| ▲ | darkpicnic 5 hours ago |
| LLMs won’t lead to AGI. Almost by definition, they can’t. The thought experiment I use constantly to explain this: Train an LLM on all human knowledge up to 1905 and see if it comes up with General Relativity. It won’t. We’ll need additional breakthroughs in AI. |
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| ▲ | johnmaguire 5 hours ago | parent | next [-] |
| I'm not sure - with tool calling, AI can both fetch and create new context. |
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| ▲ | 0xbadcafebee 5 hours ago | parent [-] | | It still can't learn. It would need to create content, experiment with it, make observations, then re-train its model on that observation, and repeat that indefinitely at full speed. That won't work on a timescale useful to a human. Reinforcement learning, on the other hand, can do that, on a human timescale. But you can't make money quickly from it. So we're hyper-tweaking LLMs to make them more useful faster, in the hopes that that will make us more money. Which it does. But it doesn't make you an AGI. | | |
| ▲ | charcircuit 4 hours ago | parent [-] | | It can learn. When my agents makes mistake they update their memories and will avoid making the same mistakes in the future. >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. | | |
| ▲ | kelnos 2 hours ago | parent | next [-] | | That's not learning, though. That's just taking new information and stacking it on top of the trained model. And that new information consumes space in the context window. So sure, it can "learn" a limited number of things, but once you wipe context, that new information is gone. You can keep loading that "memory" back in, but before too long you'll have too little context left to do anything useful. That kind of capability is not going to lead to AGI, not even close. | | |
| ▲ | charcircuit an hour ago | parent [-] | | >but before too long you'll have too little context left to do anything useful. 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. |
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| ▲ | Dansvidania 2 hours ago | parent | prev | next [-] | | That’s not learning. That’s carrying over context that you are trusting is correctly summarised over from one conversation to the next. | |
| ▲ | otabdeveloper4 2 hours ago | parent | prev [-] | | > they update their memories Their contexts, not their memories.
An LLM context is like 100k tokens. That's a fruit fly, not AGI. | | |
| ▲ | charcircuit an hour ago | parent [-] | | A human can't keep 100k tokens active in their mind at the same time. We just need a place to store them and tools to query it. You could have exabytes of memories that the AI could use. |
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| ▲ | xdennis 2 hours ago | parent | prev | next [-] |
| > Train an LLM on all human knowledge up to 1905 and see if it comes up with General Relativity. It won’t. 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. |
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| ▲ | 5 hours ago | parent | prev | next [-] |
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| ▲ | tehjoker 5 hours ago | parent | prev | next [-] |
| Part of the issue there is that the data quantity prior to 1905 is a small drop in the bucket compared to the internet era even though the logical rigor is up to par. |
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| ▲ | jerf 5 hours ago | parent | next [-] | | Yet the humans of the time, a small number of the smartest ones, did it, and on much less training data than we throw at LLMs today. 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. | |
| ▲ | antupis 5 hours ago | parent | prev [-] | | Humans need way less data. Just compare Waymo to average 16 year-old with car. | | |
| ▲ | cellis 5 hours ago | parent [-] | | A 16 year old has been training for almost 16 years to drive a car. I would argue the opposite: Waymo’s / Specific AIs need far less data than humans. Humans can generalize their training, but they definitely need a LOT of training! | | |
| ▲ | noduerme 4 hours ago | parent | next [-] | | When humans, or dogs or cats for that matter, react to novel situations they encounter, when they appear to generalize or synthesize prior diverse experience into a novel reaction, that new experience and new reaction feeds directly back into their mental model and alters it on the fly. It doesn't just tack on a new memory. New experience and new information back-propagates constantly adjusting the weights and meanings of prior memories. This is a more multi-dimensional alteration than simply re-training a model to come up with a new right answer... it also exposes to the human mental model all the potential flaws in all the previous answers which may have been sufficiently correct before. 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. | |
| ▲ | jimbokun 4 hours ago | parent | prev [-] | | No 16 year old has practiced driving a car for 16 years. | | |
| ▲ | Dansvidania 2 hours ago | parent [-] | | If you see gaining fine motor control, understanding pictographic language […] as a prerequisite to driving a car, then yes, all of them are |
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| ▲ | crazy5sheep 5 hours ago | parent | prev [-] |
| The 1905 thought experiment actually cuts both ways. Did humans "invent" the airplane? We watched birds fly for thousands of years — that's training data. The Wright brothers didn't conjure flight from pure reasoning, they synthesized patterns from nature, prior failed attempts, and physics they'd absorbed. Show me any human invention and I'll show you the training data behind it. 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. |
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| ▲ | drw85 4 hours ago | parent | next [-] | | Nice ChatGPT answer. Put some real thought and data in it too. | |
| ▲ | saagarjha an hour ago | parent | prev [-] | | > Einstein is the same story. He stood on Faraday, Maxwell, Lorentz, and Riemann. Yes, which is available to the model as data prior to 1905. |
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