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We have been using objects of the kind described in your last paragraph, called compilers since the 1950s, and with the increasing number of portability-focused
by moab 5y ago
We have been using objects of the kind described in your last paragraph, called compilers since the 1950s, and with the increasing number of portability-focused high-performance DSLs / frameworks like tensorflow or OneAPI, we are only going further down this direction. But yet 70 years after the advent of compilers, there are still people who know how to open-up the machine, improve it, and fix it, and there probably always will be.
I don't see how machine learning, at least in its current non-AGI state, will be any different. It's just that your average end-user will have no idea how to "open up the machine", but that's also true for compiler technology today.
- adrianN 5y agoThere need not be material differences if there are sufficient differences in scale. Of course we already rely on algorithmic black boxes, in fact I'd argue that we have been relying on algorithmic black boxes since before we had computers (we just call them "traditions" instead). But if a technology like neural nets expands the applicability by a sufficient margin, the resulting societal change can be huge.