Machine Learning Capabilities

ImageNet Large Scale Visual Recognition Challenge Object Recognition: 1000 classes 1.2 million images Human capability??? ~80000 nouns in English Perhaps recognise 10000 objects?

ILSVRC

Classification Error

Speech

Face Recognition

Finance Rebellion research uses ML to decide what stocks to buy/sell

Games Computer wins: Human wins: Chess – 1997 Jeopardy - 2011 Go – 2016 Human wins: Poker Bridge

Other areas Medicine Law Crime prediction Watson suggests treatment for oncology – first suggestion is appropriate 77% of the time Law http://www.rossintelligence.com/ built on top of Watson. I think it mostly does discovery at this point. Crime prediction Hitachi Predictive Crime Analytics (uses live feeds – data, video, 911, twitter, facebook etc) NCAA Basketball Kaggle Competition Best had log-loss of 0.44 – that means right approx 85% of the time

Limitations Three main ones: Data! The more data the better the result. Computation time – the more training the better, but training can take months. Experts needed to choose the architecture/training algorithms – there aren’t enough experts for all the problems. E.g. IBM has invested $1B in Watson. I can’t see any major impediments to machines being better than humans in most day-to-day endeavours Even creative areas are not exempt: https://github.com/jcjohnson/neural-style