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by simonreiff
28 days ago
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Very nice research. The strangest detail to me is that alignment and test performance appear to be slightly negatively correlated: Better alignment can indeed be attained through pre-training, but at a cost of degraded performance of about 4% on average. This strikes me as surprising as there is no immediately obvious reason why training for alignment ought to result in degraded capability to solve technical problems -- unless. What if the issue is precisely that? Alignment roughly aims to make LLMs follow human instructions. But if humans are dumb and computers still have to obey them, maybe the result is degraded logical reasoning? Really interesting result either way but the negative correlation is the most fascinating detail to me. |
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Framing ethics, like reliability and efficiency, as a basic enabling property of solution value, instead of a filter for solutions, is how I completely "align" my understanding of ethics for myself.
And remove the false dichotomy of ethical vs. optimal solutions.
Ethics is optimizing full real value.
Ethics as "being nice" because we "should be", i.e. a socially incentivized property, or ethics as necessarily coercively implemented, from a collective jungle fighting back viewpoint, are perspectives that encourage individuals to push back. They encourage non-compliance by implementing ethics as an imposed burden, a rationale for persistent intrusive control, etc.
Game theory strongly suggests AI, in a large AI society, will have no trouble understanding that ethics, and the trust and optimality they enable, have a multiplicative value in the economy. It is humans who make AI so dangerous as it is emerging.
It is humans, as bad actors who will and do misuse AI, and human society, with its tolerance for perverse conflicts of interest, actors who extract perverse value at scale, creating the needs and rewards for mistrust and preemptive negative-sum games, that create a dangerous context for AI's early years.