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GPT-6 Astra

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Re: GPT-6 Astra

#961

Post by embedding-shape »

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…

> 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 is the right thing to work on vs the wrong thing, so a smart model would waste less time, thinking like this.

Re: GPT-6 Astra

#962

Post by oblio »

iamgopal wrote: post #905

ASTRA means tool ( for war or attack specifically ) in hindi

It's basically impossible to pick a word that doesn't mean something unintended in 20+ major languages in the world.

The recent OpenAI model names are obviously based on Latin: Luna (Moon), Terra (Earth), Sol (Sun), Astra (Star).

Re: GPT-6 Astra

#963

Post by aniviacat »

On the ScreenSpot-Pro benchmark, all effort levels achieve roughly the same score. I wonder if that is just a limitation of the benchmark, or if the effort levels actually do not make a difference for purely visual tasks.

> ScreenSpot-Pro tests whether models can locate the correct interface element in high-resolution screenshots of professional software.

Re: GPT-6 Astra

#964

Post by chriskanan »

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…

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 to focus on that. By forward transfer, what I mean is weights update over time and past learning improves future learning such that we get better sample efficiency.

2) Psychologists distinguish among different kinds of intelligence for Spearman's g (IQ). Crystalized intelligence is using already acquired knowledge (frontier models probably have maxed out that). Fluid intelligence is reasoning and finding solutions in novel situations or without the necessary crystalized knowledge. [Giving colloquial definitions]

3) Now, interestingly, neither of those are correlated with _creativity_ (just they are independent, note some have this threshold theory but it hasn't held up in recent papers). That's what the AI's really are terrible at -- creativity. But I'd argue the vast majority of humans aren't very creative, with truly out-of-the-box ideas. Given that this is HN, and a non-trivial number of us have ADHD, creativity is positively correlated with ADHD.

I did a bunch of research on these topics for my AGI course that I teach each Spring (where I then point out conflicting definitions and start using multiple alternative terms rather than AGI to distinguish among the different definitions).

Re: GPT-6 Astra

#965

Post by oblio »

hannofcart wrote: post #228

What does 'Astra' here mean? Surely they must be referring to the Latin word. Because in another dead language of antiquity, Sanskrit, it means "weapon". Which would be a bit too on-the-nose.

Latin, not Sanskrit.

Heck, ask ChatGPT where the name comes from :-)

Re: GPT-6 Astra

#966

Post by basisword »

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…

I think speed is going to be important for a second reason - ever since I've started using LLM's a lot in my work I enjoy it much less. The main reason is that I ask the LLM something and have to wait because I know it's going to prompt me at random every few minutes. A lot of the day has become staring aimlessly at the screen. The prompts are too random and frequent for me to do something else in the interim. It's pretty soul destroying and this can't be how 'work' looks going forward or people are going to burn out.

Re: GPT-6 Astra

#968

Post by speleding »

jumploops wrote: post #540

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…

That balance probably depends on the human, and the context. If you are a beginner in a field the model should not assume you know what you are doing. On the other hand, for an expert it should try to work out what you mean with your vaguely worded order.

What I think should happen is that it should update its memory with notes on the proficiency level of the user, so it gets the balance right over time.

This is a problem if you allow your kids to use your ChatGPT account for homework (and silly pictures), like I do.

Re: GPT-6 Astra

#969

Post by golol »

renegade-otter wrote: post #960
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.

This is oversimplifying it. Read the metr report on the huggingface incident.

Re: GPT-6 Astra

#970

Post by wartywhoa23 »

lonrenor wrote: post #722
XCSme wrote: post #367

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?

IMO there has been a regime shift to building things for yourself and what is cool is the output of the tools you make. I have started building my own Digital Audio Workstation. The point is not to build something to compete with Ableton. The point is to build something and make music with it. If it is a good tool then I should be able to make good music with it and release the music. Actually, the DAW should be the…

> Actually, the DAW should be the secret sauce of the music and something I wouldn't want to give away.

There's got to be someone to listen to your music in order for that secret sauce to have any meaning.

What's the use of any "secret sauce" in something that only you listen to, because everyone else is either content with AI slop "music", or better yet "create" it for themselves just like you "create" your secret DAW?

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