I don't think you can use lindy on trends as if trends are static objects, but that's another conversation.
> Do we really think things will move this fast? Sort of no - between the beginning of the project last summer and the present, Daniel’s median for the intelligence explosion shifted from 2027 to 2028. We keep the scenario centered around 2027 because it’s still his modal prediction (and because it would be annoying to change). Other members of the team (including me) have medians later in the 2020s or early 2030s, and also think automation will progress more slowly. So maybe think of this as a vision of what an 80th percentile fast scenario looks like - not our precise median, but also not something we feel safe ruling out. [2]
I don't think this changes your observation that he is "personally invested" (i.e. believes this trendline will continue), but I'm pretty sure when AGI doesn't appear in 2027, many people will believe that this invalidates the arguments being made here (or in the report). The actual report was intended to give a feel for what a near-future "disaster" AGI scenario, and settled on a date to give that some concrete immediacy. The collective review that gave that as a possible, but not inevitable date is still ongoing (they originally pushed their best estimate out a bit further, but now they think, judging by the goals that are being hit, their scenario was a little too conservative). [3]
[1] https://freddiedeboer.substack.com/p/im-offering-scott-alexa... [2] https://www.astralcodexten.com/p/introducing-ai-2027 [3] https://blog.aifutures.org/p/grading-ai-2027s-2025-predictio...
LLMs are nothing close to AGI and not going to lead to it, they can’t distinguish right from wrong, they can’t count, they can’t reason, they generate plausible text from a vast databank of connected text.
Apparently that is enough to fool many people but it’s nothing close to AGI which would require internal models of the world, reasoning etc.
We are nowhere close to AGI and the fools who predicted we were will unfortunately keep lying about their stated timelines when it inevitably doesn’t arrive. You’re already hedging and trying to caveat previous predictions, as OpenAI did with their AGI predictions which they’re now furiously back-pedalling on.
Argument?
Are LLMs close to being able to significantly help AGI researchers?
They can predict likely sentences but not evaluate truth or logic. They can fairly reliably record facts about the world but not construct internal models of the world.
They do probabilistically. So do humans as a matter of fact. The best of us are better at it than LLMs, but that's not persuasive evidence of anything meaningful really.
> They can fairly reliably record facts about the world but not construct internal models of the world.
You don't know that, unless your presuppose a very specific definition of world model that necessarily precludes emergent ones.
You’re constructing a post-hoc fantasy of human thought based on how LLMs work because you are desperate for some reason to believe that they are thinking like humans, but they are not. The process is very different and the results are also different.
Do you mean "token" as in the LLM sense?
Or are you thinking that thoughts in the human brain are also constructed out of some sort of underlying "token" even though the abstract thought happens and is held before any words are used to try to communicate that thought to an external party?
Human's don't operate the same way, the thought happens and then the words are generated to reasonably describe that thought.
Thoughts don't happen in a vacuum, they are triggered by external or internal stimuli, and these stimuli/thought precursors could very easily be tokens (dense info packets), which then map to latent space vectors, which very well could be thoughts.
Claims like "humans don't operate the same way" has no basis. Not only do we literally not know how humans operate mechanistically, and so we literally don't know the logical structure of human thought, but any system that is Turing complete is so easy to create that many wildly different mechanistic systems are fundamentally equivalent/interconvertible.
Yes, possible, that's why I asked you above if that's what you meant by "token". Someone else responded and I didn't notice it wasn't you.
> Claims like "humans don't operate the same way" has no basis. Not only do we literally not know how humans operate mechanistically, and so we literally don't know the logical structure of human thought, but any system that is Turing complete is so easy to create that many wildly different mechanistic systems are fundamentally equivalent/interconvertible.
I think this position is too extreme, we do have some information.
We know how LLM's work when generating a sequence of words and I know that my brain does not work the same way for word generation because I am fully aware of the complete thought in advance of any words getting generated by me externally or internally.
I know prior to generating words that my thought is X and the words I'm about to produce need to express that thought.
But with LLM's we know that the essence of what they produce is not known in advance, that it must complete the word generation process to fully realize the end result and that multiple different end results are possible.
Additionally "from learned stats" doesn't disambiguate between a wider variety of things. I'm not aware of any other way to acquire knowledge from measurements. I'd bet that humans do this differently, based on the fact the humans can get further with less training data and that they learn actively during operation, but not so differently that 'learning stats' would be an inaccurate description.
If that were the case, then the systems would generate words based on the fully resolved idea, but that is not how the LLM systems currently work (per vendors descriptions).
They choose words sequentially and both the specifics of the input as well as the chosen output words significantly impacts not just the rest of the output but the very correctness of the output.
> but not so differently that 'learning stats' would be an inaccurate description.
Agreed, humans are generalizing using some mechanism that can be modeled with math.
But the execution of our reasoning and thought processes is not obviously similar to LLM's next word generation based on probabilities.
Anthropic says of the their model[0]:
"""Claude sometimes thinks in a conceptual space that is shared between languages, suggesting it has a kind of universal “language of thought.”
{...}
Claude will plan what it will say many words ahead, and write to get to that destination. We show this in the realm of poetry, where it thinks of possible rhyming words in advance and writes the next line to get there. This is powerful evidence that even though models are trained to output one word at a time, they may think on much longer horizons to do so."""
Anthropic also created 'golden gate claude'[1] by identifying the region of its architecture that corresponded to the concept of the golden gate bridge and activating it. What would such a region exist for if claude could only think one token at a time?
>the execution of our reasoning and thought processes is not obviously similar to LLM's
"Not obviously similar" I can agree with. I don't think you've identified a way in which they are obviously different, though.
[0] https://www.anthropic.com/research/tracing-thoughts-language...
It is broadly true that Scott believes that AGI will come in the near future and from LLMs, although his reputation runs a ways deeper than that.
It's not at all surprising that they are increasingly getting labeled a cult (they aren't by traditional definition but there are a lot similarities). I'm really surprised it hasn't hit the mainstream yet given the connections to Elon, Thiel, frontier labs, dark crypto funding, FTX/SBF, some suicides and some murders. It's all a little nuts.
Meanwhile you got all the anti-democratic NRx people on the other side of it.
I suspect this new doc coming out on HBO will spark a media frenzy.
What has he been up to since finishing the finest work of literature ever produced, Harry Potter and the Methods of Rationality? I’ve been patiently awaiting a sequel!
(I don't know about the other guy mentioned above.)
I expect benchmarks like ProgramBench will replace METR this year.
Right now we have an incredibly smart thing with severe short term memory loss, and it’s hard for us to reconcile that as it’s so different from us.
I mean, that's called "having an opinion".
Yes, that's called "having an opinion". Typically people writing argumentative pieces are doing so because they have a belief about the matter. I'm not sure what exactly you expect here.
> if he's wrong I would hope he owns up to it
I think Scott Alexander is pretty good about that.
I mean.. this is 2026 right? You're not writing that comment from 2024 or something?
We see massive problems already where photos are just not believable anymore, nor is audio, and not even video actually with many people falling for AI fake image clips from the Gaza war for example. And since then these tools are MASSIVELY more powerful. Disinformation is essentially free, and the cost of truth has been static. Meaning the "buying power" of truth has collapsed and is falling faster and faster.
Anyone who dismissed AI risks a few years ago IS ALREADY PROVEN WRONG.
It seems to me like you're trying to somehow imply that writing things to convince people of what you believe is somehow nefarious? It isn't! It's what we're all doing here right now! Putting it in a format that certain people will take more seriously doesn't make it nefarious either. I am quite confused by your point of view here.
Not interested in further arguments about this.