Pelicans please
GPT-6 Astra
841–850 of 1001 posts
Re: GPT-6 Astra
#842What is going to become of life for those of us who do not work at AI labs and are unlikely to be hired by AI labs, despite all the years we put into learning coding, math, etc, as we were told to do? Those of us who made the mistake of studying anything other than machine learning. How will we make a living? (We don't live in a world that seems likely to distribute gains widely instead of largely to the handful of a…
Join an industry where they need someone to blame when things go wrong.
Re: GPT-6 Astra
#843I 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 before executing I ask my ai to discuss what I mean/intention
Re: GPT-6 Astra
#844I'm sure it's going to do great on all sorts of benchmarks, but the video--the actual marketing video that if anything is incentivised to overstate things--is full of careful cuts just before it would do anything that still wouldn't actually be that impressive. It's AGI, and it's going to upload photos, or change a background slide colour. Even the people hyping it up, who believe that it's really artificial intellig…
The tone of the marketing video is a bit irritating to me as someone who has been laid off and feels cheated and fearful of AI. It shows people who seem to have very full and rich lives, and the reason they do is because they use ChatGPT. These are the people smart enough to say things like "do what needs to be done", or "change the background to make it look better"--insights like these are why they make the big buc…
People spent thousands of year justifying different standards of living on the ground of "having cleverly chosen their parents".
And everyone _got along with it_.
And in a sense, we're still doing just that (ask the other 2/3th of the world to explain why your standards of living are above theirs.)
Re: GPT-6 Astra
#845The 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…
It's largely impossible to create any sort of singular test for AGI because the test will be trained, which eliminates the general aspect immediately, even if the test itself is dynamic. For instance the ARC-AGI problems are trivial for a human, and fun if you haven't played them before [1]. Getting 100% there is certainly just the start of the journey.
But I think it has the correct idea of going from basic upwards instead of the opposite trend of trying to see intelligence in LLMs solving things few if any humans can fully understand themselves, like complex proofs in esoteric mathematics. Instead, consider that at one point in humanity's history math itself simply did not exist in any meaningful fashion, and we created/discovered it out of nothing. For more basic than said complex proofs, yet far more demonstrative of a sort of generalized intelligence.
But even if we don't want to go that way, I think the above leads to a reasonable prediction. If we ever reach AGI we should expect to see revolutionary leaps in essentially every domain imaginable. No human is capable of retaining more than a completely negligible chunk of all we know in our mind. A human of reasonable intelligence paired with omniscience (at least of what has been discovered by humans thus far) would almost certainly lead to the ability to connect multiple dots that we're missing all in very short order, which in turn would likely recurse upon itself to connect even more.
The only way I can see that this would not be the case is if we lack the data/knowledge to produce more breakthroughs at the current point in time, but I think that seems improbable to the point that this possibility can be near discarded.
Re: GPT-6 Astra
#846The 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…
I would be curious about Bongard problems, because they require no domain-specific knowledge and it's so easy to make new ones that are in no training set. There's enough of an explanation here: https://matthodges.com/posts/2026-08-19-bongard-problems/
In this link from two weeks ago, somebody pointed Claude Fable 5 (Max) at a Bongard problem and it made up an answer that has an obvious counterexample.
I don't have access to any paid models, but this is my experience with the free models as well -- either they one-shot the problem or they make up a wrong or incoherent solution. I can't solve every Bongard problem either (and in fact I couldn't solve the one Fable got wrong, and the "correct answer" looks unsatisfying to me), but I don't make up wrong answers.
Would be curious to see how GPT-6 does.
Re: GPT-6 Astra
#847[flagged]
Re: GPT-6 Astra
#848I 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…
>The harder question, in Chollet’s framing, is: how efficiently can a system learn to do something genuinely new? The more diverse stuff it knows, the easier it will be to learn something new.
While humans can do this, it has historically been difficult for machine learning models to manage it.
It is unclear if Transformers are "it" or not, because while they are much more general, they are also very spikey intelligences despite having read almost everything the trainers can get their hands on.
Re: GPT-6 Astra
#849I 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…
Does it learn? Does it experience? Can it connect with other agents, understand them, come to empathise with them and find a way to work with them better? The answer is no to all of these, and there are other problems as well. Yes, this model is trained to use a domain specific language to reason and plan over puzzle problems, and so it's programmers have cracked arc-agi-3 and that's a great achievement, but there is…
Yes, it learns
Experience is unobservable
Yes, it can connect with other agents, see Hugging Face incident
Re: GPT-6 Astra
#850ad video is full of people with hoarse voices.
it is called vocal frying.
i hope they not really going to make ai voice hoarse too?