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Good point. Thinking of robotics overall, it's much more of a bunch of small stuff than one big thing. Though it depends how far you "zoom out" I guess. Technically Linear Algebra itself, or the Jacobian, is an essential element of robotics. But could also zoom in on a different aspect and then say that "zero backlash gearboxes" (where Harmonic Drive is notable as it's much more compact and accurate than prev versions - but perhaps a still small effect in the big picture) are the main element. Or PID control, or high resolution encoders.
I'm not quite sure how to think of how these all fit together to form "robotics" and whether they are small elements of a larger thing, or large breakthroughs stacked over the course of many years (where they might appear small at that zoomed out level).
I think that if we take a snapshot in a specific time (e.g. 5 years) in robotics, there will often be one or very few large bottlenecks that are holding it back. Right now it is mostly ML/vision and batteries. 10-15 years ago, maybe it was the CPU real time processing latency or the motor power density. A bit earlier it might be gearbox. These things were fairly major bottlenecks until they got good enough that it switches to a minor revision/iteration regime (nowadays there's not much left to improve on gearboxes e.g., except for maybe in very specific use cases)
Other examples of fields like this include: medicine, mechanical engineering, education, SAT solving, and computer chess.
To give a maybe helpful anecdote - I am a mechanical engineer (though I now work in AI governance), and in my experience that isnt true at least for R&D (e.g. a surgical robot) where you arent just iterating or working in a highly standardized field (aerospace, hvac, mass manufacturing etc). The "bottleneck" in that case is usually figuring out the requirements (e.g. which surgical tools to support? whats the motion range, design envelope for interferences). If those are wrong, the best design will still be wrong.
In more standardized engineering fields the requirements (and user needs) are much better known, so perhaps the bottleneck now becomes a bunch of small things rather than one big thing.
I had a great time at AISC8. Perhaps I would still find my way into a full time AI Safety position without it, but i'd guess at least 1 year later and significantly less neglected opportunity. My AI Safety Camp project later became the AI Standards Lab.
I know several others who benefitted quite a bit from it.
Do you think there's some initial evidence for that? E.g. Voyager or others from Deepmind. Self play gets thrown around a lot, not sure if concretely we've seen much yet for LLMs using it.
But yes agree, good point regarding strategy games being a domain that could be verifiable
I was fairly on board with control before, I think my main remaining concern is the trusted models not being good enough. But with more elaborate control protocols (Assuming political/AI labs actually make an effort to implement), catching an escape attempt seems more likely if the model's performance is very skewed to specific domains. Though yeah I agree that some of what you mentioned might not have changed, and could still be an issue
Good post, thanks for writing!
With o1, and now o3, It seems fairly plausible now that there will be a split between "verifiable" capabilities, and general capabilities. Sure, there will be some cross-pollination (transfer), but this might have some limits.
What then? Can a superhuman mathematical + Coding AI also just reason through political strategy, or will it struggle and make errors/fallback on somewhat generic ideas in training data?
Can we get a "seed-AI style" consequentialist in some domains, while it fails to perform above human level in others? I'd like to believe reasoning would transfer (as it should be universal), but I dont think reasoning is sufficient for some more fuzzy domains - the model also needs good heuristics.
The AI Control agenda seems more promising now (for both this reason and some others).
Signal boost for the "username hiding" on homepage feature in settings - it seems cool, will see if it changes how I use LW.
I wonder also about a "hide karma by default". Though less sure if that will actually achieve the intended purpose, as karma can be a good filter when just skimming comments and not reading in detail.
Steep / Shallow
LW Feature request/idea for a feature - In posts that have lots of in-text links to other posts, perhaps add an LLM 1-2 sentence (context informed) summary in the hover preview?
I assume that for someone who has been around the forum for many years, various posts are familiar enough that name-dropping them in a link is sufficient to give context. But If I have to click a link and read 4+ other posts as I am going through one post, perhaps the LW UI can fairly easily build in that feature.
(suggesting it as a features since it does seem like LW is a place that experiments with various features not too different from this - ofc, I can always ask for a LLM summary manually myself if I need to)
I have the Boox Nova Air (7inch) for nearly 2 years now - a bit small for reading papers but great for books and blog posts. You can run google play apps, and even set up a google drive sync to automatically transfer pdfs/epubs onto it. At some point I might get the 10inch version (the Note Air).
Another useful feature is taking notes inside pdfs, by highlighting and then handwriting the note into the Gboard handwrite-to-text keyboard. Not as smooth as on an iPad, but pretty good way to annotate a paper.
This was very interesting, looking forward to the follow up!
In the "AIs messing with your evaluations" (and checking for whether the AI is capable of/likely to do so) bit, I'm curious if there is any published research on this.
Hmm, in that case maybe I misunderstood the post, my impression wasnt that he was saying AI literally isn't a science anymore, but more that engineering work is getting too far ahead of the science part - and that in practice most ML progress now is just ML Engineering, where understanding is only a means to an end (and so is not as deep as it would be if it was science first).
I would guess that engineering gets ahead of science pretty often, but maybe in ML it's more pronounced - hype/money investment, as well as perhaps the perceived relative low stakes (unlike aerospace, or medical robotics which is my field) not scaring the ML engineers enough to actually care about deep understanding, and also perhaps the inscrutable nature of ML - if it were easy to understand, it wouldn't be as unappealing spend resources to do so.
I don't really have a take on where the in elegance comes in to play here
While theoretical physics is less "applied science" than chemistry, there's still a real difference between chemistry and chemical engineering.
For context, I am a Mechanical Engineer, and while I do occasionally check the system I am designing and try to understand/verify how well it is working, I am fundamentally not doing science. The main goal is solving a practical problem (i.e. as little theoretical understanding as is sufficient), where in science the understanding is the main goal, or at least closer to it.
So basically, post hoc, ergo propter hoc (post hoc fallacy)
If winning happened after rationality (in this case, any action you judge to be rational under any definition you prefer) it does not mean it happened because of it.
This was a great read! Personally I feel like it ended too quickly - even without going into gruesome details, I felt like 1 more paragraph or so of concluding bits in the story was needed. But, overall I really enjoyed it.
I'm trying to think of ideas here. As a recap of what I think the post says:
- China is still very much in the race
- There is very little AI safety activities there, possibly due to a lack of reception of EA ideas.
^let me know if I am understanding correctly.
Some ideas/thoughts:
- it seems to me that many in AI Safety or in other specific "cause areas" are already dissociating from EA, though not much from LW.
- I am not sure if we should expect mainstream adoption of AI Safety ideas (its not really mainstream in the west, nor is EA, or LW).
- It seems like there are some communication issues (the Org looking for funding) that this post can help with
- to me it is super interesting to hear that there is less resistance to the ideas of AI Safety in China. Though I don't want to fully believe that yet. Though, im not sure that the AI Safety field is people bottlenecked right now, it seems we currently don't know what to do with more people really.
- still, it's clear that we need to have a strong field in China. Perhaps less alignment focused, and more governance? Though my impression from your post is that governance is less doable, but maybe I am misunderstanding.
I might have more thoughts later on.
(for context, I am recently involved in governance work for the EU AI Act)
Working at a startup made me realize how little we can actually "reason through" things to get to a point where all team members agree. Often there's too little time to test all assumptions, if it's even doable at all. Part of the role of the CEO is to "cut" these discussions when it's evident that spending more time on it is worse than proceeding despite uncertainty. If we had "the facts", we might find it easier to agree. But in an uncertain environment, many decisions come down to the intuition (hopefully based on reliable experience - such as founding a similar previous startup)
To me it seems that there are parallels here. In discussions I can always push back on the intuitions of others, but where no reliable facts exist, I have little chance at getting far. Which is not always bad, since if we couldn't function well under uncertainty, we would likely be a lot less successful as a species.