We’ve already discussed this in one of the other threads, but I’ll just repeat here that this isn’t correct. With overwhelmingly high probability a Gaussian matrix will satisfy the restricted isometry property, which implies that appropriately L1-regularized least squares will return the exact solution.
I do wonder if it would have been better to include something along the lines of “with probability 1” to the claim that non-Bayesian methods can solve it easily. Compressed sensing isn’t magic, even though it’s very close.
any agent that is intelligent enough to behave at all functionally close to the level of a human would be robust to context changes.
Humans get tripped up by context changes very frequently. It’s not obvious to me where you think this robustness would come from.
Compressed sensing isn’t even magic, if you’re halfway versed in signal processing. I understood compressed sensing within 30 seconds of hearing a general overview of it, and there are many related analogs in many fields.
The convex optimization guys I know are all rather impressed by compressed sensing- but that may be because they specialize in doing L1 and L2 problems, and so compressed sensing makes the things they’re good at even more important.
I do wonder if it would have been better to include something along the lines of “with probability 1” to the claim that non-Bayesian methods can solve it easily. Compressed sensing isn’t magic, even though it’s very close.
Humans get tripped up by context changes very frequently. It’s not obvious to me where you think this robustness would come from.
Compressed sensing isn’t even magic, if you’re halfway versed in signal processing. I understood compressed sensing within 30 seconds of hearing a general overview of it, and there are many related analogs in many fields.
The convex optimization guys I know are all rather impressed by compressed sensing- but that may be because they specialize in doing L1 and L2 problems, and so compressed sensing makes the things they’re good at even more important.