>Reinforcement learning, on the other hand, can do that, on a human timescale. But you can't make money quickly from it.
Tools like Claude Code and Codex have used RL to train the model how to use the harness and make a ton of money.
That kind of capability is not going to lead to AGI, not even close.
1. It's still memory, of a sort, which is learning, of a sort. 2. It's a very short hop from "I have a stack of documents" to "I have some LoRA weights." You can already see that happening.
One of the biggest boosts in LLM utility and knowledge was hooking them up to search engines. Giving them the ability to query a gigantic bank of information already has made them much more useful. The idea that it can't similarly maintain its own set of information is shortsighted in my opinion.
So in the machine learning world, it would need to be continuous re-training (I think its called fine-tuning now?). Context is not "like human memory". It's more like writing yourself a post-it note that you put in a binder and hand over to a new person to continue the task at a later date.
Its just words that you write to the next person that in LLM world happens to be a copy of the same you that started, no learning happens.
It might guide you, yes, but that's a different story.
Their contexts, not their memories. An LLM context is like 100k tokens. That's a fruit fly, not AGI.
Well, that's just, like, your opinion, man.
LLMs are artificial general intelligence, as per the Wikipedia definition:
> generalise knowledge, transfer skills between domains, and solve novel problems without task‑specific reprogramming
Even GPT-3 could meet that bar.
I think I'll just keep using AI and then explain to anyone who uses that term that there is no "I" in today's LLMs, and they shouldn't use this term for some years at least. And that when they can, we will have a big problem.
Same thing is true for humans.
If LLMs have shown us anything it is that AGI or super-human AI isn't on some line, where you either reach it or don't. It's a much higher dimensional concept. LLMs are still, at their core, language models, the term is no lie. Humans have language models in their brains, too. We even know what happens if they end up disconnected from the rest of the brain because there are some unfortunate people who have experienced that for various reasons. There's a few things that can happen, the most interesting of which is when they emit grammatically-correct sentences with no meaning in them. Like, "My green carpet is eating on the corner."
If we consider LLMs as a hypertrophied langauge model, they are blatently, grotesquely superhuman on that dimension. LLMs are way better at not just emitting grammatically-correct content but content with facts in them, related to other facts.
On the other hand, a human language model doesn't require the entire freaking Internet to be poured through it, multiple times (!), in order to start functioning. It works on multiple orders of magnitude less input.
The "is this AGI" argument is going to continue swirling in circles for the forseeable future because "is this AGI" is not on a line. In some dimensions, current LLMs are astonishingly superhuman. Find me a polyglot who is truly fluent in 20 languages and I'll show you someone who isn't also conversant with PhD-level topics in a dozen fields. And yet at the same time, they are clearly sub-human in that we do hugely more with our input data then they do, and they have certain characteristic holes in their cognition that are stubbornly refusing to go away, and I don't expect they will.
I expect there to be some sort of AI breakthrough at some point that will allow them to both fix some of those cognitive holes, and also, train with vastly less data. No idea what it is, no idea when it will be, but really, is the proposition "LLMs will not be the final manifestation of AI capability for all time" really all that bizarre a claim? I will go out on a limb and say I suspect it's either only one more step the size of "Attention is All You Need", or at most two. It's just hard to know when they'll occur.
This is why, for example, a 30 year old can lose control of a car on an icy road and then suddenly, in the span of half a second before crashing, remember a time they intentionally drifted a car on the street when they were 16 and reflect on how stupid they were. In the human or animal mental model, all events are recalled by other things, and all are constantly adapting, even adapting past things.
The tokens we take in and process are not words, nor spatial artifacts. We read a whole model as a token, and our output is a vector of weighted models that we somewhat trust and somewhat discard. Meeting a new person, you will compare all their apparent models to the ones you know: Facial models, audio models, language models, political models. You ingest their vector of models as tokens and attempt to compare them to your own existing ones, while updating yours at the same time. Only once our thoughts have arranged those competing models we hold in some kind of hierarchy do we poll those models for which ones are appropriate to synthesize words or actions from.
That being said, you don't really need training to understand a STOP sign by the time you are required to, its pretty damn clear, it being one of the simpler signs.
But you do get a lot of "cultural training" so to speak.
AGI just means human level intelligence. I couldn't come up with General Relativity. That doesn't mean I don't have general intelligence.
I don't understand why people are moving the goalposts.
It seems more like people haven't decided on what the goal post is. If AGI is just another human, that's pretty underwhelming. That's why people are imagining something that surpasses humans by heaps and bounds in terms of reasoning, leading to wondrous new discoveries.
Take the wheel. Even that wasn't invented from nothing — rolling logs, round stones, the shape of the sun. The "invention" was recognizing a pattern already present in the physical world and abstracting it. Still training data, just physical and sensory rather than textual.
And that's actually the most honest critique of current LLMs — not that they're architecturally incapable, but that they're missing a data modality. Humans have embodied training data. You don't just read about gravity, you've felt it your whole life. You don't just know fire is hot, you've been near one. That physical grounding gives human cognition a richness that pure text can't fully capture — yet.
Einstein is the same story. He stood on Faraday, Maxwell, Lorentz, and Riemann. General Relativity was an extraordinary synthesis — not a creation from void. If that's the bar for "real" intelligence, most humans don't clear it either. The uncomfortable truth is that human cognition and LLMs aren't categorically different. Everything you've ever "thought" comes from what you've seen, heard, and experienced. That's training data. The brain is a pattern-recognition and synthesis machine, and the attention mechanism in transformers is arguably our best computational model of how associative reasoning actually works.
So the question isn't whether LLMs can invent from nothing — nothing does that, not even us.
Are there still gaps? Sure. Data quality, training methods, physical grounding — these are real problems. But they're engineering problems, not fundamental walls. And we're already moving in that direction — robots learning from physical interaction, multimodal models connecting vision and language, reinforcement learning from real-world feedback. The brain didn't get smart because it has some magic ingredient. It got smart because it had millions of years of rich, embodied, high-stakes training data. We're just earlier in that journey with AI. The foundation is already there — AGI isn't a question of if anymore, it's a question of execution.
There's plenty of training data, for a human. The LLM architecture is not as efficient as the brain; perhaps we can overcome that with enough twitter posts from PhDs, and enough YouTubes of people answering "why" to their four year olds and college lectures, but that's kind of an experimental question.
Starting a network out in a contrained body and have it learn how to control that, with a social context of parents and siblings would be an interesting experiment, especially if you could give it an inherent temporality and a good similar-content-addressable persistent memory. Perhaps a bit terrifying experiment, but I guess the protocols for this would be air-gapped, not internet connected with a credit card.
Yes, which is available to the model as data prior to 1905.