No, the point is that AI x-risk is commonsensical. “If you drink much from a bottle marked poison it is certain to disagree with you sooner or later” even if you don’t know mechanism of action of poison. We don’t expect Newtonian mechanics to prove that hitting yourself with a brick is quite safe, if we’d found that Newtonian mechanics predicts hitting yourself with a brick to be safe, it would be a huge evidence for Newtonian mechanics to be wrong. Good theories usually support common intuitions.
The other thing here is an isolated demand for rigor: there is no “technical understanding of today’s deep learning systems” which predicts, say, success of AGI labs or that their final products are going to be safe.
If we accept your interpretation—that AI doom is simply the commonsense view—then doesn’t that actually reinforce my point? It suggests that the central concern driving AI doomerism isn’t a set of specific technical arguments grounded in the details of deep learning. Instead, it’s based on broader and more fundamental intuitions about the nature of artificial life and its potential risks. To borrow your analogy: the belief that a brick falling on someone’s head would cause them harm isn’t ultimately rooted in technical disputes within Newtonian mechanics. It’s based on ordinary, everyday experience. Likewise, our conversations about AI doom should focus on the intuitive, commonsense cruxes behind it, rather than pretending that the real disagreement comes from highly specific technical deep learning arguments. Instead of undermining my comment, I think your point actually strengthens it.
I don’t think the mainline doom arguments claim to be rooted in deep learning?
Mostly they’re rigorized intuitive models about the nature of agency/intelligence/goal-directedness, which may go some way toward explaining certain phenomena we see in the behavior of LLMs (ie the Palisade Stockfish experiment). They’re theoretical arguments related to a broad class of intuitions and in many cases predate deep learning as a paradigm.
We can (and many do) argue over whether our lens ought to be top-down or bottom-up, but leaning toward the top down approach isn’t the same thing as relying on a-rigorous anxieties of the kind some felt 100 years ago.
I don’t think the mainline doom arguments claim to be rooted in deep learning?
To verify this claim, we can examine the blurb in Nate and Eliezer’s new book announcement, which states:
If any company or group, anywhere on the planet, builds an artificial superintelligence using anything remotely like current techniques, based on anything remotely like the present understanding of AI, then everyone, everywhere on Earth, will die.
From this quote, I draw two main inferences. First, their primary concern seems to be driven by the nature of existing deep learning technologies. [ETA: To be clear, I mean that it’s the primary factor driving their high p(doom), not that they’d be unconcerned about AI risk without deep learning.] This is suggested by their use of the phrase “anything remotely like current techniques”, which suggests that their core worries stem largely from deep learning rather than all potential AI development pathways. Second, the statement conveys a high degree of confidence in their prediction. This is evident in the fact that the claim is presented without any hedging or uncertainty—there are no phrases like “it’s possible that” or “we think this may occur.” The absence of such qualifiers implies that they see the outcome as highly probable, rather than speculative.
Now, imagine that, using only abstract reasoning available in the 19th century, someone could reasonably arrive at a 5% estimate for the likelihood that AI would pose an existential risk. Then suppose that, after observing the development and capabilities of modern deep learning, this estimate increases to 95%. In that case, I think it would be fair to say that the central or primary source of concern is rooted in the developments in deep learning, rather than in the original abstract arguments. That’s because the bulk of the concern emerged in response to concrete evidence from deep learning, and not from the earlier theoretical reasoning alone. I think this is broadly similar to MIRI’s position, although they may not go quite as far in attributing the shift in concern to deep learning alone.
Conversely, if someone already had a 95% credence in AI posing an existential threat based solely on abstract considerations from the 19th century—before the emergence of deep learning—then it would make more sense to say that their core concern is not based on deep learning at all. Their conviction would have been established independently of modern developments. This latter view is the one I was responding to in my original comment, as it seemed inconsistent with how others—including MIRI—have characterized the origin and basis of their concerns, as I’ve outlined above.
their primary concern seems to be driven by the nature of existing deep learning technologies. This is suggested by their use of the phrase “anything remotely like current techniques”, which suggests that their core worries stem largely from deep learning rather than all potential AI development pathways
You know better! Eliezer at least has been arguing these points far before DL!
He has been warning of a significant risk of catastrophe for a long time, but unless I’m mistaken, he only began explicitly and primarily arguing for a high probability of catastrophe more recently, around the time deep learning emerged. This distinction is essential to my argument, and was highlighted explicitly by my comment.
Yes, I agree your whole comment sucks. I know you know there is a difference between p(doom) and p(doom|AGI soon), and your reasons for having a high p(doom | AGI soon) and low p(doom) can be very different. Indeed a whole factor of p(AGI soon) different!
So we can get the observed shift with most of the “highly technical DL-specific considerations” mainly updating the p(AGI soon) factor via the incredibly complicated and arcane practice of… extrapolating benchmark scores.
Indeed, the fact AGI seems to be arriving so quickly is the main reason most people are worried!
This is not to say they like deep learning. There can be additional reasons deep learning is bad in their book, but is deep learning a core part of their arguments? Hell no! Do you know how I know? I’ve actually read them! Indeed, if you type site:arbital.greaterwrong.com “deep learning” into google, you get back two results. Compare with site:arbital.greaterwrong.com “utility function”, which gives you 5 pages. Now which do you think is more central to their high p(doom | AGI in 5 years)?
I wasn’t asking for your evaluation of the rest of my comment. I was clarifying a specific point because it seemed you had misunderstood what I was saying.
So we can get the observed shift with most of the “highly technical DL-specific considerations” mainly updating the p(AGI soon) factor via the incredibly complicated and arcane practice of… extrapolating benchmark scores.
Indeed, the fact AGI seems to be arriving so quickly is the main reason most people are worried!
If someone says their high p(doom) is driven by short timelines, what they likely mean is that AGI is now expected to arrive via a certain method—namely, deep learning—that is perceived as riskier than what might have emerged under slower or more deliberate development. If that’s the case, it directly supports my core point.
This explanation makes sense to me since expecting AGI to arrive soon doesn’t by itself justify a high probability of doom. After all, it would have been reasonable to have always believed AGI would come eventually, and it would have been unjustified to increase one’s p(doom) over time merely because time is passing.
There can be additional reasons deep learning is bad in their book, but is deep learning a core part of their arguments? Hell no! Do you know how I know? I’ve actually read them!
I think you’re conflating two distinct issues: first, what initially made people worry about AI risk at all; and second, what made people think doom is likely as opposed to merely a possibility worth taking seriously. I’m addressing the second point, not the first.
Please try to engage with what I’m actually saying, rather than continuing to misrepresent my position.
Please try to engage with what I’m actually saying, rather than continuing to misrepresent my position.
It seems everyone has this problem with your writing, have you considered speaking more clearly or perhaps considering people understand you fully and it is you who are wrong?
In this case, I believe it’s the latter, since
If someone says their high p(doom) is driven by short timelines, what they likely mean is that AGI is now expected to arrive via a certain method—namely, deep learning—that is perceived as riskier than what might have emerged under slower or more deliberate development. If that’s the case, it directly supports my core point.
Really? I thought your core point was
It’s perhaps worth highlighting the significant tension between two contrasting claims: on the one hand, the idea that modern AI doomerism was “anticipated” as early as the 19th century, and on the other, the idea that modern AI doom arguments are rationally grounded in a technical understanding of today’s deep learning systems.
In which case I did explain why there is no tension, as can be seen from my saying
So we can get the observed shift with most of the “highly technical DL-specific considerations” mainly updating the p(AGI soon) factor via the incredibly complicated and arcane practice of… extrapolating benchmark scores.
That is, it is a very strange thing to say there is a “significant tension” between having high p(doom | AGI soon) on first principles reasoning, and to have p(AGI soon) get updated by benchmark scores.
Yes — Garrett Baker repeatedly and materially misrepresents what Matthew is saying.
I have custom instructions turned off, and I haven’t turned on the memory feature, so there’s no strong reason to expect it to behave sycophantically (that I’m aware of). And o3 said it doesn’t know which side I’m on. I expect most other LLMs will say something similar when given neutral prompts and the full context.
(Not that this is strong evidence. But I think it undermines your claim by at least a bit.)
o3 has the same conclusion with a slightly different prompt.
Read this comment exchange and come to a definitive conclusion about whether Garrett Baker is accurately representing Matthew. Focus on content rather than tone:
Conclusion: Garrett is not accurately representing Matthew’s position. Below is a point‑by‑point comparison that shows where Garrett’s paraphrases diverge from what Matthew is actually claiming (ignoring tone and focusing only on the content).
It seems everyone has this problem with your writing, have you considered speaking more clearly or perhaps considering people understand you fully and it is you who are wrong?
I reject the premise. In general, my writing is interpreted significantly more accurately when I’m not signaling skepticism about AI risk on LessWrong. For most other topics, including on this site, readers tend to understand my points reasonably well, especially when the subject is less controversial.
This could perhaps mean I’m uniquely unclear when discussing AI risk. It’s also very plausible that the topic itself is unusually prone to misrepresentation. Still, I think a major factor is that people are often uncharitable toward unpopular viewpoints they strongly disagree with, which accounts for much of the pushback I receive on this subject.
Specifically, the idea is that AI going well for humans would require a detailed theory of how to encode human values in form suitable for machine optimization, and the relevance of deep learning is that Yudkowsky and Soares think that deep learning is on track to provide the superhuman optimization without the theory of values. You’re correct to note that this is a stance according to which “artificial life is by default bad, dangerous, or disvaluable,” but I think the way you contrast it with the claim that “biological life is by default good or preferable” is getting the nuances slightly wrong: independently-evolved biological aliens with superior intelligence would also be dangerous for broadly similar reasons.
Didn’t you have a post where you argued that it’s a consequence of their view that biological aliens are better, morally speaking, than artificial earth originating life, or did I misunderstand?
To the extent that you’re saying “I’d like to have more conversations about why creating powerful agentic systems might not go well by default; for others this seems like a given, and I just don’t see it”, I applaud you and hope you get to talk about this a whole bunch with smart people in a mutually respectful environment. However, I do not believe analogizing the positions of those who disagree with you with luddites from the 19th century (in particular when thousands of pages of publicly available writings, with which you are familiar, exist) is the best way to invite those conversations.
Quoting the first page of a book as though it contained a detailed roadmap of the central (60,000-word) argument’s logical flow (which to you is apparently the same as a rigorous historical account of how the authors came to believe what they believe) — while it claims to do nothing of the sort — simply does not parse at all. If you read the book (which I recommend, based on your declared interests here), or modeled the pre-existing knowledge of the median book website reader, you would not think “anything remotely like current techniques” meant “we are worried exclusively about deep learning for deep learning-exclusive reasons; trust us because we know so much about deep learning.”
If you find evidence of Eliezer, Nate, or similar saying “The core reason I am concerned about AI safety is [something very specific about deep learning]; otherwise I would not be concerned”, I would take your claims about MIRI’s past messaging very seriously. As is, no evidence exists before me that I may consider in support of this claim.
Based on what you’ve said so far, you seem to think that all of the cruxes (or at least the most important ones) must either be purely intuitive or purely technical. If they’re purely intuitive, then you dismiss them as the kind of reactionary thinking someone from the 19th century might have come up with. If they’re purely technical, you’d be well-positioned to propose clever technical solutions (or else to discredit your interlocutor on the basis of their credentials).
Reality’s simply messier than that. You likely have both intuitive and technical cruxes, as well as cruxes with irreducible intuitive and technical components (that is, what you see when you survey the technical evidence is shaped by your prior, and your motivations, as is true for anyone; as was true for you when interpreting that book excerpt).
I think you’re surrounded by smart people who would be excited to pour time into talking to you about this, conditional on not opening that discussion with a straw man of their position.
I do not believe analogizing the positions of those who disagree with you with luddites from the 19th century (in particular when thousands of pages of publicly available writings, with which you are familiar, exist) is the best way to invite those conversations.
To clarify, I am not analogizing the positions of those who disagree with me with luddites from the 19th century. This is not my intention, nor was it my argument.
I think we’re talking past each other here, so I will respectfully drop this discussion.
Contemporary AI existential risk concerns originated prior to it being obvious that a dangerous AI would likely involve deep learning, so no one could claim that the arguments that existed in ~2010 involved technical details of deep learning, and you didn’t need to find anything written in the 19th century to establish this.
No, the point is that AI x-risk is commonsensical. “If you drink much from a bottle marked poison it is certain to disagree with you sooner or later” even if you don’t know mechanism of action of poison. We don’t expect Newtonian mechanics to prove that hitting yourself with a brick is quite safe, if we’d found that Newtonian mechanics predicts hitting yourself with a brick to be safe, it would be a huge evidence for Newtonian mechanics to be wrong. Good theories usually support common intuitions.
The other thing here is an isolated demand for rigor: there is no “technical understanding of today’s deep learning systems” which predicts, say, success of AGI labs or that their final products are going to be safe.
If we accept your interpretation—that AI doom is simply the commonsense view—then doesn’t that actually reinforce my point? It suggests that the central concern driving AI doomerism isn’t a set of specific technical arguments grounded in the details of deep learning. Instead, it’s based on broader and more fundamental intuitions about the nature of artificial life and its potential risks. To borrow your analogy: the belief that a brick falling on someone’s head would cause them harm isn’t ultimately rooted in technical disputes within Newtonian mechanics. It’s based on ordinary, everyday experience. Likewise, our conversations about AI doom should focus on the intuitive, commonsense cruxes behind it, rather than pretending that the real disagreement comes from highly specific technical deep learning arguments. Instead of undermining my comment, I think your point actually strengthens it.
I don’t think the mainline doom arguments claim to be rooted in deep learning?
Mostly they’re rigorized intuitive models about the nature of agency/intelligence/goal-directedness, which may go some way toward explaining certain phenomena we see in the behavior of LLMs (ie the Palisade Stockfish experiment). They’re theoretical arguments related to a broad class of intuitions and in many cases predate deep learning as a paradigm.
We can (and many do) argue over whether our lens ought to be top-down or bottom-up, but leaning toward the top down approach isn’t the same thing as relying on a-rigorous anxieties of the kind some felt 100 years ago.
To verify this claim, we can examine the blurb in Nate and Eliezer’s new book announcement, which states:
From this quote, I draw two main inferences. First, their primary concern seems to be driven by the nature of existing deep learning technologies. [ETA: To be clear, I mean that it’s the primary factor driving their high p(doom), not that they’d be unconcerned about AI risk without deep learning.] This is suggested by their use of the phrase “anything remotely like current techniques”, which suggests that their core worries stem largely from deep learning rather than all potential AI development pathways. Second, the statement conveys a high degree of confidence in their prediction. This is evident in the fact that the claim is presented without any hedging or uncertainty—there are no phrases like “it’s possible that” or “we think this may occur.” The absence of such qualifiers implies that they see the outcome as highly probable, rather than speculative.
Now, imagine that, using only abstract reasoning available in the 19th century, someone could reasonably arrive at a 5% estimate for the likelihood that AI would pose an existential risk. Then suppose that, after observing the development and capabilities of modern deep learning, this estimate increases to 95%. In that case, I think it would be fair to say that the central or primary source of concern is rooted in the developments in deep learning, rather than in the original abstract arguments. That’s because the bulk of the concern emerged in response to concrete evidence from deep learning, and not from the earlier theoretical reasoning alone. I think this is broadly similar to MIRI’s position, although they may not go quite as far in attributing the shift in concern to deep learning alone.
Conversely, if someone already had a 95% credence in AI posing an existential threat based solely on abstract considerations from the 19th century—before the emergence of deep learning—then it would make more sense to say that their core concern is not based on deep learning at all. Their conviction would have been established independently of modern developments. This latter view is the one I was responding to in my original comment, as it seemed inconsistent with how others—including MIRI—have characterized the origin and basis of their concerns, as I’ve outlined above.
You know better! Eliezer at least has been arguing these points far before DL!
He has been warning of a significant risk of catastrophe for a long time, but unless I’m mistaken, he only began explicitly and primarily arguing for a high probability of catastrophe more recently, around the time deep learning emerged. This distinction is essential to my argument, and was highlighted explicitly by my comment.
Yes, I agree your whole comment sucks. I know you know there is a difference between p(doom) and p(doom|AGI soon), and your reasons for having a high p(doom | AGI soon) and low p(doom) can be very different. Indeed a whole factor of p(AGI soon) different!
So we can get the observed shift with most of the “highly technical DL-specific considerations” mainly updating the p(AGI soon) factor via the incredibly complicated and arcane practice of… extrapolating benchmark scores.
Indeed, the fact AGI seems to be arriving so quickly is the main reason most people are worried!
This is not to say they like deep learning. There can be additional reasons deep learning is bad in their book, but is deep learning a core part of their arguments? Hell no! Do you know how I know? I’ve actually read them! Indeed, if you type
site:arbital.greaterwrong.com “deep learning”into google, you get back two results. Compare withsite:arbital.greaterwrong.com “utility function”, which gives you 5 pages. Now which do you think is more central to their high p(doom | AGI in 5 years)?I wasn’t asking for your evaluation of the rest of my comment. I was clarifying a specific point because it seemed you had misunderstood what I was saying.
If someone says their high p(doom) is driven by short timelines, what they likely mean is that AGI is now expected to arrive via a certain method—namely, deep learning—that is perceived as riskier than what might have emerged under slower or more deliberate development. If that’s the case, it directly supports my core point.
This explanation makes sense to me since expecting AGI to arrive soon doesn’t by itself justify a high probability of doom. After all, it would have been reasonable to have always believed AGI would come eventually, and it would have been unjustified to increase one’s p(doom) over time merely because time is passing.
I think you’re conflating two distinct issues: first, what initially made people worry about AI risk at all; and second, what made people think doom is likely as opposed to merely a possibility worth taking seriously. I’m addressing the second point, not the first.
Please try to engage with what I’m actually saying, rather than continuing to misrepresent my position.
It seems everyone has this problem with your writing, have you considered speaking more clearly or perhaps considering people understand you fully and it is you who are wrong?
In this case, I believe it’s the latter, since
Really? I thought your core point was
In which case I did explain why there is no tension, as can be seen from my saying
That is, it is a very strange thing to say there is a “significant tension” between having high p(doom | AGI soon) on first principles reasoning, and to have p(AGI soon) get updated by benchmark scores.
This is o3′s take, for what it’s worth:
I have custom instructions turned off, and I haven’t turned on the memory feature, so there’s no strong reason to expect it to behave sycophantically (that I’m aware of). And o3 said it doesn’t know which side I’m on. I expect most other LLMs will say something similar when given neutral prompts and the full context.
(Not that this is strong evidence. But I think it undermines your claim by at least a bit.)
o3 has the same conclusion with a slightly different prompt.
That link seems to be broken.ETA: now fixed by Thomas.oops, this was on my work account from which you can’t make public links. Replaced the link with the prompt and beginning of o3 output.
I get the same result with Claude, but when I push at all it caves & says I understand you fine.
It seems a crux is what you mean by “tension”.
Just to offer my two cents, I do not have this problem and I think Matthew is extremely clear.
Can you rephrase his argument in your own words? In particular, define what “tension” means.
I reject the premise. In general, my writing is interpreted significantly more accurately when I’m not signaling skepticism about AI risk on LessWrong. For most other topics, including on this site, readers tend to understand my points reasonably well, especially when the subject is less controversial.
This could perhaps mean I’m uniquely unclear when discussing AI risk. It’s also very plausible that the topic itself is unusually prone to misrepresentation. Still, I think a major factor is that people are often uncharitable toward unpopular viewpoints they strongly disagree with, which accounts for much of the pushback I receive on this subject.
Specifically, the idea is that AI going well for humans would require a detailed theory of how to encode human values in form suitable for machine optimization, and the relevance of deep learning is that Yudkowsky and Soares think that deep learning is on track to provide the superhuman optimization without the theory of values. You’re correct to note that this is a stance according to which “artificial life is by default bad, dangerous, or disvaluable,” but I think the way you contrast it with the claim that “biological life is by default good or preferable” is getting the nuances slightly wrong: independently-evolved biological aliens with superior intelligence would also be dangerous for broadly similar reasons.
Didn’t you have a post where you argued that it’s a consequence of their view that biological aliens are better, morally speaking, than artificial earth originating life, or did I misunderstand?
To the extent that you’re saying “I’d like to have more conversations about why creating powerful agentic systems might not go well by default; for others this seems like a given, and I just don’t see it”, I applaud you and hope you get to talk about this a whole bunch with smart people in a mutually respectful environment. However, I do not believe analogizing the positions of those who disagree with you with luddites from the 19th century (in particular when thousands of pages of publicly available writings, with which you are familiar, exist) is the best way to invite those conversations.
Quoting the first page of a book as though it contained a detailed roadmap of the central (60,000-word) argument’s logical flow (which to you is apparently the same as a rigorous historical account of how the authors came to believe what they believe) — while it claims to do nothing of the sort — simply does not parse at all. If you read the book (which I recommend, based on your declared interests here), or modeled the pre-existing knowledge of the median book website reader, you would not think “anything remotely like current techniques” meant “we are worried exclusively about deep learning for deep learning-exclusive reasons; trust us because we know so much about deep learning.”
If you find evidence of Eliezer, Nate, or similar saying “The core reason I am concerned about AI safety is [something very specific about deep learning]; otherwise I would not be concerned”, I would take your claims about MIRI’s past messaging very seriously. As is, no evidence exists before me that I may consider in support of this claim.
Based on what you’ve said so far, you seem to think that all of the cruxes (or at least the most important ones) must either be purely intuitive or purely technical. If they’re purely intuitive, then you dismiss them as the kind of reactionary thinking someone from the 19th century might have come up with. If they’re purely technical, you’d be well-positioned to propose clever technical solutions (or else to discredit your interlocutor on the basis of their credentials).
Reality’s simply messier than that. You likely have both intuitive and technical cruxes, as well as cruxes with irreducible intuitive and technical components (that is, what you see when you survey the technical evidence is shaped by your prior, and your motivations, as is true for anyone; as was true for you when interpreting that book excerpt).
I think you’re surrounded by smart people who would be excited to pour time into talking to you about this, conditional on not opening that discussion with a straw man of their position.
To clarify, I am not analogizing the positions of those who disagree with me with luddites from the 19th century. This is not my intention, nor was it my argument.
I think we’re talking past each other here, so I will respectfully drop this discussion.
Contemporary AI existential risk concerns originated prior to it being obvious that a dangerous AI would likely involve deep learning, so no one could claim that the arguments that existed in ~2010 involved technical details of deep learning, and you didn’t need to find anything written in the 19th century to establish this.