Curated Resource ( ? )
The Limits of Artificial Intelligence and Deep Learning | WIRED
Curated:
05/02/2018 from
www.wired.com/story/greedy-brittle-opaque-and-shallow-the-downsides-to-deep-learning/
my notes ( ? )
Deep learning’s advances are the product of pattern recognition: neural networks memorize classes of things ... But almost all the interesting problems in cognition aren’t classification problems at all...
The systems are greedy because they demand huge sets of training data. Brittle because when... confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks. ...
opaque because... they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases... shallow because they are programmed with little innate knowledge and possess no common sense......
our best model for intelligence is ourselves, and humans think in many different ways.
The systems are greedy because they demand huge sets of training data. Brittle because when... confronted with scenarios that differ from the examples used in training—it cannot contextualize the situation and frequently breaks. ...
opaque because... they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases... shallow because they are programmed with little innate knowledge and possess no common sense......
our best model for intelligence is ourselves, and humans think in many different ways.
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The above notes were curated from the full post www.wired.com/story/greedy-brittle-opaque-and-shallow-the-downsides-to-deep-learning/.Related reading
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