2006+ • « Neuron » is a loose inspiration (not important) • Stacked architecture of modules that compute internal abstract representations from the data • Parameters are tuned from labeled examples
~5% error rate • "It is clear that humans will soon only be able to outperform state of the art image classification models by use of significant effort, expertise, and time.” source: What I learned from competing against a ConvNet on ImageNet
Sentiment Analysis) • Machine Translation (e.g. English to French) • Speech recognition: audio to text • Speech synthesis: text to audio • Biological sequence modeling (DNA, Proteins)
Many applications already in production (e.g. speech, image indexing, translation, face recognition) • Very promising results for QA and robot control • Machine Learning is now moving from pattern recognition to higher level reasoning