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GPT-6 Astra

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Re: GPT-6 Astra

#951

Post by bloggie »

throwaway13337 wrote: post #482

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

#952

Post by yokoprime »

jeffy29 wrote: post #936
astrobiased wrote: post #402

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

#953

Post by acje »

jeffy29 wrote: post #936
astrobiased wrote: post #402

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

#954

Post by nuancebydefault »

bsenftner wrote: post #940
jeffy29 wrote: post #936

Earlier quoted context omitted.

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

Comprehension, humans have it, animals have it in limited form, trained algorithms have none at all. The training process is our wholesale replacement for no artificial comprehension. If we ever develop artificial comprehension, that is AGI all by itself, no training required.

How sure are you that comprehension is not a mere form of advanced pattern matching? We have the intuition that ideas and words appear trivially in people's mind, based on comprehension. I think chances are, that intuition is wrong.

Re: GPT-6 Astra

#955

Post by alpineman »

TrackerFF wrote: post #906
alpineman wrote: post #415

“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

#956

Post by the_duke »

danmaz74 wrote: post #939
z7 wrote: post #614

Earlier quoted context omitted.

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

I have no idea about timelines, but the current LLM architecture has no mechanism which could emulate online learning in a way similar to how it happens in humans and other animals. As another commenter wrote, there is no neuroplasticity while these systems interact with their environment in normal usage; adding information/constraints to the context partially mitigates this, but it's a completely different mechanism…

We've had that concept for quite a long time now, in the form of Lora [1] and similar fine-tuning techniques.

It first got popular for StableDiffusion to teach the image generation models new concepts.

We could easily live in a world where you can train / build Loras to encompass your entire code base history, company knowledge base, new skills, etc.

Then the models would start with a baseline that already has all the important knowledge without needing to cram it into the context.

This still isn't on the fly learning, but you could imagine daily or weekly training runs to regularly incorporate new knowledge.

I think the main reason this hasn't happened yet is that the shared batch based efficient serving architectures used today wouldn't support that structure well.

[1] https://en.wikipedia.org/wiki/LoRA_(machine_learning)

Re: GPT-6 Astra

#958

Post by calinet6 »

astrobiased wrote: post #402

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

#959

Post by calinet6 »

z7 wrote: post #614
astrobiased wrote: post #402

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

#960

Post by renegade-otter »

tappio wrote: post #950

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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