@Plinz@ritwikpavan There are plenty apps doing this but there is a fundamental hardware and user limitation. Getting high quality images of tiny, hard to access spots likely requires better lighting conditions, higher resolution cameras etc.
Teach VLAs reasoning and instruction following at training time → generate better actions without thinking at test time
🇧🇷☀️🌴Hybrid Training for VLAs is accepted to @iclr_conf
Read the full paper at: https://t.co/jIOhvVG8HP
w/ @CcansuSancaktar@mlpeschl D. Dijkman
We are launching a new arXivLabs collaboration with @HuggingFace to make demos related to papers in cs, stats, and eess directly accessible from arXiv!
@timnitGebru I don't see what effective altruism has to do with this. To be honest, I am pretty certain that most EAs would argue that these 44bn USD could have been spent much better at other places, be it for a 'longtermist' or 'shorttermist' cause.
How can we learn reward functions from experts in the presence of conflicting inputs?
Proud to present MORAL: Multi-Objective Reinforced Active Learning, accepted @aamas2022! In collaboration with @4ester @faoliehoek@lucianosiebert.
Check it out at https://t.co/hdS6iWtmbe
@fchollet Makes sense. But I suppose the manifold hypothesis persists regardless of the priors we use? Then, end to end DL will never truly get us to the 'system 2' type of capabilities. I guess the uncertainty is in whether we can find good priors to get enough ood generalization?
@fchollet I saw you were discussing this with Yoshua Bengio at the AGI conference, to which he replied that the missing piece is getting rid of the independence assumption in the latent space by assuming some additional 'modularity' prior. Do you have any comments on this?
@tudelta @ClaudiaWerker 'Vegetarian restaurants impose a certain eating style on us.'
I find this argument to be very shortsighted. Is an Italian restaurant imposing Italian culture on us just because it only serves pizza? Of course not. The same argument can be made about any type of shop/restaurant.
@mark_riedl@_KarenHao As with RL, I do like the safe RL framing of the problem, although I think that 'safe RL' as a field currently is a bit too focused on the problem of safe exploration with 'safe' only being defined in terms of simple toy problems. Talking about what 'safety' means might be good
@mark_riedl@_KarenHao Thanks for clarifying, I think I agree with you here. It seems to me that AI ethics is just a very overloaded term nowadays, and the fact that AI 'alignment' carries a generic name but points to a rather niche community does not help either.
@mark_riedl@_KarenHao For instance, consider aligning narrowly superhuman models https://t.co/90wGrTiEJW. Getting an AI to 'want' to do what humans want is, in my opinion, very central to the AI ethics debate and, regardless of an agent's complexity, will be necessary for AI alignment.
@mark_riedl@_KarenHao Even though this might be the underlying narrative for the AI alignment community, I do not agree that AI ethics is as distant from AI alignment as you suggest here. In fact, I would argue that many questions posed by AI alignment are very relevant for the AI ethics discussion...