https://www.lesswrong.com/posts/kAmgdEjq2eYQkB5PP/douglas-hofstadter-changes-his-mind-on-deep-learning-and-ai This website requires javascript to properly function. Consider activating javascript to get access to all site functionality. LESSWRONG LW Login Douglas Hofstadter changes his mind on Deep Learning & AI risk (June 2023)? by gwern 8 min read3rd Jul 202325 comments 279 Language ModelsAI RiskAI Frontpage This is a linkpost for https://www.youtube.com/watch?v=lfXxzAVtdpU&t= 1763s A podcast interview (posted 2023-06-29) with noted AI researcher Douglas Hofstadter discusses his career and current views on AI (via Edward Kmett). Hofstadter has previously energetically criticized GPT-2/3 models (and deep learning and compute-heavy GOFAI). These criticisms were widely circulated & cited, and apparently many people found Hofstadter a convincing & trustworthy authority when he was negative on deep learning capabilities & prospects, and so I found his most-recent comments (which amplify things he has been saying in private since at least 2014 of considerable interest. Below I excerpt from the second half where he discusses DL progress & AI risk: + o Q: ...Which ideas from GEB are most relevant today? o Douglas Hofstadter: ...In my book, I Am a Strange Loop, I tried to set forth what it is that really makes a self or a soul. I like to use the word "soul", not in the religious sense, but as a synonym for "I", a human "I", capital letter "I." So, what is it that makes a human being able to validly say "I"? What justifies the use of that word? When can a computer say "I" and we feel that there is a genuine "I" behind the scenes? I don't mean like when you call up the drugstore and the chatbot, or whatever you want to call it, on the phone says, "Tell me what you want. I know you want to talk to a human being, but first, in a few words, tell me what you want. I can understand full sentences." And then you say something and it says, "Do you want to refill a prescription?" And then when I say yes, it says, "Gotcha", meaning "I got you." So it acts as if there is an "I" there, but I don't have any sense whatsoever that there is an "I" there. It doesn't feel like an "I" to me, it feels like a very mechanical process. But in the case of more advanced things like ChatGPT-3 or GPT-4, it feels like there is something more there that merits the word "I." The question is, when will we feel that those things actually deserve to be thought of as being full-fledged, or at least partly fledged, "I"s? I personally worry that this is happening right now. But it's not only happening right now. It's not just that certain things that are coming about are similar to human consciousness or human selves. They are also very different, and in one way, it is extremely frightening to me. They are extraordinarily much more knowledgeable and they are extraordinarily much faster. So that if I were to take an hour in doing something, the ChatGPT-4 might take one second, maybe not even a second, to do exactly the same thing. And that suggests that these entities, whatever you want to think of them, are going to be very soon, right now they still make so many mistakes that we can't call them more intelligent than us, but very soon they're going to be, they may very well be more intelligent than us and far more intelligent than us. And at that point, we will be receding into the background in some sense. We will have handed the baton over to our successors, for better or for worse. And I can understand that if this were to happen over a long period of time, like hundreds of years, that might be okay. But it's happening over a period of a few years. It's like a tidal wave that is washing over us at unprecedented and unimagined speeds. And to me, it's quite terrifying because it suggests that everything that I used to believe was the case is being overturned. + o Q: What are some things specifically that terrify you? What are some issues that you're really... o D. Hofstadter: When I started out studying cognitive science and thinking about the mind and computation, you know, this was many years ago, around 1960, and I knew how computers worked and I knew how extraordinarily rigid they were. You made the slightest typing error and it completely ruined your program. Debugging was a very difficult art and you might have to run your program many times in order to just get the bugs out. And then when it ran, it would be very rigid and it might not do exactly what you wanted it to do because you hadn't told it exactly what you wanted to do correctly, and you had to change your program, and on and on. Computers were very rigid and I grew up with a certain feeling about what computers can or cannot do. And I thought that artificial intelligence, when I heard about it, was a very fascinating goal, which is to make rigid systems act fluid. But to me, that was a very long, remote goal. It seemed infinitely far away. It felt as if artificial intelligence was the art of trying to make very rigid systems behave as if they were fluid. And I felt that would take enormous amounts of time. I felt it would be hundreds of years before anything even remotely like a human mind would be asymptotically approaching the level of the human mind, but from beneath. I never imagined that computers would rival, let alone surpass, human intelligence. And in principle, I thought they could rival human intelligence. I didn't see any reason that they couldn't. But it seemed to me like it was a goal that was so far away, I wasn't worried about it. But when certain systems started appearing, maybe 20 years ago, they gave me pause. And then this started happening at an accelerating pace, where unreachable goals and things that computers shouldn't be able to do started toppling. The defeat of Gary Kasparov by Deep Blue, and then going on to Go systems, Go programs, well, systems that could defeat some of the best Go players in the world. And then systems got better and better at translation between languages, and then at producing intelligible responses to difficult questions in natural language, and even writing poetry. And my whole intellectual edifice, my system of beliefs... It's a very traumatic experience when some of your most core beliefs about the world start collapsing. And especially when you think that human beings are soon going to be eclipsed. It felt as if not only are my belief systems collapsing, but it feels as if the entire human race is going to be eclipsed and left in the dust soon. People ask me, "What do you mean by 'soon'?" And I don't know what I really mean. I don't have any way of knowing. But some part of me says 5 years, some part of me says 20 years, some part of me says, "I don't know, I have no idea." But the progress, the accelerating progress, has been so unexpected, so completely caught me off guard, not only myself but many, many people, that there is a certain kind of terror of an oncoming tsunami that is going to catch all humanity off guard. It's not clear whether that will mean the end of humanity in the sense of the systems we've created destroying us. It's not clear if that's the case, but it's certainly conceivable. If not, it also just renders humanity a very small phenomenon compared to something else that is far more intelligent and will become incomprehensible to us, as incomprehensible to us as we are to cockroaches. o Q: That's an interesting thought. [nervous laughter] o Hofstadter: Well, I don't think it's interesting. I think it's terrifying. I hate it. I think about it practically all the time, every single day. [Q: Wow.] And it overwhelms me and depresses me in a way that I haven't been depressed for a very long time. o Q: Wow, that's really intense. You have a unique perspective, so knowing you feel that way is very powerful. + o Q: How have LLMs, large language models, impacted your view of how human thought and creativity works? o D H: Of course, it reinforces the idea that human creativity and so forth come from the brain's hardware. There is nothing else than the brain's hardware, which is neural nets. But one thing that has completely surprised me is that these LLMs and other systems like them are all feed-forward. It's like the firing of the neurons is going only in one direction. And I would never have thought that deep thinking could come out of a network that only goes in one direction, out of firing neurons in only one direction. And that doesn't make sense to me, but that just shows that I'm naive. It also makes me feel that maybe the human mind is not so mysterious and complex and impenetrably complex as I imagined it was when I was writing Godel, Escher, Bach and writing I Am a Strange Loop. I felt at those times, quite a number of years ago, that as I say, we were very far away from reaching anything computational that could possibly rival us. It was getting more fluid, but I didn't think it was going to happen, you know, within a very short time. And so it makes me feel diminished. It makes me feel, in some sense, like a very imperfect, flawed structure compared with these computational systems that have, you know, a million times or a billion times more knowledge than I have and are a billion times faster. It makes me feel extremely inferior. And I don't want to say deserving of being eclipsed, but it almost feels that way, as if we, all we humans, unbeknownst to us, are soon going to be eclipsed, and rightly so, because we're so imperfect and so fallible. We forget things all the time, we confuse things all the time, we contradict ourselves all the time. You know, it may very well be that that just shows how limited we are. + o Q: Wow. So let me keep going through the questions. Is there a time in our history as human beings when there was something analogous that terrified a lot of smart people? o D H: Fire. o Q: You didn't even hesitate, did you? So what can we learn from that? o D H: No, I don't know. Caution, but you know, we may have already gone too far. We may have already set the forest on fire. I mean, it seems to me that we've already done that. I don't think there's any way of going back. When I saw an interview with Geoff Hinton, who was probably the most central person in the development of all of these kinds of systems, he said something striking. He said he might regret his life's work. He said, "Part of me regrets all of my life's work." The interviewer then asked him how important these developments are. "Are they as important as the Industrial Revolution? Is there something analogous in history that terrified people?" Hinton thought for a second and he said, "Well, maybe as important as the wheel." Language ModelsAI RiskAI Frontpage 279 New Comment Submit 25 comments, sorted by top scoring Click to highlight new comments since: Today at 11:00 PM [-]mishka16h343 But one thing that has completely surprised me is that these LLMs and other systems like them are all feed-forward. It's like the firing of the neurons is going only in one direction. And I would never have thought that deep thinking could come out of a network that only goes in one direction, out of firing neurons in only one direction. And that doesn't make sense to me, but that just shows that I'm naive. I felt exactly the same, until I had read this June 2020 paper: Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention. It turns out that using Transformers in the autoregressive mode (with output tokens being added back to the input by concatenating the previous input and the new output token, and sending the new versions of the input through the model again and again) results in them emulating dynamics of recurrent neural networks, and that clarifies things a lot... Reply [-]dr_s13h164 Yeah, there's obviously SOME recursion there but it's still surprising that such a relatively low bandwidth recursion can still work so well. It's more akin to me writing down my thoughts and then rereading them to gather my ideas than the kind of loops I imagine our neurons might have. That said, who knows, maybe the loops in our brain are superfluous, or only useful for learning feedback purposes, and so a neural network trained by an external system doesn't need them. Reply [-]mishka9h60 Pondering this particular recursion, I noticed that it looks like things change not too much from iteration to iteration of this autoregressive dynamics, because we just add one token each time. The key property of those artificial recurrent architectures which successfully fight the vanishing gradient problem is that a single iteration of recurrence looks like Identity + epsilon (so, X -> X + deltaX for a small deltaX on each iteration, see, for example, this 2018 paper, Overcoming the vanishing gradient problem in plain recurrent networks which explains how this is the case for LSTMs and such, and explains how to achieve this for plain recurrent networks; for a brief explanation see my review of the first version of this paper, Understanding Recurrent Identity Networks). So, I strongly suspect that it is also the case for the recurrence which is happening in Transformers used in the autoregressive mode (because the input is changing mildly from iteration to iteration). But I don't know to which extent this is also true for biological recurrent networks. On one hand, our perceptions seem to change smoothly with time, and that seems to be an argument for gradual change of the X -> X + deltaX nature in the biological case as well. But we don't understand the biological case all that well... --------------------------------------------------------------------- I think recurrence is actually quite important for LLMs. Cf. Janus' Simulator theory which is now relatively well developed (see e.g. the original Simulators or brief notes I took on the recent status of that theory May-23-2023-status-update). The fact that this is an autoregressive simulation is playing the key role. But we indeed don't know whether complexity of biological recurrences vs. relative simplicity of artificial recurrent networks matters much... Reply [-]FeepingCreature5h31 I'd speculate that our perceptions just seem to change smoothly because we encode second-order (or even third-order) dynamics in our tokens. From what I layman-understand of consciousness, I'd be surprised if it wasn't discrete. Reply [-]Sweetgum3h30 It's more akin to me writing down my thoughts and then rereading them to gather my ideas than the kind of loops I imagine our neurons might have. In a sense, that is what is happening when you think in words. It's called the phonological loop. Reply [-]Dave Dolan9h10 It could also be that LLMs don't do it like we do it and simply offer a computationally sufficient platform. Reply [-]Chris_Leong14h90 In what sense do they emulate these dynamics? Reply [-]mishka9h70 The formulas and a brief discussion are in Section 3.4 (page 5) of https://arxiv.org/abs/2006.16236 Reply [-]Chris_Leong6h30 Thanks! Reply [-]cubefox1h20 Being an autoregressive language model is like having a strange form of amnesia, where you forget everything you thought about so far as soon as you utter a new word, and you can remember only what you said before. Reply [-]gallabytesnow10 that paper is one of many claiming some linear attention mechanism that's as good as full self attention. in practice they're all sufficiently much worse that nobody uses them except the original authors in the original paper, usually not even the original authors in subsequent papers. the one exception is flash attention, which is basically just a very fancy fused kernel for the same computation (actually the same, up to numerical error, unlike all these "linear attention" papers). Reply [-]Aaro Salosensaari23m10 >It turns out that using Transformers in the autoregressive mode (with output tokens being added back to the input by concatenating the previous input and the new output token, and sending the new versions of the input through the model again and again) results in them emulating dynamics of recurrent neural networks, and that clarifies things a lot... I'll bite: Could you dumb down the implications of the paper a little bit, what is the difference between a Transformer emulating a RNN and some pre-Transformer RNNs and/or not-RNN? My much more novice-level answer to Hofstadter's intuition would have been: it's not the feedforward firing, but it is the gradient descent training of the model on massive scale (both in data and in computation). But apparently you think that something RNN-like about the model structure itself is important? Reply [-]dxu11m20 I'll bite even further, and ask for the concept of "recurrence" itself to be dumbed down. What is "recurrence", why is it important, and in what sense does e.g. a feedforward network hooked up to something like MCTS not qualify as relevantly "recurrent"? Reply [-]Ben Amitay5h3317 It is beautiful to see that many of our greatest minds are willing to Say Oops, even about their most famous works. It may not score that many winning-points, but it does restore quite a lot of dignity-points I think. Reply [-]GeneSmith6h1710 It's not clear whether that will mean the end of humanity in the sense of the systems we've created destroying us. It's not clear if that's the case, but it's certainly conceivable. If not, it also just renders humanity a very small phenomenon compared to something else that is far more intelligent and will become incomprehensible to us, as incomprehensible to us as we are to cockroaches. Q: That's an interesting thought. [nervous laughter] Hofstadter: Well, I don't think it's interesting. I think it's terrifying. I hate it. I think about it practically all the time, every single day. [Q: Wow.] And it overwhelms me and depresses me in a way that I haven't been depressed for a very long time. I don't think I've ever seen a better description of how I feel about the coming creation of artificial superintelligence. I find myself returning over and over again to that post by benkuhn about "Staring into the abyss as a core life skill" I think that is going to become a necessary core life skill for almost everyone in the coming years. It has been morbidly gratifying to see more and more people develop the same feelings about AI as I have had for about a year now. Like validation in the worst possible way. I think if people actually understood what was coming there would be a near total call to ban improvements in this technology and only allow advancement under very strict conditions. But almost no one has really thought through the consequences of making a general purpose replacement for human beings. Reply [-]Cole Wyeth19h94 It has become slightly more plausible that Melanie Mitchell could come around. Reply [-]gwern5h323 But only slightly. Hofstadter's doubts have been building for a long time in private, to an extent that his op-eds don't convey (compare his comments in OP to his comments published in the Atlantic just a week before! they are so drastically different I was wondering if this was some sort of bizarre deepfake prank, but some cursory searching made it seemed legit and no one like Mitchell was saying it was fake and the text sounds like Hofstadter). On Twitter, John Teets helpfully notes that Mitchell has a 2019 book Artificial Intelligence: A Guide for Thinking Humans where she records some private Hofstadter material I was unfamiliar with: Prologue: Terrified ...The meeting, in May 2014, had been organized by Blaise Aguera y Arcas, a young computer scientist who had recently left a top position at Microsoft to help lead Google's machine intelligence effort...The meeting was happening so that a group of select Google AI researchers could hear from and converse with Douglas Hofstadter, a legend in AI and the author of a famous book cryptically titled Godel, Escher, Bach: an Eternal Golden Braid, or more succinctly, GEB (pronounced "gee-ee-bee"). If you're a computer scientist, or a computer enthusiast, it's likely you've heard of it, or read it, or tried to read it...Chess and the First Seed of Doubt: The group in the hard-to-locate conference room consisted of about 20 Google engineers (plus Douglas Hofstadter and myself), all of whom were members of various Google AI teams. The meeting started with the usual going around the room and having people introduce themselves. Several noted that their own careers in AI had been spurred by reading GEB at a young age. They were all excited and curious to hear what the legendary Hofstadter would say about AI. Then Hofstadter got up to speak. "I have some remarks about AI research in general, and here at Google in particular." His voice became passionate. "I am terrified. Terrified." Hofstadter went on. [2. In the following sections, quotations from Douglas Hofstadter are from a follow-up interview I did with him after the Google meeting; the quotations accurately capture the content and tone of his remarks to the Google group.] He described how, when he first started working on AI in the 1970s, it was an exciting prospect but seemed so far from being realized that there was no "danger on the horizon, no sense of it actually happening." Creating machines with human-like intelligence was a profound intellectual adventure, a long-term research project whose fruition, it had been said, lay at least "one hundred Nobel prizes away." [Jack Schwartz, quoted in G.-C. Rota, Indiscrete Thoughts (Boston: Berkhauser, 1997), pg22.] Hofstadter believed AI was possible in principle: "The 'enemy' were people like John Searle, Hubert Dreyfus, and other skeptics, who were saying it was impossible. They did not understand that a brain is a hunk of matter that obeys physical law and the computer can simulate anything ... the level of neurons, neurotransmitters, et cetera. In theory, it can be done." Indeed, Hofstadter's ideas about simulating intelligence at various levels---from neurons to consciousness---were discussed at length in GEB and had been the focus of his own research for decades. But in practice, until recently, it seemed to Hofstadter that general "human-level" AI had no chance of occurring in his (or even his children's) lifetime, so he didn't worry much about it. Near the end of GEB, Hofstadter had listed "10 Questions and Speculations" about artificial intelligence. Here's one of them: "Will there be chess programs that can beat anyone?" Hofstadter's speculation was "no." "There may be programs which can beat anyone at chess, but they will not be exclusively chess players. They will be programs of general intelligence."4 At the Google meeting in 2014, Hofstadter admitted that he had been "dead wrong." The rapid improvement in chess programs in the 1980s and '90s had sown the first seed of doubt in his appraisal of AI's short-term prospects. Although the AI pioneer Herbert Simon] had predicted in 1957 that a chess program would be world champion "within 10 years", by the mid-1970s, when Hofstadter was writing GEB, the best computer chess programs played only at the level of a good (but not great) amateur. Hofstadter had befriended Eliot Hearst, a chess champion and psychology professor who had written extensively on how human chess experts differ from computer chess programs. Experiments showed that expert human players rely on quick recognition of patterns on the chessboard to decide on a move rather than the extensive brute-force look-ahead search that all chess programs use. During a game, the best human players can perceive a configuration of pieces as a particular "kind of position" that requires a certain "kind of strategy." That is, these players can quickly recognize particular configurations and strategies as instances of higher-level concepts. Hearst argued that without such a general ability to perceive patterns and recognize abstract concepts, chess programs would never reach the level of the best humans. Hofstadter was persuaded by Hearst's arguments. However, in the 1980s and '90s, computer chess saw a big jump in improvement, mostly due to the steep increase in computer speed. The best programs still played in a very unhuman way: performing extensive look-ahead to decide on the next move. By the mid-1990s, IBM's Deep Blue machine, with specialized hardware for playing chess, had reached the Grandmaster level, and in 1997 the program defeated the reigning world chess champion, Garry Kasparov, in a 6-game match. Chess mastery, once seen as a pinnacle of human intelligence, had succumbed to a brute-force approach. Music: The Bastion of Humanity... Hofstadter had been wrong about chess, but he still stood by the other speculations in GEB...Hofstadter described this speculation as "one of the most important parts of GEB---I would have staked my life on it." I sat down at my piano and I played one of EMI's mazurkas "in the style of Chopin." It didn't sound exactly like Chopin, but it sounded enough like Chopin, and like coherent music, that I just felt deeply troubled. Hofstadter then recounted a lecture he gave at the prestigious Eastman School of Music, in Rochester, New York. After describing EMI, Hofstadter had asked the Eastman audience---including several music theory and composition faculty---to guess which of two pieces a pianist played for them was a (little-known) mazurka by Chopin and which had been composed by EMI. As one audience member described later, "The first mazurka had grace and charm, but not 'true-Chopin' degrees of invention and large-scale fluidity ... The second was clearly the genuine Chopin, with a lyrical melody; large-scale, graceful chromatic modulations; and a natural, balanced form." [ 6. Quoted in D. R. Hofstadter, "Staring Emmy Straight in the Eye--and Doing My Best Not to Flinch," in Creativity, Cognition, and Knowledge, ed. T. Dartnell (Westport, Conn.: Praeger, 2002), 67-100.] Many of the faculty agreed and, to Hofstadter's shock, voted EMI for the first piece and "real-Chopin" for the second piece. The correct answers were the reverse. In the Google conference room, Hofstadter paused, peering into our faces. No one said a word. At last he went on. "I was terrified by EMI. Terrified. I hated it, and was extremely threatened by it. It was threatening to destroy what I most cherished about humanity. I think EMI was the most quintessential example of the fears that I have about artificial intelligence." Google and the Singularity: Hofstadter then spoke of his deep ambivalence about what Google itself was trying to accomplish in AI---self-driving cars, speech recognition, natural-language understanding, translation between languages, computer-generated art, music composition, and more. Hofstadter's worries were underlined by Google's embrace of Ray Kurzweil and his vision of the Singularity, in which AI, empowered by its ability to improve itself and learn on its own, will quickly reach, and then exceed, human-level intelligence. Google, it seemed, was doing everything it could to accelerate that vision. While Hofstadter strongly doubted the premise of the Singularity, he admitted that Kurzweil's predictions still disturbed him. "I was terrified by the scenarios. Very skeptical, but at the same time, I thought, maybe their timescale is off, but maybe they're right. We'll be completely caught off guard. We'll think nothing is happening and all of a sudden, before we know it, computers will be smarter than us." If this actually happens, "we will be superseded. We will be relics. We will be left in the dust. Maybe this is going to happen, but I don't want it to happen soon. I don't want my children to be left in the dust." Hofstadter ended his talk with a direct reference to the very Google engineers in that room, all listening intently: "I find it very scary, very troubling, very sad, and I find it terrible, horrifying, bizarre, baffling, bewildering, that people are rushing ahead blindly and deliriously in creating these things." Why Is Hofstadter Terrified? I looked around the room. The audience appeared mystified, embarrassed even. To these Google AI researchers, none of this was the least bit terrifying. In fact, it was old news...Hofstadter's terror was in response to something entirely different. It was not about AI becoming too smart, too invasive, too malicious, or even too useful. Instead, he was terrified that intelligence, creativity, emotions, and maybe even consciousness itself would be too easy to produce---that what he valued most in humanity would end up being nothing more than a "bag of tricks", that a superficial set of brute-force algorithms could explain the human spirit. As GEB made abundantly clear, Hofstadter firmly believes that the mind and all its characteristics emerge wholly from the physical substrate of the brain and the rest of the body, along with the body's interaction with the physical world. There is nothing immaterial or incorporeal lurking there. The issue that worries him is really one of complexity. He fears that AI might show us that the human qualities we most value are disappointingly simple to mechanize. As Hofstadter explained to me after the meeting, here referring to Chopin, Bach, and other paragons of humanity, "If such minds of infinite subtlety and complexity and emotional depth could be trivialized by a small chip, it would destroy my sense of what humanity is about." ...Several of the Google researchers predicted that general human-level AI would likely emerge within the next 30 years, in large part due to Google's own advances on the brain-inspired method of "deep learning." I left the meeting scratching my head in confusion. I knew that Hofstadter had been troubled by some of Kurzweil's Singularity writings, but I had never before appreciated the degree of his emotion and anxiety. I also had known that Google was pushing hard on AI research, but I was startled by the optimism several people there expressed about how soon AI would reach a general "human" level. My own view had been that AI had progressed a lot in some narrow areas but was still nowhere close to having the broad, general intelligence of humans, and it would not get there in a century, let alone 30 years. And I had thought that people who believed otherwise were vastly underestimating the complexity of human intelligence. I had read Kurzweil's books and had found them largely ridiculous. However, listening to all the comments at the meeting, from people I respected and admired, forced me to critically examine my own views. While assuming that these AI researchers underestimated humans, had I in turn underestimated the power and promise of current-day AI? ...Other prominent thinkers were pushing back. Yes, they said, we should make sure that AI programs are safe and don't risk harming humans, but any reports of near-term superhuman AI are greatly exaggerated. The entrepreneur and activist Mitchell Kapor advised, "Human intelligence is a marvelous, subtle, and poorly understood phenomenon. There is no danger of duplicating it anytime soon." The roboticist (and former director of MIT's AI Lab) Rodney Brooks agreed, stating that we "grossly overestimate the capabilities of machines---those of today and of the next few decades." The psychologist and AI researcher Gary Marcus went so far as to assert that in the quest to create "strong AI"---that is, general human-level AI---"there has been almost no progress." I could go on and on with dueling quotations. In short, what I found is that the field of AI is in turmoil. Either a huge amount of progress has been made, or almost none at all. Either we are within spitting distance of "true" AI, or it is centuries away. AI will solve all our problems, put us all out of a job, destroy the human race, or cheapen our humanity. It's either a noble quest or "summoning the demon." That is, whatever the snarky "don't worry, it can't happen" tone of his public writings about DL has been since ~2010, Hofstadter has been saying these things in private for at least a decade*, starting somewhere around Deep Blue which clearly falsified a major prediction of his, and his worries about the scaling paradigm intensifying ever since; what has happened is that only one of two paradigms can be true, and Hofstadter has finally flipped to the other paradigm (with ChatGPT-3.5 and then GPT-4 apparently being the straws that broke the camel's back). Mitchell, however, has heard all of this firsthand long before this podcast and appears to be completely immune to Hofstadter's concerns (publicly), so I wouldn't expect it to change her mind. * I wonder what other experts & elites have different views on AI than their public statements would lead you to believe? Reply [-]NicholasKross3h20 I heard something like this might be true for Yann also; like, allegedly being more worried about extinction-risk-from-AI in private, but then publicly doing the same snarky tweets. Reply [-]25Hour3h10 This seems doubtful to me; if Yan truly believed that AI was an imminent extinction risk, or even thought it was credible, what would Yann be hoping to do or gain by ridiculing people who are similarly worried? Reply [clickingpo]1 [-]NicholasKross2h20 That's also my confusion, yes. I could imagine someone suppressing their alignment fears temporarily, to work their way up to a position of power in a capabilities lab and then steer outcomes from there. But that doesn't seem to work, since: * The top AI capabilities labs (OpenAI, DeepMind, Anthropic) are more vocal about capabilities. Meta AI is a follow-the-leader lab anyway. * I don't think "bringing up concerns later, instead of now" is a strategically great way to do this. I don't know a ton about the politics of historical programs for e.g. atomic weapons and bioweapons. But based on my cursory knowledge, I don't think "be worried in secret" is anything like a slam-dunk for those situations. * Yann, specifically, is already the Chief AI Person at Meta/ Facebook! Unless Meta is really quick to fire people (or Yann is angling for Zuckerberg's position), what more career capital could he gain at this stage? Reply [-]JoshuaFox6h84 At the time of Hofstadter's Singularity Summit talk , I wondered why he wasn't "getting with the program", and it became clear he was a mysterian: He believed -- without being a dualist -- that some things, like the mind, are ultimately, basically, essentially, impossible to understand or describe. This 2023 interview shows that the new generation of AI has done more than chagne his mind about the potential of AI: it has struck at the core of his mysterianism the human mind is not so mysterious and complex and impenetrably complex as I imagined it was when I was writing Godel, Escher, Bach and writing I Am a Strange Loop. Reply [-]Dr_Manhattan5h112 He was only a de facto mysterian: thought mind is so complicated that it may as well be mysterious (but ofc he believed it's ultimately just physics). This position is updateable, and he clearly updated. Reply [-]Holly_Elmore4h60 But one thing that has completely surprised me is that these LLMs and other systems like them are all feed-forward. It's like the firing of the neurons is going only in one direction. And I would never have thought that deep thinking could come out of a network that only goes in one direction, out of firing neurons in only one direction. And that doesn't make sense to me, but that just shows that I'm naive. What was the argument that being feed-forward limited the potential for deep thought in principle? It makes sense that multi-directional nets could do more with fewer neurons but Hofstader seemed to think there were things that feed-forward system fundamentally couldn't do. Reply [-]Garrett Baker3h70 He explained a bunch of his position on this in Godel, Escher, Bach. If I remember correctly, it describes the limits of primitive recursive and general recursive functions this in chapter XIII. The basic idea (again, if I remember), is that a proof system can only reason about itself if its general recursive, and will always be able to reason about itself if its general recursive. Lots of what we see that makes humanity special compared to computers has to do with people having feelings and emotions and self-concepts, and reflection about past situations & thoughts. All things that really seem to require deep levels of recursion (this is a far shallower statement than what's actually written in the book). Its strange to us then that ChatGPT can mimic those same outputs with the only recursive element of its thought being that it can pass 16 bits to its next running. Reply [-]Erich_Grunewald10h30 It's not clear whether that will mean the end of humanity in the sense of the systems we've created destroying us. It's not clear if that's the case, but it's certainly conceivable. If not, it also just renders humanity a very small phenomenon compared to something else that is far more intelligent and will become incomprehensible to us, as incomprehensible to us as we are to cockroaches. It's interesting that he seems so in despair over this now. To the extent that he's worried about existential/catastrophic risks, I wonder if he is unaware of efforts to mitigate those, or if he is aware but thinks they are hopeless (or at least not guaranteed to succeed, which -- fair enough). To the extent that he's more broadly worried about human obsolescence (or anyway something more metaphysical), well, there are people trying to slow/stop AI, and others trying to enhance human capabilities -- maybe he's pessimistic about those efforts, too. Reply Moderation Log