For almost any goal an AI had, the AI would make more progress towards this goal if it became smarter.
True, but there it is likely that there are diminishing returns in how much adding more intelligence can help with other goals, including the instrumental goal of becoming smarter.
As an AI became smarter it would become better at making itself smarter.
Eventual diminishing returns, perhaps but probably long after it was smart enough to do what it wanted with Earth.
A drug that raised the IQ of human programmers would make the programmers better programmers. Also, intelligence is the ability to solve complex problems in complex environments so it does (tautologically) follow.
Eventual diminishing returns, perhaps but probably long after it was smart enough to do what it wanted with Earth.
Why?
A drug that raised the IQ of human programmers would make the programmers better programmers.
The proper analogy is with a drug that raised the IQ of researchers who invent the drugs that increase IQ. Does this lead to an intelligence explosion? Probably not. If the number of IQ points that you need to discover the next drug in a constant time increases faster than the number of IQ points that the next drug gives you, then you will run into diminishing returns.
It doesn’t seem to be much different with computers.
Algorithmic efficiency is bounded: for any given computational problem, once you have the best algorithm for it, for whatever performance measure you care for, you can’t improve on it anymore. And in fact long before you reached the perfect algorithm you’ll already have run into diminishing returns in terms of effort vs. improvement: past some point you are tweaking low-level details in order to get small performance improvements.
Once you have maxed out algorithmic efficiency, you can only improve by increasing hardware resources, but this 1) requires significant interaction with the physical world, and 2) runs into asymptotic complexity issues: for most AI problems worst-case complexity is at least exponential, average case complexity is more difficult to estimate but most likely super-linear. Take a look at the AlphaGo paper for instance, figure 4c shows how ELO rating increases with the number of CPUs/GPUs/machines. The trend is logarithmic at best, logistic at worst.
Now of course you could insist that it can’t be disproved that significant diminishing returns will kick in before AGI reaches strongly super-human level, but, as I said, this is an unfalsifiable argument from ignorance.
True, but there it is likely that there are diminishing returns in how much adding more intelligence can help with other goals, including the instrumental goal of becoming smarter.
Nope, doesn’t follow.
Eventual diminishing returns, perhaps but probably long after it was smart enough to do what it wanted with Earth.
A drug that raised the IQ of human programmers would make the programmers better programmers. Also, intelligence is the ability to solve complex problems in complex environments so it does (tautologically) follow.
Why?
The proper analogy is with a drug that raised the IQ of researchers who invent the drugs that increase IQ. Does this lead to an intelligence explosion? Probably not. If the number of IQ points that you need to discover the next drug in a constant time increases faster than the number of IQ points that the next drug gives you, then you will run into diminishing returns.
It doesn’t seem to be much different with computers.
Algorithmic efficiency is bounded: for any given computational problem, once you have the best algorithm for it, for whatever performance measure you care for, you can’t improve on it anymore. And in fact long before you reached the perfect algorithm you’ll already have run into diminishing returns in terms of effort vs. improvement: past some point you are tweaking low-level details in order to get small performance improvements.
Once you have maxed out algorithmic efficiency, you can only improve by increasing hardware resources, but this 1) requires significant interaction with the physical world, and 2) runs into asymptotic complexity issues: for most AI problems worst-case complexity is at least exponential, average case complexity is more difficult to estimate but most likely super-linear. Take a look at the AlphaGo paper for instance, figure 4c shows how ELO rating increases with the number of CPUs/GPUs/machines. The trend is logarithmic at best, logistic at worst.
Now of course you could insist that it can’t be disproved that significant diminishing returns will kick in before AGI reaches strongly super-human level, but, as I said, this is an unfalsifiable argument from ignorance.