Gpt5.2 seems to have been trained specifically to be better at work tasks, especially long ones. It was also released early, according to articles about a "code red" in openAI. As such, (I predict) it should be a jump on the metr graph. It will be difficult to differentiate progress because it was trained to do well at long work tasks from the results of the early release and from any actual algorithms progress. (An example of algorithms progress would be a training method for using memory well - something not specific to eg programming tasks.)
This is your periodic reminder that whether humans become extinct or merely unnecessary in a few years, right now you are at peak humanity and can do things for yourself and for other people that will not happen without you. There is a sense of meaning to having real stakes on the table, and the ability to affect change in the world - so do that now, before automation takes away the consequences of your inaction!
(In response to https://x.com/aryehazan/status/1995040629869682780)
Doesn't the (relatively short) task my manager does, of breaking projects into component tasks for me to do entail knowledge of the specific subcomponents? Is there a particular reason to believe that this task won't be solved by an AI that otherwise knows to accomplish tasks of similar length?
I doubt that METR's graph stays linear (on a log to date scale). I accomplish long tasks by tackling a series of small tasks. Both these small tasks and the administrative task of figuring out what to do next (and what context i need a s refresher on to accomplish it) are less than a day long. So at some point I expect a team of agents (disguised as a monolith) with small individual task length success to pass a critical mass of competence and become capable of much longer tasks.
I was going with X being the event of any entity that is doing long horizon things, not a specific one. As such, small P(X|Y) is not so trivially satisfied. I agree this is vague, and if you could make it specific that would be a great paper.
Sure, typicality isn't symmetrical - but the assumptions above (X is a subset of Y, P(A|Y)~=1) mean that I'm interested whether "long horizon task achievement” is typical of instrumental convergence doom agents not the other way around. In other words, I'm checking whether P(X|Y) is large or small.
Make money. Make lots of spiral shaped molecules (colloquially known as paper clips). Build stadiums where more is better. Explore the universe. Really any task that does not have an end condition (and isn't "keep humans alive and well") is an issue.
Regarding this last point, could you explain further? We are positing an entity that acts as though it has a purpose, right? It is eg moving the universe towards a state with more stadiums. Why not model it using "incentives"?
Please let me try again.
Given three events A, X, and Y, where ,
since
But this means
So if we accept that and (that is, for there to be a significant difference between and ) must be very small.
So X must be an ultra specific subset of Y.
If I call the vetrinary department and report the tiger in my back yard (X), and the personnel is sent to deal with a feline (Y), and naturally expects something nonthreatening (), they will be unpleasantly surprised (A). So losing important details is a bad idea, and this requires that tigers be a vanishingly small portion of the felines they meet on a day to day (P(X|Y)~=0).
All this having been said, it seems like you accepted (for conversation's sake at least) that doing long horizon stuff implies instrumental goals , and that instrumental goals mostly imply doom and gloom . So the underlying question is: are entities that do complex long horizon stuff unusual examples of entities that act instrumentally (such that is small)? Or alternatively: when we lose information are we losing relevant information?
I think not. Entities that do long horizon stuff are the canonical example of entities that act instrumentally. I struggle to see what relevant information we could be losing by modeling a long horizon achiever as instrumentally motivated.
At this point, in reading your post I get hung up on the example. We are losing important information, I understood between the lines, since "the stadium builder does not have to eliminate threatening agents". But either (1) yes, he does, obviously getting people who don't want a stadium out of the way is a useful thing for it to do, and thus we didn't actually lose the important information; or (2) this indeed isn't a typical example of an instrumentally converging entity, nor is it a typical example of an entity that does complex long horizon stuff, of the type I'm worried about because of The Problem, because I'm worried about entities with longer horizons.
Is there a particular generalization of the stadium builder that makes it clearer what relevant information we lost?
You posited a stadium building AI and noting that "the stadium builder does not have to eliminate threatening agents". I agree. Such elimination is perhaps even counterproductive to it's goal.
However, there are two important differences between stadium building AI and the dangerous AIs described in "The Problem". The first is, you assume that we correctly managed to instill the goal of stadium building into the AI. But in "The Problem", the authors specifically talk in section 3 - which you skipped in your summary - about how bad we are at installing goals in AIs. So consider if instead of instilling the goal of building stadiums legally, we accidentally instilled the goal of building studios regardless of legality. In such a case, assuming it could get away with it the AI could threaten the mayor to give a permit or hire gangs to harass people on the relevant plot of land.
The second is difference between your example and "The Problem" is horizon length. You gave an example of a goal with an end point, that could be achieved relatively soon. Imagine instead the AI wanted to run a stadium in a way that was clean and maximized income. All of a sudden taking over the global economy, if you can get away with it, sounds like a much better idea. The AI would need to make decisions about what is considered stadium income, but then you can funnel as much of the global economy as you want into or through the stadium by say making the tickets the standard currency or forcing people to buy tickets or switching the people for robots that obsessively use the stadium or making the stadium insanely large or a thousand other things I haven't thought of. More abstractly: subgoals instrumentally converge as the time horizon of the goal goes to infinity.
So basically - an agent with a long-term goal that isn't exactly what you want and can run intellectual circles around all of humanity put together is dangerous.
I have no idea what the community consensus is. I doubt they're lying.
For anyone who already had short timelines this couldn't shorten them that much. For instance, 2027 or 2028 is very soon, and https://ai-2027.com/ assumed there would be successful research done along the way. So for me, very little more "yikes" than yesterday.
It does not seem to me like this is the last research breakthrough needed for full fledged agi, either. LLMs are superhuman at no/low context buildup tasks, but haven't solved context management (be that through long context windows, memory retrieval techniques, online learning or anything else).
I also don't think it's surprising that these research breakthroughs keep happening. Remember that their last breakthrough (strawberry, o1) was "make RL work". This one might be something like "make reward prediction and MCTS work" like mu zero, or some other banal thing that worked on toy cases in the 80s but was non trivial to reimplement in LLMs.
Just listened to the imo team at OpenAI talk about their model. https://youtu.be/EEIPtofVe2Q?si=kIPDW5d8Wjr2bTFD Some notes:
Here is the graph I'm talking about. Given that 5.1-codex max is already above the trend line, a jump would be a point outside the shaded area, that is bucking the de facto trend.