How do you suppose the AGI is going to be able to wrap the sun in a dyson sphere using only the resources available on earth? Do you have evidence that there are enough resources on asteroids or nearby planets for their mining to be economically viable? At the current rate, mining an asteroid costs billions while their value is nothing. Even then we don't know if they'll have enough of the exact kind of materials necessary to make a dyson sphere around an object which has 12000x the surface area of earth. You could have von nuemman replicators do the mining but then they'd spend most of the materials on the replicators and have to go very far to get more materials, at which point they'd just settle on a new star. They could turn human atoms into usable material, but humans are a very tiny percentage of earth and our useful matter is even tinier, it certainly wouldn't be enough to envelop the sun. Even if the replicators had a perfectly efficient way to turn photoelectric power into any feasible construction of atoms, which you don't know if that's possible, it doesn't seem like it would be efficient to overcome some of the gravities and distances and related issues with other planets to make mining them economically viable either. Within our solar system and possibly even further it just doesn't seem possible to envelop the sun at all.

I'm playing within your hypotheticals here even though I think none of them will ever happen, but even within your hypotheticals it seems like the dyson sphere point is just total nonsense and we probably wont go extinct by having the sun covered up. I wont deny your other points because even though they already rest on a lot of astronomically unlikely assumptions, they do seem legitimate within that framework.

Obviously I meant some kind of approximation of consensus or acceptability derived from much greater substantiation. There is no equivalent to Climate Change or ZFC in the field of AI in terms of acceptability and standardisation. Matthew Barnett made my point better in the above comments. 

Yes, most policy has no degree of consensus. Most policy is also not asking to shut down the entire world's major industries. So there must be a high bar. A lot of policy incidentally ends up being malformed and hurting people, so it sounds like you're just making the case for more "consensus" and not less.
 

The bar is very low for me: If MIRI wants to demand the entire world shut down an entire industry, they must be an active research institution actively producing agreeable papers.

AI is not particularly unique even relative to most technologies. Our work on chemistry in the 1600's-1900's far outpaced our level of true understanding of chemistry, to the point where we only had a good model of an atom in the 20th century. And I don't think anyone will deny the potential dangers of chemistry. Other technologies followed a similar trajectory.

We don't have to agree that the range is 20-80% at all, never mind the specifics of it. Most polls demonstrate researchers find around 5-10% chance of total extinction on the high end. MIRI's own survey finds a similar result! 80% would be insanely extreme. Your landscape of experts is, I'm guessing, your own personal follower list and not statistically viable. 
 

I am not convinced MIRI has given enough evidence to support the idea that unregulated AI will kill everyone and their children. Most of their projects are either secret or old papers. The only papers which have been produced after 2019 are random irrelevant math papers.  Most of the rest of their papers are not even technical in nature and contain a lot of unverified claims. They have not even produced one paper since the breakthrough in LLM technology in 2022. Even among the papers which do indicate risk, there is no consensus among scientific peers that this is true or necessarily an extinction risk. Note: I am not asking for "peer review" as a specific process, just some actual consensus among established researchers to sift mathematical facts from conjecture.

Policymakers should not take seriously the idea of shutting down normal economic activity until this is formally addressed.

A question for all: If you are wrong and in 4/13/40 years most of this fails to come true, will you blame it on your own models being wrong or shift goalposts towards the success of the AI safety movement / government crack downs on AI development? If the latter, how will you be able to prove that AGI definitely would have come had the government and industry not slowed down development? 

To add more substance to this comment: I felt Ege came out looking the most salient here. In general, making predictions about the future should be backed by heavy uncertainty. He didn't even disagree very strongly with most of the central premises of the other participants, he just placed his estimates much more humbly and cautiously. He also brought up the mundanity of progress and boring engineering problems, something I see as the main bottleneck in the way of a singularity. I wouldn't be surprised if the singularity turns out to be a physically impossible phenomenon because of hard limits in parallelisation of compute or queueing theory or supply chains or materials processing or something.

If your goal is to get to your house, there is only one thing that will satisfy the goal: being at your house. There is a limited set of optimal solutions that will get you there. If your goal is to move as far away from your house as possible, there are infinite ways to satisfy the goal and many more solutions at your disposal.

Natural selection is a "move away" strategy, it only seeks to avoid death, not go towards anything in particular, making the possible class of problems it can solve much more open ended. Gradient Descent is a "move towards" strategy, if there is a solution that would help it reach a goal but it's not within the target direction, it mostly won't reach it without help or modification. This is why the ML industry is using evolutionary algorithms to solve global optimisation problems that GD cannot solve. The random search / brute force nature of evolution is inherently more versatile and is a well known limitation of GD.
 

Gradient descent by default would just like do, not quite the same thing, it's going to do a weirder thing, because natural selection has a much narrower information bottleneck. In one sense, you could say that natural selection was at an advantage, because it finds simpler solutions.

This is silly because it's actually the exact opposite. Gradient descent is incredibly narrow. Natural selection is the polar opposite of that kind of optimisation: an organism or even computer can come up with a complex solution to any and every problem given enough time to evolve. Evolution fundamentally overcomes global optimisation problems that are mathematically impossible for gradient descent to overcome without serious modifications, possibly not even then. It is the 'alkahest' of ML, even if it is slow and not as popular.