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GPT-6 Astra

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Re: GPT-6 Astra

#861

Post by embedding-shape »

nullbio wrote: post #680
jumploops wrote: post #475

I think the thing I'm most excited about is the increase in _user prompting_. If I give a poorly constrained/ambiguous prompt, I don't want the model one-shotting assumptions left and right. The demos of Fable/GPT-6 are impressive, but "real AGI" should act more like a collaborator than either a peon or overachiever. It's a tough balance to get right, and although this has been possible to achieve with additional pro…

This is spot on. A collaborator is exactly what real AGI is. It will figure out the perfect questions to ask, in the perfect order, by intelligently assessing the entire solution and problem space upfront, so when you leave it to go off on its own it isn't making stupid decisions for you. They really need to make this work in Codex. Claude Code has had a multi-select refinement tool since forever.

> They really need to make this work in Codex. Claude Code has had a multi-select refinement tool since forever.

I think this already exists in Codex? If you use "/plan" and something is unclear or ambiguous, Codex will ask you and present choices, and let you enter your own custom answer. Then it'll iterate like this until the plan is clear and ambiguous. Isn't this what you're talking about? If so, it has existed for a long time in Codex.

Overall I agree with you though, all the models currently don't have the right hunches nor the right approach about when things are clear enough or not.

Re: GPT-6 Astra

#862

Post by tappio »

I feel that for some time now, the biggest constraint when working with models is not their intelligence, but their speed. It does not matter how smart the model is, it will make mistakes, because the instructions are ambiguous and new facts are found during implementation. The biggest problem I've had working with software developers has always been the lag between seeing the results and steering towards the right direction, not the skills of the developer (with many exceptions of course).

Regardless, working on the wrong things is time wasted. And again, I'm procrastinating here while waiting for Fable to run a benchmark on a few solutions to a problem I have. We can guess what would work, but we only know after the benchmark. A faster model, with fewer capabilities, would've been a much better choice this time... well, "git gud" they said... and live and learn! Faster model = less time for procrastination.

PS. AI models don't live and learn; the discussion about AGI is pretty pointless imo. It's a tool. Does it matter if it is AGI or not if it does what you want it to do? Does the IQ of your colleague matter if he's good at what he's supposed to do? Or bad? Well... I guess it does matter, as many people are up in arms about whether Astro is AGI or not. Personally, I think we're past the point for that debate. These are amazing tools.

Re: GPT-6 Astra

#863

Post by bloggie »

throwaway13337 wrote: post #428

That hero video is interesting. A projector and speech. Maybe I'm in the minority here, but I find speech to text / text to speech (but not live audio mode) is quite comfortable and effective for coding now. The speech to text part can be frustrating if your local tts model does not have word match context for coding. Codex desktop does this remotely well but is slow. I've been experimenting with local software for m…

>this could bring us closer to the dream of more natural, social computing

What I saw was multiple people living alone in a small box in a warehouse (probably filled with other boxes) with all of their natural, social interactions directed at a wall. I wonder if this is foreshadowing for the future of work, at least it is what work will look like as envisioned by OpenAI.

Re: GPT-6 Astra

#864

Post by yokoprime »

jeffy29 wrote: post #850
astrobiased wrote: post #365

I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area. It seems more about coverage-driven competence. So…

Define novel intelligence in a way that would not exclude 95% of humans, yourself included.

It gets kind of out there, but what i often hear peopel refer to is that frontier models lacks the visdom component. Which I guess is in the realm of intuition, i.e. i have a feeling it might be a problem with X based on some vague signs, maybe something a colleague mentioned offhand, something that was out of alignment etc.

Re: GPT-6 Astra

#865

Post by acje »

jeffy29 wrote: post #850
astrobiased wrote: post #365

I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area. It seems more about coverage-driven competence. So…

Define novel intelligence in a way that would not exclude 95% of humans, yourself included.

I remember vaguely from a presentation by Yann LeCun "Intelligence is not what you know, but what you do when you don´t know". I find it helpful when trying to build an intuition for how to understand the LLM tool.

Re: GPT-6 Astra

#866

Post by alpineman »

TrackerFF wrote: post #823
alpineman wrote: post #377

“allowing non-technical people to create and play custom games that go beyond rudimentary elements” Proceeds to generate the most generic, rudimentary, and unoriginal clone of Mario Kart

Have you seen ads for mobile games, where there are seemingly 100 different versions of the same type of game (like tower defense types)? And they're all obviously the worst type of pay-to-play traps? I think this kind of solves that. Or at least it is the start of it. Most such games are kind of trivial. If people can easily just get AI to generate such games on the fly, then that'll hopefully be the end of predator…

The problem is, no one wants to play a game they designed themselves

Re: GPT-6 Astra

#868

Post by calinet6 »

astrobiased wrote: post #365

I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area. It seems more about coverage-driven competence. So…

Agree. Token predicting machines will continue to be token predicting machines by nature. Continued size and tuning will have the effect of making them more and more perfect at being average.

Re: GPT-6 Astra

#869

Post by calinet6 »

z7 wrote: post #545
astrobiased wrote: post #365

I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area. It seems more about coverage-driven competence. So…

Chollet writes he expects AGI now sooner than 2030, "given progress is happening faster than I expected." https://x.com/fchollet/status/2095607046129463577

Ridiculous

Re: GPT-6 Astra

#870

Post by renegade-otter »

tappio wrote: post #862

I feel that for some time now, the biggest constraint when working with models is not their intelligence, but their speed. It does not matter how smart the model is, it will make mistakes, because the instructions are ambiguous and new facts are found during implementation. The biggest problem I've had working with software developers has always been the lag between seeing the results and steering towards the right d…

AI models do not live and learn - it's worse. They actually get DUMMER if you don't start with a clean slate. This is important. One has to curate the context carefully.
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