kmmlng

I think it's more than that for two reasons: 1) Humanoid robots are general-purpose, which is great in domains where it's not economical to design specialized robots and 2) they are also drop-in replacements for humans. If you have a kitchen already, you can just drop in a humanoid robot without having to redesign your whole kitchen, which makes adoption much easier.

You have a point. I would further add that private ownership of these things requires capital concentration that cannot be healthy for society.

On the other hand, are we replacing public with private infrastructure here or is the private sector filling gaps where we didn't have any public infrastructure before in the first place?

The end of the UN seems plausible if we continue to move from a rule-based world order to a power-based one. For the same reason, there is little chance we will see the end of the EU. No European country is sufficiently powerful to really matter on the world stage, a more united Europe has the potential to be a significant world power though.

The scaling laws are also power laws, meaning that most of the big gains happen early in the curve, and improvements become more expensive the further you go along.

LeCun has already proved himself and made his mark and is now in a lucky position where he can focus on very long term goals that won't pay off for a long time (or ever). I feel like that is the best path someone like him could take.

I would say GPT itself is less an event and more the culmination of decades of research and development in algorithms, hardware, and software. Of course, to some degree, this is true for any novel development. In this case, the convergence of development in GPUs, software to utilize them well while being able to work in very high levels of abstractions, and algorithms that can scale is something I'm not sure we will see again so quickly. All this preexisting research is kind of a resource that will be completely exploited at some point. And then the only thing that can drive you forward are truly novel ideas. Reasoning models were a fairly obvious next step too as the concepts of System 1 and 2 have been around for a while.

You are completely right that the compute and funding right now are unprecedented. I don't feel confident making any predictions.

Basically what we have done the last few years is notice neural scaling laws and drive them to their logical conclusion. Those laws are power laws, which are not quite as bad as logarithmic laws, but you would still expect most of the big gains early on and then see diminishing returns.

Barring a kind of grey swan event of groundbreaking algorithmic innovation, I don't see how we get out of this. I suppose it could be that some of those diminishing returns are still big enough to bridge the gap to create an AI that can meaningfully recursively improve itself, but I personally don't see it.

At the moment, I would say everything is progressing exactly as expected and will continue to do so until it doesn't. If or when that happens is not predictable.

That's fair enough, there are problems with this way of thinking. I suppose you could say the take-away should be "Don't donate to charities where close to your whole donation will be absorbed as administrative costs". There definitely are black sheep that act this way and they probably served as the original motivation for EA. It's a logical next step to come up with a way to systematically identify these black sheep. That is probably the point where this approach should have stopped.

I suppose in the beginning, it was about finding ways to measure how effective different altruistic approaches actually are and focusing your efforts on the most effective ones. Effective then essentially means how much impact you are achieving per dollar spent. One of the more convincing ways of doing this is looking at different charitable foundations and determining how much of each dollar you donate to them actually ends up being used to fix some problem and how much ends up being absorbed by the charitable foundation itself (salaries etc.) with nothing to show for it.

They might have lost the plot somewhere along the line, but the effective altruism movement had some good ideas.

> It seems that you're saying that a therapist will be "rubbish unless they use basic Cognitive Behavioural Therapy concepts" ? i.e. that this is the only valid approach to therapy?

I believe the parent poster is saying that CBT is the only form of therapy you can trust an LLM to pull off because it's straightforward to administer.

With many drugs that are used both therapeutically and recreationally, it is the case that the average recreational dosage is much larger. Ketamine is an exception here, as therapeutic doses are actually quite high. The common mistake here is to equate intravenous dosage with intranasal dosage, when the bioavailability differs significantly between these routes of administration. And that's not even considering that most reported recreational dosages are wrong due to cutting agents.

There certainly is recreational abuse with very large dosages, but I don't think it's fair to claim that the majority of users fall into this category.

I think we have seen a general trend towards centralized platforms on the internet. Where you had many individual niche sites before, now you have a few all-encompassing platforms. There are some exceptions, but I generally find that many of those platforms want to maximize your time on the platform itself. As a consequence, they do what they can to keep you from leaving the platform via a link to some other website.

Yes, there is additional context that is not explicitly stated in the question. It is clear that you are looking for a job to earn money and live your life and everyone already knows this, so there is no need to talk about. The question is: Why did you apply (here out of all the places you could have applied to)?

It's not like the US doesn't have a problem with affordable housing, so I don't see how this plays any role in the divide.

Germany has plenty of applied research organizations, from universities (e.g. RWTH) to things like Fraunhofer. The funding schemes behind these organizations are horrible and I would argue that in many ways, they are machines to burn up potential. Even with all this, Germany has been doing okay on the publicly funded AI research front, but that is irrelevant. The US isn't leading because of publicly funded AI effort, but because of privately funded AI effort.