{"text": " of the real human data, or if that captures different answers. For example, if I say, instead of a sovereign German woman, you're a city English boy, I know, if you have explored those kind of things. Western politics of Hawaii kind of look at that as those democratic variables really matter in terms of that. Yeah, yeah, like the LLM was prompted to answer as a German woman, but then your sample is not all the German women, I guess, like the real data. So how do you account for those demographic variations when you prompt them all to answer just as one persona? You know? Yeah, yeah, yeah, that's what makes it so clear. So basically, he took like 200 persons for the survey, and all of those kind of . And did you explore like the difference between the personas that you made? Like did the LLM show some variability according to these different demographics? Yeah, yes, I had a lot of . I did run this analysis with the latest data. Previously, it was like, you could really kind of see that they kind of cast this kind of . They were like, I don't know, I'm not sure. I'm not sure. I'm not sure. I'm not sure. I'm not sure. I'm not sure. I'm not sure. I'm not sure. I don't keep them in a background. But they are like missing or some of that in contrast . Yes, there's some . There's some . Right. Well, thank you very much. The next presentation is bunun of the\u00f6mni intensivpamc towards . in order to general intelligence with a causal temper approach to the cognitive execution substrate. Yes. Okay, thank you for this opportunity. Like my two main teams I'm interested in is how is cognitive science, is it possible for cognitive science to help to build AI systems? Or is it just an engineering task? And the other one is on the next slide, but I'm going to tractability. And then some questions related to these issues. And I finalize with the hypothesis that is kind of checklist that what we should know up to this point, maybe also later. So, my method is like all the participatory design, meaning that, okay, I set a goal that, like, unfiltered. I set a feasible goal. Let's build a robot which has AGI capabilities, meaning artificial general capabilities. Obviously, that's kind of unfeasible. But the purpose is that in the process we find problems and criteria that are, you know, on the way. So, and the other one good thing is that this kind of moves from the classical symbolic paradigm, symbolic paradigm to the AI. So, I'm going to talk about the AI. So, I'm going to talk about the AI. And it's a kind of manipulation paradigm to the embodied, embedded, and enacted and extended cognition, because it's a robot and it functions in a physical world. And this causal chamber is a device from physics. It's a box where you can measure and do, measure real physical phenomena and do experiments in that way, in real physical phenomena. real world instead of in simulation world so I have this thinking tool or concept of cognitive executive substrate meaning that everything that our head is the executive organ that interprets every language data just like in computer we have processor the processor executes certain kinds of programs so like we humans we execute philosophy we understand philosophy through this to our brain to the system all the knowledge we have and all the skills we have we need that in order to interpret that so and and the point is that that we should think always what is the system we are kinda what is the system that kinda executes the theories or anything or problems that we investigate instead of like in many times in philosophy you focus in the discourse what people say and what happens in this like conceptual realm it's like same thing like in you can have extended mathematics but only part of that is relevant for physics same thing here we should kind of ground our our theories in in cognitive executive substrate that's the conceptual tool so what is artificial general intelligence there are many definitions but but I like this one it's quite practical there's similarly like self-driving cars there's this levels of autonomy and then there is this is it the general system or is it very narrow narrow system and yeah and this image shows that resistance is futile that the crafts are going that direction where the in different tasks AI is able to surpass human performance so that's why I'm interested in in knowing why how would computer science help in going that direction but there's a problem there's this practical condition thesis that states that the AGI is not computable in practice 5 minutes I have 5 minutes what left ok so but there's a problem they don't define that they define the problem problem space to broadly without any inductive biases and they think AI system as a monolithic agent instead of they should think it as a modular agent and yeah, that by its own perspective like what it like to cognize perspective without without minimal or suitable inductive biases that we throw into them how would that system kind of be able to create AGI so instead of having a thinking a model monolithic system we should have that set of modules where each module represents a cognitive capacity its module is tractable as an algorithm and also in this into input space the modules are organized in an architecture where each module is independent there's no cycles, and they are best effort broadcasters, meaning that they just function as their own and get modulated by others. Then these models should be, they can mimic sensitive periods and we can learn from, start from most digital levels like sensory levels and build on those and the whole system has different time loops where the lowest, the most digital levels are the most fastest and the highest levels are more fastest. So I woke up this morning and I saw, I realized in a dream there was something more, but I forgot it. I woke up so, so I invite you to like tell me what is missing here or what I need to consider more. Yes, any questions? I think there's time for one or two more questions, but it's probably great and then it's time for the next one. Great. Sorry. Thank you. Hi, Janne. Excellent. Thank you. Always nice to have discussions with you. You are researching on a very broad basis here. I have one very general question for you. So sorry to read it out from paper and sorry for making you to listen to it. But even if AGI, which we have, would be better in every single task than total analysis, what would you say would be the best solution for the data? So, I'm sorry. I'm sorry. I'm sorry. I'm sorry when I say this one's not the best solution. What I actually need to say is, that perhaps, creators andudio \u043d\u0430\u0439\u0442\u0438 a superior intelligence to strawberry plums that you describe not merely as man, but as an even Senhor and you know, not only as this man top human, it might not mean that it has superior intelligence. This is rest injection on the position that intelligence is made of different common tasks. There's always a possibility about an entirely new task and a need for what we call shave on creativity. The long of human intelligence. So just coming up with different cognitive tasks in a machine does not create anything more than the superhuman and all is clear. Know? more and there's some of those parts it doesn't create suddenly something this is a promise and hype of or has been of ai that suddenly there will be this emergence of the general intelligence and doesn't seem to happen we still have to teach it different tasks i don't know uh my answer would be like uh using the other mind problem and the monolithic agent problem that if we are able if if we are afraid to that ai is replacing humans then then it certainly has certain kinds of capacities and the kind of also like if the tractable cognizant thesis is true then we can also they have to say that because they are critical everybody's afraid of ai doom so i would i would ask them that how do you prove that the ai doom is not intractable i would i would kind of throw it out there that it's intractable if we are in that school but if if we are in my school then it's tractable but i don't believe they do thank you now we have a coffee break of half an hour and 2 30 is the last keynote speech thanks again to all the great presentations yes yes yes for you i thank you", "segments": [{"id": 0, "seek": 0, "start": 0.0, "end": 4.54, "text": " of the real human data, or if that captures different answers.", "tokens": [50365, 295, 264, 957, 1952, 1412, 11, 420, 498, 300, 27986, 819, 6338, 13, 50592], "temperature": 0.0, "avg_logprob": -0.6082572471804735, "compression_ratio": 1.5048543689320388, "no_speech_prob": 0.3133033215999603}, {"id": 1, "seek": 0, "start": 4.54, "end": 7.88, "text": " For example, if I say, instead of a sovereign German woman,", "tokens": [50592, 1171, 1365, 11, 498, 286, 584, 11, 2602, 295, 257, 28756, 6521, 3059, 11, 50759], "temperature": 0.0, "avg_logprob": -0.6082572471804735, "compression_ratio": 1.5048543689320388, "no_speech_prob": 0.3133033215999603}, {"id": 2, "seek": 0, "start": 7.88, "end": 11.82, "text": " you're a city English boy, I know,", "tokens": [50759, 291, 434, 257, 2307, 3669, 3237, 11, 286, 458, 11, 50956], "temperature": 0.0, "avg_logprob": -0.6082572471804735, "compression_ratio": 1.5048543689320388, "no_speech_prob": 0.3133033215999603}, {"id": 3, "seek": 0, "start": 11.82, "end": 16.02, "text": " if you have explored those kind of things.", "tokens": [50956, 498, 291, 362, 24016, 729, 733, 295, 721, 13, 51166], "temperature": 0.0, "avg_logprob": -0.6082572471804735, "compression_ratio": 1.5048543689320388, "no_speech_prob": 0.3133033215999603}, {"id": 4, "seek": 0, "start": 16.02, "end": 20.92, "text": " Western politics of Hawaii", "tokens": [51166, 8724, 7341, 295, 17930, 51411], "temperature": 0.0, "avg_logprob": -0.6082572471804735, "compression_ratio": 1.5048543689320388, "no_speech_prob": 0.3133033215999603}, {"id": 5, "seek": 0, "start": 20.92, "end": 25.48, "text": " kind of look at that as those democratic variables really", "tokens": [51411, 733, 295, 574, 412, 300, 382, 729, 15337, 9102, 534, 51639], "temperature": 0.0, "avg_logprob": -0.6082572471804735, "compression_ratio": 1.5048543689320388, "no_speech_prob": 0.3133033215999603}, {"id": 6, "seek": 0, "start": 25.48, "end": 28.88, "text": " matter in terms of that.", "tokens": [51639, 1871, 294, 2115, 295, 300, 13, 51809], "temperature": 0.0, "avg_logprob": -0.6082572471804735, "compression_ratio": 1.5048543689320388, "no_speech_prob": 0.3133033215999603}, {"id": 7, "seek": 2888, "start": 28.88, "end": 34.019999999999996, "text": " Yeah, yeah, like the LLM was prompted", "tokens": [50365, 865, 11, 1338, 11, 411, 264, 441, 43, 44, 390, 31042, 50622], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 8, "seek": 2888, "start": 34.019999999999996, "end": 37.06, "text": " to answer as a German woman, but then your sample", "tokens": [50622, 281, 1867, 382, 257, 6521, 3059, 11, 457, 550, 428, 6889, 50774], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 9, "seek": 2888, "start": 37.06, "end": 40.6, "text": " is not all the German women, I guess, like the real data.", "tokens": [50774, 307, 406, 439, 264, 6521, 2266, 11, 286, 2041, 11, 411, 264, 957, 1412, 13, 50951], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 10, "seek": 2888, "start": 40.6, "end": 43.379999999999995, "text": " So how do you account for those demographic variations", "tokens": [50951, 407, 577, 360, 291, 2696, 337, 729, 26331, 17840, 51090], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 11, "seek": 2888, "start": 43.379999999999995, "end": 46.379999999999995, "text": " when you prompt them all to answer just as one persona?", "tokens": [51090, 562, 291, 12391, 552, 439, 281, 1867, 445, 382, 472, 12184, 30, 51240], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 12, "seek": 2888, "start": 46.379999999999995, "end": 46.879999999999995, "text": " You know?", "tokens": [51240, 509, 458, 30, 51265], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 13, "seek": 2888, "start": 46.879999999999995, "end": 50.379999999999995, "text": " Yeah, yeah, yeah, that's what makes it so clear.", "tokens": [51265, 865, 11, 1338, 11, 1338, 11, 300, 311, 437, 1669, 309, 370, 1850, 13, 51440], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 14, "seek": 2888, "start": 50.379999999999995, "end": 55.26, "text": " So basically, he took like 200 persons for the survey,", "tokens": [51440, 407, 1936, 11, 415, 1890, 411, 2331, 14453, 337, 264, 8984, 11, 51684], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 15, "seek": 2888, "start": 55.26, "end": 57.26, "text": " and all of those kind of .", "tokens": [51684, 293, 439, 295, 729, 733, 295, 2411, 51784], "temperature": 0.0, "avg_logprob": -0.5094218971454991, "compression_ratio": 1.6680672268907564, "no_speech_prob": 0.01525779627263546}, {"id": 16, "seek": 5888, "start": 58.88, "end": 65.06, "text": " And did you explore like the difference between the personas", "tokens": [50365, 400, 630, 291, 6839, 411, 264, 2649, 1296, 264, 12019, 50674], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 17, "seek": 5888, "start": 65.06, "end": 65.72, "text": " that you made?", "tokens": [50674, 300, 291, 1027, 30, 50707], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 18, "seek": 5888, "start": 65.72, "end": 70.7, "text": " Like did the LLM show some variability", "tokens": [50707, 1743, 630, 264, 441, 43, 44, 855, 512, 35709, 50956], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 19, "seek": 5888, "start": 70.7, "end": 74.98, "text": " according to these different demographics?", "tokens": [50956, 4650, 281, 613, 819, 36884, 30, 51170], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 20, "seek": 5888, "start": 74.98, "end": 79.6, "text": " Yeah, yes, I had a lot of .", "tokens": [51170, 865, 11, 2086, 11, 286, 632, 257, 688, 295, 2411, 51401], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 21, "seek": 5888, "start": 79.6, "end": 82.9, "text": " I did run this analysis with the latest data.", "tokens": [51401, 286, 630, 1190, 341, 5215, 365, 264, 6792, 1412, 13, 51566], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 22, "seek": 5888, "start": 82.9, "end": 86.78, "text": " Previously, it was like, you could really kind of see that they kind of cast this kind of .", "tokens": [51566, 33606, 11, 309, 390, 411, 11, 291, 727, 534, 733, 295, 536, 300, 436, 733, 295, 4193, 341, 733, 295, 2411, 51760], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 23, "seek": 5888, "start": 86.78, "end": 87.06, "text": " They were like, I don't know, I'm not sure.", "tokens": [51760, 814, 645, 411, 11, 286, 500, 380, 458, 11, 286, 478, 406, 988, 13, 51774], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 24, "seek": 5888, "start": 87.06, "end": 87.32000000000001, "text": " I'm not sure.", "tokens": [51774, 286, 478, 406, 988, 13, 51787], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 25, "seek": 5888, "start": 87.32000000000001, "end": 87.7, "text": " I'm not sure.", "tokens": [51787, 286, 478, 406, 988, 13, 51806], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 26, "seek": 5888, "start": 87.7, "end": 87.86, "text": " I'm not sure.", "tokens": [51806, 286, 478, 406, 988, 13, 51814], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 27, "seek": 5888, "start": 87.86, "end": 88.02000000000001, "text": " I'm not sure.", "tokens": [51814, 286, 478, 406, 988, 13, 51822], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 28, "seek": 5888, "start": 88.02000000000001, "end": 88.28, "text": " I'm not sure.", "tokens": [51822, 286, 478, 406, 988, 13, 51835], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 29, "seek": 5888, "start": 88.28, "end": 88.46000000000001, "text": " I'm not sure.", "tokens": [51835, 286, 478, 406, 988, 13, 51844], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 30, "seek": 5888, "start": 88.46000000000001, "end": 88.86, "text": " I'm not sure.", "tokens": [51844, 286, 478, 406, 988, 13, 51864], "temperature": 0.4, "avg_logprob": -0.8582186889648438, "compression_ratio": 1.9375, "no_speech_prob": 0.030954735353589058}, {"id": 31, "seek": 8888, "start": 89.39999999999999, "end": 91.69999999999999, "text": " I don't keep them in a background.", "tokens": [50391, 286, 500, 380, 1066, 552, 294, 257, 3678, 13, 50506], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 32, "seek": 8888, "start": 91.69999999999999, "end": 100.39999999999999, "text": " But they are like missing or some of that", "tokens": [50506, 583, 436, 366, 411, 5361, 420, 512, 295, 300, 50941], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 33, "seek": 8888, "start": 100.39999999999999, "end": 102.74, "text": " in contrast .", "tokens": [50941, 294, 8712, 2411, 51058], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 34, "seek": 8888, "start": 102.74, "end": 104.89999999999999, "text": " Yes, there's some .", "tokens": [51058, 1079, 11, 456, 311, 512, 2411, 51166], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 35, "seek": 8888, "start": 104.89999999999999, "end": 106.19999999999999, "text": " There's some .", "tokens": [51166, 821, 311, 512, 2411, 51231], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 36, "seek": 8888, "start": 106.19999999999999, "end": 108.25999999999999, "text": " Right. Well, thank you very much.", "tokens": [51231, 1779, 13, 1042, 11, 1309, 291, 588, 709, 13, 51334], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 37, "seek": 8888, "start": 108.69999999999999, "end": 114.53999999999999, "text": " The next presentation is bunun", "tokens": [51356, 440, 958, 5860, 307, 31697, 51648], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 38, "seek": 8888, "start": 114.53999999999999, "end": 118.86, "text": " of the\u00f6mni intensivpamc towards .", "tokens": [51648, 295, 264, 32374, 3722, 14056, 592, 79, 335, 66, 3030, 2411, 51864], "temperature": 1.0, "avg_logprob": -2.6894442584063554, "compression_ratio": 1.3888888888888888, "no_speech_prob": 0.02822725661098957}, {"id": 39, "seek": 11886, "start": 118.86, "end": 122.36, "text": " in order to general intelligence with a causal temper approach", "tokens": [50365, 294, 1668, 281, 2674, 7599, 365, 257, 38755, 3393, 3109, 50540], "temperature": 0.0, "avg_logprob": -0.4844376700265067, "compression_ratio": 1.2434782608695651, "no_speech_prob": 0.01737935096025467}, {"id": 40, "seek": 11886, "start": 122.36, "end": 124.8, "text": " to the cognitive execution substrate.", "tokens": [50540, 281, 264, 15605, 15058, 27585, 13, 50662], "temperature": 0.0, "avg_logprob": -0.4844376700265067, "compression_ratio": 1.2434782608695651, "no_speech_prob": 0.01737935096025467}, {"id": 41, "seek": 11886, "start": 139.8, "end": 140.28, "text": " Yes.", "tokens": [51412, 1079, 13, 51436], "temperature": 0.0, "avg_logprob": -0.4844376700265067, "compression_ratio": 1.2434782608695651, "no_speech_prob": 0.01737935096025467}, {"id": 42, "seek": 11886, "start": 146.64, "end": 148.4, "text": " Okay, thank you for this opportunity.", "tokens": [51754, 1033, 11, 1309, 291, 337, 341, 2650, 13, 51842], "temperature": 0.0, "avg_logprob": -0.4844376700265067, "compression_ratio": 1.2434782608695651, "no_speech_prob": 0.01737935096025467}, {"id": 43, "seek": 14886, "start": 148.86, "end": 157.0, "text": " Like my two main teams I'm interested in is how is cognitive science,", "tokens": [50365, 1743, 452, 732, 2135, 5491, 286, 478, 3102, 294, 307, 577, 307, 15605, 3497, 11, 50772], "temperature": 0.0, "avg_logprob": -0.2581839341383714, "compression_ratio": 1.478527607361963, "no_speech_prob": 0.008029320277273655}, {"id": 44, "seek": 14886, "start": 157.10000000000002, "end": 163.08, "text": " is it possible for cognitive science to help to build AI systems?", "tokens": [50777, 307, 309, 1944, 337, 15605, 3497, 281, 854, 281, 1322, 7318, 3652, 30, 51076], "temperature": 0.0, "avg_logprob": -0.2581839341383714, "compression_ratio": 1.478527607361963, "no_speech_prob": 0.008029320277273655}, {"id": 45, "seek": 14886, "start": 165.3, "end": 168.08, "text": " Or is it just an engineering task?", "tokens": [51187, 1610, 307, 309, 445, 364, 7043, 5633, 30, 51326], "temperature": 0.0, "avg_logprob": -0.2581839341383714, "compression_ratio": 1.478527607361963, "no_speech_prob": 0.008029320277273655}, {"id": 46, "seek": 14886, "start": 168.32000000000002, "end": 173.84, "text": " And the other one is on the next slide,", "tokens": [51338, 400, 264, 661, 472, 307, 322, 264, 958, 4137, 11, 51614], "temperature": 0.0, "avg_logprob": -0.2581839341383714, "compression_ratio": 1.478527607361963, "no_speech_prob": 0.008029320277273655}, {"id": 47, "seek": 14886, "start": 173.84, "end": 178.22000000000003, "text": " but I'm going to tractability.", "tokens": [51614, 457, 286, 478, 516, 281, 24207, 2310, 13, 51833], "temperature": 0.0, "avg_logprob": -0.2581839341383714, "compression_ratio": 1.478527607361963, "no_speech_prob": 0.008029320277273655}, {"id": 48, "seek": 17886, "start": 178.86, "end": 183.86, "text": " And then some questions related to these issues.", "tokens": [50365, 400, 550, 512, 1651, 4077, 281, 613, 2663, 13, 50615], "temperature": 0.0, "avg_logprob": -0.4177775065104167, "compression_ratio": 1.5, "no_speech_prob": 0.0002823859394993633}, {"id": 49, "seek": 17886, "start": 184.74, "end": 187.86, "text": " And I finalize with the hypothesis that is kind of checklist", "tokens": [50659, 400, 286, 2572, 1125, 365, 264, 17291, 300, 307, 733, 295, 30357, 50815], "temperature": 0.0, "avg_logprob": -0.4177775065104167, "compression_ratio": 1.5, "no_speech_prob": 0.0002823859394993633}, {"id": 50, "seek": 17886, "start": 188.5, "end": 194.86, "text": " that what we should know up to this point, maybe also later.", "tokens": [50847, 300, 437, 321, 820, 458, 493, 281, 341, 935, 11, 1310, 611, 1780, 13, 51165], "temperature": 0.0, "avg_logprob": -0.4177775065104167, "compression_ratio": 1.5, "no_speech_prob": 0.0002823859394993633}, {"id": 51, "seek": 17886, "start": 196.20000000000002, "end": 207.62, "text": " So, my method is like all the participatory design, meaning that, okay, I set a goal that,", "tokens": [51232, 407, 11, 452, 3170, 307, 411, 439, 264, 3421, 4745, 1715, 11, 3620, 300, 11, 1392, 11, 286, 992, 257, 3387, 300, 11, 51803], "temperature": 0.0, "avg_logprob": -0.4177775065104167, "compression_ratio": 1.5, "no_speech_prob": 0.0002823859394993633}, {"id": 52, "seek": 17886, "start": 208.16000000000003, "end": 208.74, "text": " like, unfiltered.", "tokens": [51830, 411, 11, 3971, 388, 40665, 13, 51859], "temperature": 0.0, "avg_logprob": -0.4177775065104167, "compression_ratio": 1.5, "no_speech_prob": 0.0002823859394993633}, {"id": 53, "seek": 20886, "start": 208.86, "end": 209.62, "text": " I set a feasible goal.", "tokens": [50365, 286, 992, 257, 26648, 3387, 13, 50403], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 54, "seek": 20886, "start": 210.52, "end": 217.42000000000002, "text": " Let's build a robot which has AGI capabilities, meaning artificial general capabilities.", "tokens": [50448, 961, 311, 1322, 257, 7881, 597, 575, 316, 26252, 10862, 11, 3620, 11677, 2674, 10862, 13, 50793], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 55, "seek": 20886, "start": 217.58, "end": 219.62, "text": " Obviously, that's kind of unfeasible.", "tokens": [50801, 7580, 11, 300, 311, 733, 295, 517, 2106, 296, 964, 13, 50903], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 56, "seek": 20886, "start": 219.8, "end": 229.82000000000002, "text": " But the purpose is that in the process we find problems and criteria that are, you know, on the way.", "tokens": [50912, 583, 264, 4334, 307, 300, 294, 264, 1399, 321, 915, 2740, 293, 11101, 300, 366, 11, 291, 458, 11, 322, 264, 636, 13, 51413], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 57, "seek": 20886, "start": 231.18, "end": 238.04000000000002, "text": " So, and the other one good thing is that this kind of moves from the classical symbolic paradigm,", "tokens": [51481, 407, 11, 293, 264, 661, 472, 665, 551, 307, 300, 341, 733, 295, 6067, 490, 264, 13735, 25755, 24709, 11, 51824], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 58, "seek": 20886, "start": 238.24, "end": 238.74, "text": " symbolic paradigm to the AI.", "tokens": [51834, 25755, 24709, 281, 264, 7318, 13, 51859], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 59, "seek": 20886, "start": 238.74, "end": 238.82000000000002, "text": " So, I'm going to talk about the AI.", "tokens": [51859, 407, 11, 286, 478, 516, 281, 751, 466, 264, 7318, 13, 51863], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 60, "seek": 20886, "start": 238.82000000000002, "end": 238.84, "text": " So, I'm going to talk about the AI.", "tokens": [51863, 407, 11, 286, 478, 516, 281, 751, 466, 264, 7318, 13, 51864], "temperature": 0.0, "avg_logprob": -0.4521696547831386, "compression_ratio": 1.8178137651821862, "no_speech_prob": 0.001179977203719318}, {"id": 61, "seek": 23886, "start": 239.0, "end": 246.34, "text": " And it's a kind of manipulation paradigm to the embodied, embedded, and enacted and extended cognition,", "tokens": [50372, 400, 309, 311, 257, 733, 295, 26475, 24709, 281, 264, 42046, 11, 16741, 11, 293, 41313, 293, 10913, 46905, 11, 50739], "temperature": 0.8, "avg_logprob": -0.5199726383860518, "compression_ratio": 1.7079207920792079, "no_speech_prob": 0.002482631476595998}, {"id": 62, "seek": 23886, "start": 246.92000000000002, "end": 252.74, "text": " because it's a robot and it functions in a physical world.", "tokens": [50768, 570, 309, 311, 257, 7881, 293, 309, 6828, 294, 257, 4001, 1002, 13, 51059], "temperature": 0.8, "avg_logprob": -0.5199726383860518, "compression_ratio": 1.7079207920792079, "no_speech_prob": 0.002482631476595998}, {"id": 63, "seek": 23886, "start": 253.4, "end": 256.86, "text": " And this causal chamber is a device from physics.", "tokens": [51092, 400, 341, 38755, 13610, 307, 257, 4302, 490, 10649, 13, 51265], "temperature": 0.8, "avg_logprob": -0.5199726383860518, "compression_ratio": 1.7079207920792079, "no_speech_prob": 0.002482631476595998}, {"id": 64, "seek": 23886, "start": 257.64, "end": 268.1, "text": " It's a box where you can measure and do, measure real physical phenomena and do experiments in that way,", "tokens": [51304, 467, 311, 257, 2424, 689, 291, 393, 3481, 293, 360, 11, 3481, 957, 4001, 22004, 293, 360, 12050, 294, 300, 636, 11, 51827], "temperature": 0.8, "avg_logprob": -0.5199726383860518, "compression_ratio": 1.7079207920792079, "no_speech_prob": 0.002482631476595998}, {"id": 65, "seek": 23886, "start": 268.22, "end": 268.62, "text": " in real physical phenomena.", "tokens": [51833, 294, 957, 4001, 22004, 13, 51853], "temperature": 0.8, "avg_logprob": -0.5199726383860518, "compression_ratio": 1.7079207920792079, "no_speech_prob": 0.002482631476595998}, {"id": 66, "seek": 26862, "start": 268.62, "end": 277.92, "text": " real world instead of in simulation world so I have this thinking tool or", "tokens": [50365, 957, 1002, 2602, 295, 294, 16575, 1002, 370, 286, 362, 341, 1953, 2290, 420, 50830], "temperature": 0.0, "avg_logprob": -0.21640452094700025, "compression_ratio": 1.5774647887323943, "no_speech_prob": 0.03850463032722473}, {"id": 67, "seek": 26862, "start": 277.92, "end": 288.6, "text": " concept of cognitive executive substrate meaning that everything that our head is", "tokens": [50830, 3410, 295, 15605, 10140, 27585, 3620, 300, 1203, 300, 527, 1378, 307, 51364], "temperature": 0.0, "avg_logprob": -0.21640452094700025, "compression_ratio": 1.5774647887323943, "no_speech_prob": 0.03850463032722473}, {"id": 68, "seek": 26862, "start": 288.6, "end": 294.7, "text": " the executive organ that interprets every language data just like in", "tokens": [51364, 264, 10140, 1798, 300, 17489, 1373, 633, 2856, 1412, 445, 411, 294, 51669], "temperature": 0.0, "avg_logprob": -0.21640452094700025, "compression_ratio": 1.5774647887323943, "no_speech_prob": 0.03850463032722473}, {"id": 69, "seek": 29470, "start": 294.7, "end": 302.97999999999996, "text": " computer we have processor the processor executes certain kinds of programs so", "tokens": [50365, 3820, 321, 362, 15321, 264, 15321, 4454, 1819, 1629, 3685, 295, 4268, 370, 50779], "temperature": 0.4, "avg_logprob": -0.16152439946713654, "compression_ratio": 1.7953216374269005, "no_speech_prob": 0.0017934899078682065}, {"id": 70, "seek": 29470, "start": 302.97999999999996, "end": 311.02, "text": " like we humans we execute philosophy we understand philosophy through this to", "tokens": [50779, 411, 321, 6255, 321, 14483, 10675, 321, 1223, 10675, 807, 341, 281, 51181], "temperature": 0.4, "avg_logprob": -0.16152439946713654, "compression_ratio": 1.7953216374269005, "no_speech_prob": 0.0017934899078682065}, {"id": 71, "seek": 29470, "start": 311.02, "end": 316.65999999999997, "text": " our brain to the system all the knowledge we have and all the skills we", "tokens": [51181, 527, 3567, 281, 264, 1185, 439, 264, 3601, 321, 362, 293, 439, 264, 3942, 321, 51463], "temperature": 0.4, "avg_logprob": -0.16152439946713654, "compression_ratio": 1.7953216374269005, "no_speech_prob": 0.0017934899078682065}, {"id": 72, "seek": 29470, "start": 316.65999999999997, "end": 324.48, "text": " have we need that in order to interpret that so and and the point is that", "tokens": [51463, 362, 321, 643, 300, 294, 1668, 281, 7302, 300, 370, 293, 293, 264, 935, 307, 300, 51854], "temperature": 0.4, "avg_logprob": -0.16152439946713654, "compression_ratio": 1.7953216374269005, "no_speech_prob": 0.0017934899078682065}, {"id": 73, "seek": 29470, "start": 324.48, "end": 324.68, "text": " that", "tokens": [51854, 300, 51864], "temperature": 0.4, "avg_logprob": -0.16152439946713654, "compression_ratio": 1.7953216374269005, "no_speech_prob": 0.0017934899078682065}, {"id": 74, "seek": 32468, "start": 324.68, "end": 332.1, "text": " we should think always what is the system we are kinda what is the system", "tokens": [50365, 321, 820, 519, 1009, 437, 307, 264, 1185, 321, 366, 4144, 437, 307, 264, 1185, 50736], "temperature": 0.6000000000000001, "avg_logprob": -0.32174090595988486, "compression_ratio": 1.7727272727272727, "no_speech_prob": 0.007334133610129356}, {"id": 75, "seek": 32468, "start": 332.1, "end": 338.76, "text": " that kinda executes the theories or anything or problems that we investigate", "tokens": [50736, 300, 4144, 4454, 1819, 264, 13667, 420, 1340, 420, 2740, 300, 321, 15013, 51069], "temperature": 0.6000000000000001, "avg_logprob": -0.32174090595988486, "compression_ratio": 1.7727272727272727, "no_speech_prob": 0.007334133610129356}, {"id": 76, "seek": 32468, "start": 338.76, "end": 342.8, "text": " instead of like in many times in philosophy you focus in the discourse", "tokens": [51069, 2602, 295, 411, 294, 867, 1413, 294, 10675, 291, 1879, 294, 264, 23938, 51271], "temperature": 0.6000000000000001, "avg_logprob": -0.32174090595988486, "compression_ratio": 1.7727272727272727, "no_speech_prob": 0.007334133610129356}, {"id": 77, "seek": 32468, "start": 342.8, "end": 349.46000000000004, "text": " what people say and what happens in this like conceptual realm", "tokens": [51271, 437, 561, 584, 293, 437, 2314, 294, 341, 411, 24106, 15355, 51604], "temperature": 0.6000000000000001, "avg_logprob": -0.32174090595988486, "compression_ratio": 1.7727272727272727, "no_speech_prob": 0.007334133610129356}, {"id": 78, "seek": 32468, "start": 349.46000000000004, "end": 354.46000000000004, "text": " it's like same thing like in you can have extended mathematics but", "tokens": [51604, 309, 311, 411, 912, 551, 411, 294, 291, 393, 362, 10913, 18666, 457, 51854], "temperature": 0.6000000000000001, "avg_logprob": -0.32174090595988486, "compression_ratio": 1.7727272727272727, "no_speech_prob": 0.007334133610129356}, {"id": 79, "seek": 35446, "start": 354.46, "end": 361.23999999999995, "text": " only part of that is relevant for physics same thing here we should kind", "tokens": [50365, 787, 644, 295, 300, 307, 7340, 337, 10649, 912, 551, 510, 321, 820, 733, 50704], "temperature": 0.0, "avg_logprob": -0.21649665417878525, "compression_ratio": 1.5172413793103448, "no_speech_prob": 0.007833901792764664}, {"id": 80, "seek": 35446, "start": 361.23999999999995, "end": 372.64, "text": " of ground our our theories in in cognitive executive substrate that's the", "tokens": [50704, 295, 2727, 527, 527, 13667, 294, 294, 15605, 10140, 27585, 300, 311, 264, 51274], "temperature": 0.0, "avg_logprob": -0.21649665417878525, "compression_ratio": 1.5172413793103448, "no_speech_prob": 0.007833901792764664}, {"id": 81, "seek": 35446, "start": 372.64, "end": 376.71999999999997, "text": " conceptual tool so what is artificial general intelligence there are many", "tokens": [51274, 24106, 2290, 370, 437, 307, 11677, 2674, 7599, 456, 366, 867, 51478], "temperature": 0.0, "avg_logprob": -0.21649665417878525, "compression_ratio": 1.5172413793103448, "no_speech_prob": 0.007833901792764664}, {"id": 82, "seek": 37672, "start": 376.72, "end": 384.70000000000005, "text": " definitions but but I like this one it's quite practical there's similarly", "tokens": [50365, 21988, 457, 457, 286, 411, 341, 472, 309, 311, 1596, 8496, 456, 311, 14138, 50764], "temperature": 0.0, "avg_logprob": -0.1634149222538389, "compression_ratio": 1.625, "no_speech_prob": 0.0029660766012966633}, {"id": 83, "seek": 37672, "start": 384.70000000000005, "end": 390.1, "text": " like self-driving cars there's this levels of autonomy and then there is", "tokens": [50764, 411, 2698, 12, 47094, 5163, 456, 311, 341, 4358, 295, 27278, 293, 550, 456, 307, 51034], "temperature": 0.0, "avg_logprob": -0.1634149222538389, "compression_ratio": 1.625, "no_speech_prob": 0.0029660766012966633}, {"id": 84, "seek": 37672, "start": 390.1, "end": 404.68, "text": " this is it the general system or is it very narrow narrow system and yeah and", "tokens": [51034, 341, 307, 309, 264, 2674, 1185, 420, 307, 309, 588, 9432, 9432, 1185, 293, 1338, 293, 51763], "temperature": 0.0, "avg_logprob": -0.1634149222538389, "compression_ratio": 1.625, "no_speech_prob": 0.0029660766012966633}, {"id": 85, "seek": 37672, "start": 404.68, "end": 406.52000000000004, "text": " this image shows that", "tokens": [51763, 341, 3256, 3110, 300, 51855], "temperature": 0.0, "avg_logprob": -0.1634149222538389, "compression_ratio": 1.625, "no_speech_prob": 0.0029660766012966633}, {"id": 86, "seek": 40672, "start": 406.72, "end": 418.66, "text": " resistance is futile that the crafts are going that direction where the in", "tokens": [50365, 7335, 307, 1877, 794, 300, 264, 27831, 366, 516, 300, 3513, 689, 264, 294, 50962], "temperature": 0.0, "avg_logprob": -0.2761801022749681, "compression_ratio": 1.563758389261745, "no_speech_prob": 0.0021707601845264435}, {"id": 87, "seek": 40672, "start": 418.66, "end": 425.34000000000003, "text": " different tasks AI is able to surpass human performance so that's why I'm", "tokens": [50962, 819, 9608, 7318, 307, 1075, 281, 27650, 1952, 3389, 370, 300, 311, 983, 286, 478, 51296], "temperature": 0.0, "avg_logprob": -0.2761801022749681, "compression_ratio": 1.563758389261745, "no_speech_prob": 0.0021707601845264435}, {"id": 88, "seek": 40672, "start": 425.34000000000003, "end": 432.96000000000004, "text": " interested in in knowing why how would computer science help in going that", "tokens": [51296, 3102, 294, 294, 5276, 983, 577, 576, 3820, 3497, 854, 294, 516, 300, 51677], "temperature": 0.0, "avg_logprob": -0.2761801022749681, "compression_ratio": 1.563758389261745, "no_speech_prob": 0.0021707601845264435}, {"id": 89, "seek": 40672, "start": 432.96000000000004, "end": 435.22, "text": " direction", "tokens": [51677, 3513, 51790], "temperature": 0.0, "avg_logprob": -0.2761801022749681, "compression_ratio": 1.563758389261745, "no_speech_prob": 0.0021707601845264435}, {"id": 90, "seek": 43672, "start": 436.72, "end": 443.88000000000005, "text": " but there's a problem there's this practical condition thesis that states", "tokens": [50365, 457, 456, 311, 257, 1154, 456, 311, 341, 8496, 4188, 22288, 300, 4368, 50723], "temperature": 0.2, "avg_logprob": -0.5180317087376372, "compression_ratio": 1.547008547008547, "no_speech_prob": 0.0030772197060287}, {"id": 91, "seek": 43672, "start": 443.88000000000005, "end": 452.02000000000004, "text": " that the AGI is not computable in practice", "tokens": [50723, 300, 264, 316, 26252, 307, 406, 2807, 712, 294, 3124, 51130], "temperature": 0.2, "avg_logprob": -0.5180317087376372, "compression_ratio": 1.547008547008547, "no_speech_prob": 0.0030772197060287}, {"id": 92, "seek": 43672, "start": 452.02000000000004, "end": 457.54, "text": " 5 minutes I have 5 minutes what left", "tokens": [51130, 1025, 2077, 286, 362, 1025, 2077, 437, 1411, 51406], "temperature": 0.2, "avg_logprob": -0.5180317087376372, "compression_ratio": 1.547008547008547, "no_speech_prob": 0.0030772197060287}, {"id": 93, "seek": 43672, "start": 457.54, "end": 466.54, "text": " ok so but there's a problem", "tokens": [51406, 3133, 370, 457, 456, 311, 257, 1154, 51856], "temperature": 0.2, "avg_logprob": -0.5180317087376372, "compression_ratio": 1.547008547008547, "no_speech_prob": 0.0030772197060287}, {"id": 94, "seek": 46672, "start": 466.72, "end": 474.28000000000003, "text": " they don't define that they define the problem problem space to broadly without", "tokens": [50365, 436, 500, 380, 6964, 300, 436, 6964, 264, 1154, 1154, 1901, 281, 19511, 1553, 50743], "temperature": 0.0, "avg_logprob": -0.3716410468606388, "compression_ratio": 1.544776119402985, "no_speech_prob": 0.002452387008816004}, {"id": 95, "seek": 46672, "start": 474.28000000000003, "end": 481.48, "text": " any inductive biases and they think AI system as a monolithic agent instead of", "tokens": [50743, 604, 31612, 488, 32152, 293, 436, 519, 7318, 1185, 382, 257, 1108, 42878, 9461, 2602, 295, 51103], "temperature": 0.0, "avg_logprob": -0.3716410468606388, "compression_ratio": 1.544776119402985, "no_speech_prob": 0.002452387008816004}, {"id": 96, "seek": 46672, "start": 481.48, "end": 486.40000000000003, "text": " they should think it as a modular agent", "tokens": [51103, 436, 820, 519, 309, 382, 257, 31111, 9461, 51349], "temperature": 0.0, "avg_logprob": -0.3716410468606388, "compression_ratio": 1.544776119402985, "no_speech_prob": 0.002452387008816004}, {"id": 97, "seek": 46672, "start": 486.40000000000003, "end": 491.40000000000003, "text": " and", "tokens": [51349, 293, 51599], "temperature": 0.0, "avg_logprob": -0.3716410468606388, "compression_ratio": 1.544776119402985, "no_speech_prob": 0.002452387008816004}, {"id": 98, "seek": 46672, "start": 491.40000000000003, "end": 494.40000000000003, "text": " yeah", "tokens": [51599, 1338, 51749], "temperature": 0.0, "avg_logprob": -0.3716410468606388, "compression_ratio": 1.544776119402985, "no_speech_prob": 0.002452387008816004}, {"id": 99, "seek": 49672, "start": 496.72, "end": 526.72, "text": "", "tokens": [], "temperature": 0.2, "avg_logprob": -0.3568061292171478, "compression_ratio": 0.0, "no_speech_prob": 0.012232588604092598, "words": []}, {"id": 100, "seek": 52672, "start": 526.72, "end": 556.72, "text": "", "tokens": [], "temperature": 0.0, "avg_logprob": -0.09536974877119064, "compression_ratio": 0.0, "no_speech_prob": 0.006100895348936319, "words": []}, {"id": 101, "seek": 55672, "start": 556.72, "end": 586.72, "text": "", "tokens": [], "temperature": 0.2, "avg_logprob": -0.19718454778194427, "compression_ratio": 0.0, "no_speech_prob": 0.011031134985387325, "words": []}, {"id": 102, "seek": 58672, "start": 586.72, "end": 616.72, "text": "", "tokens": [], "temperature": 0.0, "avg_logprob": -0.17511120438575745, "compression_ratio": 0.0, "no_speech_prob": 0.006216233130544424, "words": []}, {"id": 103, "seek": 61672, "start": 616.72, "end": 628.5, "text": ", that by its own perspective like what it like to cognize perspective without", "tokens": [50365, 11, 300, 538, 1080, 1065, 4585, 411, 437, 309, 411, 281, 11786, 1125, 4585, 1553, 50954], "temperature": 0.0, "avg_logprob": -0.42099214949697816, "compression_ratio": 1.5533333333333332, "no_speech_prob": 0.003370600286871195}, {"id": 104, "seek": 61672, "start": 628.5, "end": 635.02, "text": " without minimal or suitable inductive biases that we throw into them how would", "tokens": [50954, 1553, 13206, 420, 12873, 31612, 488, 32152, 300, 321, 3507, 666, 552, 577, 576, 51280], "temperature": 0.0, "avg_logprob": -0.42099214949697816, "compression_ratio": 1.5533333333333332, "no_speech_prob": 0.003370600286871195}, {"id": 105, "seek": 61672, "start": 635.02, "end": 646.6800000000001, "text": " that system kind of be able to create AGI so instead of having a thinking a", "tokens": [51280, 300, 1185, 733, 295, 312, 1075, 281, 1884, 316, 26252, 370, 2602, 295, 1419, 257, 1953, 257, 51863], "temperature": 0.0, "avg_logprob": -0.42099214949697816, "compression_ratio": 1.5533333333333332, "no_speech_prob": 0.003370600286871195}, {"id": 106, "seek": 64672, "start": 646.72, "end": 654.58, "text": " model monolithic system we should have that set of modules where each module", "tokens": [50365, 2316, 1108, 42878, 1185, 321, 820, 362, 300, 992, 295, 16679, 689, 1184, 10088, 50758], "temperature": 0.8, "avg_logprob": -0.5786912352950485, "compression_ratio": 1.6455696202531647, "no_speech_prob": 0.0021173707209527493}, {"id": 107, "seek": 64672, "start": 654.58, "end": 663.4200000000001, "text": " represents a cognitive capacity its module is tractable as an algorithm and", "tokens": [50758, 8855, 257, 15605, 6042, 1080, 10088, 307, 24207, 712, 382, 364, 9284, 293, 51200], "temperature": 0.8, "avg_logprob": -0.5786912352950485, "compression_ratio": 1.6455696202531647, "no_speech_prob": 0.0021173707209527493}, {"id": 108, "seek": 64672, "start": 663.4200000000001, "end": 673.5600000000001, "text": " also in this into input space the modules are organized in an architecture", "tokens": [51200, 611, 294, 341, 666, 4846, 1901, 264, 16679, 366, 9983, 294, 364, 9482, 51707], "temperature": 0.8, "avg_logprob": -0.5786912352950485, "compression_ratio": 1.6455696202531647, "no_speech_prob": 0.0021173707209527493}, {"id": 109, "seek": 64672, "start": 673.5600000000001, "end": 676.6600000000001, "text": " where each module is independent", "tokens": [51707, 689, 1184, 10088, 307, 6695, 51862], "temperature": 0.8, "avg_logprob": -0.5786912352950485, "compression_ratio": 1.6455696202531647, "no_speech_prob": 0.0021173707209527493}, {"id": 110, "seek": 67672, "start": 676.72, "end": 686.72, "text": " there's no cycles, and they are best effort broadcasters, meaning that they just function", "tokens": [50365, 456, 311, 572, 17796, 11, 293, 436, 366, 1151, 4630, 4152, 16369, 1559, 11, 3620, 300, 436, 445, 2445, 50865], "temperature": 0.0, "avg_logprob": -0.2622427680275657, "compression_ratio": 1.4701986754966887, "no_speech_prob": 0.07128570228815079}, {"id": 111, "seek": 67672, "start": 686.72, "end": 693.0600000000001, "text": " as their own and get modulated by others.", "tokens": [50865, 382, 641, 1065, 293, 483, 1072, 6987, 538, 2357, 13, 51182], "temperature": 0.0, "avg_logprob": -0.2622427680275657, "compression_ratio": 1.4701986754966887, "no_speech_prob": 0.07128570228815079}, {"id": 112, "seek": 67672, "start": 693.0600000000001, "end": 703.24, "text": " Then these models should be, they can mimic sensitive periods and we can learn from, start", "tokens": [51182, 1396, 613, 5245, 820, 312, 11, 436, 393, 31075, 9477, 13804, 293, 321, 393, 1466, 490, 11, 722, 51691], "temperature": 0.0, "avg_logprob": -0.2622427680275657, "compression_ratio": 1.4701986754966887, "no_speech_prob": 0.07128570228815079}, {"id": 113, "seek": 70324, "start": 703.24, "end": 716.42, "text": " from most digital levels like sensory levels and build on those and the whole system has", "tokens": [50365, 490, 881, 4562, 4358, 411, 27233, 4358, 293, 1322, 322, 729, 293, 264, 1379, 1185, 575, 51024], "temperature": 0.0, "avg_logprob": -0.2114143873515882, "compression_ratio": 1.6935483870967742, "no_speech_prob": 0.0016145827248692513}, {"id": 114, "seek": 70324, "start": 716.42, "end": 721.8, "text": " different time loops where the lowest, the most digital levels are the most fastest and", "tokens": [51024, 819, 565, 16121, 689, 264, 12437, 11, 264, 881, 4562, 4358, 366, 264, 881, 14573, 293, 51293], "temperature": 0.0, "avg_logprob": -0.2114143873515882, "compression_ratio": 1.6935483870967742, "no_speech_prob": 0.0016145827248692513}, {"id": 115, "seek": 70324, "start": 721.8, "end": 723.58, "text": " the highest levels are more fastest.", "tokens": [51293, 264, 6343, 4358, 366, 544, 14573, 13, 51382], "temperature": 0.0, "avg_logprob": -0.2114143873515882, "compression_ratio": 1.6935483870967742, "no_speech_prob": 0.0016145827248692513}, {"id": 116, "seek": 70324, "start": 723.58, "end": 732.1, "text": " So I woke up this morning and I saw, I realized in a dream there was something more, but I", "tokens": [51382, 407, 286, 12852, 493, 341, 2446, 293, 286, 1866, 11, 286, 5334, 294, 257, 3055, 456, 390, 746, 544, 11, 457, 286, 51808], "temperature": 0.0, "avg_logprob": -0.2114143873515882, "compression_ratio": 1.6935483870967742, "no_speech_prob": 0.0016145827248692513}, {"id": 117, "seek": 70324, "start": 732.1, "end": 733.1, "text": " forgot it.", "tokens": [51808, 5298, 309, 13, 51858], "temperature": 0.0, "avg_logprob": -0.2114143873515882, "compression_ratio": 1.6935483870967742, "no_speech_prob": 0.0016145827248692513}, {"id": 118, "seek": 73310, "start": 733.1, "end": 743.22, "text": " I woke up so, so I invite you to like tell me what is missing here or what I need to", "tokens": [50365, 286, 12852, 493, 370, 11, 370, 286, 7980, 291, 281, 411, 980, 385, 437, 307, 5361, 510, 420, 437, 286, 643, 281, 50871], "temperature": 0.0, "avg_logprob": -0.614333805285002, "compression_ratio": 1.4819277108433735, "no_speech_prob": 0.006326767150312662}, {"id": 119, "seek": 73310, "start": 743.22, "end": 746.94, "text": " consider more.", "tokens": [50871, 1949, 544, 13, 51057], "temperature": 0.0, "avg_logprob": -0.614333805285002, "compression_ratio": 1.4819277108433735, "no_speech_prob": 0.006326767150312662}, {"id": 120, "seek": 73310, "start": 746.94, "end": 751.98, "text": " Yes, any questions?", "tokens": [51057, 1079, 11, 604, 1651, 30, 51309], "temperature": 0.0, "avg_logprob": -0.614333805285002, "compression_ratio": 1.4819277108433735, "no_speech_prob": 0.006326767150312662}, {"id": 121, "seek": 73310, "start": 751.98, "end": 760.28, "text": " I think there's time for one or two more questions, but it's probably great and then it's time", "tokens": [51309, 286, 519, 456, 311, 565, 337, 472, 420, 732, 544, 1651, 11, 457, 309, 311, 1391, 869, 293, 550, 309, 311, 565, 51724], "temperature": 0.0, "avg_logprob": -0.614333805285002, "compression_ratio": 1.4819277108433735, "no_speech_prob": 0.006326767150312662}, {"id": 122, "seek": 73310, "start": 760.28, "end": 761.28, "text": " for the next one.", "tokens": [51724, 337, 264, 958, 472, 13, 51774], "temperature": 0.0, "avg_logprob": -0.614333805285002, "compression_ratio": 1.4819277108433735, "no_speech_prob": 0.006326767150312662}, {"id": 123, "seek": 73310, "start": 761.28, "end": 762.28, "text": " Great.", "tokens": [51774, 3769, 13, 51824], "temperature": 0.0, "avg_logprob": -0.614333805285002, "compression_ratio": 1.4819277108433735, "no_speech_prob": 0.006326767150312662}, {"id": 124, "seek": 73310, "start": 762.28, "end": 762.78, "text": " Sorry.", "tokens": [51824, 4919, 13, 51849], "temperature": 0.0, "avg_logprob": -0.614333805285002, "compression_ratio": 1.4819277108433735, "no_speech_prob": 0.006326767150312662}, {"id": 125, "seek": 76310, "start": 763.1, "end": 764.1, "text": " Thank you.", "tokens": [50365, 1044, 291, 13, 50415], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 126, "seek": 76310, "start": 764.1, "end": 765.1, "text": " Hi, Janne.", "tokens": [50415, 2421, 11, 4956, 716, 13, 50465], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 127, "seek": 76310, "start": 765.1, "end": 766.1, "text": " Excellent.", "tokens": [50465, 16723, 13, 50515], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 128, "seek": 76310, "start": 766.1, "end": 767.1, "text": " Thank you.", "tokens": [50515, 1044, 291, 13, 50565], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 129, "seek": 76310, "start": 767.1, "end": 768.1, "text": " Always nice to have discussions with you.", "tokens": [50565, 11270, 1481, 281, 362, 11088, 365, 291, 13, 50615], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 130, "seek": 76310, "start": 768.1, "end": 769.1, "text": " You are researching on a very broad basis here.", "tokens": [50615, 509, 366, 24176, 322, 257, 588, 4152, 5143, 510, 13, 50665], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 131, "seek": 76310, "start": 769.1, "end": 770.1, "text": " I have one very general question for you.", "tokens": [50665, 286, 362, 472, 588, 2674, 1168, 337, 291, 13, 50715], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 132, "seek": 76310, "start": 770.1, "end": 775.1, "text": " So sorry to read it out from paper and sorry for making you to listen to it.", "tokens": [50715, 407, 2597, 281, 1401, 309, 484, 490, 3035, 293, 2597, 337, 1455, 291, 281, 2140, 281, 309, 13, 50965], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 133, "seek": 76310, "start": 775.1, "end": 786.1, "text": " But even if AGI, which we have, would be better in every single task than total analysis,", "tokens": [50965, 583, 754, 498, 316, 26252, 11, 597, 321, 362, 11, 576, 312, 1101, 294, 633, 2167, 5633, 813, 3217, 5215, 11, 51515], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 134, "seek": 76310, "start": 786.1, "end": 789.94, "text": " what would you say would be the best solution for the data?", "tokens": [51515, 437, 576, 291, 584, 576, 312, 264, 1151, 3827, 337, 264, 1412, 30, 51707], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 135, "seek": 76310, "start": 789.94, "end": 790.94, "text": " So, I'm sorry.", "tokens": [51707, 407, 11, 286, 478, 2597, 13, 51757], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 136, "seek": 76310, "start": 790.94, "end": 791.94, "text": " I'm sorry.", "tokens": [51757, 286, 478, 2597, 13, 51807], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 137, "seek": 76310, "start": 791.94, "end": 792.94, "text": " I'm sorry.", "tokens": [51807, 286, 478, 2597, 13, 51857], "temperature": 0.4, "avg_logprob": -0.6128064625298799, "compression_ratio": 1.6692015209125475, "no_speech_prob": 0.01831432431936264}, {"id": 138, "seek": 79294, "start": 792.94, "end": 795.94, "text": " I'm sorry when I say this one's not the best solution.", "tokens": [50365, 286, 478, 2597, 562, 286, 584, 341, 472, 311, 406, 264, 1151, 3827, 13, 50515], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 139, "seek": 79294, "start": 795.94, "end": 798.94, "text": " What I actually need to say is, that perhaps, creators andudio \u043d\u0430\u0439\u0442\u0438 a superior intelligence", "tokens": [50515, 708, 286, 767, 643, 281, 584, 307, 11, 300, 4317, 11, 16039, 293, 532, 1004, 31993, 257, 13028, 7599, 50665], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 140, "seek": 79294, "start": 798.94, "end": 799.98, "text": " to strawberry plums that you describe not merely as man, but as an even Senhor and you", "tokens": [50665, 281, 20440, 499, 8099, 300, 291, 6786, 406, 17003, 382, 587, 11, 457, 382, 364, 754, 43792, 293, 291, 50717], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 141, "seek": 79294, "start": 799.98, "end": 800.98, "text": " know, not only as this man top human, it might not mean that it has superior intelligence.", "tokens": [50717, 458, 11, 406, 787, 382, 341, 587, 1192, 1952, 11, 309, 1062, 406, 914, 300, 309, 575, 13028, 7599, 13, 50767], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 142, "seek": 79294, "start": 800.98, "end": 804.5, "text": " This is rest injection on the position that intelligence is made of different common tasks.", "tokens": [50767, 639, 307, 1472, 22873, 322, 264, 2535, 300, 7599, 307, 1027, 295, 819, 2689, 9608, 13, 50943], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 143, "seek": 79294, "start": 804.5, "end": 809.94, "text": " There's always a possibility about an entirely new task and a need for what we call shave", "tokens": [50943, 821, 311, 1009, 257, 7959, 466, 364, 7696, 777, 5633, 293, 257, 643, 337, 437, 321, 818, 25544, 51215], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 144, "seek": 79294, "start": 809.94, "end": 810.94, "text": " on creativity.", "tokens": [51215, 322, 12915, 13, 51265], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 145, "seek": 79294, "start": 810.94, "end": 813.74, "text": " The long of human intelligence.", "tokens": [51265, 440, 938, 295, 1952, 7599, 13, 51405], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 146, "seek": 79294, "start": 813.74, "end": 818.94, "text": " So just coming up with different cognitive tasks in a machine does not create anything", "tokens": [51405, 407, 445, 1348, 493, 365, 819, 15605, 9608, 294, 257, 3479, 775, 406, 1884, 1340, 51665], "temperature": 1.0, "avg_logprob": -2.2147752671014693, "compression_ratio": 1.7820512820512822, "no_speech_prob": 0.005965502467006445}, {"id": 147, "seek": 79294, "start": 818.94, "end": 819.9000000000001, "text": " more than the superhuman and", 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257, 6228, 293, 50729], "temperature": 0.0, "avg_logprob": -0.21662441421957576, "compression_ratio": 1.6629213483146068, "no_speech_prob": 0.04210300371050835}, {"id": 151, "seek": 82194, "start": 829.22, "end": 836.5, "text": " hype of or has been of ai that suddenly there will be this emergence of the general intelligence and", "tokens": [50729, 24144, 295, 420, 575, 668, 295, 9783, 300, 5800, 456, 486, 312, 341, 36211, 295, 264, 2674, 7599, 293, 51093], "temperature": 0.0, "avg_logprob": -0.21662441421957576, "compression_ratio": 1.6629213483146068, "no_speech_prob": 0.04210300371050835}, {"id": 152, "seek": 82194, "start": 837.0600000000001, "end": 845.3000000000001, "text": " doesn't seem to happen we still have to teach it different tasks i don't know uh my answer would be", "tokens": [51121, 1177, 380, 1643, 281, 1051, 321, 920, 362, 281, 2924, 309, 819, 9608, 741, 500, 380, 458, 2232, 452, 1867, 576, 312, 51533], "temperature": 0.0, "avg_logprob": -0.21662441421957576, "compression_ratio": 1.6629213483146068, "no_speech_prob": 0.04210300371050835}, {"id": 153, "seek": 84530, "start": 845.3, "end": 853.2199999999999, "text": " like uh using the other mind problem and the monolithic agent problem that if we are able if", "tokens": [50365, 411, 2232, 1228, 264, 661, 1575, 1154, 293, 264, 1108, 42878, 9461, 1154, 300, 498, 321, 366, 1075, 498, 50761], "temperature": 0.0, "avg_logprob": -0.12194218900468615, "compression_ratio": 1.755813953488372, "no_speech_prob": 0.0021225230302661657}, {"id": 154, "seek": 84530, "start": 853.2199999999999, "end": 862.42, "text": " if we are afraid to that ai is replacing humans then then it certainly has certain kinds of", "tokens": [50761, 498, 321, 366, 4638, 281, 300, 9783, 307, 19139, 6255, 550, 550, 309, 3297, 575, 1629, 3685, 295, 51221], "temperature": 0.0, "avg_logprob": -0.12194218900468615, "compression_ratio": 1.755813953488372, "no_speech_prob": 0.0021225230302661657}, {"id": 155, "seek": 84530, "start": 862.42, "end": 873.4599999999999, "text": " capacities and the kind of also like if the tractable cognizant thesis is true then we can", "tokens": [51221, 39396, 293, 264, 733, 295, 611, 411, 498, 264, 24207, 712, 11786, 590, 394, 22288, 307, 2074, 550, 321, 393, 51773], "temperature": 0.0, "avg_logprob": -0.12194218900468615, "compression_ratio": 1.755813953488372, "no_speech_prob": 0.0021225230302661657}, {"id": 156, "seek": 84530, "start": 873.4599999999999, "end": 875.14, "text": " also they have to say that", "tokens": [51773, 611, 436, 362, 281, 584, 300, 51857], "temperature": 0.0, "avg_logprob": -0.12194218900468615, "compression_ratio": 1.755813953488372, "no_speech_prob": 0.0021225230302661657}, {"id": 157, "seek": 87530, "start": 875.8599999999999, "end": 882.74, "text": " because they are critical everybody's afraid of ai doom so i would i would ask them that how do you", "tokens": [50393, 570, 436, 366, 4924, 2201, 311, 4638, 295, 9783, 37131, 370, 741, 576, 741, 576, 1029, 552, 300, 577, 360, 291, 50737], "temperature": 0.0, "avg_logprob": -0.12530337061200822, "compression_ratio": 1.8727272727272728, "no_speech_prob": 0.004191793035715818}, {"id": 158, "seek": 87530, "start": 882.74, "end": 889.54, "text": " prove that the ai doom is not intractable i would i would kind of throw it out there that it's", "tokens": [50737, 7081, 300, 264, 9783, 37131, 307, 406, 560, 1897, 712, 741, 576, 741, 576, 733, 295, 3507, 309, 484, 456, 300, 309, 311, 51077], "temperature": 0.0, "avg_logprob": -0.12530337061200822, "compression_ratio": 1.8727272727272728, "no_speech_prob": 0.004191793035715818}, {"id": 159, "seek": 87530, "start": 889.54, "end": 896.26, "text": " intractable if we are in that school but if if we are in my school then", "tokens": [51077, 560, 1897, 712, 498, 321, 366, 294, 300, 1395, 457, 498, 498, 321, 366, 294, 452, 1395, 550, 51413], "temperature": 0.0, "avg_logprob": -0.12530337061200822, "compression_ratio": 1.8727272727272728, "no_speech_prob": 0.004191793035715818}, {"id": 160, "seek": 87530, "start": 899.4599999999999, "end": 902.74, "text": " it's tractable but i don't believe", "tokens": [51573, 309, 311, 24207, 712, 457, 741, 500, 380, 1697, 51737], "temperature": 0.0, "avg_logprob": -0.12530337061200822, "compression_ratio": 1.8727272727272728, "no_speech_prob": 0.004191793035715818}, {"id": 161, "seek": 87530, "start": 904.74, "end": 905.14, "text": " they do", "tokens": [51837, 436, 360, 51857], "temperature": 0.0, "avg_logprob": -0.12530337061200822, "compression_ratio": 1.8727272727272728, "no_speech_prob": 0.004191793035715818}, {"id": 162, "seek": 90530, "start": 905.3, "end": 912.74, "text": " thank you", "tokens": [50365, 1309, 291, 50737], "temperature": 0.0, "avg_logprob": -0.5573034508283748, "compression_ratio": 1.3211009174311927, "no_speech_prob": 0.010627418756484985}, {"id": 163, "seek": 90530, "start": 912.74, "end": 925.8599999999999, "text": " now we have a coffee break of half an hour and 2 30 is the last keynote speech thanks again to all", "tokens": [50737, 586, 321, 362, 257, 4982, 1821, 295, 1922, 364, 1773, 293, 568, 2217, 307, 264, 1036, 33896, 6218, 3231, 797, 281, 439, 51393], "temperature": 0.0, "avg_logprob": -0.5573034508283748, "compression_ratio": 1.3211009174311927, "no_speech_prob": 0.010627418756484985}, {"id": 164, "seek": 90530, "start": 925.8599999999999, "end": 927.8599999999999, "text": " the great presentations", "tokens": [51393, 264, 869, 18964, 51493], "temperature": 0.0, "avg_logprob": -0.5573034508283748, "compression_ratio": 1.3211009174311927, "no_speech_prob": 0.010627418756484985}, {"id": 165, "seek": 90530, "start": 927.8599999999999, "end": 931.8599999999999, "text": " yes", "tokens": [51493, 2086, 51693], "temperature": 0.0, "avg_logprob": -0.5573034508283748, "compression_ratio": 1.3211009174311927, "no_speech_prob": 0.010627418756484985}, {"id": 166, "seek": 90530, "start": 931.8599999999999, "end": 933.8599999999999, "text": " yes", "tokens": [51693, 2086, 51793], "temperature": 0.0, "avg_logprob": -0.5573034508283748, "compression_ratio": 1.3211009174311927, "no_speech_prob": 0.010627418756484985}, {"id": 167, "seek": 90530, "start": 933.8599999999999, "end": 935.14, "text": " yes", "tokens": [51793, 2086, 51857], "temperature": 0.0, "avg_logprob": -0.5573034508283748, "compression_ratio": 1.3211009174311927, "no_speech_prob": 0.010627418756484985}, {"id": 168, "seek": 93530, "start": 935.3, "end": 965.3, "text": "", "tokens": [], "temperature": 0.0, "avg_logprob": -0.9565730094909668, "compression_ratio": 0.0, "no_speech_prob": 0.1667679250240326, "words": []}, {"id": 169, "seek": 96530, "start": 965.62, "end": 967.8599999999999, "text": " for you", "tokens": [50381, 337, 291, 50493], "temperature": 1.0, "avg_logprob": -2.3562284197126115, "compression_ratio": 0.7916666666666666, "no_speech_prob": 0.15477338433265686}, {"id": 170, "seek": 96530, "start": 967.8599999999999, "end": 973.8599999999999, 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