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A bear case: My predictions regarding AI progress

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Re: A bear case: My predictions regarding AI progress

#141

Post by crote »

carlosdp wrote: post #112
stego-tech wrote: post #2

> At some point there might be massive layoffs due to ostensibly competent AI labor coming onto the scene, perhaps because OpenAI will start heavily propagandizing that these mass layoffs must happen. It will be an overreaction/mistake. The companies that act on that will crash and burn, and will be outcompeted by companies that didn't do the stupid. We're already seeing this with tech doing RIFs and not backfilling…

I'll take that bet, easily. There's absolutely no way that we're not going to see a massive reduction in the need for "humans writing code" moving forward, given how good LLMs are getting at writing code. That doesn't mean people won't need devs! I think there's a real case where increased capabilities from LLMs leads to bigger demand for people that know how to direct the tools effectively, of which most would proba…

> There's absolutely no way that we're not going to see a massive reduction in the need for "humans writing code" moving forward, given how good LLMs are getting at writing code.

Sure, but in the same way that Squarespace and Wix killed web development. LLMs are going to replace a decent bunch of low-hanging fruit, but those jobs were always at risk of being outsourced to the lowest bidder over in India anyways.

The real question is, what's going to happen to the interns and the junior developers? If 10 juniors can create the same output as a single average developer equipped with a LLM, who's going to hire the juniors? And if nobody is hiring juniors, how are we supposed to get the next generation of seniors?

Similarly, what's going to happen to outsourcing? Will it be able to compete on quality and price? Will it secretly turn into nothing more than a proxy to some LLM?

Re: A bear case: My predictions regarding AI progress

#142

Post by mlsu »

roncesvalles wrote: post #56
csomar wrote: post #22

> LLMs still seem as terrible at this as they'd been in the GPT-3.5 age. Software agents break down once the codebase becomes complex enough, game-playing agents get stuck in loops out of which they break out only by accident, etc. This has been my observation. I got into Github Copilot as early as it launched back when GPT-3 was the model. By that time (late 2021) copilot can already write tests for my Rust function…

The last line has been my experience as well. I only trust what I've verified firsthand now because the Internet is just so rife with people trying to influence your thoughts in a way that benefits them, over a good faith sharing of the truth. I just recently heard this quote from a clip of Jeff Bezos: "When the data and the anecdotes disagree, the anecdotes are usually right.", and I was like... wow. That quote is t…

Revolutionary things are things that change how society actually works at a fundamental level. I can think of four technologies of the past 40 years that fit that bill:

the personal computer

the internet

the internet connected phone

social media

those technologies are revolutionary, because they caused fundamental changes to how people behave. People who behaved differently in the "old world" were forced to adapt to a "new world" with those technologies, whether they wanted to or not. A newer more convenient way of ordering a taxicab or watching a movie or music are great consumer product stories, and certainly big money makers. They don't cause complex and not fully understood changes to way people work, play, interact, self-identify, etc. the way that revolutionary technologies do.

Language models feel like they have the potential to be a full blown sociotechnological phenomenon like the above four. They don't have a convenient consumer product story beyond ChatGPT today. But they are slowly seeping into the fabric of things, especially on social media, and changing the way people apply to jobs, draft emails, do homework, maybe eventually communicate and self-identify at a basic level.

I'd almost say that the lack of a smash bang consumer product story is even more evidence that the technology is diffusing all over the place.

Re: A bear case: My predictions regarding AI progress

#144

Post by fragmede »

stego-tech wrote: post #2

> At some point there might be massive layoffs due to ostensibly competent AI labor coming onto the scene, perhaps because OpenAI will start heavily propagandizing that these mass layoffs must happen. It will be an overreaction/mistake. The companies that act on that will crash and burn, and will be outcompeted by companies that didn't do the stupid. We're already seeing this with tech doing RIFs and not backfilling…

What lasting consequences? Crowdstrike and the 2017 Equifax hack that leaked all our data didn't stop them. The shares of crowdstrike after it happened I bought are up more than the SP500. Elon went through Twitter and fired everybody but it hasn't collapsed. A carpenter has a lot of opinions about the woodworking used on cheap IKEA cabinets, but mass manufacturing and plastic means that building a good solid high quality chair is no longer the craft it used to be.

Re: A bear case: My predictions regarding AI progress

#145

Post by hangonhn »

mark_l_watson wrote: post #106

I have used neural networks for engineering problems since the 1980s. I say this as context for my opinion: I cringe at most applications of LLMs that attempt mostly autonomous behavior, but I love using LLMs as ‘side kicks’ as I work. If I have a bug in my code, I will add a few printout statements where I think my misunderstanding of my code is, show an LLM my code and output, explain the error: I very often get us…

I'll add this in case it's helpful to anyone else: LLMs are really good at regex and undoing various encodings/escaping, especially nested ones. I would go so far to say that it's better than a human at the latter.

I once spend over an hour trying to unescape JSON containing UTF8 values that's been escaped prior to being written to AWS's Cloudwatch Logs for MySQL audit logs. It was a horrific level of pain until I just asked ChatGPT to do it and it figured out all the series of escapes and encoding immediately and gave me the step to reverse them all.

LLM as a sidekick has saved me so much time. I don't really use it to generate code but for some odd tasks or API look up, it's a huge time saver.

Re: A bear case: My predictions regarding AI progress

#146

Post by mbil »

orangebread wrote: post #84

I think the author provides an interesting perspective to the AI hype, however, I think he is really downplaying the effectiveness of what you can do with the current models we have. If you've been using LLMs effectively to build agents or AI-driven workflows you understand the true power of what these models can do. So in some ways the author is being a little selective with his confirmation bias. I promise you that…

I agree with you. I recently wrote up my perspective here: https://news.ycombinator.com/item?id=43308912

Re: A bear case: My predictions regarding AI progress

#147

Post by trashtester »

Imnimo wrote: post #131

>Test-time compute/RL on LLMs: >It will not meaningfully generalize beyond domains with easy verification. To me, this is the biggest question mark. If you could get good generalized "thinking" from just training on math/code problems with verifiers, that would be a huge deal. So far, generalization seems to be limited. Is this because of a fundamental limitation, or because the post-training sets are currently too s…

> Is this because of a fundamental limitation, or because the post-training sets are currently too small (or otherwise deficient in some way) to induce good thinking patterns?

"Thinking" isn't a singular thing. Humans learn to think in layer upon layer of understandig the world, physical, social and abstract, all at many different levels.

Embodiment will allow them to use RL on the physical world, and this in combination with access to not only means of communication but also interacting in ways where there is skin in the game, will help them navigate social and digital spaces.

Re: A bear case: My predictions regarding AI progress

#148

Post by ath3nd »

lackoftactics wrote: post #116
ath3nd wrote: post #105

Earlier quoted context omitted.

I logged in to specifically downvote this comment, because it attacks the OP's position with unjustified and unsubstantiated confidence in the reverse. > It's easy to spot people who secretly hate LLMs and feel threatened by them these days. I don't think OP is threatened or hates LLM, if anything, OP is on the position that LLM are so far away from intelligence that it's laughable to consider it threatening. > In co…

I appreciate the pushback and acknowledge that my earlier comment might have conveyed too much certainty—skepticism here is justified and healthy. However, I'd like to clarify why optimism regarding AGI isn't merely wishful thinking. Historical parallels such as heavier-than-air flight, Go, and protein folding illustrate how sustained incremental progress combined with competition can result in surprising breakthroug…

I am with you that when smart people combine their efforts together and build on previous research + learnings, nothing is impossible.

Re: A bear case: My predictions regarding AI progress

#149

Post by fragmede »

arkh wrote: post #65
hattmall wrote: post #41

Earlier quoted context omitted.

There are some fields though where they can replace humans in significant capacity. Software development is probably one of the least likely for anything more than entry level, but A LOT of engineering has a very very real existential threat. Think about designing buildings. You basically just need to know a lot of rules / tables and how things interact to know what's possible and the best practices. A purpose built…

> just In my experience this word means you don't know whatever you're speaking about. "Just" almost always hide a ton of unknown unknowns. After being burned enough times nowadays when I'm going to use it I try to stop and start asking more questions.

It's a trick of human psychology. Asking "why don't you just..." leads to one reaction, when asking "what are the road blocks to completing..." leads to a different but same answer. But thinking "just" is good when you see it as a learning opportunity.

Re: A bear case: My predictions regarding AI progress

#150

Post by nyrikki »

danielbln wrote: post #78
randomNumber7 wrote: post #50

Earlier quoted context omitted.

Yes, but on the other hand I don't understand why people think something that you can train something on pattern matching and it magically becomes intelligent.

We don't know what exactly makes us humans as intelligent as we are. And while I don't think that LLMs will be general intelligent without some other advancements, I don't get the confident statements that "clearly pattern matching can't lead to intelligence" when we don't really know what leads to intelligence to begin with.

We can't even define what intelligence is.

We know or have strong hints at the limits of math/computation related to LLMs + CoT

Note how PARITY and MEDIAN is hard here:

https://arxiv.org/abs/2502.02393

We also know HALT == open frame == symbol grounding == system identification problems.

The definition of AGI is also not well defined, but given the following:

> Strong AI, also called artificial general intelligence, refers to machines possessing generalized intelligence and capabilities on par with human cognition.

We know enough for any mechanical methods with either current machines or even quantum machines, what is needed is impossible with the above definition.

Walter Pitts drank himself to death, in part because of the failure of the perceptron model.

Humans and machines are better at different things, and while ANNs are inspired by biology, they are very different.

There are some hints that the way biological neurons work is incompatible with math as we know it.

https://arxiv.org/abs/2311.00061

Computation and machine learning are incredibly powerful and useful, but are fundamentally different, and that different is both a benefit and a limit.

There are dozens of 'no effective procedure', 'no approximation', etc .. results that demonstrate that ML as we know it today is possible of most definitions of AGI.

That is why particular C* types shift the goal post, because we know that the traditional definition of strong AI is equivalent to solving HALT.

https://philarchive.org/rec/DIEEOT-2

There is another path following PAC Learning as compression an NP being about finding parsimonious reductions (P being in NP)

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