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Notes -
AI 2040: Plan A
The AI 2027 authors published a follow-up. Scott Alexander also wrote a separate blogpost and although not in the author list contributed.
It's a very speculative and optimistic timeline of AI's future evolution. It presents five ways or "plans" the US government will intervene. Unsurprisingly, the ASI-pilled authors favor strong, global regulation to ensure alignment. Summaries:
Plan A (recommended): the US makes an international treaty with China, pauses AI training (not inference, i.e. no new models but we keep using existing ones), enforces full transparency of future research, then when alignment research advances enough carefully resumes
Plan S: the US makes an international treaty with China and pauses AI training for as long as possible
Plan B: the US regulates AI at home and demands China also regulate, but doesn't negotiate with them, probably leading to a war
Plan C: the US regulates AI and ignores China, so they overtake it and reach ASI first
Plan D: the US doesn't regulate AI, we get ASI in early 2031 and it probably kills everyone
Personally, I just don't share the optimism of these guys in either direction.
I think politicians will prioritize culture war and the failing economy over AI regulation, and at most pass some executive orders suggesting companies be more careful. But I also doubt we'll have ASI that can solve the abstract problems "take over the world" or even "keep existing world leaders in power" (they're getting old and increasingly unpopular, their parties may remain in power but only if their policies significantly shift).
What I expect from AI:
Basically solve legacy code by rewriting entire codebases, applying very niche domain knowledge, and actually finding and handling edge-cases better than humans
Greatly speedup research, leading to new discoveries and inventions. Important but background things like food preservation and medicine will improve from AI-assisted discoveries. Major advancements in math and theoretical physics
Much better and cheaper education, therapy, initial medical/legal appointments, personal repairs...maybe reducing but not eliminating human jobs, because human experts will offer these services "premium"
Won't replace human artists. Some advertisements and infographics will be AI but even some will still be human. At best it will assist them in a way where the human still fully controls the output, e.g. by generating code leading to new and improved software tools to learn, practice, and create art
Used by the vast majority as a personal assistant, but doesn't replace human relations
Maybe someone here can help me with this.
What is the bull case, beyond drawing lines on a graph, for AI achieving superhuman, or even human, performance on tasks that are not quickly verifiable?
AI is quite clearly superhuman at self-contained programming problems. I haven't tried Fable, but I suspect that superhuman open ended software engineering is not far away, though I suspect that humans will have a role in architecture and problem setting as opposed to problem solving for some time more. I expect hardware work will also quickly go down this path, at least to some extent, and really anything that can be RLVR'd. That's enough to account for a huge portion of white collar work and carries serious cyber security risks. Both of those will have serious consequences, politically and militarily.
I am not convinced that AI is improving at anything like this rate for things that can't be RLVR'd, I.e. stuff where you can't generate enormous amounts of useful training data with an answer key. Radiologists continue to do just fine for themselves despite repeated promises of doom. I'm sure someone will chime in to say that the radiologists are there for liability reasons, but it's not as if they are now just hitting thumbs up/thumbs down on AI decisions all day.
Partly this is a sample efficiency question - there simply might not be enough data for them to learn this stuff to human level, and architectural advances that improve sample efficiency may lead to huge gains in quality. But it's not clear to me why people expect this to happen.
I am more uncertain about "superhuman" intelligence, but fairly confident on human intelligence (i.e. as best as the best humans).
My bull case: existing systems have a very significant flaw, in that they're very sample inefficient. They need way more data than a human brain does to learn the same things (don't tell me that a bunch of redundant sensory information counts as extra data). That's a fairly broad critique, applying not just to LLMs.
But we know that there exist systems--not just human but other animal brains--that are orders of magnitude more sample efficient. That suggests there is something fundamental missing from existing learning strategies.
But what existing LLMs do allow is a search over architectures and learning rules. Take a random ML paper off arXiv, and Fable will absolutely be able to implement a Jax Colab notebook for it. No one needs a PhD to do this.
Maybe they can suggest novel ideas, or better prune the combinatorial space of architectures and learning rules. That would speed things up. But that's not necessary: we can automate grad student descent, through brute force. Throw a couple trillion GPU hours at the problem, and if what allows human and animal brains to be as efficient as they are is efficiently implementable on GPUs, we will find it. And on the scale of years, not decades.
"if what allows human and animal brains to be as efficient as they are is efficiently implementable on GPUs" is the biggest question for me, but a negative answer to that just delays the inevitable. Admittedly pushing things a decade or two in the future: if GPUs are a dead end, we have our seasonal AI winter, until the switch to fancy neuromorphic hardware or neural organoids starts scaling.
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