19 bookmarks. First posted by kenyob 22 days ago.
"According to skeptics like Marcus, deep learning is greedy, brittle, opaque, and shallow. The systems are greedy because they demand huge sets of training data. Brittle because when a neural net is given a “transfer test”—confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks. They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography. Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases. Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology."notes
13 days ago by keithpeter
To see why modern AI is good at a few things but bad at everything else, it helps to understand how deep learning works. Deep learning is math: a statistical method where computers learn to classify patterns using neural networks. Such networks possess inputs and outputs, a little like the neurons in our own brains; they are said to be “deep” when they possess multiple hidden layers that contain many nodes, with a blooming multitude of connections. Deep learning employs an algorithm called backpropagation, or backprop, that adjusts the mathematical weights between nodes, so that an input leads to the right output.AI deep-learning google statistics
20 days ago by jchris
We've been promised a revolution in how and why nearly everything happens. But the limits of modern artificial intelligence are closer than we think. Pedro Domingos, professor of computer science and engineering at the UW, is quoted.Domingos.Pedro !UWitM 2018 natl WIRED College:Engineering Allen.School Artificial.Intelligence
22 days ago by uwnews