017 and what they are is an

architecture for token prediction and were developed in order to enable large language models so that you could give a prompt and they could predict essentially what should what should come next so you know the life and achievements of Winston church or the

history of Christ Church College where we are now um and they turned out coupled when you couple a Transformer architecture with the willingness to throw unimaginable quantities of

computer power and really mind-boggling quantities of computer power to train them and mindboggling quantities of data you get something which was remarkable and honestly AI researchers that tell

you that they were not surprised by how good it was I think is is misleading you a little bit they are genuinely remarkable they took me by surprise I didn't expect how good they were going

to be but just pause and think for a minute what we've got we've got large language models that you can have a chat about quantum mechanics the history of Christ Church College Liverpool Football

Club uh you know the origins of the first world war the economic circumstances that led to the 2008 financial crisis or recipes for um uh

for uh Arnold Bennett or whatever uh anything you can think of you can you can ask these things about and we look at that and think wow this is AI this is this is intelligence and yet we don't

have a robot that could go into your house clear the dinner table and load up the dishwasher why have we got that weird dichotomy because there is a huge

range of human activities that actually at the moment are well out of the reach of Ai and those activities are activities in the real world um doing

robotic AI uh is just very very hard um large language models succeed in remarkable ways and they are genuinely impressive achievements but they succeed

on tasks where there are huge amounts of data available and in some sense where the consequences of what they do just doesn't really matter that much you know if you get a bad omelette recipe through

chat GPT you get a bad omelet that's not the end of the world you know you build a robot that occupies the real world with human beings and it goes wrong you know it can create Havoc it can cause

real harm so an a the idea of AI which doesn't Embrace doing things in the real world is quite an impoverished version of AI I think and that's what is uh I think interesting in your critique

because you use the word disembodied and that's kind of the the intuition right and that that's what you mean by disembodied and there something else I want to pick up on there I mean you're having conversation with chat GPT you go

on holiday for two weeks and leave it hanging it's not wondering where you are it's not thinking where's wridge got to or it's not getting bored or anything like that at all it's not doing anything

it is just a computer program that's paused in a loop human intelligence animal intelligence is fundamentally different to that we exist in a world

we're aware of the world and that's what embodiment means it's not just having a body but it's actually being tightly coupled with the world we live in in

that sense I see so in your Turing lectures that you recently G gave you separated out uh two general sets of human capacities one is the embodied set

and this is uh the ability to sense one's surroundings the ability and there I agree with you right AI is just robotics lacks a lot further behind just you know natural natural language processing image processing image

Generation all sorts of stuff like that but what I found really surprising in your Turing lectures is you also listed out a series of intellectual capabilities and even there you didn't

seem to think that our current generation Foundation models are going to get us there so the things that you said were solved or solvable in the current architecture natural language

processing recall Common Sense reasoning but I was extremely surprised that you listed the following uh again not embodied but intellectual capacities as

still not being even within the Horizon uh of of current llms logical reasoning abstract reasoning planning arithmetic do you still hold that

position with gp4 and because like it can do arithmetic it can uh can it can it really do arithmetic or can it do something that looks like arithmetic I mean there is a big question mark around

whether um what large language models are doing is doing those things or whether they're doing something that looks like patent recognition so arithmetic I'll concede you probably now is is a solved problem but there's a

huge body of work looking at whether these things can actually solve problems that are not just variations of something they've already seen in their training data and the question of is it

really originally solving a problem versus just doing patent recognition at the moment that's one of the big questions and the jury is very much out on that and the weight of evidence at the moment is they are not doing problem

solving they are doing something which is much more like patent recognition so let me give you an example to illustrate this um so uh in AI we've long been concerned with problem solving and

planning and planning is is the process of here is some goal I want to achieve here is where I start out and here are some uh choices available to you some actions that you can perform that will

transform the world how do I organize th AC those actions to transform me from where I am to my goal absolutely fundamental AI capability that people have been looking at for uh for well

over half a century so can large language models do planning First Site people got very excited because it appeared that they could you can a trip

like seem exactly but uh on closer inspection suppose you do the following um suppose you obfuscate all the terms that are being used in your in your plan

so you don't use words that it's familiar with but you express a problem the same problem right uh using terms that you know that it will not have appeared in the training data can it

then solve the problem now I emphasize it's the same problem you're just using words it's never seen before and no at the moment the answer is no it can't so it can't originally solve problems we

can we can do that we have problem solving capabilities so that suggests that when it can when it's looking at planning a trip you've seen thousands of trip planning guides and trip agendas

and so on and it's doing patent matching to pick up on that uh and help you plan the trip but is it actually planning from P first principles how to organize those various actions to

plan the trip right so at the moment I say at the moment uh the weight of evidence is that it's not capable of doing uh logical reasoning or problem

solving those kinds of things not in a deep way that doesn't mean it's not useful that doesn't mean you can't use it to help plan a trip it can be used in those ways but is it actually doing

those things from first principle the weight of evidence at the moment is no right and is you're into tion that not only are these capacities not there but it's a it's an architecture issue that

we can't expect to get logical reasoning just by increasing the orders of magnitudes of data we throw at it from here because fundamentally what it's doing right for these llms is just next

word prediction yeah so is that the issue that you're that you're just is that the deeper issue you're gesturing at so that's what Transformers were designed for next word prediction and

the surprising thing was how useful and impressive that turned out to be if you were prepared to throw enough data and compute power at it um but I see no

reason to believe that that the Transformer architecture is the key for example to robotic AI That's not what it was designed for so I don't see why it should Lal reasoning or logical reasoning necessarily I mean again

that's not what it was designed for but iiz that doesn't mean it's not useful and I'm as dazzled as anybody when I use when I use this technology and I am you know daily taken by surprise when people

show me the really remarkable things that it can do um and I have to say you know we've gone this is really genuinely I think uh a watershed moment in AI

history because we've gone from a period where a lot of questions in AI were purely philosophical questions they were literally reserved for philosophers

until a few years ago uh and suddenly it's experimental science you know uh are large language models conscious well let's roll up our sleeves and do some experiment and find out no by the way

they're not uh but you know these are now practical handson questions and to have gone from not having anything in the world that you could apply those

questions to to this being actual practical Hands-On experimental science in just a few years is mind Bing right let me play a devil's advocate here um

because for me as as a philosopher studying AI has been a very humbling experience because it might reveal what how little reason actually works in

humans and here's the challenge I would like for you to respond to which is these architectures are built off of an imitation of the human mind and how the

human mind is is connected through neural networks right and so the intuition is Maybe by by just IM imitating that even though they're not designed for for logical reasoning

because we've imitated that the structure of the human brain that it's this emergent phenomenon know maybe what humans are doing is not first principles thinking maybe we're we're just pattern pattern matching maybe it's all pattern

matching down there maybe I mean I I believe I don't really believe this one but maybe we are just doing next word production when we're having a conversation and there is an entirely serious uh school of thought that thinks

actually perhaps we need to rethink what the the what humans are doing and that actually that we have overblown expectations about what beliefs about what we're what we're doing I don't

think humans are a Transformer architecture I don't think that's what we're doing I think there's a lot lot more that's going on we are animals that have evolved to inhabit planet Earth and

to interact with other human beings and to understand the fundamentals of human nature I think you have to understand those two things Transformer architectures are not that not by a long

long long way um however the point you make about emergence I think is an entirely valid one we don't understand how intelligence emerges in human beings how does all that gooey stuff in our

heads all those electrochemical processes and so on give rise to you and me we don't understand that in a deep way at all and that's what's so exciting about the present time that let's roll

up our sleeves and find out how it's actually doing this but without wishing to denigrate these systems at all there is a very real sense in which they are a hack they are an engineering hack that's

put together they are not following some deep model of mind or some deep philosophical theory about how human intelligence is or or some deep cognitive science theory of human

intelligence um they are a technological hack um and although neural networks artificial neural networks were inspired by the structures we see in human and

animal brains they are not an attempt to Faithfully recreate that in people there have actually been attempts to do that there was a very large European funded project that wanted to try to recreate a

brain an actual brain but that's not what neural networks are doing given how much success we've had about imitating a specific structure of brain right how how neurons are are linked together in

in computation should we be looking more into biomimicry and should we be studying the brain more and see if there's other structures we can replicate is that the path forward to finding out the architecture is to take us to to I think that's one that's one

way forward and I think we will surely get some insights I mean we have a very incomplete understanding of how the brain is organized I mean the brain is not just one big homogeneous neural

network even though you know it contains vast neuro multiple neural networks but it has it has some functional structure and we we understand a lot more now than

we did even 30 years about ago about the functional structure of the brain but a very incomplete understanding so that's going to be one way to go but I emphasiz again you know we are great apes that

have emerged through a process of billions of years of evolution to inhabit planet Earth at ground level at sea level roughly speaking and to be

able to um to learn about the physics of planet Earth and to be able to operate within the Dynamics of the physics of planet Earth but also to be able to interact with other great apes and those are the two key big components of human

intelligence learning about our world and learning about other Apes human beings um and we shouldn't lose sight of that when we think about the successes that AI has had you know we we're not

just a big neural network a big H modulous neural network there's an awful lot more going on than that right um but there is I think something Melancholy about what AI techniques have

worked and what haven't and let me quote to you a lovely quote from your book this is you speaking in your voice in July 2000 I was at a conference in Boston watching a presentation by one of

the Bright Young stars of the new AI I think this is when ml was starting to to pick up steam I was sitting next to a seasoned AI veteran someone who had been in AI since the Golden Age a

contemporary of McCarthy and Minsky he was contemptuous is this what passes for AI nowadays he asked where did the magic go you speaking and I could see where he

was coming from a career in AI now demanded a background not in philosophy or cognitive science or logic but in probability statistics and economics

this I think is what you were getting at about not seeming as poetic you know you you thought with with psych and artian logic that's what was going to do it well turns out like the neural net

architecture with a black box that we can barely understand more than our brains a lot of mundane mathematics exactly and you think we need more clever architecture to get our neural

Nets to behave differently well it's it turns out just increasing the scale fundamentally changes the the increasing output of of the behavior oh that is a that's a very depressing lesson I mean

the fact that you know you would think we the chief source of advances in AI is scientific developments actually no it's just more compute more data um and there's there's there's an article by

this called Rich by a guy called Rich Sutton called The Bitter lesson and rich is a very renowned uh machine learning researcher and he said look the truth is

we've made progress primarily in AI by uh you know some core ideas but actually the the big steps in progress we've seen and when we've been willing to throw 10

times more comput 10 times more data and so that that is a sobering lesson but there is still magic there in AI I mean

so uh the fact now that we have um machines like chat GPT that we can have a conversation with that we can turn the conversation to anything that we might

care to imagine compared to where we were five years ago that is simply astonishing and you know uh if I wish I was a PhD student now and having the

opportunity to explore this kind of weird new landscape of AI and to try to figure out you know what are the what are the fundamental laws that govern these systems what are the principles

try to uncover the science underneath this this technology um there is still some magic there you just have to look a bit harder to find it it might be a bit better if we needed the most advanced uh

math or we need to event this fancy architecture to study human brains very closely for decades um but I think I love your word sobering because that's

another way to frame Melancholy or disappointing and I'll end on this this one observation which is uh I'm preparing a lecture on the stoics right now and the stoics famously think that

humans are extremely rational creatures that even unbeknownst to us when I desire something I'm making an implicit proposition that that thing is good behind most human behaviors there's an

explicit or sorry there's an implicit uh true or false proposition someone on the opposite extreme is probably someone like Freud

where our unconscious is not known and perhaps even greatly unknowable to us and I think the fact that neural Nets these black boxes that we

ourselves don't really understand have gotten so much more success than something explicit like psych also tells us perhaps a sobering lesson about how our own intelligence works and that to

me again coming from a philosophical perspective is the most exciting stuff about all of this which I'll go back to which is that what were once philosophical questions have now become

experimental science all right thank you Professor thank you for fascinating interview thanks for watching my interview if you like this conversation I think you'd

also enjoy my discussion with Nick Bostrom on his new book deep Utopia it tries to imagine what there is left for humans to do after AI has surpassed Us in all domains now these interviews are

a part of an AI series that I'm producing as a fellow of the cosmos Institute a nonprofit studying philosophy and artificial intelligence you can find links to our website the

boss room interview and everything else we cover today in the description below thank you