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GPT-6 Astra

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Re: GPT-6 Astra

#561

Post by z7 »

astrobiased wrote: post #370

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

Re: GPT-6 Astra

#562

Post by kaashif »

pera wrote: post #387
intenex wrote: post #248

The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…

To each their own. Personally I will start feeling the AGI as soon as we move from chatting about benchmark results to learn that some lab just announced the discovery of tens of novel treatments for rare diseases. Maybe I'm too boring but it seems quite pointless to have this same prediction game every time a new model is released.

AGI would produce novel treatments for diseases at rates equivalent to what a human can do today.

Which is to say, not that fast.

Re: GPT-6 Astra

#563

Post by maxnevermind »

XCSme wrote: post #335

It's fun, but every new model release makes me even less interested to create cool stuff. Like, what's the point, if the next AI can do it in 5 seconds?

Maybe instead of creating cool stuff try to go and solve real problems? It seems to me that we are lacking in that department since all that LLM fuss has started 3 or so years ago.

Re: GPT-6 Astra

#564

Post by starik36 »

throwaway13337 wrote: post #439

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…

I ran into the same problem as you, so I ended up by coding a local app that is very similar to Wispr Flow, but uses the small english Whisper model on my low-end Windows laptop.

It is still a quite fast. In fact, I just typed this in using this app.

Re: GPT-6 Astra

#565

Post by karim79 »

There will probably never be AGI. This shit is just snake oil. Nor do we have a proper definition of what AGI actually is or what it's supposed to do.

There will be a small handful of billionaires claiming that AGI is just around the corner ad infinitum just to serve themselves at this moment in time, and capitalise from the hype.

There is no "AGI" endgame. This is shitty ass hypercapitalism in action and nothing more. I'll repeat: snake oil.

Re: GPT-6 Astra

#566

Post by beering »

ertgbnm wrote: post #508
abixb wrote: post #227

I want to take a step back: So, this is GPT-6 -- the natural number version release comparable to GPT-4 and GPT-5 from the past few years. The ARC-AGI-3 score is obviously impressive at 99.9% (we'll need to wait for more details on how they used the response API harness on GPT-6 Astra, wrt reasoning retention and compaction), but every other benchmarks seems to be a relatively modest improvement, comparable with any…

This is a very mundane release compared to GPT-4 and GPT-5. I think they probably scaled back a bit after the lukewarm response to the GPT-5 announcement. But it still very weird that there wasn't even a livestream,

There is simply no level of announcement that won’t have people complaining. What is so important of having a livestream?

Re: GPT-6 Astra

#567

Post by [deleted] »

ex-aws-dude wrote: post #552
astrobiased wrote: post #370

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…

They can do new tasks with in-context learning but its obviously limited by context window

[deleted]

Re: GPT-6 Astra

#568

Post by notatoad »

manlymuppet wrote: post #403

I have nothing to say about the actual model, but unrelated--why do so many of these demos include people buying things autonomously? Even if I did trust an AI to get everything right, it's not like the AI can read my mind. If I was ordering food normally and without AI, I would want more control over the process--looking over the options, prices, thinking about what I really want. People don't know what they really…

because "people will let our AI spend their money for them" is the workflow that makes their valuations reasonable.

Re: GPT-6 Astra

#569

Post by aniviacat »

rjtc wrote: post #513

I am really confused on how it can saturate ARC-AGI but still perform poorly on aggregated benchmarks: https://artificialanalysis.ai/models Perhaps if it was allowed this custom harness for all benchmarks it would similarily saturate?

This benchmark gives the same intelligence score for GPT-6 Astra (max), GPT-5.6 Sol (max), and Grok 4.6 (high)? That seems very wrong to me, unless I'm misinterpreting the visualizations.

Re: GPT-6 Astra

#570

Post by sumedh »

dingdong2026 wrote: post #485
intenex wrote: post #248

The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…

Only someone who doesn't do any work of any meaningful difficulty could think these models have anything to do with AGI. Today I spent half a day trying to solve a moderately interesting software engineering problem. I was switching between GPT-5.6 Sol and Fable 5.1 to check each other's work in Cursor. And the result was gradually driving me insane. As the models struggled to find a solution that would actually work…

Care to share the problem?
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