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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.
Humans existing and being good at these problems shows that it is possible to create an intelligence that can solve these problems to at least the skill level of a highly intelligent and competent human, without needing impossibly huge training sets to do so. The question is if we can replicate this on a computer. The bull case is that this is just a question of finding the right algorithm, and once we do, we will achieve AGI.
Since current AI can clearly help researchers write code faster, it stands to reason that the better AI we have access to, the faster we can improve the algorithm, which leads to a loop where better models are developed faster and faster. Once the models start approaching human-level intelligence they will be able to iteratively improve themselves without researcher oversight. And like that, we have justified drawing lines on the graph.
It doesn't assume that -- it rests solely on the idea that brains are physical objects. This is empirically verified by every single experiment run on a human brain. More generally, it's been borne out on every noun that interacts with the physical world.
"Humans aren't computers" is irrelevant. Brains are physical arrangements of atoms that are capable of intelligently solving problems. This assumes nothing.
(For completeness: you may be completely right about 2. You're sort-of-right about 3, in that the assumption was made and the assumption was mistaken. But I don't think you're right that the current approach avoids singularity. There are absolutely recursive feedback loops in improving the current implementation of AI, because improving AI is made out of tasks, and we can get AI to do tasks. But you're right that the original thesis had a much more directly integrated feedback loop.)
This is missing the part where the human brain is an exceptionally well-tuned physical object shaped by millenia of evolutionary pressures that arguably constitute a training set vastly bigger than the laws of physics as we currently understand them say is possible to match with an artifical model, much less do any meaningful computation with.
It is also missing the part where the human brain is the most complicated object in the universe, as it is the only currently known object capable of of understanding these questions well enough to even ask them. And even it does not fully understand itself.
Not true: a group of human brains, or a human + tools|AI, or humans + tools|AI, are smarter and more complicated.
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