Broadly, it seems that in a world where LeCun’s architecture becomes dominant, useful AI safety work looks more analogous to the kind of work that goes on now to make self-driving cars safe. It’s not difficult to understand the individual components of a self-driving car or to debug them in isolation, but emergent interactions between the components and a diverse range of environments require massive and ongoing investments in testing and redundancy.
I think this is the crux of the matter. This is why LeCun tweeted:
One cannot just “solve the AI alignment problem.” Let alone do it in 4 years. One doesn’t just “solve” the safety problem for turbojets, cars, rockets, or human societies, either. Engineering-for-reliability is always a process of continuous & iterative refinement.
LeCun, like Sam Altman, believes in an empirical, iterative approach to AI safety. This is in sharp contrast to the highly theoretical, figure-it-all-out-far-in-advance approach of MIRI.
I don’t get why some folks are so dismissive of the empirical, iterative approach. Is it because they believe in a fast takeoff?
Very lucidly written. Thanks.
I think this is the crux of the matter. This is why LeCun tweeted:
LeCun, like Sam Altman, believes in an empirical, iterative approach to AI safety. This is in sharp contrast to the highly theoretical, figure-it-all-out-far-in-advance approach of MIRI.
I don’t get why some folks are so dismissive of the empirical, iterative approach. Is it because they believe in a fast takeoff?
I think that if you mentioned cars and planes you should read this