The problem is that during the industrial revolution it also took a long time because people caught on that 40 hours per week were more effective. It is really hard to reliably measure performance in the long term. Managers are discouraged from advocating a 40 hour work week since this flies in the face of the prevailing attitude. If they fail, they will almost definitely be fired since ‘more work’->‘more productivity’ is the common sense answer, whether or not it is true. It would not be worth the risk for any individual manager to try this unless the order came from the top. Of course, this is not an argument in favor of the 40 hour week, it just shows that this could just as well be explained by a viral meme as by reasonable decisions.
This is part of the reason why I find it so hard to find any objective information on this.
I work in the area of AGI research. I specifically avoid working on practical problems and try to understand why our models work and how to improve them. While I have much less experience than the top researchers working on practical applications, I believe that my focus on basic research makes me unusually suited for understanding this topic.
I have not been very surprised by the progress of AI systems in recent years. I remember being surprised by AlphaGo, but the surprise was more about the sheer amount of resources put into that. Once I read up on details, the confusion disappeared. The GPT models did not substantially surprise me.
A disclaimer: Every researcher has their own gimmick. Take all of the below with a grain of salt. It’s possible that I have thought myself into a cul-de-sac, and the source of the AGI problem lies elsewhere.
I believe that the major hurdle we still have to pass is the switch from System 1 thinking to System 2 thinking. Every ML model we have today uses System 1. We have simply found ways to rephrase tasks that humans solve with System 2 to become solvable by System 1. Since System 1 is much faster, our ML models perform reasonably well on this despite lacking System 2 abilities.
I believe that this can not scale indefinitely. It will continue to make progress and solve amazingly many problems, but it will not go FOOM one day. There will continue to be a constant increase in capability, but there will not be a sudden takeoff until we figure out how to let AI perform System 2 reasoning effectively.
Humans can in fact compute floating point operations quickly. We do it all the time when we move our hands, which is done by System 1 processes. The problem is that doing it explicitly in System 2 is significantly slower. Consider how fast humans learn how to walk, versus how many years of schooling it takes for them to perform basic calculus. Never mind how long it takes for a human to learn how walking works and to teach a robot how to do it, or to make a model in a game perform those motions.
I expect that once we teach AI how to perform system 2 processes, it will be affected by the same slowdown. Perhaps not as much as humans, but it will still become slower to some extent. Of course this will only be a temporary reprieve, because once the AI has this capability, it will be able to learn how to self-modify and at that point all bets are off.
What does that say about the timeline?
If I am right and this is what we are missing, then it could happen at any moment. Now or in a decade. As you noticed, the field is immature and researchers keep making breakthroughs through hunches. So far none of my hunches have worked for solving this problem, but so far as I know I might randomly come up with the solution in the shower some time later this week.
Because of this, I expect that the probability of discovering the key to AGI is roughly constant per time interval. Unfortunately I have no idea how to estimate the probability per time interval that someone’s hunch for this problem will be correct. It scales with the number of researchers working on it, but the number of those is actually pretty small because the majority of ML specialists work on more practical problems instead. Those are responsible for generating money and making headlines, but they will not lead to a sudden takeoff.
To be clear, if AI never becomes AGI but the scaling of system 1 reasoning continues at the present rate, then I do think that will be dangerous. Humanity is fragile, and as you noted a single malicious person with access to this much compute could cause tremendous damage.
In a way, I expect that an unaligned AGI would be slightly safer than super-scaled narrow AI. There is at least a non-zero chance that the AGI would decide on its own, without being told about it, that it should keep humanity alive in a preserve or something, for game theoretic reasons. Unless the AGI’s values are actively detrimental for humans, keeping us alive would cost it very little and could have benefits for signalling. A narrow AI would be very unlikely to do that because thought experiments like that are not frequent in the training data we use.
Actually, it might be a good idea to start adding thought experiments like these to training data deliberately as models become more powerful. Just in case.