I think this is unfair. Tens (hundreds?) of billions of dollars have gone into deep learning and, as I understand it, most effort consists of scaling up this golden goose and presenting its "new" successes in the best light possible. Every time throwing scale at the problem produces an impressive result, billions more dollars flow in, thousands of more people are nudged into the field. We got the first orders of magnitude of scaling "free" from GPUs, but the rest are going to come at considerable cost.

Whether or not there is a better alternative, if deep learning is in fact as over-hyped as the author claims, this could be a tremendous waste of money and intellect that could be spent on literally anything else, not just machine learning (maybe they could put those resources into crypto instead /s). That alone is enough reason to want intellectually honest skeptical takes, whether or not the author has a better idea. In addition, within AI, it makes it much harder for people for people to do anything else.

If there is a contraction in the field it will likely cause another giant AI winter. Somebody should start thinking of a use for all that compute.

The internal combustion engine did not require as much of a drain on resources before it produced results. Is deep learning actually making significant amounts of money, funding and valuations excluded?