Oh, thank you for showing me his work! As far as I can tell, yes, Comprehensive AI Services seems to be what we are entering already—with GPT-3′s Codex writing functioning code a decent percentage of the time, for example! And I agree that limiting AGI would be difficult; I only suppose that it wouldn’t hurt us to restrict AGI, assuming that narrow AI does most tasks well. If narrow AI is comparable in performance, (given equal compute) then we wouldn’t be missing-out on much, and a competitor who pursues AGI wouldn’t see an overwhelming advantage. Playing it safe might be safe. :)
And, that would be my argument nudging others to avoid AGI, more than a plea founded on the risks by themselves: “Look how good narrow AI is, already—we probably wouldn’t see significant increases in performance from AGI, while AGI would put everyone at risk.” If AGI seems ‘delicious’, then it is more likely to be sought. Yet, if narrow AI is darn-good, AGI becomes less tantalizing.
And, for the FOOMing you mentioned in the other thread of replies, one source of algorithmic efficiency is a conversion to symbolic formalism that accurately models the system. Once the over-arching laws are found, modeling can be orders of magnitude faster, rapidly. [e.g. the distribution of tree-size in undisturbed forests always follows a power-law; testing a pair of points on that curve lets you accurately predict all of them!]
Yet, such a reduction to symbolic form seems to make the AI’s operations much more interpretable, as well as verifiable, and those symbols observed within its neurons by us would not be spoofed. So, I also see developments toward that DNN-to-symbolic bridge as key to BOTH a narrow-AI-powered FOOM, as well as symbolic rigor and verification to protect us. Narrow AI might be used to uncover the equations we would rather rely upon?
Oh, thank you for showing me his work! As far as I can tell, yes, Comprehensive AI Services seems to be what we are entering already—with GPT-3′s Codex writing functioning code a decent percentage of the time, for example! And I agree that limiting AGI would be difficult; I only suppose that it wouldn’t hurt us to restrict AGI, assuming that narrow AI does most tasks well. If narrow AI is comparable in performance, (given equal compute) then we wouldn’t be missing-out on much, and a competitor who pursues AGI wouldn’t see an overwhelming advantage. Playing it safe might be safe. :)
And, that would be my argument nudging others to avoid AGI, more than a plea founded on the risks by themselves: “Look how good narrow AI is, already—we probably wouldn’t see significant increases in performance from AGI, while AGI would put everyone at risk.” If AGI seems ‘delicious’, then it is more likely to be sought. Yet, if narrow AI is darn-good, AGI becomes less tantalizing.
And, for the FOOMing you mentioned in the other thread of replies, one source of algorithmic efficiency is a conversion to symbolic formalism that accurately models the system. Once the over-arching laws are found, modeling can be orders of magnitude faster, rapidly. [e.g. the distribution of tree-size in undisturbed forests always follows a power-law; testing a pair of points on that curve lets you accurately predict all of them!]
Yet, such a reduction to symbolic form seems to make the AI’s operations much more interpretable, as well as verifiable, and those symbols observed within its neurons by us would not be spoofed. So, I also see developments toward that DNN-to-symbolic bridge as key to BOTH a narrow-AI-powered FOOM, as well as symbolic rigor and verification to protect us. Narrow AI might be used to uncover the equations we would rather rely upon?