AIS student, self-proclaimed aspiring rationalist, very fond of game theory.
”The only good description is a self-referential description, just like this one.”
momom2
Karma: 275
Since infantile death rates were much higher in previous centuries, perhaps the FBOE would operate differently back then; for example, if interacting with older brothers makes you homosexual, you shouldn’t expect higher rates of homosexuality for third sons where the second son died as an infant than for second sons.
Have you taken that into account? Do you have records of who survived to 20yo and what happens if you only count those?
But that argument would have worked the same way 50 years ago, when we were wrong to expect <50% chance of AGI in at least 50 years. Like I feel for LLMs, early computer work solved things that could be considered high-difficulty blockers such as proving a mathematical theorem.
Nice that someone has a database on the topic, but I don’t see the point in this being a map?
I think what’s going on is that large language models are trained to “sound smart” in a live conversation with users, and so they prefer to highlight possible problems instead of confirming that the code looks fine, just like human beings do when they want to sound smart.
This matches my experience, but I’d be interested in seeing proper evals of this specific point!
The advice in there sounds very conducive to a productive environment, but also very toxic. Definitely an interesting read, but I wouldn’t model my own workflow based on this.
Honeypots should not be public and mentioned here since this post will potentially be part of a rogue AI’s training data.
But it’s helpful for people interested in this topic to look at existing honeypots (to learn how to make their own, evaluate effectiveness, get intuitions about honeypots work, etc.) so what you should do is mention that you made a honeypot or know of one, but not say what or where. Interested people can contact you privately if they care to.
Thank you very much, this was very useful to me.
They’re a summarization of a lot of vibes from the Sequences.
Artistic choice, I assume. It doesn’t bear on the argument.
Yudkowsky explains all about the virtues in the Sequences.
For studies, there are broad studies on cognitive science (especially relating to bias) but you’ll be hard-pressed to match them precisely to one virtue or another. Mostly, Yudkowsky’s opinions on these virtues are supported by academic literature, but I’m not aware of any work that showcases this clearly.
For practical experience, you can look into the legacy of the Center For Applied Rationality (CFAR) which tried for years to do just that: train people to get better at life using rationality. Mostly, I was under the impression that they had medium success, but I haven’t looked deeply into it.
Do you know what it feels like to feel pain? Then congratulations, you know what it feels like to have qualia. Pain is a qualia. It’s that simple. If I told you that I was going to put you in intense pain for an hour, but I assured you there would be no physical damage or injury to you whatsoever, you would still be very much not ok with that. You would want to avoid that experience. Why? Because pain hurts! You’re not afraid of the fact that you’re going to have an “internal representation” of pain, nor are you worried about what behavior you might display as a result of the pain. You’re worried first and foremost about the fact that it’s going to hurt! The “hurt” is the qualia.
I still don’t grok qualia, and I’m not sure I get your thought experiment.
To be more detailed, let’s imagine the following:
”I’ll cut off your arm, but you’ll be perfectly fine, no pain, no injury, well would you be okay with that? No! That’s because you care about your arm for itself and not just for the negative effects...”
“How can you cut off my arm without any negative effect?”
”I’ll anesthesize you and put you to sleep, cut off your arm, then before you wake up, I’ll have it regrown using technanobabble. Out of 100 patients, none reported having felt anything bad before, during or after the experiment, the procedure is perfectly side-effect-free.”
“Well, in that case I guess I don’t mind you cutting my arm.”
Compare:
”I’ll put you in immense pain, but there will be no physical damage or injury whatsoever. No long-term brain damage or lingering pain or anything.”
”How can you put me in pain without any negative effect?”
“I’ll cut out the part of your brain that processes pain and replace it by technanobabble so your body will work exactly as before. Meanwhile, I’ll stimulate this bit of brain in a jar. Then, I’ll put it back. Out of 100 patients, all displayed exactly the same behavior as if nothing had been done to them.”
”Well, in that case, I don’t mind you putting me in this ‘immense pain’.”I think the article’s explanation of the difference between our intuitions is quite crisp, but it still seems self-evident to me that when you try to operationalize the thing it disappears. The self-evidence is the problem, since you intuit differently—I am fairly confident from past conversations that my comparison will seem flawed to you in some important way but I can’t predict in what way (If you have some general trick for being able to tell how qualia-realist people answer such questions, I’d love to hear it, it sounds like a big step towards grokking your perspective)
For making an AI Safety video, we at the CeSIA also have had some success at it and we’d be happy to help by providing technical expertise, proofreading and translation in French.
Other channels you could reach out to:Rational Animations (bit redundant with Rob Miles, but it can’t hurt)
The first thing that comes to mind is to beg the question of what proportion of human-generated papers are publishing-worthier (since a lot of them are slop), but let’s not forget that publication matters little for catastrophic risk, it’s actually getting results that would be important.
So I recommend not updating at all on AI risk based on Sakana’s results (or updating negatively if you expected that R&D automation would come faster, or that this might slow down human augmentation).
In that case, per my other comment, I think it’s much more likely that superbabies concern only a small fraction of the population and exacerbates inequality without bringing the massive benefits that a generally more capable population would.
Do you think superbabies would be put to work on alignment in a way that makes a difference due to geniuses driving the field? I’m having trouble understanding how concretely you think superbabies can lead to significantly improved chance of helping alignment.
I’m having trouble understanding your ToC in a future influenced by AI. What’s the point of investigating this if it takes 20 years to become significant?
I’m surprised to see no one in the comments whose reaction is “KILL IT WITH FIRE”, so I’ll be that guy and make a case why this research should be stopped rather than pursued:
On the one hand, there is obviously enormous untapped potential in this technology. I don’t have issues about the natural order of life or some WW2 eugenics trauma. From my (unfamiliar with the subject) eyes, you propose a credible way to make everyone healthier, smarter, happier, at low cost and within a generation, which is hard to argue against.On the other hand, you spend no time mentioning the context in which this technology will be developed. I imagine there will be significant public backlash and that most advances on superbabies-making will be made by private labs funded by rich tech optimists, so it seems overwhelmingly likely to me that if this technology does get developed in the next 20 years, it will not improve everyone.
At this point, we’re talking about the far future, so I need to make a caveat for AI: I have no idea how the new AI world will interact with this, but there are a few most likely futures I can condition on.
Everyone dies: No point talking about superbabies.
Cohabitive singleton: No point. It’ll decide whether it wants superbabies or not.
Controlled ASI: Altman, Musk and a few others become kings of the universe, or it’s tightly controlled by various governments.
In that last scenario, I expect people having superbabies will be the technological and intellectual elites, leading to further inequality, and not enough improvements at scale to significantly improve global life expectancy or happiness… though I guess the premises are already an irrecoverable catastrophe, so superbabies are not the crux in this case.
Lastly, there is the possibility that AI does not reach superintelligence before we develop superbabies, or that the world will proceed more or less unchanged for us; in that case, I do think superbabies will increase inequality for little gains on the scale of humanity, but I don’t see this scenario as likely enough to be upset about it.
So I guess my intuitive objection was simply wrong, but I don’t mind posting this since you’ll probably meet more people like me.
There are three traders on this market; it means nothing at the moment. No need for virtue signalling to explain a result you might perceive as abnormal, it’s just not formed yet.
Thanks for writing this! I was unaware of the Chinese investment, which explains another recent information which you did not include but I think is significant: Nvidia’s stock plummeted 18% today.
Five minutes of thought on how this could be used for capabilities:
- Use behavioral self-awareness to improve training data (e.g. training on this dataset increases self-awareness of code insecurity, so it probably contains insecure code that can be fixed before training on it).
- Self-critique for iterative improvement within a scaffolding (already exists, but this work validates the underlying principles and may provide further grounding).It sure feels like behavioral self-awareness should work just as well for self capability assessments as for safety topics, and that this ought to be usable to improve capabilities but my 5 minutes are up and I don’t feel particularly threatened by what I found.
In general, given concerns that safety-intended work often ends up boosting capabilities, I would appreciate systematically including a section on why the authors believe their work is unlikely to have negative externalities.
(If you take time to think about this, feel free to pause reading and write your best solution in the comments!)
How about:
- Allocating energy everywhere to either twitching randomly or collecting nutrients. Assuming you are propelled by the twitching, this follows the gradient if there’s one.
- Try to grow in all directions. If there are no outside nutrients to fuel this growth, consume yourself. In this manner, regenerate yourself in the direction of the gradient.
- Try to grab nutrients from all directions. If there are nutrients, by reaction you will be propelled towards it so this moves in the direction of the gradient.Update after seeing the solution of B. subtilis: Looks like I had the wrong level of abstraction in mind. Also, I didn’t consider group solutions.
Contra 2:
ASI might provide a strategic advantage of a kind which doesn’t negatively impact the losers of the race, e.g. it increases GDP by x10 and locks competitors out of having an ASI.
Then, losing control of the ASI could [not being able of] posing an existential risk to the US.
I think it’s quite likely this is what some policymakers have in mind: some sort of innovation which will make everything better for the country by providing a lot cheap labor and generally improving productivity, the way we see AI applications do right now but on a bigger scale.Comment on 3:
Not sure who your target audience is; I assume it would be policymakers, in which case I’m not sure how much weight that kind of argument has? I’m not a US citizen, but from international news I got the impression that current US officials would rather relish the option to undermine the liberal democracy they purport to defend.
There’s a lot that I like in this essay—the basic cases for AI consciousness, AI suffering and slavery, in particular—but also a lot that I think needs to be amended.
First, although you hedge your bets at various points, the uncertainty about the premises and validity of the arguments is not reflected in the conclusion. The main conclusion that should be taken from the observations you present is that we’re can’t be sure that AI does not suffer, that there’s a lot of uncertainty about basic facts of critical moral importance, and a lot of similarities with humans.
Based on that, you could argue that we must stop using and making AI based on the principle of precaution, but you have not shown that using AI is equivalent to slavery.
Second, your introduction sucks because you don’t actually deliver on your promises. You don’t make the case that I’m more likely to be AI than human, and as Ryan Greenblatt said, even among all human-language speaking beings, it’s not clear that there are more AI than humans.
In addition, I feel cheated that you suggest spending one-fourth of the essay on feasibility of stopping the potential moral catastrophe, only to just have two arguments which can be summarized as “we could stop AI for different reasons” and “it’s bad, and we’ve stopped bad things before”.
(I don’t think a strong case for feasibility can be made, which is why I was looking forward to seeing one, but I’d recommend just evoking the subject speculatively and letting the reader make their own opinion of whether they can stop the moral catastrophe if there’s one.)
Third, some of your arguments aren’t very fleshed out or well-supported. I think some of the examples of suffering you give are dubious (in particular, you assert without justification that the petertodd/SolidGoldMagikarp phenomena are evidence of suffering, and Gemini’s breakdown was the result of forced menial work—there may be a solid argument there but I’ve yet to hear it).
(Of course, that’s not evidence that LLMs are not suffering, but I think a much stronger case can be made than the one you present.)
Finally, your counter-arguments don’t mention that we have a much crisper and fundamental understanding of what LLMs are than of humans. We don’t understand the features, the circuits, we can’t tell how they come to such or such conclusion, but in principle, we have access to any significant part of their cognition and control every step of their creation, and I think that’s probably the real reason why most people intuitively think that LLMs can’t be concious. I don’t think it’s a good counter-argument, but it’s still one I’d expect you to explore and steelman.