Even just a year ago, most people thought the practical effects in software engineering were incremental too. It took another generation of models and tooling to get to the point where it could start having a large impact.
What makes you think the same will not happen in other knowledge-based fields after another iteration or two?
Hum... Are you saying it's having clear positive (never mind "transformative") impact somewhere? Can you point any place we can see observable clear positive impact?
So the concept formal verification is as relevant as ever, and when building interconnected programs the complexity rises and verifiability becomes more difficult.
For the easy-to-verify fields like coding, you can train "domain intuitions" directly to the LLM (and some of this training should generalize to other knowledge work abilities), but for other fields you would need to supply them in the prompt as the abilities cannot be trained into the LLM directly. This will need better models but might become doable in a few generations.
Using LLMs to validate LLMs isn't a solution to this problem. If the system can't self-verify then there's no signal to tell the LLM that it's wrong. The LLM is fundamentally unreliable, that's why you need a self-verifying system to guide and constrain the token generation.
Absolutely. It's also worth noting that in the case of Tao's work, the LLM was producing Lean and Python code.
It can't because the LLM can't test its own design. Unlike with code, the LLM can't incrementally crawl its way to a solution guided by unit tests and error messages. In the real world, there are material costs for trial and error, and there is no CLI that allows every aspect of the universe to be directly manipulated with perfect precision.
OpenAI demoed training a model for a robotic hand using this approach years ago.
You clearly didn't read the post. He is talking about AI that is smarter than any human, not today's LLMs. The fact that powerful AI doesn't exist yet doesn't mean there is nothing to worry about.
This kind of petty remark is like a reverse em dash. Greetings fellow human.
Anyway, I did read it. The author's description of a future AI is basically just a more advanced version of LLMs
> By “powerful AI,” I have in mind an AI model—likely similar to today’s LLMs in form, though it might be based on a different architecture, might involve several interacting models, and might be trained differently—with the following properties:
They then go on to list several properties that meet their definition, but what I'm trying to explain in my comment is that I don't accept them all at face value. I think it's fair to critique from that perspective since the author explicitly modeled their future based on today's LLMs, unlike many AI essays that skip straight to the super intelligence meme as their premise.
No, these properties are part of his definition. To say that we have nothing to worry about because today's LLMs don't have these properties misses the point.
The unfortunate truth (for Amodei) is you cant automate true creativity and nor standardise taste. Try as they might.
Even in the software world, the effect of being able to build software a lot faster isn't really leading to a fundamentally different software landscape. Yes, you can now pump out a month's worth of CRUD in a couple days, but ultimately it's just the same CRUD, and there's no reason to expect that this will change because of LLMs.
Of course, creative people with innovative ideas will be able to achieve more, a talented engineer will be able to embark on a project that they didn't have the time to build before, and that will likely lead to some kind of software surplus that the economy feels on the margins, but in practical terms the economy will continue to chug along at a sustained pace that's mostly inline with e.g. economic projections from 10 years ago.