[0.0][S02] I think there are just[1.625] [1.625][S02]a huge number of enormously interesting[3.74] [3.74][S02]philosophical questions that AI gives rise to.[7.33] [7.33][S02]What is the nature of the human mind?[10.2] [10.2][S02]What is the nature of mind?[11.76] [11.76][S01] What about consciousness?[13.53] [13.53][S02] I do think that is the wrong question,[15.96] [15.96][S02]and I think it's wrong in many ways.[17.58] [17.58][S01] How good do you think that AI is at reasoning?[20.04] [20.04][S02] Well, that's a very interesting and open[22.67] [22.67][S02]question, and somewhat controversial.[25.35] [25.35][S02]It really is astonishing to think[26.9] [26.9][S02]that every single child born today,[29.94] [29.94][S02]they're going to grow up in a world where they've never known,[32.61] [32.61][S02]they've never known a world in which machines[35.27] [35.27][S02]can't talk to them.[37.14] [37.14][MUSIC PLAYING][39.4] [41.66][S01] Welcome back to "Google DeepMind," the podcast.[44.52] [44.52][S01]My guest on this episode is Murray Shanahan,[47.31] [47.31][S01]Professor of Cognitive Robotics at Imperial College London[50.48] [50.48][S01]and Principal Research Scientist at Google DeepMind.[53.73] [53.73][S01]Now, we have all heard the stories[55.37] [55.37][S01]about people falling in love with their chat bots,[57.86] [57.86][S01]about people pushing large language models to contemplate[61.59] [61.59][S01]their own existence, or questioning[63.99] [63.99][S01]the limits of their conceptual understanding of reality.[67.0] [67.0][S01]But these kinds of questions about self-identity and thinking[71.85] [71.85][S01]and metacognition have been puzzling philosophers[75.21] [75.21][S01]for millennia already.[77.71] [77.71][S01]And so it makes sense that they should[79.53] [79.53][S01]be turning to AI to interrogate the most profound questions[83.82] [83.82][S01]about the nature of AI's intelligence,[86.38] [86.38][S01]of its current capabilities, even its consciousness[89.31] [89.31][S01]or other ones.[90.73] [90.73][S01]Murray Shanahan has been working in the field of AI since[93.96] [93.96][S01]the 1990s.[95.14] [95.14][S01]And if you've been following this podcast for a while,[97.99] [97.99][S01]you will remember him as the man that consulted on the 2014[102.18] [102.18][S01]science fiction film "Ex Machina,"[104.68] [104.68][S01]about a computer programmer who gets the chance to test[107.67] [107.67][S01]the intelligence of a female robot, Ava,[111.22] [111.22][S01]and ultimately questions whether she is conscious.[114.16] [114.16][S01]Welcome back to the podcast, Murray.[116.1] [116.1][S01]Just thinking back, because I know that you played a key role[119.18] [119.18][S01]in "Ex machina," shall we say, the Alex Garland film.[122.07] [122.07][S01]What do you think you got right in that film[125.31] [125.31][S01]and in other science fiction films that were around[127.58] [127.58][S01]at the time?[128.25] [128.25][S01]I mean, thinking back to 10, 15 years ago,[130.979] [130.979][S01]were we on the right track?[132.75] [132.75][S02] So one respect in which "Ex Machina"[135.65] [135.65][S02]really did a great service was that it does raise a whole load[139.1] [139.1][S02]of very interesting and provocative questions about[141.59] [141.59][S02]consciousness and about AI and consciousness, and therefore,[145.13] [145.13][S02]about consciousness itself.[147.12] [147.12][S02]So that's one huge success.[150.0] [150.0][S02]But it's interesting that just very shortly before "Ex Machina"[152.72] [152.72][S02]came out, "Her" came out.[154.65] [154.65][S02]So Spike Jones's movie "Her" came out.[156.84] [156.84][S02]And at the time, I really wasn't all that keen on "Her"[160.13] [160.13][S02]as a movie, because I just thought it was so implausible[163.46] [163.46][S02]that a person could fall in love with this kind of disembodied[166.55] [166.55][S02]voice, even if it's Scarlett Johansson's.[168.392] [168.392][S01] Yeah.[169.1] [169.1][S02] I mean, how wrong was that?[170.933] [170.933][S02]As a bit of prediction, I think "Her"[173.42] [173.42][S02]really did amazingly well at predicting the world we've got[176.82] [176.82][S02]now.[177.46] [177.46][S02]Now, we don't know quite how things[179.04] [179.04][S02]are going to unfold in the next few years[180.748] [180.748][S02]because maybe robotics will progress rapidly as[185.31] [185.31][S02]well in the way that language has in AI.[189.22] [189.22][S02]But at the moment, it's all about disembodied language.[192.85] [192.85][S02]And also, "Her" showed how people can, in fact,[196.5] [196.5][S02]very much form relationships, whatever, in the broadest sense,[201.78] [201.78][S02]with disembodied AI systems, which is an extraordinary thing,[205.84] [205.84][S02]really.[206.34] [206.34][S01] OK.[206.74] [206.74][S01]We're talking 10, 15 years ago, but your involvement in AI[209.31] [209.31][S01]goes back much further than this.[210.685] [210.685][S01]You knew John McCarthy.[211.71] [211.71][S02] I did know John McCarthy.[213.46] [213.46][S02]I knew him very well.[214.75] [214.75][S02]John McCarthy was a professor of computer science[218.28] [218.28][S02]and artificial intelligence.[219.93] [219.93][S02]Back in the day, he actually coined the phrase artificial[223.14] [223.14][S02]intelligence and was one of the authors[226.47] [226.47][S02]of the proposal for the very famous Dartmouth Conference that[230.94] [230.94][S02]took place in 1956, which was the first AI[233.73] [233.73][S02]conference in the world.[235.27] [235.27][S02]And that conference really mapped out the whole field.[237.52] [237.52][S02]People just weren't thinking about this kind of thing[239.728] [239.728][S02]seriously at all.[240.59] [240.59][S02]There was just a handful.[241.64] [241.64][S02]So I think he was a real radical thinker and always was.[244.695] [244.695][S01] OK.[245.32] [245.32][S01]That choice of words, artificial intelligence, back in 1955,[248.33] [248.33][S01]was it a good choice of words?[249.833] [249.833][S02] Yeah.[250.75] [250.75][S02]I mean, I still think it was.[252.35] [252.35][S02]I mean, I know that some people don't[254.11] [254.11][S02]think that perhaps it wasn't a good choice of words, but I--[257.746] [257.746][S01] Give us some of their arguments.[259.579] [259.579][S02] So first of all, there[261.49] [261.49][S02]is the word intelligence.[263.24] [263.24][S02]So intelligence itself is, in some ways, a very contentious[267.37] [267.37][S02]concept, especially if people think[271.33] [271.33][S02]about IQ tests and that kind of thing, and the idea[274.792] [274.792][S02]that intelligence is something that[276.25] [276.25][S02]can be quantified on a straightforward, simple scale,[281.095] [281.095][S02]and then some people are more intelligent than others.[283.7] [283.7][S02]And I think in psychology, it's well-recognized today[287.413] [287.413][S02]that there are many different kinds of intelligence.[289.58] [289.58][S02]And this is a really important point, right?[291.68] [291.68][S02]There is that concern about that word there.[293.873] [293.873][S02]So what would you have used differently?[295.54] [295.54][S02]Well, maybe artificial cognition or something.[298.18] [298.18][S02]I often use the word cognition to mean[301.68] [301.68][S02]kind of thinking and processing information and so on.[305.73] [305.73][S02]But it doesn't have the same ring to it, does it?[307.833] [307.833][S02]Let's be honest.[308.5] [308.5][S01] No.[309.125] [309.125][S01]Especially not now.[310.09] [310.09][S01]I think we're too far down this road, aren't we?[312.13] [312.13][S02] Yeah.[312.42] [312.42][S02]The word artificial, I don't really[314.04] [314.04][S02]have a problem with the word artificial.[315.893] [315.893][S02]That seems like the right kind of thing.[317.56] [317.56][S02]It's alluding to the fact that it's something that we've built[320.143] [320.143][S02]and it hasn't evolved in nature, and so that[322.44] [322.44][S02]seems the right sort of word.[323.648] [323.648][S01] The objection to that word, I guess,[325.648] [325.648][S01]is that ultimately, everything that artificial intelligence is[328.59] [328.59][S01]built on is, at some level, constructed by humans.[331.543] [331.543][S02] Sure.[332.46] [332.46][S02]Yes.[333.61] [333.61][S02]But it is.[334.62] [334.62][S02]So what's wrong with the word in that case?[337.72] [337.72][S02]I mean, I think that's true.[339.545] [339.545][S01] You were working on symbolic AI, right?[341.67] [341.67][S01]Just talk to us about the difference[343.17] [343.17][S01]between that and the other types and where we're at now with--[346.12] [346.12][S02] Yeah, absolutely.[347.11] [347.11][S02]Yeah.[347.61] [347.61][S02]Yeah, so the so-called symbolic paradigm[349.74] [349.74][S02]of artificial intelligence was very much preeminent,[352.76] [352.76][S02]very much dominant for decades, for many decades.[356.3] [356.3][S02]So the idea there is that it's all about the manipulation[360.76] [360.76][S02]of symbols and of language-like sentences and symbols[366.79] [366.79][S02]and using reasoning processes with those symbols.[371.9] [371.9][S02]So the classic example would be an expert system.[374.81] [374.81][S02]So where back in the 1980s, people were building these[379.3] [379.3][S02]expert systems, and the idea there was that you would try[381.82] [381.82][S02]to encode medical knowledge, say, in a set of rules,[386.33] [386.33][S02]and the rules would be something like, oh,[388.78] [388.78][S02]if the patient has temperature of 104 and their skin is purple[396.34] [396.34][S02]and then there's a 0.75% probability that they've got[401.5] [401.5][S02]skinny-itis or something.[403.63] [403.63][S02]You can tell that I'm not a medical doctor.[406.362] [406.362][S01] Just about.[407.32] [407.32][S02] Yeah.[408.237] [408.237][S02]And then so you'd have thousands and thousands of these sorts[410.99] [410.99][S02]of rules would be put into a kind of big knowledge base,[415.11] [415.11][S02]and then you'd have what was called an inference engine which[419.03] [419.03][S02]would carry out logical reasoning over all[421.49] [421.49][S02]of these rules and come to some conclusion about what[424.4] [424.4][S02]the likely disease was in.[425.783] [425.783][S01] But it was a lot of, if this, then that.[427.95] [427.95][S02] It was a lot of, yeah, if, then type rules,[430.37] [430.37][S02]largely.[430.92] [430.92][S02]And one of the big problems with that[432.26] [432.26][S02]is that, where do the rules come from?[433.98] [433.98][S02]Well, somebody has to write them all out, basically.[437.22] [437.22][S02]And so there was a whole field of knowledge elicitation[440.09] [440.09][S02]where you go around to experts and you try and extract[442.64] [442.64][S02]from them their understanding in their domain, which[445.94] [445.94][S02]could be medical diagnosis, it could be fixing photocopiers,[450.12] [450.12][S02]it could be the law.[451.65] [451.65][S02]And you try and codify all of this[453.59] [453.59][S02]into a computer comprehensible, very precise rule.[457.95] [457.95][S02]That is a very cumbersome process.[459.43] [459.43][S02]And also what you ended up with at the end[461.18] [461.18][S02]was very, very brittle.[462.3] [462.3][S02]It would go wrong in all kinds of ways.[464.82] [464.82][S02]Another big area of research was common sense,[467.66] [467.66][S02]because often it was realized that we implicitly[472.475] [472.475][S02]have an enormous amount of common sense[474.1] [474.1][S02]knowledge about the everyday world to do[476.38] [476.38][S02]with just everyday objects.[478.19] [478.19][S02]The fact that they're solid.[479.9] [479.9][S02]The fact that they move in certain ways,[482.57] [482.57][S02]they fit into each other in certain ways,[484.84] [484.84][S02]liquids and gases and gravity and all kinds[488.08] [488.08][S02]of things like that.[489.078] [489.078][S02]And we actually bring all of that knowledge[490.87] [490.87][S02]to bear all the time in what we're doing,[492.89] [492.89][S02]but it's sort of unconscious.[494.12] [494.12][S02]So then there was a big project, there[495.55] [495.55][S02]were various big projects to try and codify all[497.44] [497.44][S02]of that common sense knowledge.[498.8] [498.8][S02]And then trying to turn that into axioms and logic[501.46] [501.46][S02]and rules and everything was a nightmare.[503.36] [503.36][S02]So eventually, I think by about the early 2000s,[507.88] [507.88][S02]I really thought that this research paradigm was kind[510.82] [510.82][S02]of doomed, to be honest.[512.72] [512.72][S02]I sort of started moving away from it.[514.917] [514.917][S01] But then, of course, along came things[517.0] [517.0][S01]like neural networks and so on.[518.72] [518.72][S02] Yes.[519.595] [519.595][S01] Which was much less about if, then rules[522.46] [522.46][S01]and much more about extracting information[525.04] [525.04][S01]from a large amount of data.[526.85] [526.85][S02] Yeah.[527.08] [527.08][S01] But then I sort of wonder now about,[529.08] [529.08][S01]now that language is effectively cracked,[531.67] [531.67][S01]have we reached a higher level of abstraction[536.04] [536.04][S01]where we can go back to some more[538.5] [538.5][S01]of those symbolic techniques, some of those more[540.81] [540.81][S01]symbolic ideas?[541.435] [541.435][S02] Yeah.[542.352] [542.352][S02]Well, we certainly have, because nowadays[544.41] [544.41][S02]one of the hot topics at the moment with large language[548.04] [548.04][S02]models is reasoning.[550.29] [550.29][S02]So you have these so-called chain of thought models that[553.47] [553.47][S02]actually carry out a whole--[555.63] [555.63][S02]rather than simply generating an answer to a question,[559.03] [559.03][S02]they generate a whole chain of reasoning[561.63] [561.63][S02]before they issue the answer.[563.49] [563.49][S02]And that can be very, very effective.[565.51] [565.51][S02]So it's interesting how that harks[567.42] [567.42][S02]back in many ways to the kind of thing[570.42] [570.42][S02]that people were looking at back in the days of symbolic AI.[574.127] [574.127][S02]But the underlying substrate for doing all of that[576.21] [576.21][S02]is very, very different indeed, because it's not[578.43] [578.43][S02]hardcoded rules.[580.17] [580.17][S02]As you mentioned, it's neural networks that have learned.[584.015] [584.015][S01] Let me pick up on that point about reasoning.[586.39] [586.39][S01]As a philosopher, background in logic,[588.61] [588.61][S01]how good do you think the AI is at reasoning?[591.25] [591.25][S02] Well, that's a very interesting and open[593.91] [593.91][S02]question, and somewhat controversial.[595.96] [595.96][S02]So computer scientists and AI people,[598.54] [598.54][S02]they have a particular notion of reasoning, a particular concept[602.01] [602.01][S02]of reasoning, which very much harks back to formal logic[605.25] [605.25][S02]and theorem proving.[606.99] [606.99][S02]So in the days of symbolic AI, for example, then you[610.29] [610.29][S02]had systems that were really very good at doing theorem[613.2] [613.2][S02]proving with formal logic.[614.865] [614.865][S02]And so people think, well, that's proper reasoning.[616.99] [616.99][S02]That's your hardcore kind of reasoning.[620.22] [620.22][S02]And today's large language models,[622.69] [622.69][S02]they can't match the performance of a hand-coded theorem prover[629.01] [629.01][S02]or logic engine of the sort that's been around for decades.[632.62] [632.62][S01] Give me an example of a type of theorem[635.01] [635.01][S01]that might be able to be proved by a hardcoded system.[637.96] [637.96][S02] So it would be where you've got[639.96] [639.96][S02]maybe 20 or 30 axioms of logic.[643.435] [643.435][S01] So it might be something like the number that[645.81] [645.81][S01]follows one is to.[646.56] [646.56][S02] Well, I mean, it could be something like that.[649.185] [649.185][S02]It could be in the domain of number theory or something[651.72] [651.72][S02]very mathematical.[652.63] [652.63][S02]But it could be something much more everyday.[655.15] [655.15][S02]So for example, suppose that you've[656.79] [656.79][S02]got some very difficult logistical planning[659.34] [659.34][S02]problem where maybe you have hundreds of lorries and depots[664.14] [664.14][S02]and goods and all kinds of things like that,[667.06] [667.06][S02]and you need to plan the routes and the deployment[670.41] [670.41][S02]of the lorries and where they're going to go.[672.31] [672.31][S02]So that's a very difficult problem, computationally,[676.66] [676.66][S02]and it can be expressed very precisely in formal rules.[680.707] [680.707][S02]And that's the kind of situation where[682.29] [682.29][S02]you might want to use a good old-fashioned, straightforward[685.2] [685.2][S02]algorithm, planning algorithm of that's[687.81] [687.81][S02]been around for a long time.[688.98] [688.98][S02]Now, contemporary large language models[691.083] [691.083][S02]are getting better and better at this kind of thing,[693.25] [693.25][S02]but they're still--[694.2] [694.2][S02]you don't have those kinds of mathematical guarantees[696.75] [696.75][S02]that they're always going to come up[698.43] [698.43][S02]with exactly the right answer.[700.09] [700.09][S02]And it's very easy to make examples[702.57] [702.57][S02]where you have more and more axioms and so on,[705.3] [705.3][S02]where they're going to slip up.[706.867] [706.867][S02]There's a whole separate research direction,[708.7] [708.7][S02]which is to try and build more hand-coded things that[712.05] [712.05][S02]combine today's AI techniques with more[714.57] [714.57][S02]old-fashioned symbolic techniques[717.54] [717.54][S02]specifically for mathematical theorem proving.[719.813] [719.813][S02]And DeepMind has done some amazing work along those lines.[722.23] [722.23][S02]But that's different from large language models.[723.79] [723.79][S02]So with large language models, we're[725.29] [725.29][S02]thinking of these chatbots that can talk[728.55] [728.55][S02]about anything under the sun.[729.88] [729.88][S02]And one of the things they happen to be able to do[731.963] [731.963][S02]is a kind of reasoning.[733.68] [733.68][S02]That's not going to be, at the moment, quite as good[736.32] [736.32][S02]as you could do by hand building something for that.[739.18] [739.18][S01] It's kind of interesting[740.68] [740.68][S01]because hand building something is,[742.77] [742.77][S01]I mean, you end up with something that's very rigid.[745.42] [745.42][S02] That's the problem.[746.17] [746.17][S02]Yeah.[746.67] [746.67][S01] And brittle.[747.91] [747.91][S02] Yes, absolutely.[748.93] [748.93][S01] But then at the same time,[750.513] [750.513][S01]the flexibility that you get from the generative AI[753.39] [753.39][S01]approach, it's too floppy, as it were.[756.61] [756.61][S01]You want the rigidity in there.[758.16] [758.16][S02] Well, maybe or maybe not.[759.91] [759.91][S02]I mean, I think many examples of human affairs[762.41] [762.41][S02]are just not as black and white as that.[766.1] [766.1][S02]You do maybe want things to be a bit more blurry, even[769.52] [769.52][S02]in simple, everyday things.[771.05] [771.05][S02]Like, what would be good flowers to put over[773.72] [773.72][S02]in this corner of the garden?[775.26] [775.26][S02]Well, we've already got some roses in that corner there,[778.83] [778.83][S02]and those roses are yellow, so we'd-- but we can't have too[781.673] [781.673][S02]much yellow, so maybe we'd need to move them to the other corner[784.34] [784.34][S02]of the garden.[785.13] [785.13][S01] But then at the same time,[786.713] [786.713][S01]though, is this real reasoning or is this just the AI mimicking[791.9] [791.9][S01]well-structured arguments that have existed[794.96] [794.96][S01]in the training data, but just in a sort of novel environment?[799.353] [799.353][S02] Yeah.[800.27] [800.27][S02]Well, of course, that begs the question,[801.937] [801.937][S02]what is real reasoning?[803.48] [803.48][S02]I don't think-- it's not written in the sky what[806.66] [806.66][S02]real reasoning is.[808.02] [808.02][S02]It's up to us to define the concept of real reasoning[811.79] [811.79][S02]or of reasoning.[813.03] [813.03][S02]And so we have that--[814.91] [814.91][S02]we were talking earlier on about mathematical reasoning[817.25] [817.25][S02]of the sort that logicians do and that[820.525] [820.525][S02]is done by theorem provers in the past and so on and today.[826.71] [826.71][S02]When people were first using the terms like reasoning,[829.627] [829.627][S02]they weren't thinking of that kind of thing.[831.46] [831.46][S02]And when we used the word reasoning in everyday life,[833.68] [833.68][S02]we're not thinking about that sort of thing.[835.513] [835.513][S02]So if you're chatting away to a large language[837.6] [837.6][S02]model about your garden and you say,[840.215] [840.215][S02]oh, I'm thinking about what plants are right,[842.09] [842.09][S02]and it says, well, maybe you should consider[844.23] [844.23][S02]this kind of plant in that kind of location[846.06] [846.06][S02]because that's best for the soil and given[847.81] [847.81][S02]you said that it's windy there.[850.59] [850.59][S02]We would just say that there is supplying reasons.[853.09] [853.09][S02]I mean, it is supplying reasons.[854.7] [854.7][S02]Now, where they come from is another matter.[856.653] [856.653][S02]So people might say, well, it's just mimicking[858.57] [858.57][S02]what's in the training set.[860.35] [860.35][S02]But it's probably never seen exactly[862.02] [862.02][S02]that kind of scenario exactly before.[864.7] [864.7][S02]So it's moving beyond the training[866.76] [866.76][S02]set to a certain extent.[868.05] [868.05][S02]I think it's just using the everyday concept of reasoning[871.47] [871.47][S02]in an everyday way to call that reasoning.[873.22] [873.22][S01] I'm just thinking back[874.637] [874.637][S01]to some of the different characteristics[876.66] [876.66][S01]that the earlier philosophers wanted artificial intelligence[879.61] [879.61][S01]to have, and reasoning being one of them.[882.52] [882.52][S01]But then also the Turing test, which, of course, gets[885.07] [885.07][S01]brought up all the time, about a way[887.56] [887.56][S01]to test for the capability of an artificial intelligence.[890.27] [890.27][S01]I mean, it's kind of controversial,[892.46] [892.46][S01]I suppose, in terms of how good it ever[894.91] [894.91][S01]would have been as a test for the capability of AI.[897.83] [897.83][S01]What was your take on it?[899.45] [899.45][S01]Do you think it was ever a good test?[901.43] [901.43][S02] No.[902.62] [902.62][S02]I've always thought it was a terrible test, but a really[905.59] [905.59][S02]great spur to philosophical discussion about things.[909.83] [909.83][S02]And again, with a bit of hindsight,[911.63] [911.63][S02]maybe I might backtrack a little bit on a few of my views,[915.23] [915.23][S02]because I was certainly very, very[916.9] [916.9][S02]much of the opinion that embodiment[919.3] [919.3][S02]was a critical facet of intelligence,[923.08] [923.08][S02]was critical for achieving intelligence.[925.17] [925.17][S01] Which doesn't come anywhere near the Turing test[927.67] [927.67][S01]at all, right?[928.325] [928.325][S02] No.[929.158] [929.158][S02]The Turing test is absolutely, explicitly[930.88] [930.88][S02]nothing to do with embodiment because in the Turing test--[933.68] [933.68][S02]so just to remind people what it is.[936.22] [936.22][S02]So in the Turing test, you have two subjects, as it were.[938.835] [938.835][S02]One is a human and the other is the computer.[940.71] [940.71][S02]And then you have a judge.[941.793] [941.793][S02]The human judge can't see which is the computer[945.952] [945.952][S02]and which is the humans, and they're only[947.66] [947.66][S02]talking to these subjects through a kind[950.69] [950.69][S02]of chat-like interface.[952.35] [952.35][S02]They can't see whether they're embodied or not,[954.84] [954.84][S02]so we can easily suppose that the computer might[958.82] [958.82][S02]be one of today's large language models.[960.72] [960.72][S02]In which case, I have to say that today they[963.05] [963.05][S02]pretty much would pass the Turing test.[964.74] [964.74][S02]I mean, we've got to that point, which is amazing, really.[967.84] [967.84][S02]So I used to think that it was a bad test because it didn't test[970.88] [970.88][S02]any of these embodied skills.[972.66] [972.66][S02]So you'd need a robot, really, to test whether something[975.74] [975.74][S02]was capable of the kind of everyday cognition[978.11] [978.11][S02]that we all put to use when we're, for example, making[981.26] [981.26][S02]a cup of tea or something.[982.86] [982.86][S01] Because otherwise it's[984.277] [984.277][S01]a very, very narrow form of intelligence.[986.887] [986.887][S02] Yes, it's all to do with language[988.97] [988.97][S02]and reasoning, and not to do with the kinds of things[992.57] [992.57][S02]that evolution developed in us and in other animals[996.5] [996.5][S02]before language, which is the ability to manipulate and move[1000.73] [1000.73][S02]around with and navigate and exploit,[1004.645] [1004.645][S02]in the best sense of the word, the everyday physical world.[1007.705] [1007.705][S01] So actually, that's really interesting.[1009.83] [1009.83][S01]That's so interesting, because I often think about how--[1013.27] [1013.27][S01]fine, maybe the large language models we have at the moment[1016.353] [1016.353][S01]can pass the Turing test, but they[1017.77] [1017.77][S01]don't flinch if you throw a ball at your computer.[1019.94] [1019.94][S02] Oh, no, indeed.[1020.98] [1020.98][S01] And in a sense, there[1022.355] [1022.355][S01]are these, as you say, these much deeper forms.[1025.7] [1025.7][S01]Maybe we wouldn't class them as intelligence in the way[1029.043] [1029.043][S01]that we talk about it.[1029.96] [1029.96][S01]But ultimately, it sort of is a form of intelligence too.[1032.756] [1032.756][S02] It very much is a form of intelligence.[1035.089] [1035.089][S02]And moreover, I think that in the biological case--[1037.243] [1037.243][S02]and now I have to caveat all these things by saying,[1039.41] [1039.41][S02]in the biological case.[1041.079] [1041.079][S02]Our ability to think and to reason and to talk[1044.44] [1044.44][S02]is very much grounded in our interaction[1046.869] [1046.869][S02]with the everyday world.[1048.02] [1048.02][S02]If you think about almost all of your everyday speech[1053.17] [1053.17][S02]is using spatial metaphors.[1055.55] [1055.55][S02]I mean, they completely permeate our everyday speech.[1058.7] [1058.7][S02]Even the word permeate.[1059.84] [1059.84][S01] Yeah.[1060.07] [1060.07][S01]Absolutely.[1060.73] [1060.73][S02] Grounded.[1061.813] [1061.813][S02]I used the word grounded.[1063.13] [1063.13][S02]So we just use those kinds of things all the time.[1066.357] [1066.357][S01] Because we're fundamentally physical beings.[1068.69] [1068.69][S02] Because we're fundamentally physical beings,[1070.16] [1070.16][S02]and because our brains have evolved[1071.89] [1071.89][S02]to help us to survive and reproduce[1075.22] [1075.22][S02]in this physical world.[1078.02] [1078.02][S01] Yeah.[1078.728] [1078.728][S02] While interacting[1080.145] [1080.145][S02]with all these other beings that are doing the same thing.[1082.84] [1082.84][S01] Because there are some alternatives when[1084.64] [1084.64][S01]you are trying to test for the capability[1086.348] [1086.348][S01]of an artificial intelligence.[1088.15] [1088.15][S01]Just talk me through some of the potential alternatives[1091.03] [1091.03][S01]that we have.[1091.572] [1091.572][S02] Well, I think perhaps you've[1093.447] [1093.447][S02]got in mind the Garland test, what I call the Garland[1095.92] [1095.92][S02]test, which is--[1097.96] [1097.96][S02]so that goes back to the film "Ex Machina,"[1100.91] [1100.91][S02]which was directed by Alex Garland, of course.[1103.49] [1103.49][S02]And there's a bit in the script where Nathan, the billionaire[1106.81] [1106.81][S02]guy, is talking to Caleb, and Caleb,[1110.02] [1110.02][S02]who's the guy who's been brought in to interact with Ava,[1113.63] [1113.63][S02]the robot, and Caleb says, oh, I'm[1115.59] [1115.59][S02]here to conduct a Turing test on Ava.[1118.87] [1118.87][S02]And Nathan says, oh, no, we're way past that.[1121.87] [1121.87][S02]Ava could pass the Turing test easily.[1124.27] [1124.27][S02]The point is to show you she's a robot[1126.66] [1126.66][S02]and see if you still think she's conscious.[1129.13] [1129.13][S01] Wow.[1129.87] [1129.87][S02] And that's what I call the Garland test.[1131.59] [1131.59][S02]And it's different from the Turing test in two respects.[1134.02] [1134.02][S02]So first of all, the judge, as it were--[1136.62] [1136.62][S02]who, in that case, is Caleb--[1139.8] [1139.8][S02]can see that she's a robot.[1141.76] [1141.76][S02]So in the Turing test, the judge can't see which is which.[1145.3] [1145.3][S02]But here, the idea is that Caleb knows that she's a robot,[1149.83] [1149.83][S02]knows that her brain is an AI brain.[1153.52] [1153.52][S01] And yet, still attributes these characteristics[1156.72] [1156.72][S01]to her.[1157.24] [1157.24][S02] And the characteristic in question[1158.79] [1158.79][S02]also is different, because it's not intelligence.[1160.66] [1160.66][S02]It's not, can she think?[1161.66] [1161.66][S02]But, is she conscious?[1162.78] [1162.78][S02]Or, is it conscious?[1164.25] [1164.25][S02]Which is an entirely different test.[1166.06] [1166.06][S02]And I think intelligence and consciousness[1168.217] [1168.217][S02]are different things that we can disentangle those two things,[1170.8] [1170.8][S02]dissociate them.[1172.11] [1172.11][S02]So when I first read the script of the film,[1175.6] [1175.6][S02]those particular lines were in there for Caleb and Nathan,[1179.2] [1179.2][S02]and I wrote next to it in my version,[1181.43] [1181.43][S02]spot on with an exclamation mark,[1183.05] [1183.05][S02]because I just thought Alex had totally nailed[1185.5] [1185.5][S02]a really important idea there.[1187.37] [1187.37][S02]And so in my writing, I call this the Garland test.[1189.535] [1189.535][S02]And quite a few people have picked up on that[1191.41] [1191.41][S02]and called it the Garland test as well.[1193.04] [1193.04][S01] Is there a test that would really[1195.79] [1195.79][S01]impress you if an AI were able of passing it?[1198.482] [1198.482][S02] So I always was very impressed[1200.44] [1200.44][S02]with Francois Chollet's ARC test.[1204.35] [1204.35][S02]And that's A-R-C, which stands for abstract reasoning corpus.[1207.92] [1207.92][S02]So these are little sequences of images of the sort that you[1212.32] [1212.32][S02]get in IQ tests and things.[1214.78] [1214.78][S02]And the images are arranged in pairs.[1217.07] [1217.07][S02]So you have the first image.[1218.96] [1218.96][S02]It's kind of pixelated image.[1220.85] [1220.85][S02]It's got little cells with little kind of things[1224.59] [1224.59][S02]that you can interpret as objects or lines[1226.57] [1226.57][S02]and so on in the images.[1228.02] [1228.02][S02]And you're interested in--[1229.333] [1229.333][S02]the challenge is to work out a rule that[1231.0] [1231.0][S02]takes you from one image to the second one.[1233.14] [1233.14][S02]Then you've got to apply that rule to a third image.[1236.95] [1236.95][S02]First of all, he held out, made completely secret[1240.54] [1240.54][S02]all of the test ones, so you couldn't game[1242.94] [1242.94][S02]it by knowing what the actual test versions were.[1247.01] [1247.01][S01] Or using it in a training set.[1248.76] [1248.76][S02] Or using it in a training set.[1249.79] [1249.79][S02]That's what I mean by gaming it.[1251.71] [1251.71][S02]And also, he very carefully designed them[1253.98] [1253.98][S02]so that it was very different rules.[1256.03] [1256.03][S02]Each rule was completely different to the other rules.[1260.658] [1260.658][S02]And you usually had to find some kind[1262.2] [1262.2][S02]of intuitive application of often our everyday common sense[1266.13] [1266.13][S02]knowledge is seeing this as a liquid that's[1268.47] [1268.47][S02]moving in this direction or imagining this thing moving,[1272.25] [1272.25][S02]growing or something.[1273.182] [1273.182][S01] So it required grounding, in a way.[1275.14] [1275.14][S02] Well, it seemed to.[1276.64] [1276.64][S02]But recently, people have been able to make[1279.12] [1279.12][S02]significant progress on these in a more brute force kind of way.[1284.67] [1284.67][S02]So I feel that the solutions are not really[1291.134] [1291.134][S02]getting at the spirit of the original test quite so much.[1293.622] [1293.622][S01] Well, that's it, I guess, in a way,[1295.58] [1295.58][S01]is that as soon as you set a metric, as soon as you[1298.39] [1298.39][S01]set a bar for once we've crossed this threshold,[1301.76] [1301.76][S01]then we will have capability intelligence and consciousness,[1305.21] [1305.21][S01]whatever it might be.[1306.585] [1306.585][S01]It sort of changes the whole nature of the test in itself.[1310.143] [1310.143][S02] Yeah.[1311.06] [1311.06][S02]Well, people are going to start--[1312.3] [1312.3][S01] Optimizing for it.[1313.12] [1313.12][S02] --for the test, right?[1314.745] [1314.745][S02]It's Goodhart's law.[1316.0] [1316.0][S01] Absolutely.[1317.05] [1317.05][S01]A lot of people who've come on this podcast[1318.842] [1318.842][S01]have expressed real need for caution[1322.51] [1322.51][S01]about anthropomorphizing these things.[1324.34] [1324.34][S01]Are you one of those people who thinks that we shouldn't?[1327.07] [1327.07][S02] Well, I think there are different ways[1328.12] [1328.12][S02]of looking at this.[1328.94] [1328.94][S02]And I think there are good and bad forms[1331.93] [1331.93][S02]of anthropomorphization.[1333.86] [1333.86][S02]So on the one hand, people can start to form relationships[1338.98] [1338.98][S02]as they see it with AI systems, friendships and companionships[1343.54] [1343.54][S02]and mentorships.[1344.96] [1344.96][S02]And that can potentially be a bad thing[1348.75] [1348.75][S02]if they are misled into thinking that they can trust[1352.565] [1352.565][S02]the thing that they're talking to[1353.94] [1353.94][S02]or that they're really in love with it,[1355.66] [1355.66][S02]or that it really cares about them.[1357.285] [1357.285][S02]On the other end of the spectrum,[1358.66] [1358.66][S02]if an AI system is just using the word I,[1362.32] [1362.32][S02]then I think that that's a pretty harmless sort of form[1366.18] [1366.18][S02]of self-anthropomorphization.[1368.01] [1368.01][S02]We even see buses that say things like, on the side,[1370.74] [1370.74][S02]I am out of service.[1371.583] [1371.583][S02]And we don't have a problem with that kind of thing,[1373.75] [1373.75][S02]so I don't see why we should have[1374.67] [1374.67][S02]a problem with that with large language models either.[1377.23] [1377.23][S02]But I think we do tend to anthropomorphize things.[1380.02] [1380.02][S02]When we had satnavs in cars that weren't just in our phones,[1382.69] [1382.69][S02]I used to anthropomorphize the satnav all the time.[1384.82] [1384.82][S02]I used to think, oh, stupid thing.[1386.41] [1386.41][S02]It thinks we're doing this.[1388.11] [1388.11][S02]It's a natural human tendency, I think.[1390.135] [1390.135][S01] What about the other words that we use?[1392.26] [1392.26][S01]I mean, the example that you gave of the satnav saying,[1394.752] [1394.752][S01]oh, it thinks we're in the car park[1396.21] [1396.21][S01]or, oh, it believes that this is--[1399.78] [1399.78][S01]it got this wrong.[1400.96] [1400.96][S01]It misunderstood this.[1402.36] [1402.36][S01]Those are all very human-centric words, aren't they?[1405.16] [1405.16][S02] Yeah, yeah.[1406.327] [1406.327][S02]Absolutely.[1407.47] [1407.47][S02]There are examples of what philosophers often[1409.65] [1409.65][S02]call folk psychology.[1411.04] [1411.04][S02]So we have this folk psychology where we use words like belief.[1414.06] [1414.06][S02]We have concepts like belief, desire, and intention, which[1416.88] [1416.88][S02]we can apply not just to other humans and other animals,[1421.72] [1421.72][S02]but we can apply to objects as well.[1424.63] [1424.63][S02]It's what the philosopher Dan Dennett called[1427.59] [1427.59][S02]taking the intentional stance.[1429.55] [1429.55][S02]So we adopt the intentional stance towards something[1432.45] [1432.45][S02]if we talk about it and think about it[1434.61] [1434.61][S02]as if it acted on the basis of having beliefs and goals[1439.53] [1439.53][S02]and carrying out rational decisions for what it does[1442.47] [1442.47][S02]on the basis of those things.[1444.25] [1444.25][S02]And that's a very useful way of thinking[1447.03] [1447.03][S02]about many, many things, such as even our satnav or a chess[1449.94] [1449.94][S02]computer.[1450.97] [1450.97][S02]So for Dan Dennett, that was one of the examples that he used.[1453.93] [1453.93][S02]A chess computer, that, oh, it wants to get the queen forward[1457.47] [1457.47][S02]because it thinks I'm going to use my rook to defend this rank[1461.91] [1461.91][S02]or something.[1462.7] [1462.7][S02]And that's full of this kind of intentional folk psychological[1466.71] [1466.71][S02]language about beliefs and goals and things.[1469.027] [1469.027][S01] Is that problematic, then,[1470.61] [1470.61][S01]if we start using that idea of beliefs and intentions[1473.46] [1473.46][S01]and desires about the AI?[1475.442] [1475.442][S02] So it's only problematic[1477.15] [1477.15][S02]if we start to use these things in ways that mislead us[1481.53] [1481.53][S02]into thinking that things have capabilities[1485.67] [1485.67][S02]that they don't really have.[1486.97] [1486.97][S02]So I think that's where it becomes problematic.[1489.76] [1489.76][S02]So the Encyclopedia Britannica, the physical volume[1493.98] [1493.98][S02]of the Encyclopedia Britannica doesn't[1495.93] [1495.93][S02]know that Argentina won the World Cup in this[1498.81] [1498.81][S02]because it's too old.[1500.86] [1500.86][S02]So if you made that remark, it would make perfect sense.[1504.905] [1504.905][S02]You might say that and it's fine.[1506.57] [1506.57][S02]But if somebody said to you, why don't you[1508.32] [1508.32][S02]have a conversation with it about England's football prowess[1512.22] [1512.22][S02]or lack thereof, that would be ridiculous.[1515.02] [1515.02][S02]Right?[1515.52] [1515.52][S02]Now, the interesting thing is that now[1517.103] [1517.103][S02]we've got these large language models,[1518.77] [1518.77][S02]you can have a conversation with them.[1520.75] [1520.75][S02]You can tell it things so that it kind of pushes[1524.37] [1524.37][S02]the boundary of where we might start to say, well,[1527.56] [1527.56][S02]it doesn't really XYZ.[1529.33] [1529.33][S02]It pushes that a little bit further out.[1531.1] [1531.1][S01] I wonder if there's something even deeper here[1533.517] [1533.517][S01]about this human need, or maybe it's just a desire,[1537.9] [1537.9][S01]to really want AI to have these characteristics,[1543.06] [1543.06][S01]to be anthropomorphized.[1544.51] [1544.51][S02] Yeah.[1544.86] [1544.86][S02]Yeah.[1545.36] [1545.36][S02]Well, that's a really interesting question, isn't it?[1547.75] [1547.75][S02]So I don't think it kind of comes back to that.[1549.708] [1549.708][S02]It comes back to language.[1551.01] [1551.01][S02]In this case, we're inclined to anthropomorphize things[1554.7] [1554.7][S02]because they're really good at using language.[1556.772] [1556.772][S02]And for us, the only things that are good at using language[1559.23] [1559.23][S02]are other humans.[1560.26] [1560.26][S02]And so it's very strange, in a way, to be suddenly in a world[1563.46] [1563.46][S02]where we have language using things.[1567.375] [1567.375][S02]It's not just humans that can talk.[1569.32] [1569.32][S02]That's astonishing.[1570.4] [1570.4][S01] Yeah.[1571.38] [1571.38][S01]I mean, it is astonishing.[1572.71] [1572.71][S02] It is astonishing.[1573.39] [1573.39][S02]So it really is astonishing to think[1575.25] [1575.25][S02]that every single child born today,[1578.082] [1578.082][S02]they're going to grow up in a world[1579.54] [1579.54][S02]where they've never known--[1580.96] [1580.96][S02]they've never known a world in which[1582.97] [1582.97][S02]machines can't talk to them.[1585.79] [1585.79][S02]Isn't that an extraordinary thing?[1588.262] [1588.262][S01] Yeah.[1588.97] [1588.97][S02] I mean, it really is.[1590.553] [1590.553][S02]And so what the implications are of that for us[1594.1] [1594.1][S02]all is really hard to say.[1595.773] [1595.773][S01] I'm just thinking back[1597.19] [1597.19][S01]to what you were saying about how grounded humans are[1602.23] [1602.23][S01]in the physical world.[1604.13] [1604.13][S02] Yes.[1605.57] [1605.57][S01] It does feel like the kind[1607.27] [1607.27][S01]of embodied aspect of AI has lagged behind this language[1612.37] [1612.37][S01]aspect quite a bit.[1614.12] [1614.12][S01]Do you think that we're going to see[1616.06] [1616.06][S01]a big up-step in intelligence, however you want to define it,[1620.93] [1620.93][S01]or broader capabilities once we get good and effective embodied[1625.45] [1625.45][S01]AI?[1626.268] [1626.268][S02] Well, I think it might make a big difference[1628.81] [1628.81][S02]because the large language models we have at the moment,[1631.46] [1631.46][S02]it's really difficult to discern, actually,[1633.59] [1633.59][S02]to be honest, right now, where the limits are[1636.25] [1636.25][S02]for how good they're going to get,[1638.12] [1638.12][S02]whether we really are on the road to producing[1641.64] [1641.64][S02]general intelligence that is comparable[1643.5] [1643.5][S02]to human general intelligence.[1646.12] [1646.12][S02]And often when you get to the boundaries of the capabilities[1651.21] [1651.21][S02]of these kinds of things, sometimes you[1653.16] [1653.16][S02]get the impression that the AI system doesn't really quite[1656.13] [1656.13][S02]grok something.[1657.99] [1657.99][S02]It doesn't really deeply understand something.[1659.95] [1659.95][S02]You reach some kind of limit and you[1662.22] [1662.22][S02]realize that it's been faking it a little bit.[1665.17] [1665.17][S02]But it may be that that sort of general ability[1668.13] [1668.13][S02]to really get things on a deep level,[1670.87] [1670.87][S02]on a deep, common sense level, maybe, that that does still[1675.09] [1675.09][S02]require a bit of embodiment.[1677.53] [1677.53][S02]It does still basically require training data[1680.31] [1680.31][S02]that involves interacting with a real world of physical objects[1684.99] [1684.99][S02]with their spatial organization.[1687.01] [1687.01][S02]And there's something fundamental about that.[1689.02] [1689.02][S01] OK.[1689.645] [1689.645][S01]If understanding, then, however we define it,[1692.61] [1692.61][S01]is something that can emerge as just a consequence of more[1697.86] [1697.86][S01]and more data, what about consciousness?[1700.79] [1700.79][S01]I mean, I'm sure you've been asked a thousand times about AI[1704.23] [1704.23][S01]consciousness and whether it's something[1706.09] [1706.09][S01]that we can expect to happen or has already happened.[1709.373] [1709.373][S02] Yeah, yeah.[1710.54] [1710.54][S02]The very first thing to point out[1712.48] [1712.48][S02]is that I do think we can dissociate intelligence[1716.41] [1716.41][S02]or cognition and cognitive capabilities,[1719.42] [1719.42][S02]we can dissociate that from consciousness.[1721.67] [1721.67][S02]So I think we can imagine things that are very capable[1726.13] [1726.13][S02]and that we want to say are very intelligent because of the way[1730.78] [1730.78][S02]they can achieve their goals and so on,[1732.56] [1732.56][S02]but that we don't want to ascribe consciousness to.[1735.47] [1735.47][S02]But actually, what does that even mean,[1737.95] [1737.95][S02]to ascribe consciousness to something at all?[1740.59] [1740.59][S02]I think the concept of consciousness[1742.09] [1742.09][S02]itself can be broken down into many parts.[1745.64] [1745.64][S02]It's a multifaceted concept.[1748.37] [1748.37][S02]So, for example, we might talk about awareness of the world.[1751.99] [1751.99][S02]In the scientific study of consciousness,[1754.4] [1754.4][S02]there are all of these experimental protocols[1756.82] [1756.82][S02]and paradigms, and many of them are to do with perception.[1760.58] [1760.58][S02]You're looking at whether a person is aware of something,[1764.16] [1764.16][S02]is consciously perceiving something in the world.[1766.89] [1766.89][S02]Large language models are not aware of the world[1769.31] [1769.31][S02]at all in that respect.[1771.177] [1771.177][S02]But there are other facets of consciousness.[1773.01] [1773.01][S02]We also have self-awareness.[1774.89] [1774.89][S02]Now our self-awareness, part of that[1776.42] [1776.42][S02]is awareness of our own body and where it is in space.[1780.08] [1780.08][S02]But another aspect of self-awareness[1781.58] [1781.58][S02]is a kind of awareness of our own inner machinations[1784.94] [1784.94][S02]of our stream of consciousness, as William James called it.[1788.52] [1788.52][S02]So we have that kind of self-awareness as well.[1791.34] [1791.34][S02]And we have what some people call metacognition as well.[1794.91] [1794.91][S02]We have the ability to think about what we know.[1797.7] [1797.7][S02]And then additionally, there's the emotional side[1800.39] [1800.39][S02]or the feeling side of consciousness or sentience.[1803.61] [1803.61][S02]So the capacity to feel, the capacity to suffer.[1808.64] [1808.64][S02]And that's another aspect of consciousness.[1810.66] [1810.66][S02]Now, I think we can dissociate all of these things.[1812.84] [1812.84][S02]Now in humans, they all come as a big package, a big bundle.[1815.47] [1815.47][S02]We only actually have to think about non-human animals[1817.72] [1817.72][S02]to realize that we can start to separate these things[1821.23] [1821.23][S02]a little bit, because I think that much as I love cats,[1825.11] [1825.11][S02]I think there's a limited self-awareness going on in cats.[1828.918] [1828.918][S01] How dare you.[1829.96] [1829.96][S02] Well, I'm a big cat person, I have to say,[1832.418] [1832.418][S02]so I do say that with some hesitation.[1834.925] [1834.925][S01] There's little metacognition, shall we say.[1837.35] [1837.35][S02] Well, yeah.[1838.16] [1838.16][S02]Certainly they don't have an awareness[1839.743] [1839.743][S02]of their own ongoing stream of verbal consciousness[1842.2] [1842.2][S02]because they don't have it.[1844.15] [1844.15][S02]They're not thinking about what they did yesterday[1846.79] [1846.79][S02]in verbal terms or what they want to do with their lives.[1850.04] [1850.04][S02]So if we think about robots, you may[1851.95] [1851.95][S02]have a very sophisticated robot, even your robot vacuum cleaner.[1855.65] [1855.65][S02]And you may say that, well, it does actually[1857.74] [1857.74][S02]have a kind of awareness of the world.[1859.76] [1859.76][S02]And that's not an inappropriate use[1861.94] [1861.94][S02]of that phrase, awareness of the world.[1863.582] [1863.582][S02]Do I want to call it consciousness?[1865.04] [1865.04][S02]Well, then I seem to be bringing on board all of this other stuff[1867.87] [1867.87][S02]as well.[1868.37] [1868.37][S02]But you don't have to.[1869.66] [1869.66][S02]You can break down the concept of consciousness[1871.81] [1871.81][S02]into these different aspects.[1873.018] [1873.018][S01] Because your robot vacuum[1874.56] [1874.56][S01]can know exactly where it is in a space and how--[1876.83] [1876.83][S02] Yeah, and respond[1878.247] [1878.247][S02]in an intelligent and sensitive way to where it is[1880.517] [1880.517][S02]and the objects around it.[1881.6] [1881.6][S01] Achieve its ends.[1882.14] [1882.14][S02] And achieve its ends and so on.[1884.14] [1884.14][S02]So there's a kind of awareness of the world there.[1886.43] [1886.43][S02]There's no self-awareness.[1887.757] [1887.757][S02]There's certainly no capacity for suffering.[1889.59] [1889.59][S02]And so in a large language model,[1891.03] [1891.03][S02]there might not be awareness of the world[1893.33] [1893.33][S02]in that perceptual sense, but maybe there's[1896.21] [1896.21][S02]some kind of self awareness or reflexive capabilities,[1901.32] [1901.32][S02]reflexive cognitive capabilities.[1903.3] [1903.3][S02]They can talk about the things that they've[1905.96] [1905.96][S02]talked about earlier in the conversation, for example,[1908.21] [1908.21][S02]and can do so in a reflective manner, which kind of feels[1913.25] [1913.25][S02]a little bit like some aspects of self-awareness[1916.16] [1916.16][S02]that we have a little bit.[1918.09] [1918.09][S02]I don't think that it's appropriate to think of them[1920.45] [1920.45][S02]in terms of having feelings.[1923.13] [1923.13][S02]They can't experience pain because they don't have a body.[1925.68] [1925.68][S02]I think we can take the concept apart, basically.[1928.59] [1928.59][S01] So then is the question,[1930.15] [1930.15][S01]can AI be conscious or not, as though it's a binary thing?[1933.82] [1933.82][S01]It's the wrong question from the off?[1935.68] [1935.68][S02] I do think that is the wrong question,[1938.02] [1938.02][S02]and I think it's wrong in many ways.[1939.61] [1939.61][S02]So just then we were talking about the fact[1942.03] [1942.03][S02]that it's actually a sort of multifaceted concept.[1945.1] [1945.1][S02]But also I think that we tend to have[1946.68] [1946.68][S02]these very deep metaphysical commitments[1950.37] [1950.37][S02]to the idea of consciousness as some sort of magical thing[1955.41] [1955.41][S02]that is a metaphysical thing.[1958.87] [1958.87][S02]So the question of whether something[1960.63] [1960.63][S02]is conscious or is not a matter of consensus[1964.53] [1964.53][S02]or a matter of just our language,[1966.07] [1966.07][S02]but it's something that is out there[1968.19] [1968.19][S02]in the metaphysical reality or in the mind of God[1970.77] [1970.77][S02]or in the platonic heaven or something like that.[1972.91] [1972.91][S02]But ultimately, I do think that that's the wrong way of thinking[1975.577] [1975.577][S02]about consciousness.[1976.66] [1976.66][S01] Let's take one aspect of consciousness, then,[1979.035] [1979.035][S01]that you described about an emotional side,[1981.4] [1981.4][S01]an ability to suffer, but not necessarily physical pain.[1984.46] [1984.46][S01]Emotional pain, too, and a sense of self in the emotional way.[1989.17] [1989.17][S01]Do you think this is something that[1991.89] [1991.89][S01]will just emerge as a natural consequence of intelligence.[1995.417] [1995.417][S01]If you build something that is intelligent enough,[1997.5] [1997.5][S01]at some point, this is going to happen.[1999.45] [1999.45][S01]Or is there something unique about biological creatures.[2002.81] [2002.81][S01]And I guess the process of evolution[2004.42] [2004.42][S01]that we've been through that has resulted in that that can't[2006.94] [2006.94][S01]be replicated in a machine.[2008.27] [2008.27][S02] I don't think there is a right or wrong answer[2010.96] [2010.96][S02]to your question there.[2012.52] [2012.52][S02]I think we just have to wait and see[2014.14] [2014.14][S02]what things we bring into the world[2016.6] [2016.6][S02]and how we end up treating them and talking about them[2019.39] [2019.39][S02]and thinking about them.[2020.45] [2020.45][S02]And I don't think we really know until they're[2023.77] [2023.77][S02]among us, as it were, these things that we're building.[2026.08] [2026.08][S02]Then we will just be led to think about them[2029.14] [2029.14][S02]and talk about them and treat them in a particular way.[2033.26] [2033.26][S02]So an example I like to think in this regard is the octopus.[2038.0] [2038.0][S02]So octopuses have recently been brought[2041.47] [2041.47][S02]into, UK legislation, brought into the category of things[2045.4] [2045.4][S02]that we have to care about the welfare of.[2047.84] [2047.84][S02]That's as a result of lots of things, I think, happening.[2051.78] [2051.78][S02]So the public has been exposed to being with octopuses[2056.51] [2056.51][S02]a lot more.[2057.21] [2057.21][S02]Now, you don't have to literally be under the water[2059.63] [2059.63][S02]and poking around with octopuses to know what it's[2062.887] [2062.887][S02]like to be with them, because there's[2064.429] [2064.429][S02]all kinds of wonderful documentaries[2066.05] [2066.05][S02]and wonderful books by--[2067.672] [2067.672][S02]Peter Godfrey-Smith has these great books[2069.38] [2069.38][S02]about interacting with octopuses and so on.[2072.679] [2072.679][S02]So those sort of narratives and documentaries,[2074.875] [2074.875][S02]they give us a feel for what it's[2076.25] [2076.25][S02]like to be with an octopus, what it's like to have[2078.56] [2078.56][S02]an encounter with an octopus.[2080.969] [2080.969][S02]And then you can't help yourself but to see it[2084.05] [2084.05][S02]as a fellow conscious creature.[2086.48] [2086.48][S02]But complementing that is the scientific progress as well.[2089.46] [2089.46][S02]So at the same time, scientists study the nervous systems[2093.199] [2093.199][S02]of octopuses and realize the extent[2097.16] [2097.16][S02]to which their nervous systems are similar to ours in the way[2100.34] [2100.34][S02]that we experience pain.[2103.08] [2103.08][S02]You can find analogous aspects of their nervous systems[2106.88] [2106.88][S02]to ours.[2108.03] [2108.03][S02]So taking all these things together,[2109.53] [2109.53][S02]I think that tends to affect the way we think about them[2112.48] [2112.48][S02]and the way we talk about them and the way we treat them.[2115.46] [2115.46][S02]So I think the same kind of thing[2117.73] [2117.73][S02]is going to happen with AI systems.[2120.29] [2120.29][S02]Do I think there's a right or wrong answer to,[2123.67] [2123.67][S02]could we be misled there?[2126.08] [2126.08][S02]I think that's a really, really deep and difficult[2128.68] [2128.68][S02]metaphysical philosophical question.[2130.945] [2130.945][S01] I do wonder, though--[2132.32] [2132.32][S01]I mean, that point about suffering to me[2134.44] [2134.44][S01]seems different to the others, because metacognition,[2138.71] [2138.71][S01]the sense of the world, et cetera,[2140.71] [2140.71][S01]there's not these ethical implications necessarily[2143.98] [2143.98][S01]about those.[2144.74] [2144.74][S01]But I think with suffering, you wouldn't want your shoes[2147.67] [2147.67][S01]to be conscious.[2148.45] [2148.45][S01]You know?[2149.17] [2149.17][S01]You wouldn't want a forklift truck to be sort of conscious.[2152.091] [2152.091][S02] No, no.[2153.091] [2153.091][S02]Unless they happen to really like being a forklift truck.[2155.54] [2155.54][S01] Sure, sure.[2156.53] [2156.53][S01]But then do we have to be a tiny bit more[2158.238] [2158.238][S01]careful about that particular aspect of it?[2160.34] [2160.34][S02] I think we do.[2161.632] [2161.632][S02]If there were the prospect of bringing into being something[2164.92] [2164.92][S02]that is genuinely capable of suffering,[2166.97] [2166.97][S02]then we should think very hard about whether we should do it[2168.95] [2168.95][S02]or not.[2169.53] [2169.53][S02]I tend to think that that's not the case with anything[2172.34] [2172.34][S02]that we've got at the moment.[2173.85] [2173.85][S02]But some people will push back against that.[2178.32] [2178.32][S02]We take the example of large language models.[2180.27] [2180.27][S02]Well, OK.[2180.96] [2180.96][S02]So there's one level in which what they do[2183.53] [2183.53][S02]is next token prediction, next word prediction.[2186.63] [2186.63][S02]But in order to be able to do that really,[2190.62] [2190.62][S02]really, really well in the way that they can at the moment,[2194.13] [2194.13][S02]then they've had to learn and acquire[2198.11] [2198.11][S02]all kinds of emergent mechanisms.[2200.01] [2200.01][S02]So who knows whether or not there's[2202.07] [2202.07][S02]some kind of emergent mechanism has[2203.99] [2203.99][S02]been learned in the weights of this enormous, staggeringly huge[2207.38] [2207.38][S02]number, hundreds of billions of weights in a language[2209.93] [2209.93][S02]model, whether some mechanism hasn't been learned there that[2215.195] [2215.195][S02]has, for example, genuine understanding in it,[2217.83] [2217.83][S02]whatever that means, or even consciousness.[2220.71] [2220.71][S02]Coming back to embodiment again, I have always been of the view[2225.05] [2225.05][S02]that it's only really legitimate to talk about consciousness[2227.79] [2227.79][S02]in the context of something we can share a world with and have[2233.1] [2233.1][S02]that kind of encounter with that we have[2234.81] [2234.81][S02]with an octopus or a dog or a horse,[2237.24] [2237.24][S02]whatever, and being together in the world with that animal[2240.45] [2240.45][S02]and responding to things together.[2242.56] [2242.56][S02]Then I'm in no doubt that they are conscious.[2245.377] [2245.377][S02]That's the kind of primal case for me.[2246.96] [2246.96][S02]Now with a large language model, you[2248.46] [2248.46][S02]can't be in the same world as them in that kind of way,[2251.44] [2251.44][S02]and you can't hang out with them and interact[2254.31] [2254.31][S02]with physical objects with today's large language models.[2257.61] [2257.61][S02]So to my mind, using the language of consciousness[2260.34] [2260.34][S02]in that context is what Wittgenstein would say,[2263.44] [2263.44][S02]it's taking language on holiday.[2265.15] [2265.15][S02]It's using it so far outside of its normal use,[2268.8] [2268.8][S02]maybe it's inappropriate.[2270.01] [2270.01][S02]But that can change.[2271.05] [2271.05][S02]And the more I interact with large language models,[2273.725] [2273.725][S02]the more I have these sophisticated and interesting[2275.85] [2275.85][S02]conversations with them, the more I'm inclined to think,[2279.58] [2279.58][S02]well, maybe I want to extend the language of consciousness,[2282.64] [2282.64][S02]bend it, change it, distort it, make up some new words,[2286.43] [2286.43][S02]break it apart in ways that are going[2288.37] [2288.37][S02]to fit these new things that I'm interacting with all the time.[2292.76] [2292.76][S01] I know you've spent a lot of time interacting[2295.24] [2295.24][S01]with these large language models.[2296.615] [2296.615][S01]I've actually seen you described as a renowned prompt whisperer.[2299.78] [2299.78][S01]What's your secret?[2300.675] [2300.675][S02] Well, one secret[2302.05] [2302.05][S02]is to talk to the large language model as if it were human.[2305.195] [2305.195][S02]So if you think that what they're doing[2306.82] [2306.82][S02]is role playing a human character, such[2309.61] [2309.61][S02]as, say, a very smart and helpful intern,[2313.223] [2313.223][S02]then you should treat them like a smart and helpful intern[2315.64] [2315.64][S02]and talk to them as if they were a smart and helpful intern.[2318.95] [2318.95][S02]For example, just being polite and saying, is that clear[2322.67] [2322.67][S02]and please and thank you.[2323.78] [2323.78][S02]And in my experience, you get better responses out of things[2327.59] [2327.59][S02]if you do things that way.[2329.237] [2329.237][S01] Do you say please and thank you?[2331.07] [2331.07][S02] You can say please and thank you.[2332.69] [2332.69][S02]Yeah.[2333.19] [2333.19][S02]Now there's a good reason, good scientific reason[2335.74] [2335.74][S02]why that might get--[2337.96] [2337.96][S02]again, it just depends.[2339.79] [2339.79][S02]Models are changing all the time.[2341.38] [2341.38][S02]Why that might get better performance out of it.[2344.71] [2344.71][S02]Because if it's role playing, say it's role[2347.73] [2347.73][S02]playing a very smart intern.[2349.74] [2352.54][S02]Then it's going to just role play, maybe[2354.48] [2354.48][S02]being a bit more stroppy if they're not[2356.58] [2356.58][S02]being treated politely.[2358.83] [2358.83][S02]It's just mimicking what humans would do in that scenario.[2364.3] [2364.3][S02]So the mimicry might extend to being a bit more--[2368.43] [2368.43][S02]not being as responsive if their boss is a bit of a stroppy--[2375.103] [2375.103][S01] So-and-so.[2376.02] [2376.02][S02] Bossy boss.[2377.187] [2377.187][S01] I absolutely love that.[2378.73] [2378.73][S01]I think I want to return to where we started, which[2381.57] [2381.57][S01]is about how we think of that AI and the language[2386.04] [2386.04][S01]we use to describe it, and how we frame it in our minds.[2390.12] [2390.12][S01]Do you think that we need a new way of talking about AI?[2393.673] [2393.673][S02] I do.[2394.59] [2394.59][S01] Both acknowledges its potential[2397.08] [2397.08][S01]without overestimating it, but then similarly,[2400.32] [2400.32][S01]isn't dismissive of the things that it can do.[2402.752] [2402.752][S02] I think that's exactly what we need.[2404.96] [2404.96][S02]In one of my papers, I used the phrase[2406.543] [2406.543][S02]exotic mind-like entities to describe large language models.[2411.47] [2411.47][S02]So I think that they are, to a degree,[2413.92] [2413.92][S02]exotic mind-like entities.[2416.06] [2416.06][S01] Lovely.[2416.972] [2416.972][S02] So they are kind of mind-like,[2418.93] [2418.93][S02]and they're increasingly mind-like.[2420.98] [2420.98][S02]Now there's a very important reason[2422.62] [2422.62][S02]for using the little hyphen like there,[2424.97] [2424.97][S02]which is because I want to hedge my bets as to whether they[2428.47] [2428.47][S02]really qualify as minds.[2430.1] [2430.1][S02]And so I can wriggle out of that problem by just using mind-like.[2433.24] [2433.24][S02]They're exotic because they're not like us, language use,[2436.258] [2436.258][S02]but in other respects, they're disembodied.[2438.05] [2438.05][S02]For a start, there's really weird conceptions[2440.65] [2440.65][S02]of self-hood that are applicable to them, maybe.[2443.21] [2443.21][S02]But so they are quite exotic entities as well.[2446.09] [2446.09][S02]So I think of them as exotic, mind-like entities,[2449.06] [2449.06][S02]and we just don't have the right kind of conceptual framework[2453.85] [2453.85][S02]and vocabulary for talking about these exotic, mind-like entities[2457.13] [2457.13][S02]yet.[2457.63] [2457.63][S02]We're working on it.[2459.26] [2459.26][S02]And the more they are around us, the more[2463.43] [2463.43][S02]we'll develop new kinds of ways of talking and thinking[2466.85] [2466.85][S02]about them.[2467.67] [2467.67][S01] It is interesting, though, that you are still[2470.045] [2470.045][S01]going for the Turing-like approach of a creature,[2473.61] [2473.61][S01]almost, rather than the [INAUDIBLE].[2476.302] [2476.302][S02] Well, entity is a pretty neutral term, isn't it?[2479.01] [2479.01][S02]I suppose you could just say thing.[2480.98] [2480.98][S02]Exotic, mind-like thing, if you prefer.[2483.468] [2483.468][S01] Yeah, let's go with that.[2485.01] [2485.01][S01]I think let's push for that for the new name.[2488.367] [2488.367][S02] OK.[2489.2] [2489.2][S02]OK.[2489.7] [2489.7][S02]But I mean, I can't, Hannah, because I've[2492.29] [2492.29][S02]used the word entity in that context in many publications[2495.08] [2495.08][S02]now, so.[2496.065] [2496.065][S01] Exotic, mind-like entities.[2497.69] [2497.69][S01]I like it.[2498.45] [2498.45][S01]I like it a lot.[2499.44] [2499.44][S01]Murray, thank you so much for joining us.[2501.45] [2501.45][S02] It's been a pleasure, Hannah.[2502.65] [2502.65][S02]Thank you.[2503.37] [2503.37][S01] One of the nice things[2504.787] [2504.787][S01]about having done this podcast for a number of years[2507.02] [2507.02][S01]is that you really get to see how[2509.96] [2509.96][S01]the people at the frontier of AI, how their opinions change[2513.92] [2513.92][S01]and shift over time.[2516.08] [2516.08][S01]The last few years have been a real game changer[2518.99] [2518.99][S01]in all sorts of ways about the extent to which intelligence[2523.54] [2523.54][S01]requires a physical body, about how much[2527.2] [2527.2][S01]we need to expand our definition of consciousness[2530.54] [2530.54][S01]to account for the subtly different ways[2532.99] [2532.99][S01]that these mind-like entities can operate.[2536.45] [2536.45][S01]And the next few years, well, who knows?[2538.91] [2538.91][S01]But if past predictions are any indication,[2542.06] [2542.06][S01]the only thing we know about tomorrow's science[2544.45] [2544.45][S01]and technology is that it will be radically different to what[2547.9] [2547.9][S01]we imagine today.[2549.59] [2549.59][S01]You have been listening to "Google DeepMind,"[2551.93] [2551.93][S01]the podcast with me, Professor Hannah Fry.[2554.35] [2554.35][S01]If you enjoyed this episode, then[2556.12] [2556.12][S01]do subscribe to our YouTube channel.[2558.08] [2558.08][S01]You can also find us on your favorite podcast platform.[2561.38] [2561.38][S01]And of course, we have plenty more episodes[2564.16] [2564.16][S01]on a whole range of topics to come, so do check those out.[2568.04] [2568.04][S01]See you next time.[2570.06]