I think that would be more of ASI than AGI, AGI is a smart human, I'd say we're pretty close if not already there for most stuff we've been focusing on like coding. ASI is a super genius, beyond the smartest human, and we're nowhere near that. But as we've already seen with LLMs you don't need to wait for ASI to solve hard problems in math and physics.

I find the distinction between AGI and ASI to be a red herring. In my mind, we humans already have enough intelligence to basically solve any problem given enough time and a 'scratch pad'. It's sort of like Turing-completeness but for intelligence.

Saying that a computer exhibits artificial-SUPER-intelligence feels a little like saying that one universal Turing machine is more expressive than another universal Turing machine. The thing that sets the two Turing machines apart is not how expressive they are but how quickly and efficiently they can arrive at the given output.

It's hard for me to imagine a task that ASI could complete which a human with enough time and determination couldn't also complete, although I can imagine such tasks for dogs which don't exhibit the same level of intelligence as humans. Maybe I'm too human-centric and naive. Maybe there are other tasks out there that are beyond human intelligence even given infinite time.

If, however, my view of intelligence is correct, then the distinction between ASI and AGI is illusory, and the real distinction that we would see between systems is just their speed, efficiency, determination, etc.

In my mind, once we achieve AGI, we immediately have ASI: just run that AGI on a faster computer, across many nodes, etc. Instead of having humans solve quantum gravity over the next 1000 years, just have AGI solve it over the next month.

But is it byte for byte identical ?

You're right to push back.

Your experience sounds really interesting, can you tell us more ?

I mean sure, you can add a third, and a fourth and a fifth one if ur ok with the added cost, latency and it actually helps. Redundancy is a core concept in software and CS and at the heart of making many systems, complex or otherwise, reliable.

What you want can be accomplished with an extra doc, call it the Log, where the llm appends things tried, lessons learned, failed experiments etc.. while leaving the comments as terse accurate snapshot of the current state. I've been using this log pattern and it works well.

It was communicated fine, can you be more specific ?

No, it wasn't. For example: https://news.ycombinator.com/item?id=48019226

you really went digging with that 3 months old hn comment. What's wrong with that comment btw? Are you saying he was lying and they did in fact commit to the rewrite at that point?

I didn't dig. It was prominent at the time this occurred, because like I said, it wasn't communicated at all until they were already in the middle of it.

And yes, I think he was not genuine there and that they had indeed fully committed to the rewrite prior to that point and comment.

If you're not aware of any of this, how are you arguing it was communicated clearly?

it's not the way to go if you hit the limits very quickly and have to waste immense time migrating.

From my experience the limits are way higher than people think.

from my experience they're actually much lower than people think, especially when using Postgres for things it's not well-suited for like queuing, and the problem is you rarely can just throw more hardware at it to solve the problem and it's really not a good place to be.

Thw bottleneck become compiling and running the code every iteration so it wont be seconds

JIT compilers largely solve that. The real question in my mind is whether fast LLMs bias stuff towards languages that either don't have a compile/typecheck phase at all or have one that's very fast (e.g. Java).

This has not been my experience at all with gpt sol on ultra/max. Some concurrent code is just really really hard to write and even in those cases LLMs can help, the barrier for extensive thorough testing and writing your own custom simulator, liveness and correctness tests is simply so much lower now.

Do you think diffusion models or models that generate video and image are conscious? I'd say most people who think LLMs are conscious don't think the same way about non-chattng models ( unless you ask them then they might say "sure" in an attempt to remain consistent).

My point is it's the interaction medium that's causing the confusion and causinf this psychosis that's tricking people into feeling like they're talking to someone.

LLMs are not conscious, sams as google chrome, same as GTA 5, same as your latest python script.