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GPT-6 Astra

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Re: GPT-6 Astra

#881

Post by amazingamazing »

embedding-shape wrote: post #866
tappio wrote: post #857

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…

> It does not matter how smart the model is, it will make mistakes It does matter, otherwise why are we all using GPT 5.6 rather than GPT 3.5? Because it's way smarter, makes less mistakes and therefore finishes tasks faster. The smarter the model is, the faster it can complete what you actually wanted. > Regardless, working on the wrong things is time wasted Agreed. And "smart" for me, would mean understanding what…

Not everyone is using 5.6 or even the best model so your premise is already wrong.

Re: GPT-6 Astra

#884

Post by i_have_an_idea »

x312 wrote: post #98

Hmm, 61 on ArtificialAnalysis, effectively matching GPT-5.6 and trailing the new Meta model. How is that possible along with the other metrics they shared? Insanely jagged intelligence?

ArtificialAnalysis are famous Anthropic fanboys.

Re: GPT-6 Astra

#885

Post by somethingsome »

chriskanan wrote: post #869
astrobiased wrote: post #366

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…

You are conflating multiple things. 1) First, you are talking about positive forward transfer in continual learning. I've been giving talks for the past 6-7 years about how that community (I was one of the founders) went astray and wasn't focusing enough on that topic, but continual learning of the kind you are thinking isn't in any of these systems right now. I think some people left the Grok team to make a start-up…

Very interesting!

I was looking at your website and wondering if there is a way to have access to the course material/videos?

In particular: Spring 2025 @ UR : CSC 209/409 Seminar on Artificial General Intelligence

Fall 2024 @ UR : CSC 277/477 End-to-End Deep Learning

Spring 2023 @ UR : CSC 266/466 Frontiers in Deep Learning

Spring 2022 @ Cornell Tech : CS 5787 – Deep Learning

Fall 2021 @ RIT : IMGS 684 – Deep Learning for Vision

Re: GPT-6 Astra

#886

Post by logicchains »

jeffy29 wrote: post #845
astrobiased wrote: post #366

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.

Memorize most of the street names in London and the quickest routes between them. Most humans could do it if they put in the effort (it's required to become a London taxi driver; a test called The Knowledge), but that information won't fit in 1m tokens of context so can't be learned by an LLM that wasn't explicitly trained to memorize it. Human brains are biological, so they can physically grow to encompass the extra information: https://www.pnas.org/doi/10.1073/pnas.070039597

Re: GPT-6 Astra

#887

Post by harrouet »

astrobiased wrote: post #366

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…

I have the same feeling. It is not unlike old-Siri receiving hard-coded workflows for each type of question. It will not scale.

Re: GPT-6 Astra

#888

Post by pmarreck »

astrobiased wrote: post #366

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…

My take on it is that even if there was no "novel" discovery (leaving that up to the reader to define), if you consider human knowledge to be a sphere in N-dimensional state space, "within that surface area" is Swiss cheese, and AI seems to at minimum be able to fill in some of those holes.

And those hole-fillings, for all intents and purposes, look to us like novelty, even if much of it was simply overlooked by us, or, perhaps, unable to attain due to time or other constraints.

Now if you want to talk beyond the sphere, let's call it the "novel novel discovery of the unknown unknowns", then you may have a point, and AI may be more limited than humans in discovering the things that we don't know we don't know. Especially the as-yet-unmodelable things i.e. intuition.

Plenty of discovery left just working from first principles, however. Which I cautiously suggest current frontier AI is good enough to model to a significant enough extent that it is useful for discovery.

Re: GPT-6 Astra

#889

Post by druskacik »

enraged_camel wrote: post #691
jumploops wrote: post #476

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…

>>> The demos of Fable/GPT-6 are impressive, but "real AGI" should act more like a collaborator than either a peon or overachiever. I don't really agree. The thing that makes Fable feel like an actual collaborator is its ability to sus out your real intent when you give ambiguous instructions. It's really good at it. I watched some reviews today and came way with the impression that Astra is not better than Sol in th…

Interesting. I actually prefer when agents don't commit on my behalf unless I explicitly say so, I even had to add a custom instruction for Claude to stop doing it (Codex never does it). Even if I don't read all the code line-by-line, I at least want to see the changes at glance and commit myself. Git Fork[0] is a great tool for that, by the way.

In general, I don't like when I have to prompt models to NOT do something. It's probably difficult for the AI companies to get this right, they should understand ambiguity but still not over-do simple instructions.

[0] https://git-fork.com/

Re: GPT-6 Astra

#890

Post by monkeydust »

chriskanan wrote: post #869
astrobiased wrote: post #366

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…

You are conflating multiple things. 1) First, you are talking about positive forward transfer in continual learning. I've been giving talks for the past 6-7 years about how that community (I was one of the founders) went astray and wasn't focusing enough on that topic, but continual learning of the kind you are thinking isn't in any of these systems right now. I think some people left the Grok team to make a start-up…

> That's what the AI's really are terrible at -- creativity

Good thought piece here "We Are Losing the Ability to Discover What We Didn’t Know to Ask[1]" By Anne-Laure Le Cunff

It keeps playing on my mind as I see people at work follow some predetermined AI workflow to get their jobs done, the art of being curious and exploring around the problem is so important to the really big innovations. Been thinking about how to address this through some of the harnesses we are developing in the knowledge working space.

[1] https://archive.is/IAxf9

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