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None of the existing tools seem effective enough, given their inputs, to lead to runaway exponential increase in capability. Coding assistants seem like they'll be very useful, but the blocking factors on making a more powerful AI don't seem to be the fact that writing code might be slow. It's compute, data, and new clever ideas and algorithms. And those seem like it still takes a lot of work to make an AI that can do those things--comparable to doing the work yourself. AlphaTensor, for example, involved a lot of training data and cleverly reframing the problem to achieve:
Increasing the speed at which you multiply matrices is obviously helpful for training new AI, but these results represent (at best) a minor speedup after an enormous effort. And every improvement you make means that further improvements are harder. In the case of multiplying matrices, there's some mathematical limit to how few operations you can perform; more complex problems aren't necessarily like this, but they could easily have a difficulty curve that scales similarly. I think previous data on benchmarks shows some evidence of this (e.g. linear or at best exponential improvement in performance with an exponential increase in parameters, see e.g. https://slatestarcodex.com/2020/06/10/the-obligatory-gpt-3-post/) although it's difficult to say for sure with few data points and that may be partially an artifact of how the benchmarks are scored (I recall seeing graphs that show logistic curve performance as a function of parameters, where the model does poorly for a long time, then suddenly starts performing much better very quickly and then hitting a performance ceiling).
The gpt 4 technical paper doesn't have these exact same graphs to compare, but it does seem like they're getting more mileage out of new training methods and new ideas rather than just brute-forcing with more parameters and compute. For example figure 6 shows only modest improvement from gpt 2 to to 3 (100x parameters) or 3 to 4 (unknown, maybe 10x) but gpt 4 does much better than chat gpt 4 (which I think is in part due to specifically trying to improve these measures).
Sure but the inputs are growing rapidly. There's still plenty of space at the bottom, the fundamental limits for computing are very generous. All our chips are still basically 2D!
Maybe our current machines can only produce a few nice-to-haves like this. But the next generation will produce more and better. Parameters get cheaper as transistors get smaller, as architecture gets better and algorithms improve. The amount of money we put in continually grows. And then our training methods improve as well. We're already starting to reap interest on the 'architecture improvement' front. Compound interest starts really slow but it gets powerful very quickly.
The human brain shows you can do a hell of a lot with 20 watts, at 20 hertz, on a shoestring materials budget, fitting the whole thing through a woman's hips! We have every element on the periodic table, endless lasers, acids and refinement techniques, we have gigawatts and gigahertz, thousands of cubic meters to spend. Our methods are incredibly primitive compared to what's already proven possible, there's so much low-hanging fruit we're yet to find.
The question is not whether current technology will help you make better technology, or whether AGI is theoretically possible. The question is how quickly change happens, and to what extent advances make future advances faster: You have better tools but the problem has also become harder. So far, it seems to me like the latter effect is winning out. GPT 4 can write (allegedly) working code, use documentation, bug fix, etc. But is it good enough to make writing GPT 5 substantially easier or faster than making GPT 4 was?
Well I doubt 'Open'AI would tell us, they like keeping things secret nowadays. Nevertheless, existing demonstrated capabilities seem to be accelerating progress. I'm not a subject matter technical expert but it seems this is happening: https://www.hpcwire.com/2022/04/18/nvidia-rd-chief-on-how-ai-is-improving-chip-design/
I can't judge how significant this is because I'm not an expert. But my intuition is that compound interest balloons outwards and there's plenty of physics/computing space for it to balloon outwards into. This is a fundamentally new kind of compound interest that is different to whatever input scaling we were already doing to keep up with Moore's law. In addition to increasing the amount of wealth and human intellect going in quantitatively, we get some qualitatively superior (albeit specialized) inhuman intellect too.
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