But only slightly. It appears that Hofstadter’s doubts have been building for a long time in private, even to organizing informal conferences/meetings about it, 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 Agüera 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 Gödel, 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: Berkhäuser, 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.”<sup>4</sup>
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?
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 wouldlead youtobelieve?
So . . . chess-playing fell to computers? I don’t feel particularly threatened or upset; after all, sheer computation had decades earlier fallen to computers as well. So a computer had outdone <a href=”https://en.wikipedia.org/wiki/Daniel_Shanks“>Daniel Shanks</a> in the calculation of digits of π—did it matter? Did that achievement in any way lower human dignity? Of course not! It simply taught us that calculation is more mechanical than we had realized.
Likewise, <a href=”https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)“>Deep Blue</a> taught us that chess is more mechanical than we had realized. These lessons serve as interesting pieces of information about various domains of expertise, but to my mind they hardly seem to threaten the notion, which I then cherished and which I still cherish, that human intelligence is extraordinarily profound and mysterious.
It is not, I hasten to add, that I am a mystic who thinks that intelligence intrinsically resists implantation in physical entities. To the contrary, I look upon brains themselves as very complex machines, and, unlike <a href=”https://en.wikipedia.org/wiki/John_Searle“>John Searle</a> and <a href=”https://en.wikipedia.org/wiki/Roger_Penrose“>Roger Penrose</a>, I have always maintained that the precise nature of the physicochemical substrate of thinking and consciousness is irrelevant.
I can imagine silicon-based thought as easily as I can imagine carbon-based thought; I can imagine ideas and meanings and emotions and a first-person awareness of the world (an “inner light”, a “ghost in the machine”) emerging from electronic circuitry as easily as from proteins and nucleic acids. I simply have always run on faith that when “genuine artificial intelligence” (sorry for the oxymoron) finally arises, it will do so precisely because the same degree of complexity and the same overall kind of abstract mental architecture will have come to exist in a new kind of hardware.
What I do not expect, however, is that full human intelligence will emerge from something far simpler, architecturally speaking, than a human brain.
Q: …Okay, so I want to respect your time and not go too long over an hour, but I’d love to ask you some slightly more personal questions. One about Douglas Hofstadter.
You open up your book with a very interesting anecdote about him giving a talk at Google and basically telling all the Google Engineers that AI could be calamitous, but not in the ways that we typically hear about. It seems like he was more worried about the possibility that we would succeed in building AI and this would mean that current approaches worked and would sort of trivialize human intelligence in some sense. We would lose the magic of our thinking.
I’m wondering if you could tell us a bit more about that and then, you seem to have different concerns. I’m wondering about your journey of deviating from his thinking.
Melanie Mitchell: Interestingly, Hofstadter’s worry stems from him reading some of Ray Kurzweil’s books about the Singularity. They range between very science fiction-like to somewhat compelling arguments about technology and its exponential increase in progress. Hofstadter was pretty worried that something like what Kurzweil was describing might actually happen. He kept saying he didn’t want this to happen in the lifetime of his children. He didn’t want the human race to be made irrelevant because these machines are now much smarter, much more creative than humans. He didn’t think it was going to happen but he was kind of worried about it. He actually organized two different conferences about this topic and he invited Ray Kurzweil and a bunch of other people. It was kind of an early version of what you might call the current things that we see on AI future predictions and AI alignment kind of stuff.
That was one of the things that really worried him and this would have been in the 1980s or 1990s. He started organizing these conferences in the 1990s. Kurzweil had already come up with the singularity idea. I was a lot less worried about the singularity scenario for some reason. I just didn’t see AI going in that direction at all. Kurzweil’s arguments were all about hardware and the exponential increase in hardware. But clearly, software is different from hardware and doesn’t follow the same exponential rules. Our knowledge and our ideas about how intelligence works in the biological world were not increasing exponentially and so I didn’t see us being able to replicate biological AI anytime soon.
There was actually a program at DARPA in the 90s where they were trying to recreate the intelligence of a cat using neural networks and it was a total failure. [Mitchell seems to misremember here and be referring to a 2008 program where $5m [$7.2m ~2023] was spent on an IBM spiking-neural net chip hardware project, DARPA’s System of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project, Ananthanarayanan et al 2009] It really impressed me that that’s actually super hard and we’re just so far away from that. How can we think that in just like 20–30 years we’re going to get to human-level intelligence? Hofstadter and I have had a lot of discussions about this. He’s obviously disturbed by these large language models and their behavior. He’s not sure what to think, like a lot of us. You see their behavior and it’s amazing at least some of the time but it’s also you realize we don’t have intuitions about how to deal with statistical models at that scale.
I don’t think of myself as an AI skeptic necessarily. I work in AI. I think it’s a very hard problem but I feel like a lot of times people who criticize current approaches are kind of labeled as overall AI skeptics. I even saw an article that called people like that AI deniers. I don’t like being labeled as an AI skeptic or AI critic because I do think that AI is really interesting and is going to produce a lot of really interesting insights and results. I just don’t think it’s going to be as easy to achieve something like human-level intelligence.
Q: I have a couple of questions about analogies because that’s a huge area of your thought that I’d just love to know more about. I know that with Hofstadter, your PhD thesis was on analogy making. I had never thought of analogies as being particularly insightful into human intelligence but I’m curious what your interest in analogies is and what we all stand to learn from the study of analogy.
M. Mitchell: I think most people have a narrow view of what analogy means. We all take IQ tests or SATs that have these single-word analogies and those are a lot less interesting. But I think analogy is much broader than that. It’s when we notice some kind of abstract similarity between two different situations or two different events. If you’ve ever had somebody tell you a little story about something that happened in their life and you say “oh the same thing happened to me”, you’re making an analogy. It’s not the same thing but it’s reminding you of something that is abstractly similar.
In science, analogy in scientific invention is paramount. There’s a nice article I saw today about this recent result from DeepMind about matrix multiplication. The idea was that the researchers saw that matrix multiplication could be mapped into a kind of game-playing framework and therefore they had these reinforcement systems that could be applied to that framework. But it was that initial leap of analogy that allowed them to apply these AI systems.
We also are constantly in our language making analogies. One of Hofstadter’s examples is anytime there’s a scandal we say “oh it’s another Watergate” or we call it something-gate. That’s an analogy. That kind of thing is just all over the place. I think it’s a really important part of transfer learning which is sort of the Holy Grail of machine learning. It’s about learning something in one domain and applying it to another domain. That’s really about making analogies. It’s about saying what is it that I’ve learned that’s important here that I can now apply to this new situation.
Q: I have one final question. Do you have advice for someone who wants to both be engaged in academic research but also sort of keep their head above the water and write general articles and be able to engage with a general audience? I’m impressed by the ability to be writing NeurIPS papers but also writing books for a general audience and articles. I’m curious whether you have any advice for someone who would like to do that and how you sort of resist the pressure to get caught in the academic rabbit hole where it’s just like always the next paper because there’s always something new to do. How do you balance that with writing a New York Times op-ed or something?
Mitchell: I haven’t really published papers in NeurIPS and all those places as much as many people who are prominent in AI and machine learning. I try to have time to think which is often hard if you’re under pressure all the time to publish the next paper. It’s a challenge because if you’re in an academic position and you’re on a tenure track, there’s all kinds of pressure to publish and get citations for your publications and publish in top-tier venues. Some of the people I know who are really successful at writing for the popular audience often aren’t publishing as much in academic venues.
Publishing for a popular audience is hard because imagine trying to explain your research to someone in your family who isn’t a technical person. It’s pretty hard to do. Learning how to do that is like learning how to have a theory of mind of people. I think that’s also the key to being a good teacher. It’s about having a theory of mind of the students and sort of knowing what they don’t know and making sure that you address that. It’s a challenge and it takes practice.
Q: Thank you so much for coming on the podcast. This was a very wonderful and enlightening conversation. Where can our audience find more of your work?
Q: We’ll put links into the show notes. I just want everyone to explore your work and enjoy it as much as we have.
M: Great, well thank you very much. It’s been a lot of fun.
(The DARPA cat example is a weird one. If I’d heard of it, I’d long since forgotten it, and I’m not sure why she’d put so much weight on it; $5m was a drop in the bucket then for chip development - $5m often doesn’t even cover simple NREs when it comes to chip designing/fabbing—especially compared to Blue Brain, and it’s not like one could train useful spiking networks in the first place. I hope she doesn’t really put as much weight on that as a reason to dismiss DL scaling as she seems to.)
I think the conferences Mitchell refers to are the same ones mentioned by Chalmers 2010:
...With some exceptions: discussions by academics include Bostrom (1998; 2003), Hanson (2008), Hofstadter (2005), and Moravec (1988; 1998). Hofstadter organized symposia on the prospect of superintelligent machines at Indiana University in 1999 and at Stanford University in 2000, and more recently, Bostrom’s Future of Humanity Institute at the University of Oxford has organized a number of relevant activities.
(The 1 April 2000 conference was covered at length by Ellen Ullman; it’s an interesting piece, if only for showing how far the AI zeitgeist was then from now in 2003.)
Another Mitchell followup: https://www.science.org/doi/10.1126/science.adj5957 tldr: argues that LLMs still aren’t intelligent and explains away everything they do as dataset contamination, bad benchmarks and sloppy evaluation, or shallow heuristics.
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.
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?
It often crosses my mind that public discourse about AI safety might not be useful. Tell men that AGI is powerful and they’ll start trying harder to acquire it. Tell legislators and, perhaps Yann thinks they’ll just start an arms race and complicate the work and not do much else.
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?
I wonder how many AI experts hold back their thoughts because they remember what happened to Copernicus when he presented that the Earth was not the center of the universe.
Thank you for your post. I’m new here and am, therefore, not permitted to upvote it, but I would, if I could.
It has become slightly more plausible that Melanie Mitchell could come around.
But only slightly. It appears that Hofstadter’s doubts have been building for a long time in private, even to organizing informal conferences/meetings about it, 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:
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?
† Hofstadter’s semi-mysterian/irreducible-complexity view:
So the question becomes, why the front of optimism, even after this conversation?
Also on Twitter, Experimental Learning highlights a 2022-11-01 podcast by Melanie Mitchell which confirms the book description, “Increments Podcast: #45---4 Central Fallacies of AI Research (with Melanie Mitchell)”:
(The DARPA cat example is a weird one. If I’d heard of it, I’d long since forgotten it, and I’m not sure why she’d put so much weight on it; $5m was a drop in the bucket then for chip development - $5m often doesn’t even cover simple NREs when it comes to chip designing/fabbing—especially compared to Blue Brain, and it’s not like one could train useful spiking networks in the first place. I hope she doesn’t really put as much weight on that as a reason to dismiss DL scaling as she seems to.)
I think the conferences Mitchell refers to are the same ones mentioned by Chalmers 2010:
(The 1 April 2000 conference was covered at length by Ellen Ullman; it’s an interesting piece, if only for showing how far the AI zeitgeist was then from now in 2003.)
Another Mitchell followup: https://www.science.org/doi/10.1126/science.adj5957 tldr: argues that LLMs still aren’t intelligent and explains away everything they do as dataset contamination, bad benchmarks and sloppy evaluation, or shallow heuristics.
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.
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?
It often crosses my mind that public discourse about AI safety might not be useful. Tell men that AGI is powerful and they’ll start trying harder to acquire it. Tell legislators and, perhaps Yann thinks they’ll just start an arms race and complicate the work and not do much else.
I wonder if that’s what he’s thinking.
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?
I wonder how many AI experts hold back their thoughts because they remember what happened to Copernicus when he presented that the Earth was not the center of the universe. Thank you for your post. I’m new here and am, therefore, not permitted to upvote it, but I would, if I could.