1. Machine Learning Capabilities

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

3. ILSVRC

4. Classification Error

5. Speech

6. Face Recognition

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

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

9. Other areas Medicine Law Crime prediction
Watson suggests treatment for oncology – first suggestion is appropriate 77% of the time
Law
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

10. 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: