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comparison org/sloman.org @ 57:a72ac82bb785

add dylan's sloman transcript.

author	Robert McIntyre <rlm@mit.edu>
date	Tue, 13 Aug 2013 00:47:01 -0400
parents
children	414a10d51d9f

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+#+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.

Mercurial > thoughts

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