Pedro Domingos, Professor, University of Washington at MLconf ATL - 9/18/15
The Five Tribes of Machine Learning, and What You Can Take from Each: There are five main schools of thought in machine learning, and each has its own master algorithm – a general-purpose learner that can in principle be applied to any domain. The symbolists have inverse deduction, the connectionists have backpropagation, the evolutionaries have genetic programming, the Bayesians have probabilistic inference, and the analogizers have support vector machines. What we really need, however, is a single algorithm combining the key features of all of them. In this talk I will describe my work toward this goal, including in particular Markov logic networks, and speculate on the new applications that such a universal learner will enable, and how society will change as a result.
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Pedro Domingos, Professor, University of Washington at MLconf ATL - 9/18/15
- 1. And What You Can Take from Each Pedro Domingos University of Washington
- 4. Most of the knowledge in the world in the future is going to be extracted by machines and will reside in machines. – Yann LeCun, Director of AI Research, Facebook
- 5. 1. Fill in gaps in existing knowledge 2. Emulate the brain 3. Simulate evolution 4. Systematically reduce uncertainty 5. Notice similarities between old and new
- 6. Tribe Origins Master Algorithm Symbolists Logic, philosophy Inverse deduction Connectionists Neuroscience Backpropagation Evolutionaries Evolutionary biology Genetic programming Bayesians Statistics Probabilistic inference Analogizers Psychology Kernel machines
- 7. Tom Mitchell Steve Muggleton Ross Quinlan
- 8. Addition Subtraction 2 + 2 ――― = ? ―― 2 + ? ――― = 4 ――
- 9. Deduction Socrates is human + Humans are mortal . ――――――――――― = ? Induction Socrates is human + ? ――――――――――― = Socrates is mortal ―――――――――― ――――――――――
- 11. Yann LeCun Geoff Hinton Yoshua Bengio
- 16. John Koza John Holland Hod Lipson
- 20. David Heckerman Judea Pearl Michael Jordan
- 24. Peter Hart Vladimir Vapnik Douglas Hofstadter
- 28. Tribe Problem Solution Symbolists Knowledge composition Inverse deduction Connectionists Credit assignment Backpropagation Evolutionaries Structure discovery Genetic programming Bayesians Uncertainty Probabilistic inference Analogizers Similarity Kernel machines
- 29. Tribe Problem Solution Symbolists Knowledge composition Inverse deduction Connectionists Credit assignment Backpropagation Evolutionaries Structure discovery Genetic programming Bayesians Uncertainty Probabilistic inference Analogizers Similarity Kernel machines But what we really need is a single algorithm that solves all five!
- 30. Representation Probabilistic logic (e.g., Markov logic networks) Weighted formulas → Distribution over states Evaluation Posterior probability User-defined objective function Optimization Formula discovery: Genetic programming Weight learning: Backpropagation
- 31. Much remains to be done . . . We need your ideas
- 32. Home Robots Cancer Cures 360o Recommenders World Wide Brains
- 33. If we used all our technology resources, we could actually give people personalized recommendations for every step of your life. – Aneesh Chopra, former CTO of the U.S.