5 ms·

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

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-alexander-a-wager#:~:text=said%20in%20the-,dwarkesh%20podcast,-and%20elsewhere%2C%20my https://freddiedeboer.substack.com/p/im-offering-scott-alexa... [2] https://www.astralcodexten.com/p/introducing-ai-2027 https://www.astralcodexten.com/p/introducing-ai-2027 [3] https://blog.aifutures.org/p/grading-ai-2027s-2025-predictions https://blog.aifutures.org/p/grading-ai-2027s-2025-predictio...

grey-area 5mo ago
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.
- par1970 5mo ago
  > 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?
- naasking 5mo ago
  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.
  - grey-area 5mo ago
    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 5mo ago
      > 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 5mo ago
        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.

grey-area 5mo ago

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.

par1970 5mo ago
> 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?
naasking 5mo ago
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.
- grey-area 5mo ago
  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 5mo ago
    > 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 5mo ago
      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.