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OpenAI, Google and Anthropic are struggling to build more advanced AI

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Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#351

Earlier quoted context omitted.

Novel as in requiring novel reasoning to sort out. One of the classic ways to expose the issue is to take a common puzzle and introduce irrelevant details and perhaps trivialize the solution. LLMs pattern match on the general form of the puzzle and then wander down the garden path to an incorrect solution that no human would fall for. The sort of generalization these things can do seems to mostly be the trivial sort:…

Well the problem with that approach is that LLMs are still both incredibly dumb and small, at least compared to the what, 700T params of a human brain? Can't compare the two directly, especially when one has a massive recall advantage that skews the perception of that. But there is still some inteligence under there that's not just memorization. Not much, but some. So if you present a novel problem it would need to b…

It’s not fair to ask me to judge them based on their size. I’m judging them based on the claims of their vendors.

Anyway the novel problems I’m talking about are extremely simple. Basically they’re variations on the “farmer, 3 animals, and a rowboat” problem. People keep finding trivial modifications to the problem that fool the LLMs but wouldn’t fool a child. Then the vendors come along and patch the model to deal with them. This is what I mean by whack-a-mole.

Searle’s Chinese Room thought experiment tells us that enough games of whack-a-mole could eventually get us to a pretty good facsimile of reasoning without ever achieving the genuine article.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#352

Earlier quoted context omitted.

It's strange to me that's your takeaway. The reason that the internet was overhyped in the 2000's is because it was and also heavily overvalued. It took a massive correction and seriously disruptive bubble burst to break the delusion and move on to something more sustainable.

I disagree that it was over hyped. It has transformed our society so much that I would argue it was vastly under-hyped. Sure, there were a lot of silly companies that sprang up and went away because they weren't sound, but so much of the modern economy is based on the internet that it is hard to say any business isn't somehow internet related today. You would be hard pressed to find any business anywhere that doesn't…

There were no smartphones in 2000, so the Web was overvalued at that point in time... until we all started carrying the Web in our pockets in the form of a portable rectangle.

Given that this is the case, why can't this be analogously true of “AI” as well? There's plenty of reason to believe that we're hitting a wall, such that, to progress further, said wall must be overcome by means of one or more breakthroughs.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#353
post #248

Earlier quoted context omitted.

> Except our users experience the failed attempts (LLM replies that are wrong, even when backed by RAG) and it's incredibly hard to hide those from them. This has been my team's experience (and frustration) as well, and has led us to look at using LLMs for classifying / structuring, but not entrusting an LLM with making a decision based on things like a database schema or business logic. I think the technology and to…

> "let's only allow the LLM to do things we know it is rock-solid at." Even this is insanely hard in my opinion. The one thing that you would assume LLM to excel at is spelling and grammar checking for the English language, but even the top model (GPT-4o) can be insanely stupid/unpredictable at times. Take the following example from my tool: https://app.gitsense.com/?doc=6c9bada92&model=GPT-4o&samples... 5 models are…

I was using an LLM to help spot passive voice in my documents and it told me "We're making" was passive and I should change it to "we are making" to make it active.

Leaving aside "we're" and "we are" are the same, it is absolutely active voice

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#354

A few important things to remember here: The best engineering minds have been focused on scaling transformer pre and post training for the last three years because they had good reason to believe it would work, and it has up until now. Progress has been measured against benchmarks which are / were largely solvable with scale. There is another emerging paradigm which is still small(er) scale but showing remarkable res…

> The best engineering minds have been focused on scaling transformer pre and post training for the last three years because they had good reason to believe it would work, and it has up until now.

Or because the people running companies who have fooled investors into believing it will work can afford to pay said engineers life-changing amounts of money.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#355

Earlier quoted context omitted.

By itself, sure, but there are many sources all pointing to the same thing. Sutskever, recently ex. OpenAI, one of the first to believe in scaling, now says it is plateauing. Do OpenAI have something secret he was unaware of? I doubt it. FWIW, GPT-2 and GPT-3 were about a year apart (2019 "Language models are Unsupervised Multitask Learners" to 2020 "Language Models are Few-Shot Learners"). Dario Amodei recently said…

>Sutskever, recently ex. OpenAI, one of the first to believe in scaling, now says it is plateauing. Blind scaling sure (for whatever reason)* but this is the same Sutskever who believes in ASI within a decade off the back of what we have today. * Not like anyone is telling us any details. After all, Open AI and Microsoft are still trying to create a 100B data center. In my opinion, there's a difference between scalin…

> After all, Open AI and Microsoft are still trying to create a 100B data center.

Yes - it'll be interesting to see if there are any signs of these plans being adjusted. Apparently Microsoft's first step is to build optical links between existing data centers to create a larger distributed cluster, which must be less of a financial commitment.

Meta seem to have an advantage here in that they have massive inference needs to run their own business, so they are perhaps making less of a bet by building out data centers.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#356

Earlier quoted context omitted.

Well the problem with that approach is that LLMs are still both incredibly dumb and small, at least compared to the what, 700T params of a human brain? Can't compare the two directly, especially when one has a massive recall advantage that skews the perception of that. But there is still some inteligence under there that's not just memorization. Not much, but some. So if you present a novel problem it would need to b…

It’s not fair to ask me to judge them based on their size. I’m judging them based on the claims of their vendors. Anyway the novel problems I’m talking about are extremely simple. Basically they’re variations on the “farmer, 3 animals, and a rowboat” problem. People keep finding trivial modifications to the problem that fool the LLMs but wouldn’t fool a child. Then the vendors come along and patch the model to deal w…

Well that's true and has been pretty glaring, but they've needed to do that in cases where models seem to fail to grasp the some concept across the board and not in cases where they don't.

Like, every time an LLM gets something right we assume they've seen it somewhere in the training data, and every time they fail we presume they haven't. But that may not always be the case, it's just extremely hard to prove it one way or the other unless you search the entire dataset. Ironically the larger the dataset, the more likely the model is generalizing while also making it harder to prove if it's really so.

To give a human example, in a school setting you have teachers tasked with figuring out that exact thing for students. Sometimes people will read the question wrong with full understanding and fail, while other times they won't know anything and make it through with a lucky guess. If LLMs (and their vendors) have learned anything it's that confidently bullshitting gets you very far which makes it even harder to tell in cases where they aren't. Somehow it's also become ubiquitous to tune models to never even say "I don't know" because it boosts benchmark scores slightly.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#358

Earlier quoted context omitted.

I disagree that it was over hyped. It has transformed our society so much that I would argue it was vastly under-hyped. Sure, there were a lot of silly companies that sprang up and went away because they weren't sound, but so much of the modern economy is based on the internet that it is hard to say any business isn't somehow internet related today. You would be hard pressed to find any business anywhere that doesn't…

There were no smartphones in 2000, so the Web was overvalued at that point in time... until we all started carrying the Web in our pockets in the form of a portable rectangle. Given that this is the case, why can't this be analogously true of “AI” as well? There's plenty of reason to believe that we're hitting a wall, such that, to progress further, said wall must be overcome by means of one or more breakthroughs.

'smartphones' needed a reason to exist, the internet provided that. I doubt we would have had them without it. AI will drive whole new product categories that didn't exist that will then transform our society even more.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#359

Earlier quoted context omitted.

> Why does knowing how things work under the hood make you think its not on the path towards AGI? Because I had no idea how these were built until I read the paper, so couldn’t really tell what sort of tree they’re barking up. The failure-modes of LLMs and ways prompts affect output made a ton more sense after I updated my mental model with that information.

Right, but its behavior didn't change after you learned more about it. Why should that cause you to update in the negative? Why does learning how it work not update you in the direction of "so that's how thinking works!" rather than, "clearly its not doing any thinking"? Why do you have a preconception of how thinking works such that learning about the internals of LLMs updates you against it thinking?

If you didn’t know what an airplane was, and saw one for the first time, you might wonder why it doesn’t flap its wings. Is it just not very good at being a bird yet? Is it trying to flap, but cannot? Why, there’s a guy over there with a company called OpenBird and he is saying all kinds of stuff about how bird-like they are. Where’s the flapping? I don’t see any pecking at seed, either. Maybe the engineers just haven’t finished making the flapping and pecking parts yet?

Then on learning how it works, you might realize flapping just isn’t something they’re built to do, and it wouldn’t make much sense if they did flap their wings, given how they work instead.

And yet—damn, they fly fast! That’s impressive, and without a single flap! Amazing. Useful!

At no point did their behavior change, but your ability to understand how and why they do what they do, and why they fail the ways they fail instead of the ways birds fail, got better. No more surprises from expecting them to be more bird-like than they are supposed to, or able to be!

And now you can better handle that guy over there talking about how powerful and scary these “metal eagles” (his words) are, how he’s working so hard to make sure they don’t eat us with their beaks (… beaks? Where?), they’re so powerful, imagine these huge metal raptors ruling the sky, roaming and eating people as they please, while also… trying to sell you airplanes? Actively seeking further investment in making them more capable? Huh. One begins to suspect the framing of these things as scary birds that (spooky voice) EVEN THEIR CREATORS FEAR FOR THEIR BIRD-LIKE QUALITIES (/spooky voice) was part of a marketing gimmick.

Re: OpenAI, Google and Anthropic are struggling to build more advanced AI

#360
post #212
post #115

Question for the group here: do we honestly feel like we've exhausted the options for delivering value on top of the current generation of LLMs? I lead a team exploring cutting edge LLM applications and end-user features. It's my intuition from experience that we have a LONG way to go. GPT-4o / Claude 3.5 are the go-to models for my team. Every combination of technical investment + LLMs yields a new list of potential…

Right. I've been saying for a while that if all LLM development stopped entirely and we were stuck with the models we have right now (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.1/2, Qwen 2.5 etc) we could still get multiple years worth of advances just out of those existing models. There is SO MUCH we haven't figured out about how to use them yet.

LLMs use historic data to help create useful current data. It works well sometimes.

I find that a human is able to solve a P=NP situation, and an LLM can’t quite yet do that. When they can the game changes.

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