AI is causing everything to change, small basically trivial oss projects are no longer really needed - that doesn’t seem too bad, we can focus on the more exciting things now!
I am shocked how people can deny that solving Navier Stokes requires some sort of intelligence. Even Doctorow talks of "brute-forcing" a solution. Brute-forcing leads to combinatorial explosion, so there must be something more going on here. Otherwise you could just put this problem into an automated theorem prover (we've had those forever, they are actually just brute-forcing it).
> Otherwise you could just put this problem into an automated theorem prover (we've had those forever, they are actually just brute-forcing it).
There are many automatic theorem provers that do very clever stuff, just as the underlying theroy describes.
> I am shocked how people can deny that solving Navier Stokes requires some sort of intelligence.
It is absurd to waste time discussing whether it is inteligent or not. It is just an algorithm, we know how it works, and it does exactly what we expect it to do. LLMs are not magical things. The main difference is the scale: for Navier-Stokes they spent in 3 days more money that the whole mathematical community over the last 20 years easily.
By the way, I'm not saying that LLM's are useless, that I'm anti-AI or anything like that.
Intelligence does not seem to be magic either, as LLMs are proving now. It is indeed a waste of time to argue that LLMs are not intelligent in their own way, they obviously are. If Navier Stokes doesn't convince you, nothing will.
I just used a £89 Codex subscription to do very intelligent things with it, stuff that I would have had to sit down and ponder and work on for quite a while, and I have a PhD in that. I didn't need to do anything special except explaining the problem(s) to the AI, and my theory of it so far. It took it from there. If that is not intelligence, nothing is.
We know what calculations it does. We have some hazy idea of some bits of how those calculations lead to something that at least somewhat resembles intelligent behaviour. But that's a far cry from actually knowing how it works.
For instance, suppose you give one of today's frontier models some of those chain-of-cubes rotation puzzles (the sort that infamously men are about 1sd better at than women, statistically speaking). How well will it do? I have absolutely no idea and I'm quite sure that a more detailed understanding of the transformer architecture would not make my guesses any better. (Actually, I do kinda have some guesses but they're based on a vague notion about how the models might be partitioned between vision-y bits and language-y bits, and it's very possible that that notion is out of date.)
> it does exactly what we expect it to do
Were you, let's say 6 months ago, expecting it to resolve one of the Millennium Prize problems?
(I do agree that it is more productive to ask "what can and can't they do?" than "should we classify that as intelligent or not?".)
> for Navier-Stokes they spent in 3 days more money than the whole mathematical community over the last 20 years easily.
Are you sure?
(The numbers I've heard, which I admittedly have no very strong reason to trust, don't seem that way to me.)
> Were you, let's say 6 months ago, expecting it to resolve one of the Millennium Prize problems?
I didn't expect them to throw millions of dollars at each famous math problem. But one year ago we already had LLMs that solved IMO problems, no?
> Are you sure? (The numbers I've heard, which I admittedly have no very strong reason to trust, don't seem that way to me.)
Math has very little founding compared to other science domains. Also, if you filter mathematicians by specialization in PDE and that have worked on Navier-Stokes, then you end up with a very niche community.
> For instance, suppose you give one of today's frontier models some of those chain-of-cubes rotation puzzles. How well will it do?
I feel like this is not the correct way of thinking about it. We can also ask, for instance, how well a state-of-the-art algorithm for the salesman problem works on a particular graph topology. People do PhD thesis on topics like that, so the answer is not obvious at all. For LLMs we still don't have a curated theory that explains what they're good/bad at, and that you don't see how to extract an answer from the definitions is no surprise since this is obviously not an easy problem. But all this is normal because this is a rather new topic (models of this scale appeared when? 3 years ago? That's nothing for science).
Anthropomorphizing LLMs has added so much noise to this discussion.
Yes, one year ago we had LLM-based AI systems solving some IMO problems. My impression is that most observers at that time didn't expect them to be solving Millennium Prize problems within a year.
> Math has very little funding compared to other science domains.
True. But to whatever extent the numbers I've seen are correct, for the whole mathematical community to have spent less on Navier-Stokes than OpenAI did -- even if we value the tokens they spent at something like market rate rather than at what the compute actually costs them (which might be right since any capacity they use internally can't be sold to customers) -- the average number of mathematicians working on Navier-Stokes since 2000 would need to be somewhere around four (depending of course on how well paid they are), and that seems too low to me.
> I feel like this is not the correct way of thinking about it.
It seems to me that if you say "It is absurd to waste time discussing whether it is intelligent or not. It is just an algorithm, we know how it works, and it does exactly what we expect it to do." then this only makes any sense if your "knowing how it works" and "what we expect it to do" enable you to predict what it can and can't do.
(I repeat that I agree that what matters is what it can do, not whether we choose to apply the term "intelligent" to it. But unless I misunderstood you were saying somewhat more than that.)
> Anthropomorphizing LLMs has added so much noise to this discussion.
I think sometimes it helps, sometimes it hurts, and sometimes it's indifferent, because LLMs are like us in some ways and unlike us in some ways. (The same goes for many other things, but LLMs are much more like us in some important ways than any other human-made artefacts.)
arguably the job application process is automated and staffed by the ignorant to such a degree that it's an adversarial game where you don't need to be optimising for correctness. Which is the perfect tool to be mass spamming job ads with
Edit: I think this is a reasonable solution because the hurdles and context-free nonsensical rejections you'll face if you interact with this process in good faith are far too high. It's dehumanising.
Just want to make it clear that what I am talking about is not mass spamming. The agent gets a list of jobs from various portals through a script, and then through an google sheets they can be marked to be further processed. Neither me or my wife feel comfortable completely automating anything. But its much easier to see dashboards and give instructions on how to build things from master resumes than it is to do this repetitive part yourself.
At least writing scripts you know what it will and won’t do, with open claw it will probably do what you want and maybe won’t do anything you don’t want.
You can check the scripts and cronjobs, als ask it to summarize the scripts actions (using another agent/model if you want). All in all, LLMs are not 100% trustworthy but generally things go as intended.
I have heard some stories about people's clawbot doing things like deleting their entire email inbox or buying random shit with their credit cards. it's difficult to tell how many of those stories are real or if they were just stories that would be funny.
Just focus on putting your phone in a draw for as long as you can, leave a stopwatch on it running so you can see how long you have left it - it will be short to start with but with practice you’ll regain the ability to live without consuming information.
>Just focus on putting your phone in a draw for as long as you can,
I've had a smartphone since the iPhone 4, I think. And while I find it useful, I honestly don't understand how anyone can be addicted to it. Mostly stays in my pocket. If I need navigation, it's out in the car while I find the address. I use it for instant messaging for my wife and kids (rarely the phone call anymore... don't know the last time I answered a call, it's all robot scammers and telemarketers).
The screen's not big enough to read anything, not large enough to watch anything. The sorts of games I enjoyed as a kid just aren't available. Sometimes I'll have an ebook on it, that's ok (it's nearly as big as a paperback used to be), but remembering to load one onto my phone before I got somewhere I'll be waiting doesn't always work out.
I can spend hours online, if I'm not careful. At home, in front of the big monitor. How does anyone get addicted on a tiny little phone screen, with dumbed down apps? I can't even look at pages on Firefox on the phone, without adblock it's just abysmal.
You don’t need to spend all that money to get your mojo back, make the changes that you would make I.e. leave the MacBook on your desk and see if life improves.
I fear this is just the beginning, I’ve seen nothing to say the voting American public are any less into divisive politics than they have been for the last decade
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