I struggle to believe that AI R&D is ingenious

They literally generate stuff that's averages of other stuff.

It's not interesting in the least

Morbid fascination.

Interesting because pure creativity is the most prominent thing they can't do by design.

How many people on this planet are experts on every topic?

>An LLM is like a low IQ person with vast knowledge.

Are you aware that frontier models have scored in the 135-195 IQ range when they have been tested? How smart are you?

E.g. consider the best answer describes how to achieve something with some CLI, but the CLI's changed and the command is now broken. It can discover that, combine with the 'best answer' about what changed in the CLI, and fix it to work for the original problem even if that's completely novel and there were no extant answers about solving the problem with the new CLI form, or that it had broken that use case.

It doesn't change fundamentally how they work of course, but 'thinking', creating that feedback loop, made them significantly more useful IMO.

Hard disagree.

Claude is able to come up with solutions in very niche areas where there is little discussion on the web, because it takes steps to digest the source of (for instance) frameworks and libraries, and can then use/advise on lots of esoteric options, edge-cases and all sorts of stuff that doesn't get discussed on the open web.

The LLMs are vastly superior for any subject matter with even the slightest hint of danger or otherwise high stakes because while both LLMs and Reddit will whine at you and talk down to you the LLM will actually knock it off when told.

Like I'm asking you about sheetmetal thickness vs joist spacing for re-skinning a goddamn carport with roofing steel, read the room, this is not the time to whine about permits and snow loads and whatnot because the project is happening with or without your advice.

And it's not just mundane stuff. Some guy who's retrofitting new chinesium controls onto 50yo industrial equipment that had a prior generation of controls cobbled on 20yr ago doesn't need to get talked down at about stainless on stainless corrosion when he's asking a question about a solenoid or something. If the LLM talks down to him he can brush it aside. If he tries that on reddit they'll dig in their heels and fling poo.

Cutting out the "negative value" middle man of having to painstakingly make the case about the validity of your big picture goal before getting answers to simple questions is of substantial value. Stuff that people would've painstakingly researched themselves rather than ask Reddit or HN or Stack Overflow because dealing with those people is so onerous they now just punch into their LLM of choice and get the best of both worlds.

Such a good turn of phrase.

I now have a newspaper that doesn't give me that feeling.

Actually, we do. We do dismiss calculator results. We are all generally aware of a calculator's abilities. We know it has limits. So when a calculator outputs 9999e999999 we know to distrust that result. It was something out of the calculators concept of understanding. So too a calculator's version of Pi. "3.14159265359" is not Pi and every user knows it is an approximation not be used for anything too important or precise. The same approach should apply to LLMs... but their limitations remain nebulous and ever-changing.

As a concrete example, when you ask Claude Sonnet 5.5 to generate a small-to-medium-sized one-shot Python project, it will always fall into the same code patterns. Same structures of functions. Same file layout. Same package layout. They're not even good patterns, because if you throw a few common linters at it (e.g. flake8, isort, mypy) there are a ton of linting errors to clean up.

The only way that it escapes this mediocrity is through context. If you give it enough guardrails or sample data (e.g. Python code to emulate), it will start to go off in a "novel" direction that is defined by that context.

Now, what's the most likely way that "novelty" will be generated in an automated system like this? Randomly pulling in context and going for a random walk through the compressed language-based knowledge storage. It remains to be seen whether that's an effective way of producing more than just parlor tricks for investors. Human+LLM is overwhelmingly more novel and effective than only LLM.

Ultimately any claim that there exist only combinations of old ideas must tackle this chicken-and-egg problem convincingly. For example- explain art. There was no art until it appeared fully formed and out of nowhere in early anatomically modern humans' dwellings (Neanderthals also had art but it was simpler and it is subject to the same chicken-and-eggness). So where did art come from? What pre-existing ideas were combined to bring it into existence from non-existence?

Btw, since I've had this conversation before- if the answer is that we looked around us in the world and saw things; yes, but we didn't see art. Where did art come from? Where did the idea come from of abstracting shapes and colours so that they resemble the shapes and colours of existing things, without actually being those things, or being composed of pieces of those things? How about numbers? How about writing? How about all our technology and science? Where did those things come from? What did we combine to create them?

Obviously I think that the argument that there are only new combinations is wrong: we are a creative species. We can create new things. Somehow. It would be very interesting to understand how. We have not yet created machines that can match us in that and we can't understand the "how" by studying the machines we have now. By denying the mystery of this our ability, by denying that it is a mystery, by dismissing it and accepting any facile explanation that presents itself in our current era -it's god, it's nature, we're like LLMs- we are making ourselves a great epistemic disservice, denying ourselves the chance to find out what it all means, to understand what kind of creature we truly are.

it is nothing but combining known knowledge that begets new.

The ingenious aspect seems to be that we've found simpler ways to apply computing power to a broader range of problems, which it's important to note is as much a testament to the developers of proof assistants like Lean as it is to LLM developers, to say nothing of the countless mathematicians over many centuries that contributed training data.

Whether this will lead to many revolutions, or a handful of impressive results followed by a long dark spell remains to be seen.

I, for one, wouldn't place large bets on either.

On the other hand, there's no evidence that work will dry up merely because it becomes more efficient, or even if it does, that this cannot lead to a better world. The trick is finding a way to prevent rapid changes in the nature of work from destroying too many lives, and that's a very human problem machines alone can't solve.

For example, there's a model we have in production whose sensitivity has to be balanced based on which of our customers / partners is consuming its inference. It's a cat-and-mouse game where we are increasingly taking into account additional features to use in the model based on feedback directly from these customers and partners. Adjusting the model based on this feedback is much more art than science because there's really no "correct" adjustment to make, just an adjustment that produces inferences that convince our customers and partners that their concerns have been addressed.

Sure, we could encode every conversation that we've had both internally and across the internal<->external interface, both formal and informal, as text that an LLM could consume. But we find it much easier to just have a human (me, or one of my teammates) do the same thing and then guide an LLM to design a system that satisfies the requirements collected from those conversations.

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That being said, I'm equally amazed at the quality of suggestions that LLMs sometimes come up with for improvements to systems as I am dismayed by the complete dead-ends it sometimes hits when it doesn't have a good solution in its knowledge base. It's not limited to just ML topics, but also very esoteric things that have very little documentation or even contradictory documentation!

So yes, you're right that if the frontier model providers aim their reinforcement learning laser beams at improving a specific topic they will probably succeed. But, no, I don't think there's a panacea.

This recent work which tried to get LLMs to reinvent methods they haven't seen before (given the same problem setting) also raised some of the same issues I've noticed in my own usage: https://epoch.ai/publications/innovationeval

Now I have this DAG with something like 40 specialized subagents. It's sort of a monstrosity, but it works much better for 100x fewer tokens. I've found that basically every single judgment call (which is characteristic of ux) really wants an agent solely focused on it with plenty of concrete examples.

It seems like LLMs are just guiding an extreme brute-force search (there's a reason they didn't use a single Astra agent, but 10s of thousands).

I'm curious if there are maybe a few million mathematical statements, and only a certain subset will connect at the search nodes you're on. If we had such a DB (which is currently the LLM), how effective such a search would be? This is a bit like program induction but on a math theory. Perhaps the limit so far has been lack of such a database, no one has put every math paper into a formal LEAN proof so it can be leveraged.

A lot of key breakthroughs so far seem to just be "recombination" of existing things (not a massive quantum leap in new math), so perhaps we just need to automate this via search (and not with a very expensive intermediate database of LLMs).

Of course, I've read that just compiling the core math lib of LEAN takes 10+ minutes, so its not exactly a fast process at the moment, so quality of guess is probably a limiting factor.

I have a gut feeling this is the last juice because for 2 years now they have been "beating benchmarks" yet relatively simple tasks they still can't do sometimes

Arcane is not the same as actually intellectually hard. Programming is arcane.

Same with circuit layout, which is also amenable to things like genetic algorithms, and yes I believe math proofs might be easier than we thought.

It seems to be that you can mix some intelligence and a learned bag of motifs with iterative gradient descent and solve math proofs like a hard jagged optimization problem. I feel like this is what has been demonstrated. You get a huge blob of Lean but it works.

We might learn there are domains like, say, nursing or writing (good writing) or blue collar work or being a secretary for a large company that require a lot more fluid intelligence than fields that have historically been smug about their high IQ requirements.