To be clear, the jump from Opus to Fable was like the jump from pre o3 -> o3 for me. Very sharp improvement, not incremental. But that could be explained by dummy long thinking times.

It one shot a task that Opus burned hundreds of dollars on to get nowhere. Very tricky semantic refactor, got it right. Granted, again, the semantics Opus and I fleshed out 3 months prior, but Opus couldn't execute on the vision. Fable could.

Then I discussed some philosophy and it was actually both pleasant (GPT constantly "corrected" you for the sake of correction without clarification, also still often just wrong; it's like it refused to think critically about philosphy) and accurate, and actually helped resolve some deep but subtle misconceptions I had around representationalism. When talking with GPT I felt like I was talking with someone who either was sycophantic or "anything that is not absolute truth is relativism" - Fable actually discussed.

Both is exciting and kind of makes me depressed. I can definitely see why people are getting hyped about AGI again. All the models were extremely strong technically but I felt like couldn't match the developer's tacit state - Fable definitely did, and that's a basic quailty to be considered "usefully intelligent" IMO, at least to me.

Shame that it's going away in 2 weeks and probably going to be nerfed if/when it's re-released.

On one project, through a variety of circumstances, dead code elimination was straight up not working, but we wanted to show the theoretical improvement of some approach - but we couldn't figure out why at the moment (we did spend a whole week chasing down the root cause after - maybe worth in hindsight...).

We were doing it by hand at one point, but someone suggested using CReduce for shrinking the code. Definitely was an interesting test-iterate loop...

But Claude models seem to be better at long term problems or more ambiguous problems.

I'm curious as to what the primary benefit here. Are there secret improvements in training? There hasn't been much in fundamental model architecture, I don't think. What about harnesses? I wonder what's pushing the AI. It seems like harnesses is the main thing pushing AI ever since CoT.

The only way to test this is to test it out, in real life. Sometimes people see results, sometimes people don't. Note that yes, I am including the entire iteration process - even after iterating, people still don't see results with AI.

I have had both positive and negative experiences with AI, over multi-week projects. But apparently on hackernews, anything positive about AI is proof that AI is superhuman and taking over, and all follies about AI are lies by stupid humans who secretly have psychological dispositions to fear AI. Sometimes the AI genuinely isn't good enough. Are we not allowed to say that now? We might not know why, but it's just the truth.

The other solution is to formally analyze the entire space of possible actions the agent can take a priori. Then yes, you can definitively say whether or not the principle breaks or not. Can you, though? Can you give a formal specification for the space of possible actions for AI and show that your loop never breaks, or breaks less than humans, or any other sensible criteria? If not, then you can't just give an abstract principle and start making inferences from that.

OK, the agents don't click in the same way as humans. You learn that, what about mouse hovering telemetry, time spent, etc. And one of the most extreme is to force biometrics - a lot of telemetry, breaks the interface a lot - but hey, you have assurance.

And none of these tradeoffs require understanding the deep processes of the human mind. Just, map is not the territory, how you do game the map harder and harder and how do the mapmakers respond to that?

Whereas self-supervised, allows learning without explicit annotation of data ; but it doesn't matter if the models already trained on the entire Internet, and it's not like a game where it can come up with effectively new training data for itself?

Everything is an accident, an anecdote, only trust the state with your authoritative quantitative data! There's surely no philosophical issues with that! There's no issues with definitional authority!

I'm not even sure why they were invoked. Even disregarding the big techinical debunks such as two dogmas, sociologically and even by talking to real mathematicians (see Lakatos, historically, but this is true anecdotally too), it's (ironically) a complete non-question to wonder about mathematics in a logical positivist way.

Something about that background, all the discussions about definitions and representation, the original article talking about dualisms.... It's certainly an experience.

1 week turnaround I guess is what they meant.

You can't be a dick on this platform without fancy prose I guess.

This has very specific implications in symbolic ai specifically where historically the goal was mapping out the 'correct' representation of the space, then running formal analysis over it. That's why it's not a black box - you can trace out all of the steps. The issue is, is that symbolic AI just doesn't work. To my knowledge, as compared to all the DL wins we have.

I think the win of transformers proves that symbolic AI isn't the way. At the very least, the complex interactions that arise from in-context learning clearly in no way imply some fixed universal meaning for words, which is a big problem for symbolic AI.

Doesn't symbolic AI have a lot of philosophical problems? Think back to Quine's two dogmas - you can't just say, "Let's understand the true meanings of these words and understand the proper mappings". There is no such thing as fixed meaning. I don't see how you get around that.

Deep learning is admittedly an ugly solution, but it works better than symbolic AI at least.

And to be clear, falsification and being empirical & skeptical about theoretical claims is great. What I see all too often on the Internet is just pattern matching to the words "observable" and "falsification" without a second thought, without actually looking into how science develops, and any and all narratives are historically rewritten to fit only those two categories.

Which is why it's even more impressive to be a real scientist, to actually be able to navigate the muddy waters properly where it's not just some simple adjective checklists to run through. (As a non scientist)

In some sense, this isn't really different than how society was headed anyways? The trend was already going on that more and more sections of the population were getting deemed irrational and you're just stupid/evil for disagreeing with the state.

But that reality was still probably at least a century out, without AI. With AI, you have people making that narrative right now. It makes me wonder if these people really even respect humanity at all.

Yes, you can prod slippery slope and go from "superintelligent beings exist" to effectively totalitarianism, but you'll find so many bad commitments there.