john bronskill - machine learning is everywhere
TRANSCRIPT
Machine Learning is EverywhereJohn Bronskill Partner Architect Microsoft Research Cambridge
Who is this?
Paul the Octopus: Correctly predicted 85% of Germany’s 2010 World Cup matches!
Paul died in October 2010 and the pressure was on to find a replacement…
There is a better way to make predictions…That is Machine Learning
Machine Learning entails the use of algorithms that can learn from and make predictions on data.
Machine Learning is Everywhere
All of these applications would be difficult or impossible to program by hand.
Information Retrieval
Fraud DetectionRecommendations
Predictive Maintenance
How does machine learning work?
Predict the sale price of a home
Training
Home Features - Size - No. of bedrooms - Distance to school - Interest rate - …
Sale Prices ($, £, €)
Learned Model
Learn from previous sales
Home Features Sale Price ($, £, €)Learned Model
Predict current values
• Can be extremely effective, but: • Need a large number of labelled training examples • Getting labelled training data can be manpower intensive • Doesn’t generalize well to new domains
Learning Algorithms
Logistic Regression Neural NetworkRandom Forest
Human in the loop
▪ Facebook M ▪ Hybrid system: AI + supervision by humans
▪ “Everything the trainers do, we record every step,” ▪ Websites they visit ▪ Telephone conversations ▪ What they type in response to M users, etc.
▪ In the future, this data can help train a more advanced system.
Why machine learning use is ubiquitous
cloud computing2011 ➔ 2016 5x increase
inexpensive storage
+ powerful compute
Moore’s Law
90% of the data in the world today has been created in the last two years alone
data explosion
connected customers
1B+200M10.4M 160M
Movement to the Cloud
Machine Learning at Microsoft Research Cambridge
Kinnect learned body tracking
front view top viewside viewinput depth inferred body parts
Visual Studio Developer Assistant
Visual Studio Developer Assistant
Office 365 Clutter
Summary
▪Machines can learn ▪ But they are not yet intelligent ▪ Poor generalization of learning ▪ Extensive human interaction still required
Any Questions?