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Reflections on AI at the End of 2025

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Re: Reflections on AI at the End of 2025

#291

Post by chickensong »

markdown wrote: post #278
chickensong wrote: post #275

Earlier quoted context omitted.

> Imagine if it could start gently shilling a particular brand of antidepressant if you started talking to it about how you're feeling lonely and down. I'm not saying you should do that, but people definitely do. Doctors already shill for big pharma. There are trust issues all the way down.

> Doctors already shill for big pharma. This is not the norm worldwide.

I hope you're right and that it remains that way, but TBH my hopes aren't high.

Big pharma corps are multinational powerhouses, who behave like all other big corps, doing whatever they can to increase profits. It may not be direct product placement, kickbacks, or bribery on the surface, but how about an expense-paid trip to a sponsored conference or a small research grant? Soft money gets their foot in the door.

Re: Reflections on AI at the End of 2025

#292

Post by yladiz »

threethirtytwo wrote: post #231
mjr00 wrote: post #151

Earlier quoted context omitted.

> we still see claims that LLMs are "just next token predictors" and "just regurgitate code they read online". These are just uninformed and wrong views. It's fair to say that these people were (are!) wrong. I don't think it's fair to say that at all. How are LLMs not statistical models that predict tokens? It's a big oversimplification but it doesn't seem wrong , the same way that "computers are electricity running…

It's wrong because it’s deliberately used to mischaracterize the current abilities of AI. Technically it's not wrong but the context of usage in basically every case is that the person saying it is deliberately trying to use the concept to downplay AI as just a pattern matching machine.

I'm a bit confused. You say it's wrong, but then later say it's not wrong, and just because it can be used to downplay advancements in AI doesn't mean that it's wrong and saying it's wrong because it can be used that way is a bit disingenuous.

Re: Reflections on AI at the End of 2025

#293

Post by crystal_revenge »

krackers wrote: post #289
crystal_revenge wrote: post #287

Earlier quoted context omitted.

Only if the computer you use does not have memory. Definitionally if you are writing and reading from memory, you are not using an FSM.

No, it can still be modeled as a finite state machine. Each state just encodes the configuration of your memory. I.e. if you have 8 bits of memory, your state space just encodes 2^8 states for each memory configuration. Any real-world deterministic thing can be encoded as a FSM if you make your state space big enough, since it by definition there has only a finite number of states.

You could model a specific instance of using your computer this way, but you could not capture the fact that you can execute arbitrary programs with your PC represented as an FSM.

Your computer is strictly more computationally powerful than an FSM or PDA, even though you could represent particular states of your computer this way.

The fact that you can model an arbitrary CFG as an regular language with limited recursion depth does not mean there’s no meaningful distinction between regular languages and CFG.

Re: Reflections on AI at the End of 2025

#294

Post by [deleted] »

emp17344 wrote: post #225
grodriguez100 wrote: post #10

Earlier quoted context omitted.

I would say yes, everyone should care about it. There is plenty of material on the topic. See for example https://ai-2027.com/ or https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a...

The fact that people here take AI 2027 seriously is embarrassing. The authors are already beginning to walk back these claims: https://x.com/eli_lifland/status/1992004724841906392?s=20

[deleted]

Re: Reflections on AI at the End of 2025

#295

Post by krackers »

crystal_revenge wrote: post #293
krackers wrote: post #289

Earlier quoted context omitted.

No, it can still be modeled as a finite state machine. Each state just encodes the configuration of your memory. I.e. if you have 8 bits of memory, your state space just encodes 2^8 states for each memory configuration. Any real-world deterministic thing can be encoded as a FSM if you make your state space big enough, since it by definition there has only a finite number of states.

You could model a specific instance of using your computer this way, but you could not capture the fact that you can execute arbitrary programs with your PC represented as an FSM. Your computer is strictly more computationally powerful than an FSM or PDA, even though you could represent particular states of your computer this way. The fact that you can model an arbitrary CFG as an regular language with limited recurs…

> you can execute arbitrary programs with your PC represented as an FSM

You cannot execute arbitrary programs with your PC, your PC is limited in how much memory and storage it has access to.

>Your computer is strictly more computationally powerful

The abstract computer is, but _your_ computer is not.

>model an arbitrary CFG as an regular language with limited recursion depth does not mean there’s no meaningful distinction between regular languages and CFG

Yes this I agree. But going back to your argument, claiming that LLMs with a fixed context-window are basically markov chains so they can't do anything useful is reductio ad absurdum in the exact same way as claiming that real-world computers are finite state machines.

A more useful argument on the upper-bound of computational power would be along the lines of circuit complexity I think. But even this does not really matter. An LLM does not need to be turing complete even conceptually. When paired with tool-use, it suffices that the LLM can merely generate programs that are then fed into an interpreter. (And the grammar of turing-complete programming languages can be made simple enough, you can encode Brainfuck in a CFG). So even if an LLM could only ever produce programs with a CFG grammar, the combination of LLM + brainfuck executor would give turing completeness.

Edit: There was this recent HN article along those lines. https://news.ycombinator.com/item?id=46267862.

Re: Reflections on AI at the End of 2025

#296

Post by jodrellblank »

abathur wrote: post #268
jodrellblank wrote: post #249

Earlier quoted context omitted.

Here[1] is a recent submission from Simon Willison using GPT-5.2 to port a Python HTML-parsing library to JavaScript in 4.5 hours. The code passes the 9,200 test cases of html5lib-tests used by web browsers. That's a workable, usable, standards-compliant (as much as the test cases are) HTML parser in Almost no human could port 3000 lines of Python to JavaScript and test it in their spare time while watching TV and de…

I think both of those experiments do a good job of demonstrating utility on a certain kind of task. But this is cherry-picking. In the grand scheme of the work we all collectively do, very few programming projects entail something even vaguely like generating an Nth HTML parser in a language that already has several wildly popular HTML parsers--or porting that parser into another language that has several wildly popu…

> "it's hard to imagine a more favorable situation"

Granted, but this reads a bit like a headline from The Onion: "'Hard to imagine a more favourable situation than pressing nails into wood' said local man unimpressed with neighbour's new hammer".

I think it's a strong enough example to disprove "they're an interesting phenomenon that people have convinced themselves MUST BE USEFUL ... either through ignorance or a sense of desperation". Not enough to claim they are always useful in all situations or to all people, but I wasn't trying for that. You (or the person I was replying to) basically have to make the case that Simon Willison is ignorant about LLMs and programming, is desperate about something, or is deluding himself that the port worked when it actually didn't, to keep the original claim. And I don't think you can. He isn't hyping an AI startup, he has no profit motive to delude him. He isn't a non-technical business leader who can't code being baffled by buzzwords. He isn't new to LLMs and wowed by the first thing. He gave a conference talk showing that LLMs cannot draw pelicans on bicycles so he is able to admit their flaws and limitations.

> "But this is cherry-picking."

Is it? I can't use an example where they weren't useful or failed. It makes no sense to try and argue how many successes vs. failures, even if I had any way to know that; any number of people failing at plumbing a bathroom sink don't prove that plumbing is impossible or not useful. One success at plumbing a bathroom sink is enough to demonstrate that it is possible and useful - it doesn't need dozens of examples - even if the task is narrowly scoped and well-trodden. If a Tesla humanoid robot could plumb in a bathroom sink, it might not be good value for money, but it would be a useful task. If it could do it for $30 it might be good value for money as well even if it couldn't do any other tasks at all, right?

Re: Reflections on AI at the End of 2025

#297

Post by thayne »

laterium wrote: post #233
etra0 wrote: post #127

LLMs have certainly become extremely useful for Software Engineers, they're very convincing (and pleasers, too) and I'm still unsure about the future of our day-to-day job. But one thing that has scared me the most, is the trust of LLMs output to the general society. I believe that for software engineers it's really easy to see if it's being useful or not -- We can just run the code and see if the output is what we e…

The issue you're overlooking is the scarcity of experts. You're comparing the current situation to an alternative universe where every person can ask a doctor their questions 10 times a day and instantly get an accurate response. That is not the reality we're living in. Doctors barely give you 5 minutes even if you get an appointment days or weeks in advance. There is just nobody to ask. The alternatives today are 1)…

But he LLM was probably trained on all the sponsered posts and scams. It isn't clear to me that an LLM response is any more reliable than sifting through google results.

Re: Reflections on AI at the End of 2025

#298

Post by monkaiju »

dwaltrip wrote: post #103
seu wrote: post #22

> And I've vibe coded entire ephemeral apps just to find a single bug because why not - code is suddenly free, ephemeral, malleable, discardable after single use. Vibe coding will terraform software and alter job descriptions. I'm not super up-to-date on all that's happening in AI-land, but in this quote I can find something that most techno-enthusiast seem to have decided to ignore: no, code is not free. There are i…

Can you provide numbers relative to things many of us already do? - drive to the store or to work - take a shower - eat meat - fly on vacation And so on... thanks!

Whats the point of this question? These are things (some) people do while existing as humans, not sure how thats relevant? The AI is consuming vast resources while note existing as a human, it doesnt get some innate privilege to consume some amount of resources like we do.

Re: Reflections on AI at the End of 2025

#299

Post by jowea »

grodriguez100 wrote: post #10
fleebee wrote: post #7

> The fundamental challenge in AI for the next 20 years is avoiding extinction. That's a weird thing to end on. Surely it's worth more than one sentence if you're serious about it? As it stands, it feels a bit like the fearmongering Big Tech CEOs use to drive up the AI stocks. If AI is really that powerful and I should care about it, I'd rather hear about it without the scare tactics.

I would say yes, everyone should care about it. There is plenty of material on the topic. See for example https://ai-2027.com/ or https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a...

And I thought the rest of the thread was anxiety-inducing. Thanks for the nightmares lol.

Re: Reflections on AI at the End of 2025

#300

Post by crystal_revenge »

krackers wrote: post #295
crystal_revenge wrote: post #293

Earlier quoted context omitted.

You could model a specific instance of using your computer this way, but you could not capture the fact that you can execute arbitrary programs with your PC represented as an FSM. Your computer is strictly more computationally powerful than an FSM or PDA, even though you could represent particular states of your computer this way. The fact that you can model an arbitrary CFG as an regular language with limited recurs…

> you can execute arbitrary programs with your PC represented as an FSM You cannot execute arbitrary programs with your PC, your PC is limited in how much memory and storage it has access to. >Your computer is strictly more computationally powerful The abstract computer is, but _your_ computer is not. >model an arbitrary CFG as an regular language with limited recursion depth does not mean there’s no meaningful disti…

> so they can't do anything useful

I never claimed that. They demonstrate just how powerful Markov chains can be with sophisticated state representations. Obviously LLMs are useful, I have never claimed otherwise.

Additionally, it doesn’t require any logical leaps to understand decoder only LLMs as Markov Chains, they preserve the Markov Property and otherwise be have exactly like them. It’s worth noting that encoder-decoder LLMs do not preserve the Markov property and can not be considered Markov chains.

Edit: I saw that post and at the time was disappointed by how confused the author was about those topics and how they apply to the subject.

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