[dannyobrien]

So, this is not quite right: Alexander contributed to the report, but his personal opinion is more like the mid-2030s[1]. Freddie feels like this is him backing down from the original statement, but in fact he said this at the time the report was published, and in fact pointed out a graf below the quote that Freddie claims does tie him to 2027:

> 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...

[grey-area]

AI boosters really are detached from reality.

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.

[naasking]

This is all speculative. We don't understand intelligence, so you literally have no idea whether what we recognize as intelligence is some suitable arrangement of "statistical token generation", especially once you add feedbacks loops.

[RaftPeople]

> "We don't understand intelligence, so you literally have no idea whether what we recognize as intelligence is some suitable arrangement of "statistical token generation""

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?

[ToValueFunfetti]

LLMs also don't run on tokens internally, they're just the inputs and outputs. The reasoning models do operate (partially) in the token space, but then so do I.

[RaftPeople]

LLM's generate their output words sequentially based on probability (from learned stats).

Human's don't operate the same way, the thought happens and then the words are generated to reasonably describe that thought.

[naasking]

> 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.

[ToValueFunfetti]

What I'm saying is that this is incorrect. An "idea" exists within a model before it generates tokens. This property does not distinguish humans from LLMs.

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.

[grey-area]

We understand it enough to see the obvious massive deficiencies in LLMs.

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.

[naasking]

> They can predict likely sentences but not evaluate truth or logic.

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.

[grey-area]

Humans do not reason by guessing the next most likely token/word. They use logic, morality and systems of thought they have constructed and shared to help them reason and don’t in any way predict tokens in a sequence - we use words to represent our thoughts and feelings about the world, not to construct them.

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.

[par1970]

> 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.

Argument?

Are LLMs close to being able to significantly help AGI researchers?