How would AI or gene editing make a difference to this?
Wondering why this has so many disagreement votes. Perhaps people don't like to see the serious topic of "how much time do we have left", alongside evidence that there's a population of AI entrepreneurs who are so far removed from consensus reality, that they now think they're living in a simulation.
(edit: The disagreement for @JenniferRM's comment was at something like -7. Two days later, it's at -2)
For those who are interested, here is a summary of posts by @False Name due to Claude Pro:
I offer, no consensus, but my own opinions:
Will AI get takeover capability? When?
0-5 years.
Single ASI or many AGIs?
There will be a first ASI that "rules the world" because its algorithm or architecture is so superior. If there are further ASIs, that will be because the first ASI wants there to be.
Will we solve technical alignment?
Contingent.
Value alignment, intent alignment, or CEV?
For an ASI you need the equivalent of CEV: values complete enough to govern an entire transhuman civilization.
Defense>offense or offense>defense?
Offense wins.
Is a long-term pause achievable?
It is possible, but would require all the great powers to be convinced, and every month it is less achievable, owing to proliferation. The open sourcing of Llama-3 400b, if it happens, could be a point of no return.
These opinions, except the first and the last, predate the LLM era, and were formed from discussions on Less Wrong and its precursors. Since ChatGPT, the public sphere has been flooded with many other points of view, e.g. that AGI is still far off, that AGI will naturally remain subservient, or that market discipline is the best way to align AGI. I can entertain these scenarios, but they still do not seem as likely as: AI will surpass us, it will take over, and this will not be friendly to humanity by default.
I couldn't swallow Eliezer's argument, I tried to read Guzey but couldn't stay awake, Hanson's argument made me feel ill, and I'm not qualified to judge Caplan.
In Engines of Creation ("Will physics again be upended?"), @Eric Drexler pointed out that prior to quantum mechanics, physics had no calculable explanations for the properties of atomic matter. "Physics was obviously and grossly incomplete... It was a gap not in the sixth place of decimals but in the first."
That gap was filled, and it's an open question whether the truth about the remaining phenomena can be known by experiment on Earth. I believe in trying to know, and it's very possible that some breakthrough in e.g. the foundations of string theory or the hard problem of consciousness, will have decisive implications for the interpretation of quantum mechanics.
If there's an empirical breakthrough that could do it, my best guess is some quantum-gravitational explanation for the details of dark matter phenomenology. But until that happens, I think it's legitimate to think deeply about "standard model plus gravitons" and ask what it implies for ontology.
In applied quantum physics, you have concrete situations (Stern-Gerlach experiment is a famous one), theory gives you the probabilities of outcomes, and repeating the experiment many times, gives you frequencies that converge on the probabilities.
Can you, or Chris, or anyone, explain, in terms of some concrete situation, what you're talking about?
Congratulations to Anthropic for getting an LLM to act as a Turing machine - though that particular achievement shouldn't be surprising. Of greater practical interest is, how efficiently can it act as a Turing machine, and how efficiently should we want it to act. After all, it's far more efficient to implement your Turing machine as a few lines of specialized code.
On the other hand, the ability to be a (universal) Turing machine could, in principle, be the foundation of the ability to reliably perform complex rigorous calculation and cognition - the kind of tasks where there is an exact right answer, or exact constraints on what is a valid next step, and so the ability to pattern-match plausibly is not enough. And that is what people always say is missing from LLMs.
I also note the claim that "given only existing tapes, it learns the rules and computes new sequences correctly". Arguably this ability is even more important than the ability to follow rules exactly, since this ability is about discovering unknown exact rules, i.e., the LLM inventing new exact models and theories. But there are bounds on the ability to extrapolate sequences correctly (e.g. complexity bounds), so it would be interesting to know how closely Claude approaches those bounds.
You could have a Q&A superintelligence that is passive and reactive - it gives the best answer to a question, on the basis of what it already knows, but it takes no steps to acquire more information, and when it's not asked a question, it just sits there... But any agent that uses it, would de facto become a superintelligence with agency.