271–277 of 277 posts
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You can't take notes before you've learned language, so if that's how you propose to learn then you are doomed to build something with less learning capability than an human brain. Maybe you don't care - if you just want to push to see how far an LLM can go that's fine. but it's not going to be superhuman-level, or even human-level, if it doesn't have the learning ability of a human. If it relies on language and note…
Okay, nowhere did I say it would be before they learned language. I said we could use models that can trivially build notetaking systems to auto-improve themselves by taking, refining, and updating notes as a memory system to exceed the x token context threshold. > but it's not going to be superhuman-level, or even human-level, if it doesn't have the learning ability of a human. If it relies on language and notes to…
You're talking about building an "alien" intelligence, apparently something based on an LLM, where language is baked in from the start and can therefore be used as the basis of some type of learning (or at least memorization).
Sure an LLM can be super-human at specifc things like math where the traditional computer strengths of compute, memory, etc apply, but in the context of AGI (generality, not a bag of narrow intelligences) you can't call something super-human if it can't at least also do the majority of things that a human can do, and not having learning ability as powerful as a human seems to me to be a pretty massive omission.
If WE didn't have ability to learn language, then we'd not be here talking about it, and there would be no LLMs. Having a fundamental ability to learn, isn't just useful to learn language from scratch, or for learning all the non-linguistic skills a human is capable of, but would also support an AI that can learn things that we cannot (e.g. could give it additional "senses" like global pressure/temperature inputs so it could "see" the weather and learn global patterns).
(Posting partly so I can revisit my predictions when they open access more widely) A big problem I have with OpenAI's models (and of course Claude) is that they tend to write the most over-engineered pieces of code, beyond the imagination of any architecture's astronaut. Just this week I asked 5.6-sol-ultra to update a 1000 LOC python script I had, to "incorporate the key lessons learned when using it for another pro…
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Well, let's learn to walk first. Right now we have just massively jagged intelligence that performs stunning feats in math and single-shotting three.js games, and falls flat on it's face in many every day scenarios. And, it can't even learn.
It can learn very well within its context. And it can leave notes for itself to read into future contexts. That's learning, whether you agree or not, and whether you like it or not. Baby steps, yes, but walking nevertheless.
Yes, I'm aware of the linear regression example, and a few others, but these appear to just be specific capabilities that were learnt during pre-training, presumably pursuant to reducing errors on similar-but-different training samples, not any kind of generic run-time learning capability.
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It's a lot more than just commercially meaningful. Even if you don't care about money, "highly autonomous systems that outperform humans at most economically valuable work" has a lot more weight on "how close we are to achieving human intelligence" than most of the 'AGI' thresholds I've seen yet.
Traditionally, AGI means being capable of learning everything (not necessarily at once) that can be learned by the same AI agent. It differs from the commercially meaningful definition in that a standard specific-purpose-built AI can still do most tasks if you give it enough specific purposes, but it will be just as incapable of human intelligence as ever.
Shane Legg would a few years later go on to co-found DeepMind, with creating AGI as their declared mission. Legg's personal definition of AGI is not just generalist AI, but specifically human-level generalist.
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This stood out: "Artificial Analysis Intelligence Index v4.1.1 61.2" So on the Metacritic of LLM benchmarks, it's.. basically where everyone else is (except for Fable 5.1, which is a bit ahead).
On their Agentic Index, GPT-6 Astra (both max/xhigh) has the same result as Qwen3.8-27b. Weird.
2.5x more expensive than Sol. Can expect 2.5x more usage in Codex subscription. Sol is already brutal (even after their recent fixes, it's just a token-hungry model: I go through a full 20x account per day, on Sol Med/High standard speed, with ~2 threads). Note that Tibo recommended using Sol Med as daily driver. When I'm doing less complicated work, I can't even make it past 2-3 days with Sol Med, whereas I was able…
>token-hungry model It's kind of funny how this is the exact opposite of the truth. It's one of the most token-efficient models ever. The claims aren't bullshit. Every conceivable benchmark and test you can throw at it shows Sol being good for token efficiency.
If Astra is per benchmarks so much more token efficient than Sol, why did they limit its use in ChatGPT to ~16% as many messages compare to Sol? When Sol is 40% the price of Astra, why do they give Sol Pro (in ChatGPT) 6x as many tasks?
If it were strictly true that token efficiency makes Astra cost around the same per task as Sol then there'd be no need to limit it to 16% as much access.
It's because per-task token use is highly variable, not as universally true as you claim