3 ms·

I remember taking a class at UCSD in 1971 or 1972 that touched on Perceptrons (among a lot of other things, it was basically a survey course). I wonder sometime

I remember taking a class at UCSD in 1971 or 1972 that touched on Perceptrons (among a lot of other things, it was basically a survey course). I wonder sometimes what the world would be like today if they had realized the importance of hidden layers back then.

rococode 5y ago
Data and compute power in the 70s probably would've limited the usefulness and led people to try other things (perhaps not even "probably" - that could be what really happened), though maybe it could've been revisited with success by the late 90s. Makes you wonder if there are other abandoned techniques that might be worth circling back to nowadays...
- mrDmrTmrJ 5y ago
  k-NN methods always love more data and can be embarrassingly competitive with far fancier algorithms. Should always be considered as a classification baseline: https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
- MaysonL 5y ago
  A former colleague of mine, Terry Koken, who worked with Rosenblatt at Cornell for a while and is quoted in the article, dropped a comment last year saying pretty much that: https://blogs.umass.edu/comphon/2017/06/15/did-frank-rosenblatt-invent-deep-learning-in-1962/#comment-7831 https://blogs.umass.edu/comphon/2017/06/15/did-frank-rosenbl...
sjg007 5y ago
Empirical methods/experimentation have classically taken a back seat to mathematical proofs in computer science.