thoughts: org/sloman-old.html annotate

annotate org/sloman-old.html @ 126:72c6ede12806

fix dangling SOM description.

author	Robert McIntyre <rlm@mit.edu>
date	Thu, 05 Jun 2014 16:11:29 -0400
parents	414a10d51d9f
children

rev	line source
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rlm@109	6 <title>Transcript of Aaron Sloman - Artificial Intelligence - Psychology - Oxford Interview</title>
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rlm@109	89
rlm@109	90
rlm@109	91 <div id="content">
rlm@109	92 <h1 class="title">Transcript of Aaron Sloman - Artificial Intelligence - Psychology - Oxford Interview</h1>
rlm@109	93
rlm@109	94
rlm@109	95 <blockquote>
rlm@109	96
rlm@109	97
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rlm@109	110
rlm@109	111
rlm@109	112 <p>
rlm@109	113 <b>Editor's note:</b> This is a working draft transcript which I made of
rlm@109	114 <a href="http://www.youtube.com/watch?feature=player_detailpage&v=iuH8dC7Snno">this nice interview</a> of Aaron Sloman. Having just finished one
rlm@109	115 iteration of transcription, I still need to go in and clean up the
rlm@109	116 formatting and fix the parts that I misheard, so you can expect the
rlm@109	117 text to improve significantly in the near future.
rlm@109	118 </p>
rlm@109	119 <p>
rlm@109	120 To the extent that this is my work, you have my permission to make
rlm@109	121 copies of this transcript for your own purposes. Also, feel free to
rlm@109	122 e-mail me with comments or corrections.
rlm@109	123 </p>
rlm@109	124 <p>
rlm@109	125 You can send mail to <code>transcript@aurellem.org</code>.
rlm@109	126 </p>
rlm@109	127 <p>
rlm@109	128 Cheers,
rlm@109	129 </p>
rlm@109	130 <p>
rlm@109	131 —Dylan
rlm@109	132 </p>
rlm@109	133 </blockquote>
rlm@109	134
rlm@109	135
rlm@109	136
rlm@109	137
rlm@109	138
rlm@109	139 <div id="table-of-contents">
rlm@109	140 <h2>Table of Contents</h2>
rlm@109	141 <div id="text-table-of-contents">
rlm@109	142 <ul>
rlm@109	143 <li><a href="#sec-1">1 Introduction</a>
rlm@109	144 <ul>
rlm@109	145 <li><a href="#sec-1-1">1.1 Aaron Sloman evolves into a philosopher of AI</a></li>
rlm@109	146 <li><a href="#sec-1-2">1.2 AI is hard, in part because there are tempting non-problems.</a></li>
rlm@109	147 </ul>
rlm@109	148 </li>
rlm@109	149 <li><a href="#sec-2">2 What problems of intelligence did evolution solve?</a>
rlm@109	150 <ul>
rlm@109	151 <li><a href="#sec-2-1">2.1 Intelligence consists of solutions to many evolutionary problems; no single development (e.g. communication) was key to human-level intelligence.</a></li>
rlm@109	152 <li><a href="#sec-2-2">2.2 Speculation about how communication might have evolved from internal lanagues.</a></li>
rlm@109	153 </ul>
rlm@109	154 </li>
rlm@109	155 <li><a href="#sec-3">3 How do language and internal states relate to AI?</a>
rlm@109	156 <ul>
rlm@109	157 <li><a href="#sec-3-1">3.1 In AI, false assumptions can lead investigators astray.</a></li>
rlm@109	158 <li><a href="#sec-3-2">3.2 Example: Vision is not just about finding surfaces, but about finding affordances.</a></li>
rlm@109	159 <li><a href="#sec-3-3">3.3 Online and offline intelligence</a></li>
rlm@109	160 <li><a href="#sec-3-4">3.4 Example: Even toddlers use sophisticated geometric knowledge</a></li>
rlm@109	161 </ul>
rlm@109	162 </li>
rlm@109	163 <li><a href="#sec-4">4 Animal intelligence</a>
rlm@109	164 <ul>
rlm@109	165 <li><a href="#sec-4-1">4.1 The priority is <i>cataloguing</i> what competences have evolved, not ranking them.</a></li>
rlm@109	166 <li><a href="#sec-4-2">4.2 AI can be used to test philosophical theories</a></li>
rlm@109	167 </ul>
rlm@109	168 </li>
rlm@109	169 <li><a href="#sec-5">5 Is abstract general intelligence feasible?</a>
rlm@109	170 <ul>
rlm@109	171 <li><a href="#sec-5-1">5.1 It's misleading to compare the brain and its neurons to a computer made of transistors</a></li>
rlm@109	172 <li><a href="#sec-5-2">5.2 For example, brains may rely heavily on chemical information processing</a></li>
rlm@109	173 <li><a href="#sec-5-3">5.3 Brain algorithms may simply be optimized for certain kinds of information processing other than bit manipulations</a></li>
rlm@109	174 <li><a href="#sec-5-4">5.4 Example: find the shortest path by dangling strings</a></li>
rlm@109	175 <li><a href="#sec-5-5">5.5 In sum, we know surprisingly little about the kinds of problems that evolution solved, and the manner in which they were solved.</a></li>
rlm@109	176 </ul>
rlm@109	177 </li>
rlm@109	178 <li><a href="#sec-6">6 A singularity of cognitive catch-up</a>
rlm@109	179 <ul>
rlm@109	180 <li><a href="#sec-6-1">6.1 What if it will take a lifetime to learn enough to make something new?</a></li>
rlm@109	181 </ul>
rlm@109	182 </li>
rlm@109	183 <li><a href="#sec-7">7 Spatial reasoning: a difficult problem</a>
rlm@109	184 <ul>
rlm@109	185 <li><a href="#sec-7-1">7.1 Example: Spatial proof that the angles of any triangle add up to a half-circle</a></li>
rlm@109	186 <li><a href="#sec-7-2">7.2 Geometric results are fundamentally different than experimental results in chemistry or physics.</a></li>
rlm@109	187 </ul>
rlm@109	188 </li>
rlm@109	189 <li><a href="#sec-8">8 Is near-term artificial general intelligence likely?</a>
rlm@109	190 <ul>
rlm@109	191 <li><a href="#sec-8-1">8.1 Two interpretations: a single mechanism for all problems, or many mechanisms unified in one program.</a></li>
rlm@109	192 </ul>
rlm@109	193 </li>
rlm@109	194 <li><a href="#sec-9">9 Abstract General Intelligence impacts</a></li>
rlm@109	195 </ul>
rlm@109	196 </div>
rlm@109	197 </div>
rlm@109	198
rlm@109	199 <div id="outline-container-1" class="outline-2">
rlm@109	200 <h2 id="sec-1"><span class="section-number-2">1</span> Introduction</h2>
rlm@109	201 <div class="outline-text-2" id="text-1">
rlm@109	202
rlm@109	203
rlm@109	204
rlm@109	205 </div>
rlm@109	206
rlm@109	207 <div id="outline-container-1-1" class="outline-3">
rlm@109	208 <h3 id="sec-1-1"><span class="section-number-3">1.1</span> Aaron Sloman evolves into a philosopher of AI</h3>
rlm@109	209 <div class="outline-text-3" id="text-1-1">
rlm@109	210
rlm@109	211 <p>[0:09] My name is Aaron Sloman. My first degree many years ago in
rlm@109	212 Capetown University was in Physics and Mathematics, and I intended to
rlm@109	213 go and be a mathematician. I came to Oxford and encountered
rlm@109	214 philosophers — I had started reading philosophy and discussing
rlm@109	215 philosophy before then, and then I found that there were philosophers
rlm@109	216 who said things about mathematics that I thought were wrong, so
rlm@109	217 gradually got more and more involved in [philosophy] discussions and
rlm@109	218 switched to doing philosophy DPhil. Then I became a philosophy
rlm@109	219 lecturer and about six years later, I was introduced to artificial
rlm@109	220 intelligence when I was a lecturer at Sussex University in philosophy
rlm@109	221 and I very soon became convinced that the best way to make progress in
rlm@109	222 both areas of philosophy (including philosophy of mathematics which I
rlm@109	223 felt i hadn't dealt with adequately in my DPhil) about the philosophy
rlm@109	224 of mathematics, philosophy of mind, philsophy of language and all
rlm@109	225 those things—the best way was to try to design and test working
rlm@109	226 fragments of mind and maybe eventually put them all together but
rlm@109	227 initially just working fragments that would do various things.
rlm@109	228 </p>
rlm@109	229 <p>
rlm@109	230 [1:12] And I learned to program and ~ with various other people
rlm@109	231 including ~Margaret Boden whom you've interviewed, developed—helped
rlm@109	232 develop an undergraduate degree in AI and other things and also began
rlm@109	233 to do research in AI and so on which I thought of as doing philosophy,
rlm@109	234 primarily.
rlm@109	235 </p>
rlm@109	236 <p>
rlm@109	237 [1:29] And then I later moved to the University of Birmingham and I
rlm@109	238 was there — I came in 1991 — and I've been retired for a while but
rlm@109	239 I'm not interested in golf or gardening so I just go on doing full
rlm@109	240 time research and my department is happy to keep me on without paying
rlm@109	241 me and provide space and resources and I come, meeting bright people
rlm@109	242 at conferences and try to learn and make progress if I can.
rlm@109	243 </p>
rlm@109	244 </div>
rlm@109	245
rlm@109	246 </div>
rlm@109	247
rlm@109	248 <div id="outline-container-1-2" class="outline-3">
rlm@109	249 <h3 id="sec-1-2"><span class="section-number-3">1.2</span> AI is hard, in part because there are tempting non-problems.</h3>
rlm@109	250 <div class="outline-text-3" id="text-1-2">
rlm@109	251
rlm@109	252
rlm@109	253 <p>
rlm@109	254 One of the things I learnt and understood more and more over the many
rlm@109	255 years — forty years or so since I first encountered AI — is how
rlm@109	256 hard the problems are, and in part that's because it's very often
rlm@109	257 tempting to <i>think</i> the problem is something different from what it
rlm@109	258 actually is, and then people design solutions to the non-problems, and
rlm@109	259 I think of most of my work now as just helping to clarify what the
rlm@109	260 problems are: what is it that we're trying to explain — and maybe
rlm@109	261 this is leading into what you wanted to talk about:
rlm@109	262 </p>
rlm@109	263 <p>
rlm@109	264 I now think that one of the ways of getting a deep understanding of
rlm@109	265 that is to find out what were the problems that biological evolution
rlm@109	266 solved, because we are a product of <i>many</i> solutions to <i>many</i>
rlm@109	267 problems, and if we just try to go in and work out what the whole
rlm@109	268 system is doing, we may get it all wrong, or badly wrong.
rlm@109	269 </p>
rlm@109	270
rlm@109	271 </div>
rlm@109	272 </div>
rlm@109	273
rlm@109	274 </div>
rlm@109	275
rlm@109	276 <div id="outline-container-2" class="outline-2">
rlm@109	277 <h2 id="sec-2"><span class="section-number-2">2</span> What problems of intelligence did evolution solve?</h2>
rlm@109	278 <div class="outline-text-2" id="text-2">
rlm@109	279
rlm@109	280
rlm@109	281
rlm@109	282 </div>
rlm@109	283
rlm@109	284 <div id="outline-container-2-1" class="outline-3">
rlm@109	285 <h3 id="sec-2-1"><span class="section-number-3">2.1</span> Intelligence consists of solutions to many evolutionary problems; no single development (e.g. communication) was key to human-level intelligence.</h3>
rlm@109	286 <div class="outline-text-3" id="text-2-1">
rlm@109	287
rlm@109	288
rlm@109	289 <p>
rlm@109	290 [2:57] Well, first I would challenge that we are the dominant
rlm@109	291 species. I know it looks like that but actually if you count biomass,
rlm@109	292 if you count number of species, if you count number of individuals,
rlm@109	293 the dominant species are microbes — maybe not one of them but anyway
rlm@109	294 they're the ones who dominate in that sense, and furthermore we are
rlm@109	295 mostly — we are largely composed of microbes, without which we
rlm@109	296 wouldn't survive.
rlm@109	297 </p>
rlm@109	298
rlm@109	299 <p>
rlm@109	300 [3:27] But there are things that make humans (you could say) best at
rlm@109	301 those things, or worst at those things, but it's a combination. And I
rlm@109	302 think it was a collection of developments of which there isn't any
rlm@109	303 single one. [] there might be, some people say, human language which
rlm@109	304 changed everything. By our human language, they mean human
rlm@109	305 communication in words, but I think that was a later development from
rlm@109	306 what must have started as the use of <i>internal</i> forms of
rlm@109	307 representation — which are there in nest-building birds, in
rlm@109	308 pre-verbal children, in hunting mammals — because you can't take in
rlm@109	309 information about a complex structured environment in which things can
rlm@109	310 change and you may have to be able to work out what's possible and
rlm@109	311 what isn't possible, without having some way of representing the
rlm@109	312 components of the environment, their relationships, the kinds of
rlm@109	313 things they can and can't do, the kinds of things you might or might
rlm@109	314 not be able to do — and <i>that</i> kind of capability needs internal
rlm@109	315 languages, and I and colleagues [at Birmingham] have been referring to
rlm@109	316 them as generalized languages because some people object to
rlm@109	317 referring…to using language to refer to something that isn't used
rlm@109	318 for communication. But from that viewpoint, not only humans but many
rlm@109	319 other animals developed abilities to do things to their environment to
rlm@109	320 make them more friendly to themselves, which depended on being able to
rlm@109	321 represent possible futures, possible actions, and work out what's the
rlm@109	322 best thing to do.
rlm@109	323 </p>
rlm@109	324 <p>
rlm@109	325 [5:13] And nest-building in corvids for instance—crows, magpies,
rlm@109	326 [hawks], and so on — are way beyond what current robots can do, and
rlm@109	327 in fact I think most humans would be challenged if they had to go and
rlm@109	328 find a collection of twigs, one at a time, maybe bring them with just
rlm@109	329 one hand — or with your mouth — and assemble them into a
rlm@109	330 structure that, you know, is shaped like a nest, and is fairly rigid,
rlm@109	331 and you could trust your eggs in them when wind blows. But they're
rlm@109	332 doing it, and so … they're not our evolutionary ancestors, but
rlm@109	333 they're an indication — and that example is an indication — of
rlm@109	334 what must have evolved in order to provide control over the
rlm@109	335 environment in <i>that</i> species.
rlm@109	336 </p>
rlm@109	337 </div>
rlm@109	338
rlm@109	339 </div>
rlm@109	340
rlm@109	341 <div id="outline-container-2-2" class="outline-3">
rlm@109	342 <h3 id="sec-2-2"><span class="section-number-3">2.2</span> Speculation about how communication might have evolved from internal lanagues.</h3>
rlm@109	343 <div class="outline-text-3" id="text-2-2">
rlm@109	344
rlm@109	345 <p>[5:56] And I think hunting mammals, fruit-picking mammals, mammals
rlm@109	346 that can rearrange parts of the environment, provide shelters, needed
rlm@109	347 to have …. also needed to have ways of representing possible
rlm@109	348 futures, not just what's there in the environment. I think at a later
rlm@109	349 stage, that developed into a form of communication, or rather the
rlm@109	350 <i>internal</i> forms of representation became usable as a basis for
rlm@109	351 providing [context] to be communicated. And that happened, I think,
rlm@109	352 initially through performing actions that expressed intentions, and
rlm@109	353 probably led to situtations where an action (for instance, moving some
rlm@109	354 large object) was performed more easily, or more successfully, or more
rlm@109	355 accurately if it was done collaboratively. So someone who had worked
rlm@109	356 out what to do might start doing it, and then a conspecific might be
rlm@109	357 able to work out what the intention is, because that person has the
rlm@109	358 <i>same</i> forms of representation and can build theories about what's
rlm@109	359 going on, and might then be able to help.
rlm@109	360 </p>
rlm@109	361 <p>
rlm@109	362 [7:11] You can imagine that if that started happening more (a lot of
rlm@109	363 collaboration based on inferred intentions and plans) then sometimes
rlm@109	364 the inferences might be obscure and difficult, so the <i>actions</i> might
rlm@109	365 be enhanced to provide signals as to what the intention is, and what
rlm@109	366 the best way is to help, and so on.
rlm@109	367 </p>
rlm@109	368 <p>
rlm@109	369 [7:35] So, this is all handwaving and wild speculation, but I think
rlm@109	370 it's consistent with a large collection of facts which one can look at
rlm@109	371 — and find if one looks for them, but one won't know if [some]one
rlm@109	372 doesn't look for them — about the way children, for instance, who
rlm@109	373 can't yet talk, communicate, and the things they'll do, like going to
rlm@109	374 the mother and turning the face to point in the direction where the
rlm@109	375 child wants it to look and so on; that's an extreme version of action
rlm@109	376 indicating intention.
rlm@109	377 </p>
rlm@109	378 <p>
rlm@109	379 [8:03] Anyway. That's a very long roundabout answer to one conjecture
rlm@109	380 that the use of communicative language is what gave humans their
rlm@109	381 unique power to create and destroy and whatever, and I'm saying that
rlm@109	382 if by that you mean <i>communicative</i> language, then I'm saying there
rlm@109	383 was something before that which was <i>non</i>-communicative language, and I
rlm@109	384 suspect that noncommunicative language continues to play a deep role
rlm@109	385 in <i>all</i> human perception —in mathematical and scientific reasoning, in
rlm@109	386 problem solving — and we don't understand very much about it.
rlm@109	387 </p>
rlm@109	388 <p>
rlm@109	389 [8:48]
rlm@109	390 I'm sure there's a lot more to be said about the development of
rlm@109	391 different kinds of senses, the development of brain structures and
rlm@109	392 mechanisms is above all that, but perhaps I've droned on long enough
rlm@109	393 on that question.
rlm@109	394 </p>
rlm@109	395
rlm@109	396 </div>
rlm@109	397 </div>
rlm@109	398
rlm@109	399 </div>
rlm@109	400
rlm@109	401 <div id="outline-container-3" class="outline-2">
rlm@109	402 <h2 id="sec-3"><span class="section-number-2">3</span> How do language and internal states relate to AI?</h2>
rlm@109	403 <div class="outline-text-2" id="text-3">
rlm@109	404
rlm@109	405
rlm@109	406 <p>
rlm@109	407 [9:09] Well, I think most of the human and animal capabilities that
rlm@109	408 I've been referring to are not yet to be found in current robots or
rlm@109	409 [computing] systems, and I think there are two reasons for that: one
rlm@109	410 is that it's intrinsically very difficult; I think that in particular
rlm@109	411 it may turn out that the forms of information processing that one can
rlm@109	412 implement on digital computers as we currently know them may not be as
rlm@109	413 well suited to performing some of these tasks as other kinds of
rlm@109	414 computing about which we don't know so much — for example, I think
rlm@109	415 there may be important special features about <i>chemical</i> computers
rlm@109	416 which we might [talk about in a little bit? find out about].
rlm@109	417 </p>
rlm@109	418
rlm@109	419 </div>
rlm@109	420
rlm@109	421 <div id="outline-container-3-1" class="outline-3">
rlm@109	422 <h3 id="sec-3-1"><span class="section-number-3">3.1</span> In AI, false assumptions can lead investigators astray.</h3>
rlm@109	423 <div class="outline-text-3" id="text-3-1">
rlm@109	424
rlm@109	425 <p>[9:57] So, one of the problems then is that the tasks are hard … but
rlm@109	426 there's a deeper problem as to why AI hasn't made a great deal of
rlm@109	427 progress on these problems that I'm talking about, and that is that
rlm@109	428 most AI researchers assume things—and this is not just AI
rlm@109	429 researchers, but [also] philsophers, and psychologists, and people
rlm@109	430 studying animal behavior—make assumptions about what it is that
rlm@109	431 animals or humans do, for instance make assumptions about what vision
rlm@109	432 is for, or assumptions about what motivation is and how motivation
rlm@109	433 works, or assumptions about how learning works, and then they try ---
rlm@109	434 the AI people try — to model [or] build systems that perform those
rlm@109	435 assumed functions. So if you get the <i>functions</i> wrong, then even if
rlm@109	436 you implement some of the functions that you're trying to implement,
rlm@109	437 they won't necessarily perform the tasks that the initial objective
rlm@109	438 was to imitate, for instance the tasks that humans, and nest-building
rlm@109	439 birds, and monkeys and so on can perform.
rlm@109	440 </p>
rlm@109	441 </div>
rlm@109	442
rlm@109	443 </div>
rlm@109	444
rlm@109	445 <div id="outline-container-3-2" class="outline-3">
rlm@109	446 <h3 id="sec-3-2"><span class="section-number-3">3.2</span> Example: Vision is not just about finding surfaces, but about finding affordances.</h3>
rlm@109	447 <div class="outline-text-3" id="text-3-2">
rlm@109	448
rlm@109	449 <p>[11:09] I'll give you a simple example — well, maybe not so simple,
rlm@109	450 but — It's often assumed that the function of vision in humans (and
rlm@109	451 in other animals with good eyesight and so on) is to take in optical
rlm@109	452 information that hits the retina, and form into the (maybe changing
rlm@109	453 — or, really, in our case definitely changing) patterns of
rlm@109	454 illumination where there are sensory receptors that detect those
rlm@109	455 patterns, and then somehow from that information (plus maybe other
rlm@109	456 information gained from head movement or from comparisons between two
rlm@109	457 eyes) to work out what there was in the environment that produced
rlm@109	458 those patterns, and that is often taken to mean “where were the
rlm@109	459 surfaces off which the light bounced before it came to me”. So
rlm@109	460 you essentially think of the task of the visual system as being to
rlm@109	461 reverse the image formation process: so the 3D structure's there, the
rlm@109	462 lens causes the image to form in the retina, and then the brain goes
rlm@109	463 back to a model of that 3D structure there. That's a very plausible
rlm@109	464 theory about vision, and it may be that that's a <i>subset</i> of what
rlm@109	465 human vision does, but I think James Gibson pointed out that that kind
rlm@109	466 of thing is not necessarily going to be very useful for an organism,
rlm@109	467 and it's very unlikely that that's the main function of perception in
rlm@109	468 general, namely to produce some physical description of what's out
rlm@109	469 there.
rlm@109	470 </p>
rlm@109	471 <p>
rlm@109	472 [12:37] What does an animal <i>need</i>? It needs to know what it can do,
rlm@109	473 what it can't do, what the consequences of its actions will be
rlm@109	474 …. so, he introduced the word <i>affordance</i>, so from his point of
rlm@109	475 view, the function of vision, perception, are to inform the organism
rlm@109	476 of what the <i>affordances</i> are for action, where that would mean what
rlm@109	477 the animal, <i>given</i> its morphology (what it can do with its mouth, its
rlm@109	478 limbs, and so on, and the ways it can move) what it can do, what its
rlm@109	479 needs are, what the obstacles are, and how the environment supports or
rlm@109	480 obstructs those possible actions.
rlm@109	481 </p>
rlm@109	482 <p>
rlm@109	483 [13:15] And that's a very different collection of information
rlm@109	484 structures that you need from, say, “where are all the
rlm@109	485 surfaces?”: if you've got all the surfaces, <i>deriving</i> the
rlm@109	486 affordances would still be a major task. So, if you think of the
rlm@109	487 perceptual system as primarily (for biological organisms) being
rlm@109	488 devices that provide information about affordances and so on, then the
rlm@109	489 tasks look very different. And most of the people working, doing
rlm@109	490 research on computer vision in robots, I think haven't taken all that
rlm@109	491 on board, so they're trying to get machines to do things which, even
rlm@109	492 if they were successful, would not make the robots very intelligent
rlm@109	493 (and in fact, even the ones they're trying to do are not really easy
rlm@109	494 to do, and they don't succeed very well— although, there's progress;
rlm@109	495 I shouldn't disparage it too much.)
rlm@109	496 </p>
rlm@109	497 </div>
rlm@109	498
rlm@109	499 </div>
rlm@109	500
rlm@109	501 <div id="outline-container-3-3" class="outline-3">
rlm@109	502 <h3 id="sec-3-3"><span class="section-number-3">3.3</span> Online and offline intelligence</h3>
rlm@109	503 <div class="outline-text-3" id="text-3-3">
rlm@109	504
rlm@109	505
rlm@109	506 <p>
rlm@109	507 [14:10] It gets more complex as animals get more sophisticated. So, I
rlm@109	508 like to make a distinction between online intelligence and offline
rlm@109	509 intelligence. So, for example, if I want to pick something up — like
rlm@109	510 this leaf <he plucks a leaf from the table> — I was able to select
rlm@109	511 it from all the others in there, and while moving my hand towards it,
rlm@109	512 I was able to guide its trajectory, making sure it was going roughly
rlm@109	513 in the right direction — as opposed to going out there, which
rlm@109	514 wouldn't have been able to pick it up — and these two fingers ended
rlm@109	515 up with a portion of the leaf between them, so that I was able to tell
rlm@109	516 when I'm ready to do that <he clamps the leaf between two fingers>
rlm@109	517 and at that point, I clamped my fingers and then I could pick up the
rlm@109	518 leaf.
rlm@109	519 </p>
rlm@109	520 <p>
rlm@109	521 [14:54] Whereas, — and that's an example of online intelligence:
rlm@109	522 during the performance of an action (both from the stage where it's
rlm@109	523 initiated, and during the intermediate stages, and where it's
rlm@109	524 completed) I'm taking in information relevant to controlling all those
rlm@109	525 stages, and that relevant information keeps changing. That means I
rlm@109	526 need stores of transient information which gets discarded almost
rlm@109	527 immediately and replaced or something. That's online intelligence. And
rlm@109	528 there are many forms; that's just one example, and Gibson discussed
rlm@109	529 quite a lot of examples which I won't try to replicate now.
rlm@109	530 </p>
rlm@109	531 <p>
rlm@109	532 [15:30] But in offline intelligence, you're not necessarily actually
rlm@109	533 <i>performing</i> the actions when you're using your intelligence; you're
rlm@109	534 thinking about <i>possible</i> actions. So, for instance, I could think
rlm@109	535 about how fast or by what route I would get back to the lecture room
rlm@109	536 if I wanted to [get to the next talk] or something. And I know where
rlm@109	537 the door is, roughly speaking, and I know roughly which route I would
rlm@109	538 take, when I go out, I should go to the left or to the right, because
rlm@109	539 I've stored information about where the spaces are, where the
rlm@109	540 buildings are, where the door was that we came out — but in using
rlm@109	541 that information to think about that route, I'm not actually
rlm@109	542 performing the action. I'm not even <i>simulating</i> it in detail: the
rlm@109	543 precise details of direction and speed and when to clamp my fingers,
rlm@109	544 or when to contract my leg muscles when walking, are all irrelevant to
rlm@109	545 thinking about a good route, or thinking about the potential things
rlm@109	546 that might happen on the way. Or what would be a good place to meet
rlm@109	547 someone who I think [for an acquaintance in particular] — [barber]
rlm@109	548 or something — I don't necessarily have to work out exactly <i>where</i>
rlm@109	549 the person's going to stand, or from what angle I would recognize
rlm@109	550 them, and so on.
rlm@109	551 </p>
rlm@109	552 <p>
rlm@109	553 [16:46] So, offline intelligence — which I think became not just a
rlm@109	554 human competence; I think there are other animals that have aspects of
rlm@109	555 it: Squirrels are very impressive as you watch them. Gray squirrels at
rlm@109	556 any rate, as you watch them defeating squirrel-proof birdfeeders, seem
rlm@109	557 to have a lot of that [offline intelligence], as well as the online
rlm@109	558 intelligence when they eventually perform the action they've worked
rlm@109	559 out [] that will get them to the nuts.
rlm@109	560 </p>
rlm@109	561 <p>
rlm@109	562 [17:16] And I think that what happened during our evolution is that
rlm@109	563 mechanisms for acquiring and processing and storing and manipulating
rlm@109	564 information that is more and more remote from the performance of
rlm@109	565 actions developed. An example is taking in information about where
rlm@109	566 locations are that you might need to go to infrequently: There's a
rlm@109	567 store of a particular type of material that's good for building on
rlm@109	568 roofs of houses or something out around there in some
rlm@109	569 direction. There's a good place to get water somewhere in another
rlm@109	570 direction. There are people that you'd like to go and visit in
rlm@109	571 another place, and so on.
rlm@109	572 </p>
rlm@109	573 <p>
rlm@109	574 [17:59] So taking in information about an extended environment and
rlm@109	575 building it into a structure that you can make use of for different
rlm@109	576 purposes is another example of offline intelligence. And when we do
rlm@109	577 that, we sometimes use only our brains, but in modern times, we also
rlm@109	578 learned how to make maps on paper and walls and so on. And it's not
rlm@109	579 clear whether the stuff inside our heads has the same structures as
rlm@109	580 the maps we make on paper: the maps on paper have a different
rlm@109	581 function; they may be used to communicate with others, or meant for
rlm@109	582 <i>looking</i> at, whereas the stuff in your head you don't <i>look</i> at; you
rlm@109	583 use it in some other way.
rlm@109	584 </p>
rlm@109	585 <p>
rlm@109	586 [18:46] So, what I'm getting at is that there's a great deal of human
rlm@109	587 intelligence (and animal intelligence) which is involved in what's
rlm@109	588 possible in the future, what exists in distant places, what might have
rlm@109	589 happened in the past (sometimes you need to know why something is as
rlm@109	590 it is, because that might be relevant to what you should or shouldn't
rlm@109	591 do in the future, and so on), and I think there was something about
rlm@109	592 human evolution that extended that offline intelligence way beyond
rlm@109	593 that of animals. And I don't think it was <i>just</i> human language, (but
rlm@109	594 human language had something to do with it) but I think there was
rlm@109	595 something else that came earlier than language which involves the
rlm@109	596 ability to use your offline intelligence to discover something that
rlm@109	597 has a rich mathematical structure.
rlm@109	598 </p>
rlm@109	599 </div>
rlm@109	600
rlm@109	601 </div>
rlm@109	602
rlm@109	603 <div id="outline-container-3-4" class="outline-3">
rlm@109	604 <h3 id="sec-3-4"><a name="example-gap" id="example-gap"></a><span class="section-number-3">3.4</span> Example: Even toddlers use sophisticated geometric knowledge</h3>
rlm@109	605 <div class="outline-text-3" id="text-3-4">
rlm@109	606
rlm@109	607 <p>[19:44] I'll give you a simple example: if you look through a gap, you
rlm@109	608 can see something that's on the other side of the gap. Now, you
rlm@109	609 <i>might</i> see what you want to see, or you might see only part of it. If
rlm@109	610 you want to see more of it, which way would you move? Well, you could
rlm@109	611 either move <i>sideways</i>, and see through the gap—and see it roughly
rlm@109	612 the same amount but a different part of it [if it's a ????], or you
rlm@109	613 could move <i>towards</i> the gap and then your view will widen as you
rlm@109	614 approach the gap. Now, there's a bit of mathematics in there, insofar
rlm@109	615 as you are implicitly assuming that information travels in straight
rlm@109	616 lines, and as you go closer to a gap, the straight lines that you can
rlm@109	617 draw from where you are through the gap, widen as you approach that
rlm@109	618 gap. Now, there's a kind of theorem of Euclidean geometry in there
rlm@109	619 which I'm not going to try to state very precisely (and as far as I
rlm@109	620 know, wasn't stated explicitly in Euclidean geometry) but it's
rlm@109	621 something every toddler— human toddler—learns. (Maybe other
rlm@109	622 animals also know it, I don't know.) But there are many more things,
rlm@109	623 actions to perform, to get you more information about things, actions
rlm@109	624 to perform to conceal information from other people, actions that will
rlm@109	625 enable you to operate, to act on a rigid object in one place in order
rlm@109	626 to produce an effect on another place. So, there's a lot of stuff that
rlm@109	627 involves lines and rotations and angles and speeds and so on that I
rlm@109	628 think humans (maybe, to a lesser extent, other animals) develop the
rlm@109	629 ability to think about in a generic way. That means that you could
rlm@109	630 take out the generalizations from the particular contexts and then
rlm@109	631 re-use them in a new contexts in ways that I think are not yet
rlm@109	632 represented at all in AI and in theories of human learning in any []
rlm@109	633 way — although some people are trying to study learning of mathematics.
rlm@109	634 </p>
rlm@109	635 </div>
rlm@109	636 </div>
rlm@109	637
rlm@109	638 </div>
rlm@109	639
rlm@109	640 <div id="outline-container-4" class="outline-2">
rlm@109	641 <h2 id="sec-4"><span class="section-number-2">4</span> Animal intelligence</h2>
rlm@109	642 <div class="outline-text-2" id="text-4">
rlm@109	643
rlm@109	644
rlm@109	645
rlm@109	646 </div>
rlm@109	647
rlm@109	648 <div id="outline-container-4-1" class="outline-3">
rlm@109	649 <h3 id="sec-4-1"><span class="section-number-3">4.1</span> The priority is <i>cataloguing</i> what competences have evolved, not ranking them.</h3>
rlm@109	650 <div class="outline-text-3" id="text-4-1">
rlm@109	651
rlm@109	652 <p>[22:03] I wasn't going to challenge the claim that humans can do more
rlm@109	653 sophisticated forms of [tracking], just to mention that there are some
rlm@109	654 things that other animals can do which are in some ways comparable,
rlm@109	655 and some ways superior to [things] that humans can do. In particular,
rlm@109	656 there are species of birds and also, I think, some rodents ---
rlm@109	657 squirrels, or something — I don't know enough about the variety ---
rlm@109	658 that can hide nuts and remember where they've hidden them, and go back
rlm@109	659 to them. And there have been tests which show that some birds are able
rlm@109	660 to hide tens — you know, [eighteen] or something nuts — and to
rlm@109	661 remember which ones have been taken, which ones haven't, and so
rlm@109	662 on. And I suspect most humans can't do that. I wouldn't want to say
rlm@109	663 categorically that maybe we couldn't, because humans are very
rlm@109	664 [varied], and also [a few] people can develop particular competences
rlm@109	665 through training. But it's certainly not something I can do.
rlm@109	666 </p>
rlm@109	667
rlm@109	668 </div>
rlm@109	669
rlm@109	670 </div>
rlm@109	671
rlm@109	672 <div id="outline-container-4-2" class="outline-3">
rlm@109	673 <h3 id="sec-4-2"><span class="section-number-3">4.2</span> AI can be used to test philosophical theories</h3>
rlm@109	674 <div class="outline-text-3" id="text-4-2">
rlm@109	675
rlm@109	676 <p>[23:01] But I also would like to say that I am not myself particularly
rlm@109	677 interested in trying to align animal intelligences according to any
rlm@109	678 kind of scale of superiority; I'm just trying to understand what it
rlm@109	679 was that biological evolution produced, and how it works, and I'm
rlm@109	680 interested in AI <i>mainly</i> because I think that when one comes up with
rlm@109	681 theories about how these things work, one needs to have some way of
rlm@109	682 testing the theory. And AI provides ways of implementing and testing
rlm@109	683 theories that were not previously available: Immanuel Kant was trying
rlm@109	684 to come up with theories about how minds work, but he didn't have any
rlm@109	685 kind of a mechanism that he could build to test his theory about the
rlm@109	686 nature of mathematical knowledge, for instance, or how concepts were
rlm@109	687 developed from babyhood onward. Whereas now, if we do develop a
rlm@109	688 theory, we have a criterion of adequacy, namely it should be precise
rlm@109	689 enough and rich enough and detailed to enable a model to be
rlm@109	690 built. And then we can see if it works.
rlm@109	691 </p>
rlm@109	692 <p>
rlm@109	693 [24:07] If it works, it doesn't mean we've proved that the theory is
rlm@109	694 correct; it just shows it's a candidate. And if it doesn't work, then
rlm@109	695 it's not a candidate as it stands; it would need to be modified in
rlm@109	696 some way.
rlm@109	697 </p>
rlm@109	698 </div>
rlm@109	699 </div>
rlm@109	700
rlm@109	701 </div>
rlm@109	702
rlm@109	703 <div id="outline-container-5" class="outline-2">
rlm@109	704 <h2 id="sec-5"><span class="section-number-2">5</span> Is abstract general intelligence feasible?</h2>
rlm@109	705 <div class="outline-text-2" id="text-5">
rlm@109	706
rlm@109	707
rlm@109	708
rlm@109	709 </div>
rlm@109	710
rlm@109	711 <div id="outline-container-5-1" class="outline-3">
rlm@109	712 <h3 id="sec-5-1"><span class="section-number-3">5.1</span> It's misleading to compare the brain and its neurons to a computer made of transistors</h3>
rlm@109	713 <div class="outline-text-3" id="text-5-1">
rlm@109	714
rlm@109	715 <p>[24:27] I think there's a lot of optimism based on false clues:
rlm@109	716 the…for example, one of the false clues is to count the number of
rlm@109	717 neurons in the brain, and then talk about the number of transistors
rlm@109	718 you can fit into a computer or something, and then compare them. It
rlm@109	719 might turn out that the study of the way synapses work (which leads
rlm@109	720 some people to say that a typical synapse [] in the human brain has
rlm@109	721 computational power comparable to the Internet a few years ago,
rlm@109	722 because of the number of different molecules that are doing things,
rlm@109	723 the variety of types of things that are being done in those molecular
rlm@109	724 interactions, and the speed at which they happen, if you somehow count
rlm@109	725 up the number of operations per second or something, then you get
rlm@109	726 these comparable figures).
rlm@109	727 </p>
rlm@109	728 </div>
rlm@109	729
rlm@109	730 </div>
rlm@109	731
rlm@109	732 <div id="outline-container-5-2" class="outline-3">
rlm@109	733 <h3 id="sec-5-2"><span class="section-number-3">5.2</span> For example, brains may rely heavily on chemical information processing</h3>
rlm@109	734 <div class="outline-text-3" id="text-5-2">
rlm@109	735
rlm@109	736 <p>Now even if the details aren't right, there may just be a lot of
rlm@109	737 information processing that…going on in brains at the <i>molecular</i>
rlm@109	738 level, not the neural level. Then, if that's the case, the processing
rlm@109	739 units will be orders of magnitude larger in number than the number of
rlm@109	740 neurons. And it's certainly the case that all the original biological
rlm@109	741 forms of information processing were chemical; there weren't brains
rlm@109	742 around, and still aren't in most microbes. And even when humans grow
rlm@109	743 their brains, the process of starting from a fertilized egg and
rlm@109	744 producing this rich and complex structure is, for much of the time,
rlm@109	745 under the control of chemical computations, chemical information
rlm@109	746 processing—of course combined with physical sorts of materials and
rlm@109	747 energy and so on as well.
rlm@109	748 </p>
rlm@109	749 <p>
rlm@109	750 [26:25] So it would seem very strange if all that capability was
rlm@109	751 something thrown away when you've got a brain and all the information
rlm@109	752 processing, the [challenges that were handled in making a brain],
rlm@109	753 … This is handwaving on my part; I'm just saying that we <i>might</i>
rlm@109	754 learn that what brains do is not what we think they do, and that
rlm@109	755 problems of replicating them are not what we think they are, solely in
rlm@109	756 terms of numerical estimate of time scales, the number of components,
rlm@109	757 and so on.
rlm@109	758 </p>
rlm@109	759 </div>
rlm@109	760
rlm@109	761 </div>
rlm@109	762
rlm@109	763 <div id="outline-container-5-3" class="outline-3">
rlm@109	764 <h3 id="sec-5-3"><span class="section-number-3">5.3</span> Brain algorithms may simply be optimized for certain kinds of information processing other than bit manipulations</h3>
rlm@109	765 <div class="outline-text-3" id="text-5-3">
rlm@109	766
rlm@109	767 <p>[26:56] But apart from that, the other basis of skepticism concerns
rlm@109	768 how well we understand what the problems are. I think there are many
rlm@109	769 people who try to formalize the problems of designing an intelligent
rlm@109	770 system in terms of streams of information thought of as bit streams or
rlm@109	771 collections of bit streams, and they think of as the problems of
rlm@109	772 intelligence as being the construction or detection of patterns in
rlm@109	773 those, and perhaps not just detection of patterns, but detection of
rlm@109	774 patterns that are useable for sending <i>out</i> streams to control motors
rlm@109	775 and so on in order to []. And that way of conceptualizing the problem
rlm@109	776 may lead on the one hand to oversimplification, so that the things
rlm@109	777 that <i>would</i> be achieved, if those goals were achieved, maybe much
rlm@109	778 simpler, in some ways inadequate. Or the replication of human
rlm@109	779 intelligence, or the matching of human intelligence—or for that
rlm@109	780 matter, squirrel intelligence—but in another way, it may also make
rlm@109	781 the problem harder: it may be that some of the kinds of things that
rlm@109	782 biological evolution has achieved can't be done that way. And one of
rlm@109	783 the ways that might turn out to be the case is not because it's not
rlm@109	784 impossible in principle to do some of the information processing on
rlm@109	785 artificial computers-based-on-transistors and other bit-manipulating
rlm@109	786 []—but it may just be that the computational complexity of solving
rlm@109	787 problems, processes, or finding solutions to complex problems, are
rlm@109	788 much greater and therefore you might need a much larger universe than
rlm@109	789 we have available in order to do things.
rlm@109	790 </p>
rlm@109	791 </div>
rlm@109	792
rlm@109	793 </div>
rlm@109	794
rlm@109	795 <div id="outline-container-5-4" class="outline-3">
rlm@109	796 <h3 id="sec-5-4"><span class="section-number-3">5.4</span> Example: find the shortest path by dangling strings</h3>
rlm@109	797 <div class="outline-text-3" id="text-5-4">
rlm@109	798
rlm@109	799 <p>[28:55] Then if the underlying mechanisms were different, the
rlm@109	800 information processing mechanisms, they might be better tailored to
rlm@109	801 particular sorts of computation. There's a [] example, which is
rlm@109	802 finding the shortest route if you've got a collection of roads, and
rlm@109	803 they may be curved roads, and lots of tangled routes from A to B to C,
rlm@109	804 and so on. And if you start at A and you want to get to Z — a place
rlm@109	805 somewhere on that map — the process of finding the shortest route
rlm@109	806 will involve searching through all these different possibilities and
rlm@109	807 rejecting some that are longer than others and so on. But if you make
rlm@109	808 a model of that map out of string, where these strings are all laid
rlm@109	809 out on the maps and so have the lengths of the routes. Then if you
rlm@109	810 hold the two knots in the string – it's a network of string — which
rlm@109	811 correspond to the start point and end point, then <i>pull</i>, then the
rlm@109	812 bits of string that you're left with in a straight line will give you
rlm@109	813 the shortest route, and that process of pulling just gets you the
rlm@109	814 solution very rapidly in a parallel computation, where all the others
rlm@109	815 just hang by the wayside, so to speak.
rlm@109	816 </p>
rlm@109	817 </div>
rlm@109	818
rlm@109	819 </div>
rlm@109	820
rlm@109	821 <div id="outline-container-5-5" class="outline-3">
rlm@109	822 <h3 id="sec-5-5"><span class="section-number-3">5.5</span> In sum, we know surprisingly little about the kinds of problems that evolution solved, and the manner in which they were solved.</h3>
rlm@109	823 <div class="outline-text-3" id="text-5-5">
rlm@109	824
rlm@109	825 <p>[30:15] Now, I'm not saying brains can build networks of string and
rlm@109	826 pull them or anything like that; that's just an illustration of how if
rlm@109	827 you have the right representation, correctly implemented—or suitably
rlm@109	828 implemented—for a problem, then you can avoid very combinatorially
rlm@109	829 complex searches, which will maybe grow exponentially with the number
rlm@109	830 of components in your map, whereas with this thing, the time it takes
rlm@109	831 won't depend on how many strings you've [got on the map]; you just
rlm@109	832 pull, and it will depend only on the shortest route that exists in
rlm@109	833 there. Even if that shortest route wasn't obvious on the original map.
rlm@109	834 </p>
rlm@109	835
rlm@109	836 <p>
rlm@109	837 [30:59] So that's a rather long-winded way of formulating the
rlm@109	838 conjecture which—of supporting, a roundabout way of supporting the
rlm@109	839 conjecture that there may be something about the way molecules perform
rlm@109	840 computations where they have the combination of continuous change as
rlm@109	841 things move through space and come together and move apart, and
rlm@109	842 whatever — and also snap into states that then persist, so [as you
rlm@109	843 learn from] quantum mechanics, you can have stable molecular
rlm@109	844 structures which are quite hard to separate, and then in catalytic
rlm@109	845 processes you can separate them, or extreme temperatures, or strong
rlm@109	846 forces, but they may nevertheless be able to move very rapidly in some
rlm@109	847 conditions in order to perform computations.
rlm@109	848 </p>
rlm@109	849 <p>
rlm@109	850 [31:49] Now there may be things about that kind of structure that
rlm@109	851 enable searching for solutions to <i>certain</i> classes of problems to be
rlm@109	852 done much more efficiently (by brain) than anything we could do with
rlm@109	853 computers. It's just an open question.
rlm@109	854 </p>
rlm@109	855 <p>
rlm@109	856 [32:04] So it <i>might</i> turn out that we need new kinds of technology
rlm@109	857 that aren't on the horizon in order to replicate the functions that
rlm@109	858 animal brains perform —or, it might not. I just don't know. I'm not
rlm@109	859 claiming that there's strong evidence for that; I'm just saying that
rlm@109	860 it might turn out that way, partly because I think we know less than
rlm@109	861 many people think we know about what biological evolution achieved.
rlm@109	862 </p>
rlm@109	863 <p>
rlm@109	864 [32:28] There are some other possibilities: we may just find out that
rlm@109	865 there are shortcuts no one ever thought of, and it will all happen
rlm@109	866 much more quickly—I have an open mind; I'd be surprised, but it
rlm@109	867 could turn up. There <i>is</i> something that worries me much more than the
rlm@109	868 singularity that most people talk about, which is machines achieving
rlm@109	869 human-level intelligence and perhaps taking over [the] planet or
rlm@109	870 something. There's what I call the <i>singularity of cognitive catch-up</i> …
rlm@109	871 </p>
rlm@109	872 </div>
rlm@109	873 </div>
rlm@109	874
rlm@109	875 </div>
rlm@109	876
rlm@109	877 <div id="outline-container-6" class="outline-2">
rlm@109	878 <h2 id="sec-6"><span class="section-number-2">6</span> A singularity of cognitive catch-up</h2>
rlm@109	879 <div class="outline-text-2" id="text-6">
rlm@109	880
rlm@109	881
rlm@109	882
rlm@109	883 </div>
rlm@109	884
rlm@109	885 <div id="outline-container-6-1" class="outline-3">
rlm@109	886 <h3 id="sec-6-1"><span class="section-number-3">6.1</span> What if it will take a lifetime to learn enough to make something new?</h3>
rlm@109	887 <div class="outline-text-3" id="text-6-1">
rlm@109	888
rlm@109	889 <p>… SCC, singularity of cognitive catch-up, which I think we're close
rlm@109	890 to, or maybe have already reached—I'll explain what I mean by
rlm@109	891 that. One of the products of biological evolution—and this is one of
rlm@109	892 the answers to your earlier questions which I didn't get on to—is
rlm@109	893 that humans have not only the ability to make discoveries that none of
rlm@109	894 their ancestors have ever made, but to shorten the time required for
rlm@109	895 similar achievements to be reached by their offspring and their
rlm@109	896 descendants. So once we, for instance, worked out ways of complex
rlm@109	897 computations, or ways of building houses, or ways of finding our way
rlm@109	898 around, we don't need…our children don't need to work it out for
rlm@109	899 themselves by the same lengthy trial and error procedure; we can help
rlm@109	900 them get there much faster.
rlm@109	901 </p>
rlm@109	902 <p>
rlm@109	903 Okay, well, what I've been referring to as the singularity of
rlm@109	904 cognitive catch-up depends on the fact that—fairly obvious, and it's
rlm@109	905 often been commented on—that in case of humans, it's not necessary
rlm@109	906 for each generation to learn what previous generations learned <i>in the same way</i>. And we can speed up learning once something has been
rlm@109	907 learned, [it is able to] be learned by new people. And that has meant
rlm@109	908 that the social processes that support that kind of education of the
rlm@109	909 young can enormously accelerate what would have taken…perhaps
rlm@109	910 thousands [or] millions of years for evolution to produce, can happen in
rlm@109	911 a much shorter time.
rlm@109	912 </p>
rlm@109	913
rlm@109	914 <p>
rlm@109	915 [34:54] But here's the catch: in order for a new advance to happen ---
rlm@109	916 so for something new to be discovered that wasn't there before, like
rlm@109	917 Newtonian mechanics, or the theory of relativity, or Beethoven's music
rlm@109	918 or [style] or whatever — the individuals have to have traversed a
rlm@109	919 significant amount of what their ancestors have learned, even if they
rlm@109	920 do it much faster than their ancestors, to get to the point where they
rlm@109	921 can see the gaps, the possibilities for going further than their
rlm@109	922 ancestors, or their parents or whatever, have done.
rlm@109	923 </p>
rlm@109	924 <p>
rlm@109	925 [35:27] Now in the case of knowledge of science, mathematics,
rlm@109	926 philosophy, engineering and so on, there's been a lot of accumulated
rlm@109	927 knowledge. And humans are living a <i>bit</i> longer than they used to, but
rlm@109	928 they're still living for [whatever it is], a hundred years, or for
rlm@109	929 most people, less than that. So you can imagine that there might come
rlm@109	930 a time when in a normal human lifespan, it's not possible for anyone
rlm@109	931 to learn enough to understand the scope and limits of what's already
rlm@109	932 been achieved in order to see the potential for going beyond it and to
rlm@109	933 build on what's already been done to make that…those future steps.
rlm@109	934 </p>
rlm@109	935 <p>
rlm@109	936 [36:10] So if we reach that stage, we will have reached the
rlm@109	937 singularity of cognitive catch-up because the process of education
rlm@109	938 that enables individuals to learn faster than their ancestors did is
rlm@109	939 the catching-up process, and it may just be that we at some point
rlm@109	940 reach a point where catching up can only happen within a lifetime of
rlm@109	941 an individual, and after that they're dead and they can't go
rlm@109	942 beyond. And I have some evidence that there's a lot of that around
rlm@109	943 because I see a lot of people coming up with what <i>they</i> think of as
rlm@109	944 new ideas which they've struggled to come up with, but actually they
rlm@109	945 just haven't taken in some of what was…some of what was done [] by
rlm@109	946 other people, in other places before them. And I think that despite
rlm@109	947 the availability of search engines which make it <i>easier</i> for people
rlm@109	948 to get the information—for instance, when I was a student, if I
rlm@109	949 wanted to find out what other people had done in the field, it was a
rlm@109	950 laborious process—going to the library, getting books, and
rlm@109	951 —whereas now, I can often do things in seconds that would have taken
rlm@109	952 hours. So that means that if seconds [are needed] for that kind of
rlm@109	953 work, my lifespan has been extended by a factor of ten or
rlm@109	954 something. So maybe that <i>delays</i> the singularity, but it may not
rlm@109	955 delay it enough. But that's an open question; I don't know. And it may
rlm@109	956 just be that in some areas, this is more of a problem than others. For
rlm@109	957 instance, it may be that in some kinds of engineering, we're handing
rlm@109	958 over more and more of the work to machines anyways and they can go on
rlm@109	959 doing it. So for instance, most of the production of computers now is
rlm@109	960 done by a computer-controlled machine—although some of the design
rlm@109	961 work is done by humans— a lot of <i>detail</i> of the design is done by
rlm@109	962 computers, and they produce the next generation, which then produces
rlm@109	963 the next generation, and so on.
rlm@109	964 </p>
rlm@109	965 <p>
rlm@109	966 [37:57] I don't know if humans can go on having major advances, so
rlm@109	967 it'll be kind of sad if we can't.
rlm@109	968 </p>
rlm@109	969 </div>
rlm@109	970 </div>
rlm@109	971
rlm@109	972 </div>
rlm@109	973
rlm@109	974 <div id="outline-container-7" class="outline-2">
rlm@109	975 <h2 id="sec-7"><span class="section-number-2">7</span> Spatial reasoning: a difficult problem</h2>
rlm@109	976 <div class="outline-text-2" id="text-7">
rlm@109	977
rlm@109	978
rlm@109	979 <p>
rlm@109	980 [38:15] Okay, well, there are different problems [ ] mathematics, and
rlm@109	981 they have to do with properties. So for instance a lot of mathematics
rlm@109	982 that can be expressed in terms of logical structures or algebraic
rlm@109	983 structures and those are pretty well suited for manipulation and…on
rlm@109	984 computers, and if a problem can be specified using the
rlm@109	985 logical/algebraic notation, and the solution method requires creating
rlm@109	986 something in that sort of notation, then computers are pretty good,
rlm@109	987 and there are lots of mathematical tools around—there are theorem
rlm@109	988 provers and theorem checkers, and all kinds of things, which couldn't
rlm@109	989 have existed fifty, sixty years ago, and they will continue getting
rlm@109	990 better.
rlm@109	991 </p>
rlm@109	992
rlm@109	993 <p>
rlm@109	994 But there was something that I was <a href="#sec-3-4">alluding to earlier</a> when I gave the
rlm@109	995 example of how you can reason about what you will see by changing your
rlm@109	996 position in relation to a door, where what you are doing is using your
rlm@109	997 grasp of spatial structures and how as one spatial relationship
rlm@109	998 changes namely you come closer to the door or move sideways and
rlm@109	999 parallel to the wall or whatever, other spatial relationships change
rlm@109	1000 in parallel, so the lines from your eyes through to other parts of
rlm@109	1001 the…parts of the room on the other side of the doorway change,
rlm@109	1002 spread out more as you go towards the doorway, and as you move
rlm@109	1003 sideways, they don't spread out differently, but focus on different
rlm@109	1004 parts of the internal … that they access different parts of the
rlm@109	1005 … of the room.
rlm@109	1006 </p>
rlm@109	1007 <p>
rlm@109	1008 Now, those are examples of ways of thinking about relationships and
rlm@109	1009 changing relationships which are not the same as thinking about what
rlm@109	1010 happens if I replace this symbol with that symbol, or if I substitute
rlm@109	1011 this expression in that expression in a logical formula. And at the
rlm@109	1012 moment, I do not believe that there is anything in AI amongst the
rlm@109	1013 mathematical reasoning community, the theorem-proving community, that
rlm@109	1014 can model the processes that go on when a young child starts learning
rlm@109	1015 to do Euclidean geometry and is taught things about—for instance, I
rlm@109	1016 can give you a proof that the angles of any triangle add up to a
rlm@109	1017 straight line, 180 degrees.
rlm@109	1018 </p>
rlm@109	1019
rlm@109	1020 </div>
rlm@109	1021
rlm@109	1022 <div id="outline-container-7-1" class="outline-3">
rlm@109	1023 <h3 id="sec-7-1"><span class="section-number-3">7.1</span> Example: Spatial proof that the angles of any triangle add up to a half-circle</h3>
rlm@109	1024 <div class="outline-text-3" id="text-7-1">
rlm@109	1025
rlm@109	1026 <p>There are standard proofs which involves starting with one triangle,
rlm@109	1027 then adding a line parallel to the base one of my former students,
rlm@109	1028 Mary Pardoe, came up with which I will demonstrate with this <he holds
rlm@109	1029 up a pen> — can you see it? If I have a triangle here that's got
rlm@109	1030 three sides, if I put this thing on it, on one side — let's say the
rlm@109	1031 bottom—I can rotate it until it lies along the second…another
rlm@109	1032 side, and then maybe move it up to the other end ~. Then I can rotate
rlm@109	1033 it again, until it lies on the third side, and move it back to the
rlm@109	1034 other end. And then I'll rotate it again and it'll eventually end up
rlm@109	1035 on the original side, but it will have changed the direction it's
rlm@109	1036 pointing in — and it won't have crossed over itself so it will have
rlm@109	1037 gone through a half-circle, and that says that the three angles of a
rlm@109	1038 triangle add up to the rotations of half a circle, which is a
rlm@109	1039 beautiful kind of proof and almost anyone can understand it. Some
rlm@109	1040 mathematicians don't like it, because they say it hides some of the
rlm@109	1041 assumptions, but nevertheless, as far as I'm concerned, it's an
rlm@109	1042 example of a human ability to do reasoning which, once you've
rlm@109	1043 understood it, you can see will apply to any triangle — it's got to
rlm@109	1044 be a planar triangle — not a triangle on a globe, because then the
rlm@109	1045 angles can add up to more than … you can have three <i>right</i> angles
rlm@109	1046 if you have an equator…a line on the equator, and a line going up to
rlm@109	1047 to the north pole of the earth, and then you have a right angle and
rlm@109	1048 then another line going down to the equator, and you have a right
rlm@109	1049 angle, right angle, right angle, and they add up to more than a
rlm@109	1050 straight line. But that's because the triangle isn't in the plane,
rlm@109	1051 it's on a curved surface. In fact, that's one of the
rlm@109	1052 differences…definitional differences you can take between planar and
rlm@109	1053 curved surfaces: how much the angles of a triangle add up to. But our
rlm@109	1054 ability to <i>visualize</i> and notice the generality in that process, and
rlm@109	1055 see that you're going to be able to do the same thing using triangles
rlm@109	1056 that stretch in all sorts of ways, or if it's a million times as
rlm@109	1057 large, or if it's made…you know, written on, on…if it's drawn in
rlm@109	1058 different colors or whatever — none of that's going to make any
rlm@109	1059 difference to the essence of that process. And that ability to see
rlm@109	1060 the commonality in a spatial structure which enables you to draw some
rlm@109	1061 conclusions with complete certainty—subject to the possibility that
rlm@109	1062 sometimes you make mistakes, but when you make mistakes, you can
rlm@109	1063 discover them, as has happened in the history of geometrical theorem
rlm@109	1064 proving. Imre Lakatos had a wonderful book called <a href="http://en.wikipedia.org/wiki/Proofs_and_Refutations"><i>Proofs and Refutations</i></a> — which I won't try to summarize — but he has
rlm@109	1065 examples: mistakes were made; that was because people didn't always
rlm@109	1066 realize there were subtle subcases which had slightly different
rlm@109	1067 properties, and they didn't take account of that. But once they're
rlm@109	1068 noticed, you rectify that.
rlm@109	1069 </p>
rlm@109	1070 </div>
rlm@109	1071
rlm@109	1072 </div>
rlm@109	1073
rlm@109	1074 <div id="outline-container-7-2" class="outline-3">
rlm@109	1075 <h3 id="sec-7-2"><span class="section-number-3">7.2</span> Geometric results are fundamentally different than experimental results in chemistry or physics.</h3>
rlm@109	1076 <div class="outline-text-3" id="text-7-2">
rlm@109	1077
rlm@109	1078 <p>[43:28] But it's not the same as doing experiments in chemistry and
rlm@109	1079 physics, where you can't be sure it'll be the same on [] or at a high
rlm@109	1080 temperature, or in a very strong magnetic field — with geometric
rlm@109	1081 reasoning, in some sense you've got the full information in front of
rlm@109	1082 you; even if you don't always notice an important part of it. So, that
rlm@109	1083 kind of reasoning (as far as I know) is not implemented anywhere in a
rlm@109	1084 computer. And most people who do research on trying to model
rlm@109	1085 mathematical reasoning, don't pay any attention to that, because of
rlm@109	1086 … they just don't think about it. They start from somewhere else,
rlm@109	1087 maybe because of how they were educated. I was taught Euclidean
rlm@109	1088 geometry at school. Were you?
rlm@109	1089 </p>
rlm@109	1090 <p>
rlm@109	1091 (Adam ford: Yeah)
rlm@109	1092 </p>
rlm@109	1093 <p>
rlm@109	1094 Many people are not now. Instead they're taught set theory, and
rlm@109	1095 logic, and arithmetic, and [algebra], and so on. And so they don't use
rlm@109	1096 that bit of their brains, without which we wouldn't have built any of
rlm@109	1097 the cathedrals, and all sorts of things we now depend on.
rlm@109	1098 </p>
rlm@109	1099 </div>
rlm@109	1100 </div>
rlm@109	1101
rlm@109	1102 </div>
rlm@109	1103
rlm@109	1104 <div id="outline-container-8" class="outline-2">
rlm@109	1105 <h2 id="sec-8"><span class="section-number-2">8</span> Is near-term artificial general intelligence likely?</h2>
rlm@109	1106 <div class="outline-text-2" id="text-8">
rlm@109	1107
rlm@109	1108
rlm@109	1109
rlm@109	1110 </div>
rlm@109	1111
rlm@109	1112 <div id="outline-container-8-1" class="outline-3">
rlm@109	1113 <h3 id="sec-8-1"><span class="section-number-3">8.1</span> Two interpretations: a single mechanism for all problems, or many mechanisms unified in one program.</h3>
rlm@109	1114 <div class="outline-text-3" id="text-8-1">
rlm@109	1115
rlm@109	1116
rlm@109	1117 <p>
rlm@109	1118 [44:35] Well, this relates to what's meant by general. And when I
rlm@109	1119 first encountered the AGI community, I thought that what they all
rlm@109	1120 meant by general intelligence was <i>uniform</i> intelligence ---
rlm@109	1121 intelligence based on some common simple (maybe not so simple, but)
rlm@109	1122 single powerful mechanism or principle of inference. And there are
rlm@109	1123 some people in the community who are trying to produce things like
rlm@109	1124 that, often in connection with algorithmic information theory and
rlm@109	1125 computability of information, and so on. But there's another sense of
rlm@109	1126 general which means that the system of general intelligence can do
rlm@109	1127 lots of different things, like perceive things, understand language,
rlm@109	1128 move around, make things, and so on — perhaps even enjoy a joke;
rlm@109	1129 that's something that's not nearly on the horizon, as far as I
rlm@109	1130 know. Enjoying a joke isn't the same as being able to make laughing
rlm@109	1131 noises.
rlm@109	1132 </p>
rlm@109	1133 <p>
rlm@109	1134 Given, then, that there are these two notions of general
rlm@109	1135 intelligence—there's one that looks for one uniform, possibly
rlm@109	1136 simple, mechanism or collection of ideas and notations and algorithms,
rlm@109	1137 that will deal with any problem that's solvable — and the other
rlm@109	1138 that's general in the sense that it can do lots of different things
rlm@109	1139 that are combined into an integrated architecture (which raises lots
rlm@109	1140 of questions about how you combine these things and make them work
rlm@109	1141 together) and we humans, certainly, are of the second kind: we do all
rlm@109	1142 sorts of different things, and other animals also seem to be of the
rlm@109	1143 second kind, perhaps not as general as humans. Now, it may turn out
rlm@109	1144 that in some near future time, who knows—decades, a few
rlm@109	1145 decades—you'll be able to get machines that are capable of solving
rlm@109	1146 in a time that will depend on the nature of the problem, but any
rlm@109	1147 problem that is solvable, and they will be able to do it in some sort
rlm@109	1148 of tractable time — of course, there are some problems that are
rlm@109	1149 solvable that would require a larger universe and a longer history
rlm@109	1150 than the history of the universe, but apart from that constraint,
rlm@109	1151 these machines will be able to do anything []. But to be able to do
rlm@109	1152 some of the kinds of things that humans can do, like the kinds of
rlm@109	1153 geometrical reasoning where you look at the shape and you abstract
rlm@109	1154 away from the precise angles and sizes and shapes and so on, and
rlm@109	1155 realize there's something general here, as must have happened when our
rlm@109	1156 ancestors first made the discoveries that eventually put together in
rlm@109	1157 Euclidean geometry.
rlm@109	1158 </p>
rlm@109	1159 <p>
rlm@109	1160 It may be that that requires mechanisms of a kind that we don't know
rlm@109	1161 anything about at the moment. Maybe brains are using molecules and
rlm@109	1162 rearranging molecules in some way that supports that kind of
rlm@109	1163 reasoning. I'm not saying they are — I don't know, I just don't see
rlm@109	1164 any simple…any obvious way to map that kind of reasoning capability
rlm@109	1165 onto what we currently do on computers. There is—and I just
rlm@109	1166 mentioned this briefly beforehand—there is a kind of thing that's
rlm@109	1167 sometimes thought of as a major step in that direction, namely you can
rlm@109	1168 build a machine (or a software system) that can represent some
rlm@109	1169 geometrical structure, and then be told about some change that's going
rlm@109	1170 to happen to it, and it can predict in great detail what'll
rlm@109	1171 happen. And this happens for instance in game engines, where you say
rlm@109	1172 we have all these blocks on the table and I'll drop one other block,
rlm@109	1173 and then [the thing] uses Newton's laws and properties of rigidity of
rlm@109	1174 the parts and the elasticity and also stuff about geometries and space
rlm@109	1175 and so on, to give you a very accurate representation of what'll
rlm@109	1176 happen when this brick lands on this pile of things, [it'll bounce and
rlm@109	1177 go off, and so on]. And you just, with more memory and more CPU power,
rlm@109	1178 you can increase the accuracy— but that's totally different than
rlm@109	1179 looking at <i>one</i> example, and working out what will happen in a whole
rlm@109	1180 <i>range</i> of cases at a higher level of abstraction, whereas the game
rlm@109	1181 engine does it in great detail for <i>just</i> this case, with <i>just</i> those
rlm@109	1182 precise things, and it won't even know what the generalizations are
rlm@109	1183 that it's using that would apply to others []. So, in that sense, [we]
rlm@109	1184 may get AGI — artificial general intelligence — pretty soon, but
rlm@109	1185 it'll be limited in what it can do. And the other kind of general
rlm@109	1186 intelligence which combines all sorts of different things, including
rlm@109	1187 human spatial geometrical reasoning, and maybe other things, like the
rlm@109	1188 ability to find things funny, and to appreciate artistic features and
rlm@109	1189 other things may need forms of pattern-mechanism, and I have an open
rlm@109	1190 mind about that.
rlm@109	1191 </p>
rlm@109	1192 </div>
rlm@109	1193 </div>
rlm@109	1194
rlm@109	1195 </div>
rlm@109	1196
rlm@109	1197 <div id="outline-container-9" class="outline-2">
rlm@109	1198 <h2 id="sec-9"><span class="section-number-2">9</span> Abstract General Intelligence impacts</h2>
rlm@109	1199 <div class="outline-text-2" id="text-9">
rlm@109	1200
rlm@109	1201
rlm@109	1202 <p>
rlm@109	1203 [49:53] Well, as far as the first type's concerned, it could be useful
rlm@109	1204 for all kinds of applications — there are people who worry about
rlm@109	1205 where there's a system that has that type of intelligence, might in
rlm@109	1206 some sense take over control of the planet. Well, humans often do
rlm@109	1207 stupid things, and they might do something stupid that would lead to
rlm@109	1208 disaster, but I think it's more likely that there would be other
rlm@109	1209 things [] lead to disaster— population problems, using up all the
rlm@109	1210 resources, destroying ecosystems, and whatever. But certainly it would
rlm@109	1211 go on being useful to have these calculating devices. Now, as for the
rlm@109	1212 second kind of them, I don't know—if we succeeded at putting
rlm@109	1213 together all the parts that we find in humans, we might just make an
rlm@109	1214 artificial human, and then we might have some of them as your friends,
rlm@109	1215 and some of them we might not like, and some of them might become
rlm@109	1216 teachers or whatever, composers — but that raises a question: could
rlm@109	1217 they, in some sense, be superior to us, in their learning
rlm@109	1218 capabilities, their understanding of human nature, or maybe their
rlm@109	1219 wickedness or whatever — these are all issues in which I expect the
rlm@109	1220 best science fiction writers would give better answers than anything I
rlm@109	1221 could do, but I did once fantasize when I [back] in 1978, that perhaps
rlm@109	1222 if we achieved that kind of thing, that they would be wise, and gentle
rlm@109	1223 and kind, and realize that humans are an inferior species that, you
rlm@109	1224 know, have some good features, so they'd keep us in some kind of
rlm@109	1225 secluded…restrictive kind of environment, keep us away from
rlm@109	1226 dangerous weapons, and so on. And find ways of cohabitating with
rlm@109	1227 us. But that's just fantasy.
rlm@109	1228 </p>
rlm@109	1229 <p>
rlm@109	1230 Adam Ford: Awesome. Yeah, there's an interesting story <i>With Folded Hands</i> where [the computers] want to take care of us and want to
rlm@109	1231 reduce suffering and end up lobotomizing everybody [but] keeping them
rlm@109	1232 alive so as to reduce the suffering.
rlm@109	1233 </p>
rlm@109	1234 <p>
rlm@109	1235 Aaron Sloman: Not all that different from <i>Brave New World</i>, where it
rlm@109	1236 was done with drugs and so on, but different humans are given
rlm@109	1237 different roles in that system, yeah.
rlm@109	1238 </p>
rlm@109	1239 <p>
rlm@109	1240 There's also <i>The Time Machine</i>, H.G. Wells, where the … in the
rlm@109	1241 distant future, humans have split in two: the Eloi, I think they were
rlm@109	1242 called, they lived underground, they were the [] ones, and then—no,
rlm@109	1243 the Morlocks lived underground; Eloi lived on the planet; they were
rlm@109	1244 pleasant and pretty but not very bright, and so on, and they were fed
rlm@109	1245 on by …
rlm@109	1246 </p>
rlm@109	1247 <p>
rlm@109	1248 Adam Ford: [] in the future.
rlm@109	1249 </p>
rlm@109	1250 <p>
rlm@109	1251 Aaron Sloman: As I was saying, if you ask science fiction writers,
rlm@109	1252 you'll probably come up with a wide variety of interesting answers.
rlm@109	1253 </p>
rlm@109	1254 <p>
rlm@109	1255 Adam Ford: I certainly have; I've spoken to [] of Birmingham, and
rlm@109	1256 Sean Williams, … who else?
rlm@109	1257 </p>
rlm@109	1258 <p>
rlm@109	1259 Aaron Sloman: Did you ever read a story by E.M. Forrester called <i>The Machine Stops</i> — very short story, it's <a href="http://archive.ncsa.illinois.edu/prajlich/forster.html">on the Internet somewhere</a>
rlm@109	1260 — it's about a time when people sitting … and this was written in
rlm@109	1261 about [1914 ] so it's about…over a hundred years ago … people are
rlm@109	1262 in their rooms, they sit in front of screens, and they type things,
rlm@109	1263 and they communicate with one another that way, and they don't meet;
rlm@109	1264 they have debates, and they give lectures to their audiences that way,
rlm@109	1265 and then there's a woman whose son says “I'd like to see
rlm@109	1266 you” and she says “What's the point? You've got me at
rlm@109	1267 this point ” but he wants to come and talk to her — I won't
rlm@109	1268 tell you how it ends, but.
rlm@109	1269 </p>
rlm@109	1270 <p>
rlm@109	1271 Adam Ford: Reminds me of the Internet.
rlm@109	1272 </p>
rlm@109	1273 <p>
rlm@109	1274 Aaron Sloman: Well, yes; he invented … it was just extraordinary
rlm@109	1275 that he was able to do that, before most of the components that we
rlm@109	1276 need for it existed.
rlm@109	1277 </p>
rlm@109	1278 <p>
rlm@109	1279 Adam Ford: [Another person who did that] was Vernor Vinge [] <i>True Names</i>.
rlm@109	1280 </p>
rlm@109	1281 <p>
rlm@109	1282 Aaron Sloman: When was that written?
rlm@109	1283 </p>
rlm@109	1284 <p>
rlm@109	1285 Adam Ford: The seventies.
rlm@109	1286 </p>
rlm@109	1287 <p>
rlm@109	1288 Aaron Sloman: Okay, well a lot of the technology was already around
rlm@109	1289 then. The original bits of internet were working, in about 1973, I was
rlm@109	1290 sitting … 1974, I was sitting at Sussex University trying to
rlm@109	1291 use…learn LOGO, the programming language, to decide whether it was
rlm@109	1292 going to be useful for teaching AI, and I was sitting [] paper
rlm@109	1293 teletype, there was paper coming out, transmitting ten characters a
rlm@109	1294 second from Sussex to UCL computer lab by telegraph cable, from there
rlm@109	1295 to somewhere in Norway via another cable, from there by satellite to
rlm@109	1296 California to a computer Xerox [] research center where they had
rlm@109	1297 implemented a computer with a LOGO system on it, with someone I had
rlm@109	1298 met previously in Edinburgh, Danny Bobrow, and he allowed me to have
rlm@109	1299 access to this sytem. So there I was typing. And furthermore, it was
rlm@109	1300 duplex typing, so every character I typed didn't show up on my
rlm@109	1301 terminal until it had gone all the way there and echoed back, so I
rlm@109	1302 would type, and the characters would come back four seconds later.
rlm@109	1303 </p>
rlm@109	1304 <p>
rlm@109	1305 [55:26] But that was the Internet, and I think Vernor Vinge was
rlm@109	1306 writing after that kind of thing had already started, but I don't
rlm@109	1307 know. Anyway.
rlm@109	1308 </p>
rlm@109	1309 <p>
rlm@109	1310 [55:41] Another…I mentioned H.G. Wells, <i>The Time Machine</i>. I
rlm@109	1311 recently discovered, because <a href="http://en.wikipedia.org/wiki/David_Lodge_(author)">David Lodge</a> had written a sort of
rlm@109	1312 semi-novel about him, that he had invented Wikipedia, in advance— he
rlm@109	1313 had this notion of an encyclopedia that was free to everybody, and
rlm@109	1314 everybody could contribute and [collaborate on it]. So, go to the
rlm@109	1315 science fiction writers to find out the future — well, a range of
rlm@109	1316 possible futures.
rlm@109	1317 </p>
rlm@109	1318 <p>
rlm@109	1319 Adam Ford: Well the thing is with science fiction writers, they have
rlm@109	1320 to maintain some sort of interest for their readers, after all the
rlm@109	1321 science fiction which reaches us is the stuff that publishers want to
rlm@109	1322 sell, and so there's a little bit of a … a bias towards making a
rlm@109	1323 plot device there, and so the dramatic sort of appeals to our
rlm@109	1324 amygdala, our lizard brain; we'll sort of stay there obviously to some
rlm@109	1325 extent. But I think that they do come up with sort of amazing ideas; I
rlm@109	1326 think it's worth trying to make these predictions; I think that we
rlm@109	1327 should more time on strategic forecasting, I mean take that seriously.
rlm@109	1328 </p>
rlm@109	1329 <p>
rlm@109	1330 Aaron Sloman: Well, I'm happy to leave that to others; I just want to
rlm@109	1331 try to understand these problems that bother me about how things
rlm@109	1332 work. And it may be that some would say that's irresponsible if I
rlm@109	1333 don't think about what the implications will be. Well, understanding
rlm@109	1334 how humans work <i>might</i> enable us to make [] humans — I suspect it
rlm@109	1335 wont happen in this century; I think it's going to be too difficult.
rlm@109	1336 </p></div>
rlm@109	1337 </div>
rlm@109	1338 </div>
rlm@109	1339
rlm@109	1340 <div id="postamble">
rlm@109	1341 <p class="date">Date: 2013-10-04 18:49:53 UTC</p>
rlm@109	1342 <p class="author">Author: Dylan Holmes</p>
rlm@109	1343 <p class="creator">Org version 7.7 with Emacs version 23</p>
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