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.

This seems like the classic shifting of goalposts to determine when AI has actually become intelligent. Is the ability to communicate not a form of intelligence? We don't have to pretend like these models are super intelligent, but to deny them any intelligence seems too far for me.

I keep seeing this argument, but I don't buy it at all. I want a phone with an AGI, not a phone that is only AGI. Often it's just easier to press a button rather than talk to an AI, regardless how smart it is. I have no interest in natural language being the only interface to my device, that sounds awful. In public, I want to preserve my privacy. I do not want to have everyone listening in on what I'm doing.

If we can create an AGI that can literally read my mind, okay, maybe that's a better interface than the current one, but we are far away from that scenario.

Until then, I'm convinced users will prefer a phone with AI functionalities rather than the reverse. It's easier for a phone company to create such a phone than it is for an AI company.

This is clearly a joke, but just for completeness: This would be terrible idea as dosing is very important with this drug. Too little doesn't make sense, too much is extremely dangerous.

It is true that Germany (and Europe as a whole) suffers from a less than ideal investment and innovation landscape. The companies you mentioned, however, worked on products that barely make any sense. It is clear that those kinds of companies will not (and should not) survive outside of 0% interest paradigms.

> humans are infinitely better for this purpose and way cheaper to produce.

Most of the western world is having demographic issues. We are having trouble getting people to reproduce in sufficient numbers to keep our societies going. This is an ugly trajectory to be on because you have an increasingly big group of old people who need to be supported by an increasingly small number of young people.

I feel like as an individual user, I'd rather have my social media data siphoned off by a foreign government than my own. On a societal level, having everyone's data siphoned off by a foreign government and being subjected to political influence is undesirable.

It does seem that there are periods and places where things are in equilibrium, or stable. At some point, something typically comes along and disturbs that balance. This can be seen in the fossil record, where you have lots of species going extinct during fairly short periods of time, while there are other times when not much happens.

What people are concerned about is that we, as humans, are the thing that is disturbing the balance at the moment.

You sound like you have a good overview. Is there any chance you could point me into the direction of good literature? I'm used to reading scientific literature and would love to learn a bit more, ideally through reviews and meta-analyses.

I've been applying to jobs for the last three months and many companies use systems that automatically parse your resume and then let you manually correct the parsed data. It's nearly always a mess, and I use a very straightforward linear resume template.

It's possible that there is some selection bias here, where mediocre parsing systems give you the option to manually correct everything because they know they are mediocre. I remain skeptical.

foobar2000 is actually available for Mac.

I think it's important to distinguish between two directions here: 1) can someone figure out who I am based on my profile and 2) can someone find my profile if they know who I am. I care much more about the second, because the odds of someone attempting the second are much higher.

Well, at least Google Scholar is aligned with Google's core business: search. It seems silly for Google to scrap search features. On the other hand, I'm not sure if Google Scholar is aligned with their real core business: ads.

I think you both have a point. Clearly, something about the way we build LLMs today makes them inept at these kinds of tasks. It's unclear how fundamental this problem is as far as I'm concerned.

Clearly, humans also need training in these things and almost no one figures out how to do basic things like long division by themselves. Some people sometimes figure things out, and more importantly, they do so by building on what came before.

The difference between humans and LLMs is that even after being trained and given access to near everything that came before, LLMs are terrible at this stuff.

I used to be in the same camp, but have since abandoned this position. The world has changed. Very serious people use the term "AI" nowadays. I don't find that it's a high-precision signal for identifying charlatans anymore (though it is high-recall).

Would it have mattered in this case? The advent of LLMs would have endangered their position just the same, no?

I always got the feeling that the distributions were different for men and women. Male professionals seem to be normally distributed, with a lot of mediocrity, some excellence, and some incompetence. In women, the mediocrity part of the population seems to be missing, so the distribution looks more bimodal.

If that's actually the case and not just a warped perception on my part, it could easily happen that, depending on your own skill-level and environment, you'll be more likely to work with one of the two groups in the bimodal distribution.