abstractcontrol

I am always doing everything alone, so I am liking the LLMs a lot. My own code is very good, but nobody but myself cared about that. Even the one job that I got which allowed me to gather the capital needed to move into trading feels like complete luck. This is a new golden age for entrepreneurs and apocalypse for wage workers. Nobody whose status depends on his own productivity would complain about it going up sharply.

Probably the LLMs saved me because deep down I knew that my own power by itself wouldn't be enough to let me do what I really want, but right now the future does seem bright.

I wouldn't take those benchmarks too seriously; the companies are benchmaxing as there is a lot of money at stake.

The intelligence of LLMs is a mirage masked by their utility. It makes more sense to assume that ML models not fitted to a particular game would be bad at it than otherwise. If you're relating how good a human with inate capabilities of the modern LLMs would be, then yes, it might make sense to assume that they'd be good at anything, but that isn't the situation in reality.

It isn't nonsense. If AI was such a broad umbrella term, why would companies be tripping over themselves to use it? If it meant video game NPC level of intelligence, nobody would be in a hurry to adopt it. It's very clear that when Nvidia or OpenAI use the term AI, they really want you to think these LLMs are actual human level intelligence. That is why they are doing it.

When I use nonsense like "real AI" I think you very well know what I mean, and don't find it in any way confusing or ambiguous.

Are those important questions that need to be answered? Rather, you should be asking whether you could use backprop and NNs to create something other than text predictors. What have LLMs changed about your conception of intelligence?

As far as I can see, none of the hard problems of intelligence that we had a decade ago got touched. What LLMs ultimately changed is the way I see language. I've underestimated its power, and I will admit, the companies making these models went far further than I could have imagined with them.

But intelligence and AI have a high hurdle to clear, and it won't be done with LLMs.

Revealing the secrets of intelligence will involve new concepts and understanding that that we don't have now. LLMs will be helpful with the plumbing and as replacement for natural evolution, but not so much natural intelligence.

Consider that maybe AI should mean AGI. That was what its original meaning was supposed to be, and calling generative models AI is watering down the original term and accepting the frame of the LLM makers.

What's really disappointing about current LLM models isn't that they aren't useful, but that they really tell us much about intelligence at all. The techniques used to make LLMs aren't generalizable at all, and I cannot use them to solve problems of interest in the real world.

That wouldn't be the case with real AI. I want better methods and algorithms that could live up to the expectations, and I am sure we'll get them at some point even if we don't have them now.

The history of AI is people deciding that it really means AGI but then getting enamored with some method of the time and calling it AI, before going back to the original meaning. We're just living through the latest iteration of this where LLMs mean AI, and we're forced to invent the term AGI for what used to be AI.

I'll admit that to the people who don't know how LLMs work, which includes most investors, the lie seems very plausible so of course the CEOs running the companies are going to frame it as such.

If I were an artist, I wouldn't really care that a machine took over the role of drawing lines on paper, and as a programmer, I don't care that the machines are taking the role of writing code.

The thing that actually matters is understanding the problem domain, and thinking about and visualizing the program I am trying to make, so I am happy to be doing less drudge work so I can focus on what really matters.

I've been doing quant research for the past year, and the amount of time (and tedium) LLMs have saved me is huge.

LLMs aren't even AI, if this amount of disruption is causing difficulty, having actual AI will give these people breakdowns.

This is the internet, so I cannot tell at all whether you're being sarcastic or not. In my view, what LLMs should get us to reconsider isn't whether there is more to intelligence, but whether there is more to language. It's the latter which I underestimated.

Fun story, but I really wish OpenAI got its act together and started making actual AI breakthroughs instead of funneling compute into LLMs. I'd really like some new algorithms to get me excited about the field again. Kuddos to them for making LLMs really useful, but this is not the ride I wanted to get on.

Dumb people aren't going to be doing the work of the smart people ever, no matter how tech evolves. The actual implication of AI being on the level of sentient beings instead of hyped-up token predictors is that the era of humanity is over. If AIs were literally capable of doing what CEOs want them to, we'd have far bigger problems to deal with than employment.

Why don't they just pass the time into the RNG in order to randomize it instead of using fixed seeds?

Yes, this is what I mean. It's not that talent doesn't exist, but it's never the case that people on the top of their domain work less hard than others. In fact, it's the opposite. People don't have talent, the talent has them.

> Probably one of the most ego-crushing realizations (if you're a nerd) is to discover that there are people out there MUCH more talented and higher performing than what you'll ever be, but with none of the obsession or pride. In other profession that's not really a topic. You can be a top performer in other professions, without a deep interest, clock out 4 daily, and never think about work outside work.

You could clock out, but I don't think the top performers ever stop thinking about work. Everything you've written here has to be wrong.

The job market is somewhat of a crapshot. A dice roll if you will. I got rejected from all kinds of place before I got a highly paid one from a Reddit post of all places. I got rejected from all kinds of places that only paid like 1/2 or 1/3 of what that job was paying, and I wasn't even looking for it at that time. It's been over for over half a year, and I wouldn't be able to replicate that sort of performance at will. Though at this point, I am not even looking for one and just working on my own projects since I have a lot saved up and barely spend anything.

One thing that I understand at my age of nearly 39 now is that success comes pretty hard, and luck plays a large role in it. As programmers all we can do is build and develop our skills, even if the world doesn't validate our efforts.

You're asking yourself what you are doing wrong, but you should be asking yourself what your goal is and focus on that. Is it really to just work for other people?

What can you do for yourself?