[Update 4 (12/23/2023): Tamay has now conceded.]

[Update 3 (3/16/2023): Matthew has now conceded.]

[Update 2 (11/4/2022): Matthew Barnett now thinks he will probably lose this bet. You can read a post about how he's updated his views here.]

[Update 1: we have taken this bet with two people, as detailed in a comment below.]

Recently, a post claimed,

it seems very possible (>30%) that we are now in the crunch-time section of a short-timelines world, and that we have 3-7 years until Moore's law and organizational prioritization put these systems at extremely dangerous levels of capability.

We (Tamay Besiroglu and I) think this claim is strongly overstated, and disagree with the suggestion that “It's time for EA leadership to pull the short-timelines fire alarm.” This post received a fair amount of attention, and we are concerned about a view of the type expounded in the post causing EA leadership to try something hasty and ill-considered.

To counterbalance this view, we express our disagreement with the post. To substantiate and make concrete our disagreement, we are offering to bet up to $1000 against the idea that we are in the “crunch-time section of a short-timelines”.

In particular, we are willing to bet at at 1:1 odds that no more than one of the following events will occur by 2026-01-01, or alternatively, 3:1 odds (in our favor) that no more than one of the following events will occur by 2030-01-01.

A model/ensemble of models achieves >80% on all tasks in the MMLU benchmark
A credible estimate reveals that an AI lab deployed EITHER >10^30 FLOPs OR hardware that would cost $1bn if purchased through competitive cloud computing vendors at the time on a training run to develop a single ML model (excluding autonomous driving efforts)
A model/ensemble of models will achieve >90% on the MATH dataset using a no-calculator rule
A model/ensemble of models achieves >80% top-1 strict accuracy on competition-level problems on the APPS benchmark
A gold medal for the IMO Grand Challenge (conditional on it being clear that the questions were not in the training set)
A robot that can, from beginning to end, reliably wash dishes, take them out of an ordinary dishwasher and stack them into a cabinet, without breaking any dishes, and at a comparable speed to humans (<120% the average time)
Tesla’s full-self-driving capability makes fewer than on

...

AI Races and Macrostrategy