#+TITLE:Transcript of Aaron Sloman - Artificial Intelligence - Psychology - Oxford Interview

 #+AUTHOR:Dylan Holmes

 #+EMAIL:

 #+STYLE: <link rel="stylesheet" type="text/css" href="../css/sloman.css" /> 

 

 

 #+BEGIN_QUOTE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 *Editor's note:* This is a working draft transcript which I made of

 [[http://www.youtube.com/watch?feature=player_detailpage&v=iuH8dC7Snno][this nice interview]] of Aaron Sloman. Having just finished one

 iteration of transcription, I still need to go in and clean up the

 formatting and fix the parts that I misheard, so you can expect the

 text to improve significantly in the near future.

 

 To the extent that this is my work, you have my permission to make

 copies of this transcript for your own purposes. Also, feel free to

 e-mail me with comments or corrections.

 

 You can send mail to =transcript@aurellem.org=.

 

 Cheers,

 

 ---Dylan

 #+END_QUOTE

 

 

 

 * Introduction

 

 ** Aaron Sloman evolves into a philosopher of AI

 [0:09] My name is Aaron Sloman. My first degree many years ago in

 Capetown University was in Physics and Mathematics, and I intended to

 go and be a mathematician. I came to Oxford and encountered

 philosophers --- I had started reading philosophy and discussing

 philosophy before then, and then I found that there were philosophers

 who said things about mathematics that I thought were wrong, so

 gradually got more and more involved in [philosophy] discussions and

 switched to doing philosophy DPhil. Then I became a philosophy

 lecturer and about six years later, I was introduced to artificial

 intelligence when I was a lecturer at Sussex University in philosophy

 and I very soon became convinced that the best way to make progress in

 both areas of philosophy (including philosophy of mathematics which I

 felt i hadn't dealt with adequately in my DPhil) about the philosophy

 of mathematics, philosophy of mind, philsophy of language and all

 those things---the best way was to try to design and test working

 fragments of mind and maybe eventually put them all together but

 initially just working fragments that would do various things.

 

 [1:12] And I learned to program and ~ with various other people

 including ~Margaret Boden whom you've interviewed, developed---helped

 develop an undergraduate degree in AI and other things and also began

 to do research in AI and so on which I thought of as doing philosophy,

 primarily.

 

 [1:29] And then I later moved to the University of Birmingham and I

 was there --- I came in 1991 --- and I've been retired for a while but

 I'm not interested in golf or gardening so I just go on doing full

 time research and my department is happy to keep me on without paying

 me and provide space and resources and I come, meeting bright people

 at conferences and try to learn and make progress if I can.

 

 ** AI is hard, in part because there are tempting non-problems.

 

 One of the things I learnt and understood more and more over the many

 years --- forty years or so since I first encountered AI --- is how

 hard the problems are, and in part that's because it's very often

 tempting to /think/ the problem is something different from what it

 actually is, and then people design solutions to the non-problems, and

 I think of most of my work now as just helping to clarify what the

 problems are: what is it that we're trying to explain --- and maybe

 this is leading into what you wanted to talk about:

 

 I now think that one of the ways of getting a deep understanding of

 that is to find out what were the problems that biological evolution

 solved, because we are a product of /many/ solutions to /many/

 problems, and if we just try to go in and work out what the whole

 system is doing, we may get it all wrong, or badly wrong.

 

 

 * What problems of intelligence did evolution solve?

 

 ** Intelligence consists of solutions to many evolutionary problems; no single development (e.g. communication) was key to human-level intelligence.

 

 [2:57] Well, first I would challenge that we are the dominant

 species. I know it looks like that but actually if you count biomass,

 if you count number of species, if you count number of individuals,

 the dominant species are microbes --- maybe not one of them but anyway

 they're the ones who dominate in that sense, and furthermore we are

 mostly --- we are largely composed of microbes, without which we

 wouldn't survive.

 

 

 # ** Many nonlinguistic competences require sophisticated internal representations

 [3:27] But there are things that make humans (you could say) best at

 those things, or worst at those things, but it's a combination.  And I

 think it was a collection of developments of which there isn't any

 single one. [] there might be, some people say, human language which

 changed everything. By our human language, they mean human

 communication in words, but I think that was a later development from

 what must have started as the use of /internal/ forms of

 representation --- which are there in nest-building birds, in

 pre-verbal children, in hunting mammals --- because you can't take in

 information about a complex structured environment in which things can

 change and you may have to be able to work out what's possible and

 what isn't possible, without having some way of representing the

 components of the environment, their relationships, the kinds of

 things they can and can't do, the kinds of things you might or might

 not be able to do --- and /that/ kind of capability needs internal

 languages, and I and colleagues [at Birmingham] have been referring to

 them as generalized languages because some people object to

 referring...to using language to refer to something that isn't used

 for communication. But from that viewpoint, not only humans but many

 other animals developed abilities to do things to their environment to

 make them more friendly to themselves, which depended on being able to

 represent possible futures, possible actions, and work out what's the

 best thing to do.

 

 [5:13] And nest-building in corvids for instance---crows, magpies,

  [hawks], and so on --- are way beyond what current robots can do, and

  in fact I think most humans would be challenged if they had to go and

  find a collection of twigs, one at a time, maybe bring them with just

  one hand --- or with your mouth --- and assemble them into a

  structure that, you know, is shaped like a nest, and is fairly rigid,

  and you could trust your eggs in them when wind blows. But they're

  doing it, and so ... they're not our evolutionary ancestors, but

  they're an indication --- and that example is an indication --- of

  what must have evolved in order to provide control over the

  environment in /that/ species.

 

 ** Speculation about how communication might have evolved from internal lanagues.

 [5:56] And I think hunting mammals, fruit-picking mammals, mammals

 that can rearrange parts of the environment, provide shelters, needed

 to have .... also needed to have ways of representing possible

 futures, not just what's there in the environment. I think at a later

 stage, that developed into a form of communication, or rather the

 /internal/ forms of representation became usable as a basis for

 providing [context] to be communicated. And that happened, I think,

 initially through performing actions that expressed intentions, and

 probably led to situtations where an action (for instance, moving some

 large object) was performed more easily, or more successfully, or more

 accurately if it was done collaboratively. So someone who had worked

 out what to do might start doing it, and then a conspecific might be

 able to work out what the intention is, because that person has the

 /same/ forms of representation and can build theories about what's

 going on, and might then be able to help.

 

 [7:11] You can imagine that if that started happening more (a lot of

 collaboration based on inferred intentions and plans) then sometimes

 the inferences might be obscure and difficult, so the /actions/ might

 be enhanced to provide signals as to what the intention is, and what

 the best way is to help, and so on.

 

 [7:35] So, this is all handwaving and wild speculation, but I think

 it's consistent with a large collection of facts which one can look at

 --- and find if one looks for them, but one won't know if [some]one

 doesn't look for them --- about the way children, for instance, who

 can't yet talk, communicate, and the things they'll do, like going to

 the mother and turning the face to point in the direction where the

 child wants it to look and so on; that's an extreme version of action

 indicating intention.

 

 [8:03] Anyway. That's a very long roundabout answer to one conjecture

 that the use of communicative language is what gave humans their

 unique power to create and destroy and whatever, and I'm saying that

 if by that you mean /communicative/ language, then I'm saying there

 was something before that which was /non/-communicative language, and I

 suspect that noncommunicative language continues to play a deep role

 in /all/ human perception ---in mathematical and scientific reasoning, in

 problem solving --- and we don't understand very much about it.

 

 [8:48]

 I'm sure there's a lot more to be said about the development of

 different kinds of senses, the development of brain structures and

 mechanisms is above all that, but perhaps I've droned on long enough

 on that question.

 

 

 * How do language and internal states relate to AI?

 

 [9:09] Well, I think most of the human and animal capabilities that

 I've been referring to are not yet to be found in current robots or

 [computing] systems, and I think there are two reasons for that: one

 is that it's intrinsically very difficult; I think that in particular

 it may turn out that the forms of information processing that one can

 implement on digital computers as we currently know them may not be as

 well suited to performing some of these tasks as other kinds of

 computing about which we don't know so much --- for example, I think

 there may be important special features about /chemical/ computers

 which we might [talk about in a little bit? find out about]. 

 

 ** In AI, false assumptions can lead investigators astray.

 [9:57] So, one of the problems then is that the tasks are hard ... but

 there's a deeper problem as to why AI hasn't made a great deal of

 progress on these problems that I'm talking about, and that is that

 most AI researchers assume things---and this is not just AI

 researchers, but [also] philsophers, and psychologists, and people

 studying animal behavior---make assumptions about what it is that

 animals or humans do, for instance make assumptions about what vision

 is for, or assumptions about what motivation is and how motivation

 works, or assumptions about how learning works, and then they try ---

 the AI people try --- to model [or] build systems that perform those

 assumed functions. So if you get the /functions/ wrong, then even if

 you implement some of the functions that you're trying to implement,

 they won't necessarily perform the tasks that the initial objective

 was to imitate, for instance the tasks that humans, and nest-building

 birds, and monkeys and so on can perform. 

 

 ** Example: Vision is not just about finding surfaces, but about finding affordances.

 [11:09] I'll give you a simple example --- well, maybe not so simple,

 but --- It's often assumed that the function of vision in humans (and

 in other animals with good eyesight and so on) is to take in optical

 information that hits the retina, and form into the (maybe changing

 --- or, really, in our case definitely changing) patterns of

 illumination where there are sensory receptors that detect those

 patterns, and then somehow from that information (plus maybe other

 information gained from head movement or from comparisons between two

 eyes) to work out what there was in the environment that produced

 those patterns, and that is often taken to mean \ldquo{}where were the

 surfaces off which the light bounced before it came to me\rdquo{}. So

 you essentially think of the task of the visual system as being to

 reverse the image formation process: so the 3D structure's there, the

 lens causes the image to form in the retina, and then the brain goes

 back to a model of that 3D structure there. That's a very plausible

 theory about vision, and it may be that that's a /subset/ of what

 human vision does, but I think James Gibson pointed out that that kind

 of thing is not necessarily going to be very useful for an organism,

 and it's very unlikely that that's the main function of perception in

 general, namely to produce some physical description of what's out

 there.

 

 [12:37] What does an animal /need/? It needs to know what it can do,

 what it can't do, what the consequences of its actions will be

 .... so, he introduced the word /affordance/, so from his point of

 view, the function of vision, perception, are to inform the organism

 of what the /affordances/ are for action, where that would mean what

 the animal, /given/ its morphology (what it can do with its mouth, its

 limbs, and so on, and the ways it can move) what it can do, what its

 needs are, what the obstacles are, and how the environment supports or

 obstructs those possible actions.

 

 [13:15] And that's a very different collection of information

 structures that you need from, say, \ldquo{}where are all the

 surfaces?\rdquo{}: if you've got all the surfaces, /deriving/ the

 affordances would still be a major task. So, if you think of the

 perceptual system as primarily (for biological organisms) being

 devices that provide information about affordances and so on, then the

 tasks look very different. And most of the people working, doing

 research on computer vision in robots, I think haven't taken all that

 on board, so they're trying to get machines to do things which, even

 if they were successful, would not make the robots very intelligent

 (and in fact, even the ones they're trying to do are not really easy

 to do, and they don't succeed very well--- although, there's progress;

 I shouldn't disparage it too much.)

 

 ** Online and offline intelligence

 

 [14:10] It gets more complex as animals get more sophisticated. So, I

 like to make a distinction between online intelligence and offline

 intelligence. So, for example, if I want to pick something up --- like

 this leaf <he plucks a leaf from the table> --- I was able to select

 it from all the others in there, and while moving my hand towards it,

 I was able to guide its trajectory, making sure it was going roughly

 in the right direction --- as opposed to going out there, which

 wouldn't have been able to pick it up --- and these two fingers ended

 up with a portion of the leaf between them, so that I was able to tell

 when I'm ready to do that <he clamps the leaf between two fingers>

 and at that point, I clamped my fingers and then I could pick up the

 leaf. 

 

 [14:54] Whereas, --- and that's an example of online intelligence:

 during the performance of an action (both from the stage where it's

 initiated, and during the intermediate stages, and where it's

 completed) I'm taking in information relevant to controlling all those

 stages, and that relevant information keeps changing. That means I

 need stores of transient information which gets discarded almost

 immediately and replaced or something. That's online intelligence. And

 there are many forms; that's just one example, and Gibson discussed

 quite a lot of examples which I won't try to replicate now.

 

 [15:30] But in offline intelligence, you're not necessarily actually

 /performing/ the actions when you're using your intelligence; you're

 thinking about /possible/ actions. So, for instance, I could think

 about how fast or by what route I would get back to the lecture room

 if I wanted to [get to the next talk] or something. And I know where

 the door is, roughly speaking, and I know roughly which route I would

 take, when I go out, I should go to the left or to the right, because

 I've stored information about where the spaces are, where the

 buildings are, where the door was that we came out --- but in using

 that information to think about that route, I'm not actually

 performing the action. I'm not even /simulating/ it in detail: the

 precise details of direction and speed and when to clamp my fingers,

 or when to contract my leg muscles when walking, are all irrelevant to

 thinking about a good route, or thinking about the potential things

 that might happen on the way. Or what would be a good place to meet

 someone who I think [for an acquaintance in particular] --- [barber]

 or something --- I don't necessarily have to work out exactly /where/

 the person's going to stand, or from what angle I would recognize

 them, and so on.

 

 [16:46] So, offline intelligence --- which I think became not just a

 human competence; I think there are other animals that have aspects of

 it: Squirrels are very impressive as you watch them. Gray squirrels at

 any rate, as you watch them defeating squirrel-proof birdfeeders, seem

 to have a lot of that [offline intelligence], as well as the online

 intelligence when they eventually perform the action they've worked

 out [] that will get them to the nuts. 

 

 [17:16] And I think that what happened during our evolution is that

 mechanisms for acquiring and processing and storing and manipulating

 information that is more and more remote from the performance of

 actions developed. An example is taking in information about where

 locations are that you might need to go to infrequently: There's a

 store of a particular type of material that's good for building on

 roofs of houses or something out around there in some

 direction. There's a good place to get water somewhere in another

 direction. There are people that you'd like to go and visit in

 another place, and so on. 

 

 [17:59] So taking in information about an extended environment and

 building it into a structure that you can make use of for different

 purposes is another example of offline intelligence. And when we do

 that, we sometimes use only our brains, but in modern times, we also

 learned how to make maps on paper and walls and so on. And it's not

 clear whether the stuff inside our heads has the same structures as

 the maps we make on paper: the maps on paper have a different

 function; they may be used to communicate with others, or meant for

 /looking/ at, whereas the stuff in your head you don't /look/ at; you

 use it in some other way.

 

 [18:46] So, what I'm getting at is that there's a great deal of human

 intelligence (and animal intelligence) which is involved in what's

 possible in the future, what exists in distant places, what might have

 happened in the past (sometimes you need to know why something is as

 it is, because that might be relevant to what you should or shouldn't

 do in the future, and so on), and I think there was something about

 human evolution that extended that offline intelligence way beyond

 that of animals. And I don't think it was /just/ human language, (but

 human language had something to do with it) but I think there was

 something else that came earlier than language which involves the

 ability to use your offline intelligence to discover something that

 has a rich mathematical structure. 

 

 ** Example: Even toddlers use sophisticated geometric knowledge

 #+<<example-gap>>

 [19:44] I'll give you a simple example: if you look through a gap, you

 can see something that's on the other side of the gap. Now, you

 /might/ see what you want to see, or you might see only part of it. If

 you want to see more of it, which way would you move? Well, you could

 either move /sideways/, and see through the gap---and see it roughly

 the same amount but a different part of it [if it's a ????], or you

 could move /towards/ the gap and then your view will widen as you

 approach the gap. Now, there's a bit of mathematics in there, insofar

 as you are implicitly assuming that information travels in straight

 lines, and as you go closer to a gap, the straight lines that you can

 draw from where you are through the gap, widen as you approach that

 gap. Now, there's a kind of theorem of Euclidean geometry in there

 which I'm not going to try to state very precisely (and as far as I

 know, wasn't stated explicitly in Euclidean geometry) but it's

 something every toddler--- human toddler---learns. (Maybe other

 animals also know it, I don't know.) But there are many more things,

 actions to perform, to get you more information about things, actions

 to perform to conceal information from other people, actions that will

 enable you to operate, to act on a rigid object in one place in order

 to produce an effect on another place. So, there's a lot of stuff that

 involves lines and rotations and angles and speeds and so on that I

 think humans (maybe, to a lesser extent, other animals) develop the

 ability to think about in a generic way. That means that you could

 take out the generalizations from the particular contexts and then

 re-use them in a new contexts in ways that I think are not yet

 represented at all in AI and in theories of human learning in any []

 way --- although some people are trying to study learning of mathematics.

 

 * Animal intelligence

 

 ** The priority is /cataloguing/ what competences have evolved, not ranking them.

 [22:03] I wasn't going to challenge the claim that humans can do more

 sophisticated forms of [tracking], just to mention that there are some

 things that other animals can do which are in some ways comparable,

 and some ways superior to [things] that humans can do. In particular,

 there are species of birds and also, I think, some rodents ---

 squirrels, or something --- I don't know enough about the variety ---

 that can hide nuts and remember where they've hidden them, and go back

 to them. And there have been tests which show that some birds are able

 to hide tens --- you know, [eighteen] or something nuts --- and to

 remember which ones have been taken, which ones haven't, and so

 on. And I suspect most humans can't do that. I wouldn't want to say

 categorically that maybe we couldn't, because humans are very

 [varied], and also [a few] people can develop particular competences

 through training. But it's certainly not something I can do.

 

 

 ** AI can be used to test philosophical theories

 [23:01] But I also would like to say that I am not myself particularly

 interested in trying to align animal intelligences according to any

 kind of scale of superiority; I'm just trying to understand what it

 was that biological evolution produced, and how it works, and I'm

 interested in AI /mainly/ because I think that when one comes up with

 theories about how these things work, one needs to have some way of

 testing the theory. And AI provides ways of implementing and testing

 theories that were not previously available: Immanuel Kant was trying

 to come up with theories about how minds work, but he didn't have any

 kind of a mechanism that he could build to test his theory about the

 nature of mathematical knowledge, for instance, or how concepts were

 developed from babyhood onward. Whereas now, if we do develop a

 theory, we have a criterion of adequacy, namely it should be precise

 enough and rich enough and detailed to enable a model to be

 built. And then we can see if it works. 

 

 [24:07] If it works, it doesn't mean we've proved that the theory is

 correct; it just shows it's a candidate. And if it doesn't work, then

 it's not a candidate as it stands; it would need to be modified in

 some way.

 

 * Is abstract general intelligence feasible?

 

 ** It's misleading to compare the brain and its neurons to a computer made of transistors

 [24:27] I think there's a lot of optimism based on false clues:

 the...for example, one of the false clues is to count the number of

 neurons in the brain, and then talk about the number of transistors

 you can fit into a computer or something, and then compare them. It

 might turn out that the study of the way synapses work (which leads

 some people to say that a typical synapse [] in the human brain has

 computational power comparable to the Internet a few years ago,

 because of the number of different molecules that are doing things,

 the variety of types of things that are being done in those molecular

 interactions, and the speed at which they happen, if you somehow count

 up the number of operations per second or something, then you get

 these comparable figures).

 

 ** For example, brains may rely heavily on chemical information processing

 Now even if the details aren't right, there may just be a lot of

 information processing that...going on in brains at the /molecular/

 level, not the neural level. Then, if that's the case, the processing

 units will be orders of magnitude larger in number than the number of

 neurons. And it's certainly the case that all the original biological

 forms of information processing were chemical; there weren't brains

 around, and still aren't in most microbes. And even when humans grow

 their brains, the process of starting from a fertilized egg and

 producing this rich and complex structure is, for much of the time,

 under the control of chemical computations, chemical information

 processing---of course combined with physical sorts of materials and

 energy and so on as well.

 

 [26:25] So it would seem very strange if all that capability was

 something thrown away when you've got a brain and all the information

 processing, the [challenges that were handled in making a brain],

 ... This is handwaving on my part; I'm just saying that we /might/

 learn that what brains do is not what we think they do, and that

 problems of replicating them are not what we think they are, solely in

 terms of numerical estimate of time scales, the number of components,

 and so on.

 

 ** Brain algorithms may simply be optimized for certain kinds of information processing other than bit manipulations

 [26:56] But apart from that, the other basis of skepticism concerns

 how well we understand what the problems are. I think there are many

 people who try to formalize the problems of designing an intelligent

 system in terms of streams of information thought of as bit streams or

 collections of bit streams, and they think of as the problems of

 intelligence as being the construction or detection of patterns in

 those, and perhaps not just detection of patterns, but detection of

 patterns that are useable for sending /out/ streams to control motors

 and so on in order to []. And that way of conceptualizing the problem

 may lead on the one hand to oversimplification, so that the things

 that /would/ be achieved, if those goals were achieved, maybe much

 simpler, in some ways inadequate. Or the replication of human

 intelligence, or the matching of human intelligence---or for that

 matter, squirrel intelligence---but in another way, it may also make

 the problem harder: it may be that some of the kinds of things that

 biological evolution has achieved can't be done that way. And one of

 the ways that might turn out to be the case is not because it's not

 impossible in principle to do some of the information processing on

 artificial computers-based-on-transistors and other bit-manipulating

 []---but it may just be that the computational complexity of solving

 problems, processes, or finding solutions to complex problems, are

 much greater and therefore you might need a much larger universe than

 we have available in order to do things.

 

 ** Example: find the shortest path by dangling strings

 [28:55] Then if the underlying mechanisms were different, the

 information processing mechanisms, they might be better tailored to

 particular sorts of computation. There's a [] example, which is

 finding the shortest route if you've got a collection of roads, and

 they may be curved roads, and lots of tangled routes from A to B to C,

 and so on. And if you start at A and you want to get to Z --- a place

 somewhere on that map --- the process of finding the shortest route

 will involve searching through all these different possibilities and

 rejecting some that are longer than others and so on. But if you make

 a model of that map out of string, where these strings are all laid

 out on the maps and so have the lengths of the routes. Then if you

 hold the two knots in the string -- it's a network of string --- which

 correspond to the start point and end point, then /pull/, then the

 bits of string that you're left with in a straight line will give you

 the shortest route, and that process of pulling just gets you the

 solution very rapidly in a parallel computation, where all the others

 just hang by the wayside, so to speak.

 

 ** In sum, we know surprisingly little about the kinds of problems that evolution solved, and the manner in which they were solved.

 [30:15] Now, I'm not saying brains can build networks of string and

 pull them or anything like that; that's just an illustration of how if

 you have the right representation, correctly implemented---or suitably

 implemented---for a problem, then you can avoid very combinatorially

 complex searches, which will maybe grow exponentially with the number

 of components in your map, whereas with this thing, the time it takes

 won't depend on how many strings you've [got on the map]; you just

 pull, and it will depend only on the shortest route that exists in

 there. Even if that shortest route wasn't obvious on the original map.

 

 

 [30:59] So that's a rather long-winded way of formulating the

 conjecture which---of supporting, a roundabout way of supporting the

 conjecture that there may be something about the way molecules perform

 computations where they have the combination of continuous change as

 things move through space and come together and move apart, and

 whatever --- and also snap into states that then persist, so [as you

 learn from] quantum mechanics, you can have stable molecular

 structures which are quite hard to separate, and then in catalytic

 processes you can separate them, or extreme temperatures, or strong

 forces, but they may nevertheless be able to move very rapidly in some

 conditions in order to perform computations.

 

 [31:49] Now there may be things about that kind of structure that

 enable searching for solutions to /certain/ classes of problems to be

 done much more efficiently (by brain) than anything we could do with

 computers. It's just an open question.

 

 [32:04] So it /might/ turn out that we need new kinds of technology

 that aren't on the horizon in order to replicate the functions that

 animal brains perform ---or, it might not. I just don't know. I'm not

 claiming that there's strong evidence for that; I'm just saying that

 it might turn out that way, partly because I think we know less than

 many people think we know about what biological evolution achieved.

 

 [32:28] There are some other possibilities: we may just find out that

 there are shortcuts no one ever thought of, and it will all happen

 much more quickly---I have an open mind; I'd be surprised, but it

 could turn up. There /is/ something that worries me much more than the

 singularity that most people talk about, which is machines achieving

 human-level intelligence and perhaps taking over [the] planet or

 something. There's what I call the /singularity of cognitive

 catch-up/ ...

 

 * A singularity of cognitive catch-up

 

 ** What if it will take a lifetime to learn enough to make something new?

 ... SCC, singularity of cognitive catch-up, which I think we're close

 to, or maybe have already reached---I'll explain what I mean by

 that. One of the products of biological evolution---and this is one of

 the answers to your earlier questions which I didn't get on to---is

 that humans have not only the ability to make discoveries that none of

 their ancestors have ever made, but to shorten the time required for

 similar achievements to be reached by their offspring and their

 descendants. So once we, for instance, worked out ways of complex

 computations, or ways of building houses, or ways of finding our way

 around, we don't need...our children don't need to work it out for

 themselves by the same lengthy trial and error procedure; we can help

 them get there much faster.

 

 Okay, well, what I've been referring to as the singularity of

 cognitive catch-up depends on the fact that---fairly obvious, and it's

 often been commented on---that in case of humans, it's not necessary

 for each generation to learn what previous generations learned /in the

 same way/. And we can speed up learning once something has been

 learned, [it is able to] be learned by new people. And that has meant

 that the social processes that support that kind of education of the

 young can enormously accelerate what would have taken...perhaps

 thousands [or] millions of years for evolution to produce, can happen in

 a much shorter time. 

 

 

 [34:54] But here's the catch: in order for a new advance to happen ---

 so for something new to be discovered that wasn't there before, like

 Newtonian mechanics, or the theory of relativity, or Beethoven's music

 or [style] or whatever --- the individuals have to have traversed a

 significant amount of what their ancestors have learned, even if they

 do it much faster than their ancestors, to get to the point where they

 can see the gaps, the possibilities for going further than their

 ancestors, or their parents or whatever, have done.

 

 [35:27] Now in the case of knowledge of science, mathematics,

 philosophy, engineering and so on, there's been a lot of accumulated

 knowledge. And humans are living a /bit/ longer than they used to, but

 they're still living for [whatever it is], a hundred years, or for

 most people, less than that. So you can imagine that there might come

 a time when in a normal human lifespan, it's not possible for anyone

 to learn enough to understand the scope and limits of what's already

 been achieved in order to see the potential for going beyond it and to

 build on what's already been done to make that...those future steps.

 

 [36:10] So if we reach that stage, we will have reached the

 singularity of cognitive catch-up because the process of education

 that enables individuals to learn faster than their ancestors did is

 the catching-up process, and it may just be that we at some point

 reach a point where catching up can only happen within a lifetime of

 an individual, and after that they're dead and they can't go

 beyond. And I have some evidence that there's a lot of that around

 because I see a lot of people coming up with what /they/ think of as

 new ideas which they've struggled to come up with, but actually they

 just haven't taken in some of what was...some of what was done [] by

 other people, in other places before them. And I think that despite

 the availability of search engines which make it /easier/ for people

 to get the information---for instance, when I was a student, if I

 wanted to find out what other people had done in the field, it was a

 laborious process---going to the library, getting books, and

 ---whereas now, I can often do things in seconds that would have taken

 hours. So that means that if seconds [are needed] for that kind of

 work, my lifespan has been extended by a factor of ten or

 something. So maybe that /delays/ the singularity, but it may not

 delay it enough. But that's an open question; I don't know. And it may

 just be that in some areas, this is more of a problem than others. For

 instance, it may be that in some kinds of engineering, we're handing

 over more and more of the work to machines anyways and they can go on

 doing it.  So for instance, most of the production of computers now is

 done by a computer-controlled machine---although some of the design

 work is done by humans--- a lot of /detail/ of the design is done by

 computers, and they produce the next generation, which then produces

 the next generation, and so on.

 

 [37:57] I don't know if humans can go on having major advances, so

 it'll be kind of sad if we can't.

 

 * Spatial reasoning: a difficult problem

 

 [38:15] Okay, well, there are different problems [ ] mathematics, and

 they have to do with properties. So for instance a lot of mathematics

 that can be expressed in terms of logical structures or algebraic

 structures and those are pretty well suited for manipulation and...on

 computers, and if a problem can be specified using the

 logical/algebraic notation, and the solution method requires creating

 something in that sort of notation, then computers are pretty good,

 and there are lots of mathematical tools around---there are theorem

 provers and theorem checkers, and all kinds of things, which couldn't

 have existed fifty, sixty years ago, and they will continue getting

 better.

 

 

 But there was something that I was [[example-gap][alluding to earlier]] when I gave the

 example of how you can reason about what you will see by changing your

 position in relation to a door, where what you are doing is using your

 grasp of spatial structures and how as one spatial relationship

 changes namely you come closer to the door or move sideways and

 parallel to the wall or whatever, other spatial relationships change

 in parallel, so the lines from your eyes through to other parts of

 the...parts of the room on the other side of the doorway change,

 spread out more as you go towards  the doorway, and as you move

 sideways, they don't spread out differently, but focus on different

 parts of the internal ... that they access different parts of the

 ... of the room.

 

 Now, those are examples of ways of thinking about relationships and

 changing relationships which are not the same as thinking about what

 happens if I replace this symbol with that symbol, or if I substitute

 this expression in that expression in a logical formula.  And at the

 moment, I do not believe that there is anything in AI amongst the

 mathematical reasoning community, the theorem-proving community, that

 can model the processes that go on when a young child starts learning

 to do Euclidean geometry and is taught things about---for instance, I

 can give you a proof that the angles of any triangle add up to a

 straight line, 180 degrees. 

 

 ** Example: Spatial proof that the angles of any triangle add up to a half-circle

 There are standard proofs which involves starting with one triangle,

 then adding a line parallel to the base one of my former students,

 Mary Pardoe, came up with which I will demonstrate with this <he holds

 up a pen> --- can you see it? If I have a triangle here that's got

 three sides, if I put this thing on it, on one side --- let's say the

 bottom---I can rotate it until it lies along the second...another

 side, and then maybe move it up to the other end ~. Then I can rotate

 it again, until it lies on the third side, and move it back to the

 other end. And then I'll rotate it again and it'll eventually end up

 on the original side, but it will have changed the direction it's

 pointing in --- and it won't have crossed over itself so it will have

 gone through a half-circle, and that says that the three angles of a

 triangle add up to the rotations of half a circle, which is a

 beautiful kind of proof and almost anyone can understand it. Some

 mathematicians don't like it, because they say it hides some of the

 assumptions, but nevertheless, as far as I'm concerned, it's an

 example of a human ability to do reasoning which, once you've

 understood it, you can see will apply to any triangle --- it's got to

 be a planar triangle --- not a triangle on a globe, because then the

 angles can add up to more than ... you can have three /right/ angles

 if you have an equator...a line on the equator, and a line going up to

 to the north pole of the earth, and then you have a right angle and

 then another line going down to the equator, and you have a right

 angle, right angle, right angle, and they add up to more than a

 straight line. But that's because the triangle isn't in the plane,

 it's on a curved surface. In fact, that's one of the

 differences...definitional differences you can take between planar and

 curved surfaces: how much the angles of a triangle add up to. But our

 ability to /visualize/ and notice the generality in that process, and

 see that you're going to be able to do the same thing using triangles

 that stretch in all sorts of ways, or if it's a million times as

 large, or if it's made...you know, written on, on...if it's drawn in

 different colors or whatever --- none of that's going to make any

 difference to the essence of that process. And that ability to see

 the commonality in a spatial structure which enables you to draw some

 conclusions with complete certainty---subject to the possibility that

 sometimes you make mistakes, but when you make mistakes, you can

 discover them, as has happened in the history of geometrical theorem

 proving. Imre Lakatos had a wonderful book called [[http://en.wikipedia.org/wiki/Proofs_and_Refutations][/Proofs and

 Refutations/]] --- which I won't try to summarize --- but he has

 examples: mistakes were made; that was because people didn't always

 realize there were subtle subcases which had slightly different

 properties, and they didn't take account of that. But once they're

 noticed, you rectify that. 

 

 ** Geometric results are fundamentally different than experimental results in chemistry or physics.

 [43:28] But it's not the same as doing experiments in chemistry and

 physics, where you can't be sure it'll be the same on [] or at a high

 temperature, or in a very strong magnetic field --- with geometric

 reasoning, in some sense you've got the full information in front of

 you; even if you don't always notice an important part of it. So, that

 kind of reasoning (as far as I know) is not implemented anywhere in a

 computer. And most people who do research on trying to model

 mathematical reasoning, don't pay any attention to that, because of

 ... they just don't think about it. They start from somewhere else,

 maybe because of how they were educated. I was taught Euclidean

 geometry at school. Were you?

 

 (Adam ford: Yeah)

 

 Many people are not now. Instead they're taught set theory, and

 logic, and arithmetic, and [algebra], and so on. And so they don't use

 that bit of their brains, without which we wouldn't have built any of

 the cathedrals, and all sorts of things we now depend on.

 

 * Is near-term artificial general intelligence likely? 

 

 ** Two interpretations: a single mechanism for all problems, or many mechanisms unified in one program.

 

 [44:35] Well, this relates to what's meant by general. And when I

 first encountered the AGI community, I thought that what they all

 meant by general intelligence was /uniform/ intelligence ---

 intelligence based on some common simple (maybe not so simple, but)

 single powerful mechanism or principle of inference. And there are

 some people in the community who are trying to produce things like

 that, often in connection with algorithmic information theory and

 computability of information, and so on. But there's another sense of

 general which means that the system of general intelligence can do

 lots of different things, like perceive things, understand language,

 move around, make things, and so on --- perhaps even enjoy a joke;

 that's something that's not nearly on the horizon, as far as I

 know. Enjoying a joke isn't the same as being able to make laughing

 noises. 

 

 Given, then, that there are these two notions of general

 intelligence---there's one that looks for one uniform, possibly

 simple, mechanism or collection of ideas and notations and algorithms,

 that will deal with any problem that's solvable --- and the other

 that's general in the sense that it can do lots of different things

 that are combined into an integrated architecture (which raises lots

 of questions about how you combine these things and make them work

 together) and we humans, certainly, are of the second kind: we do all

 sorts of different things, and other animals also seem to be of the

 second kind, perhaps not as general as humans. Now, it may turn out

 that in some near future time, who knows---decades, a few

 decades---you'll be able to get machines that are capable of solving

 in a time that will depend on the nature of the problem, but any

 problem that is solvable, and they will be able to do it in some sort

 of tractable time --- of course, there are some problems that are

 solvable that would require a larger universe and a longer history

 than the history of the universe, but apart from that constraint,

 these machines will be able to do anything [].  But to be able to do

 some of the kinds of things that humans can do, like the kinds of

 geometrical reasoning where you look at the shape and you abstract

 away from the precise angles and sizes and shapes and so on, and

 realize there's something general here, as must have happened when our

 ancestors first made the discoveries that eventually put together in

 Euclidean geometry. 

 

 It may be that that requires mechanisms of a kind that we don't know

 anything about at the moment. Maybe brains are using molecules and

 rearranging molecules in some way that supports that kind of

 reasoning. I'm not saying they are --- I don't know, I just don't see

 any simple...any obvious way to map that kind of reasoning capability

 onto what we currently do on computers. There is---and I just

 mentioned this briefly beforehand---there is a kind of thing that's

 sometimes thought of as a major step in that direction, namely you can

 build a machine (or a software system) that can represent some

 geometrical structure, and then be told about some change that's going

 to happen to it, and it can predict in great detail what'll

 happen. And this happens for instance in game engines, where you say

 we have all these blocks on the table and I'll drop one other block,

 and then [the thing] uses Newton's laws and properties of rigidity of

 the parts and the elasticity and also stuff about geometries and space

 and so on, to give you a very accurate representation of what'll

 happen when this brick lands on this pile of things, [it'll bounce and

 go off, and so on]. And you just, with more memory and more CPU power,

 you can increase the accuracy--- but that's totally different than

 looking at /one/ example, and working out what will happen in a whole

 /range/ of cases at a higher level of abstraction, whereas the game

 engine does it in great detail for /just/ this case, with /just/ those

 precise things, and it won't even know what the generalizations are

 that it's using that would apply to others []. So, in that sense, [we]

 may get AGI --- artificial general intelligence --- pretty soon, but

 it'll be limited in what it can do. And the other kind of general

 intelligence which combines all sorts of different things, including

 human spatial geometrical reasoning, and maybe other things, like the

 ability to find things funny, and to appreciate artistic features and

 other things may need forms of pattern-mechanism, and I have an open

 mind about that.

 

 * Abstract General Intelligence impacts

 

 [49:53] Well, as far as the first type's concerned, it could be useful

 for all kinds of applications --- there are people who worry about

 where there's a system that has that type of intelligence, might in

 some sense take over control of the planet. Well, humans often do

 stupid things, and they might do something stupid that would lead to

 disaster, but I think it's more likely that there would be other

 things [] lead to disaster--- population problems, using up all the

 resources, destroying ecosystems, and whatever. But certainly it would

 go on being useful to have these calculating devices. Now, as for the

 second kind of them, I don't know---if we succeeded at putting

 together all the parts that we find in humans, we might just make an

 artificial human, and then we might have some of them as your friends,

 and some of them we might not like, and some of them might become

 teachers or whatever, composers --- but that raises a question: could

 they, in some sense, be superior to us, in their learning

 capabilities, their understanding of human nature, or maybe their

 wickedness or whatever --- these are all issues in which I expect the

 best science fiction writers would give better answers than anything I

 could do, but I did once fantasize when I [back] in 1978, that perhaps

 if we achieved that kind of thing, that they would be wise, and gentle

 and kind, and realize that humans are an inferior species that, you

 know, have some good features, so they'd keep us in some kind of

 secluded...restrictive kind of environment, keep us away from

 dangerous weapons, and so on. And find ways of cohabitating with

 us. But that's just fantasy.

 

 Adam Ford: Awesome. Yeah, there's an interesting story /With Folded

 Hands/ where [the computers] want to take care of us and want to

 reduce suffering and end up lobotomizing everybody [but] keeping them

 alive so as to reduce the suffering. 

 

 Aaron Sloman: Not all that different from /Brave New World/, where it

 was done with drugs and so on, but different humans are given

 different roles in that system, yeah.

 

 There's also /The Time Machine/, H.G. Wells, where the ... in the

 distant future, humans have split in two: the Eloi, I think they were

 called, they lived underground, they were the [] ones, and then---no,

 the Morlocks lived underground; Eloi lived on the planet; they were

 pleasant and pretty but not very bright, and so on, and they were fed

 on by ...

 

 Adam Ford: [] in the future.

 

 Aaron Sloman: As I was saying, if you ask science fiction writers,

 you'll probably come up with a wide variety of interesting answers. 

 

 Adam Ford: I certainly have; I've spoken to [] of Birmingham, and

 Sean Williams, ... who else? 

 

 Aaron Sloman: Did you ever read a story by E.M. Forrester called /The

 Machine Stops/ --- very short story, it's [[http://archive.ncsa.illinois.edu/prajlich/forster.html][on the Internet somewhere]]

 --- it's about a time when people sitting ... and this was written in

 about [1914 ] so it's about...over a hundred years ago ... people are

 in their rooms, they sit in front of screens, and they type things,

 and they communicate with one another that way, and they don't meet;

 they have debates, and they give lectures to their audiences that way,

 and then there's a woman whose son says \ldquo{}I'd like to see

 you\rdquo{} and she says \ldquo{}What's the point? You've got me at

 this point \rdquo{} but he wants to come and talk to her --- I won't

 tell you how it ends, but.

 

 Adam Ford: Reminds me of the Internet.

 

 Aaron Sloman: Well, yes; he invented ... it was just extraordinary

 that he was able to do that, before most of the components that we

 need for it existed.

 

 Adam Ford: [Another person who did that] was Vernor Vinge [] /True

 Names/. 

 

 Aaron Sloman: When was that written?

 

 Adam Ford: The seventies.

 

 Aaron Sloman: Okay, well a lot of the technology was already around

 then. The original bits of internet were working, in about 1973, I was

 sitting ... 1974, I was sitting at Sussex University trying to

 use...learn LOGO, the programming language, to decide whether it was

 going to be useful for teaching AI, and I was sitting [] paper

 teletype, there was paper coming out, transmitting ten characters a

 second from Sussex to UCL computer lab by telegraph cable, from there

 to somewhere in Norway via another cable, from there by satellite to

 California to a computer Xerox [] research center where they had

 implemented a computer with a LOGO system on it, with someone I had

 met previously in Edinburgh, Danny Bobrow, and he allowed me to have

 access to this sytem. So there I was typing. And furthermore, it was

 duplex typing, so every character I typed didn't show up on my

 terminal until it had gone all the way there and echoed back, so I

 would type, and the characters would come back four seconds later.

 

 [55:26] But that was the Internet, and I think Vernor Vinge was

 writing after that kind of thing had already started, but I don't

 know. Anyway.

 

 [55:41] Another...I mentioned H.G. Wells, /The Time Machine/. I

 recently discovered, because [[http://en.wikipedia.org/wiki/David_Lodge_(author)][David Lodge]] had written a sort of

 semi-novel about him, that he had invented Wikipedia, in advance--- he

 had this notion of an encyclopedia that was free to everybody, and

 everybody could contribute and [collaborate on it]. So, go to the

 science fiction writers to find out the future --- well, a range of

 possible futures.

 

 Adam Ford: Well the thing is with science fiction writers, they have

 to maintain some sort of interest for their readers, after all the

 science fiction which reaches us is the stuff that publishers want to

 sell, and so there's a little bit of a ... a bias towards making a

 plot device there, and so the dramatic sort of appeals to our

 amygdala, our lizard brain; we'll sort of stay there obviously to some

 extent. But I think that they do come up with sort of amazing ideas; I

 think it's worth trying to make these predictions; I think that we

 should more time on strategic forecasting, I mean take that seriously.

 

 Aaron Sloman: Well, I'm happy to leave that to others; I just want to

 try to understand these problems that bother me about how things

 work. And it may be that some would say that's irresponsible if I

 don't think about what the implications will be. Well, understanding

 how humans work /might/ enable us to make [] humans --- I suspect it

 wont happen in this century; I think it's going to be too difficult.