I don’t see this as being the case. As Vadim pointed out, we don’t even know what we mean by “aligned versions” of algos, ATM. So we wouldn’t know if we’re succeeding or failing (until it’s too late and we have a treacherous turn).
“Orthogonality implies that alignment shouldn’t cost performance, but says nothing about the costs of ‘value loading’ (i.e. teaching an AI human values and verifying its value learning procedure and/or the values it has learned). Furthermore, value loading will probably be costly, because we don’t know how to do it, competitive dynamics make the opportunity cost of working on it large, and we don’t even have clear criteria for success.”
I don’t see this as being the case. As Vadim pointed out, we don’t even know what we mean by “aligned versions” of algos, ATM. So we wouldn’t know if we’re succeeding or failing (until it’s too late and we have a treacherous turn).
It looks to me like Wei Dai shares my views on “safety-performance trade-offs” (grep it here: http://graphitepublications.com/the-beginning-of-the-end-or-the-end-of-beginning-what-happens-when-ai-takes-over/).
I’d paraphrase what he’s said as:
“Orthogonality implies that alignment shouldn’t cost performance, but says nothing about the costs of ‘value loading’ (i.e. teaching an AI human values and verifying its value learning procedure and/or the values it has learned). Furthermore, value loading will probably be costly, because we don’t know how to do it, competitive dynamics make the opportunity cost of working on it large, and we don’t even have clear criteria for success.”
Which I emphatically agree with.