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Thought I would clarify, add, and answer your questions. Reading back over my post and your response has made me realize what I forgot to make obvious and how others were interpreting the format of the timeline differently. Some of what I wrote may already be obvious to some, but I wanted to write what was obvious to me that I didn’t see others also making obvious. Also I rarely sometimes think something is obviously true when it actually isn’t, so let me know if I am being shortsighted. For the sake of brevity and avoiding interruptions to what I am saying, I didn’t put in clear transitions.
Having a format of early, middle and late progress estimates of 2026 and then switching to more certain month by month predictions in 2027 doesn’t make a lot of sense to me. What happens if something they thought was going to happen in middle of 2026 happens in early 2026(which is extremely likely)? Wouldn’t that throw off the whole timeline? It’d be like if you planned out your schedule hour by hour for every day, not for next year, but for the year after with food, entertainment, work, etc. So you go through next year with no plan, but then when the year you planned comes up, you end up having to switch cities for a new job and everything you planned needs to be scrapped as it’s now irrelevant.
Arguing over what is going to happen exactly when is a way to only be prepared if things happen exactly that way. Would we be more prepared if we knew a certain milestone would be met in August or September of 2026 right now? If our approach wouldn’t change, then how soon it happens doesn’t matter. All that matters is how likely it is that something is going to happen soon, and how prepared we are for that likelihood.
AI 2027 goes into lots of stuff that could happen, but doesn’t include obvious things that definitely will happen. People will use AI more(obvious). Internet availability will increase(obvious). Context length will increase(obvious). These are things that can be expanded and reasoned on and can have numbers attached for data analysis. It’s pointless to say non obvious things as nobody will agree, and it also degrades all the other obvious things said. The more obvious, the more useful, and a lot of these predictions aren’t obvious at all. They skip the obvious facts in favor of speculative opinions. AI 2027 has lots of statements like “The President defers to his advisors, tech industry leaders”. This is a wild speculation for an extremely important decision, not an obvious fact that can be relied on to happen. So immediately all the rest of the essay gets called into question and can no longer be relied on for its obviousness and absolutely trueness(the DOD would not accept this).
If humans do lose control, it will because of incompetence and a lack of basic understanding of what is happening, which unfortunately is looking likely to be the case.
I say decision makers will start to care once and if it becomes obvious they should. They will in turn write obviously, and avoid the “fun speculation” AI 2027 engages in that infantilizes such an important issue(so common).
Alignment will be a lot easier once we can convert weights to what they represent and predict how a model with a given weights will respond to any prompt. Ideally, we will be able to verify what an AI will do before it does it. We could also verify by having an AI describe a high level overview of its plan without actually implementing anything, and then just monitor and see if it deviated. As long as we can maintain logs and monitoring of those logs of all AI activities, it may be a lot harder for an ASI to engage in malign behavior.