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The Master Algorithm: Revolutionizing Learning Machines

Discover how the quest for the ultimate learning machine is reshaping our world. From traditional programming to advanced data algorithms, explore the five tribes of machine learning and unravel the power of evolutionary and probabilistic inference. Can we find a single algorithm to solve it all? Join the journey toward a universal learner to unlock potentials in home robots, global brain networks, cancer treatments, and revolutionary recommenders. Your ideas are needed for the next breakthrough!

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The Master Algorithm: Revolutionizing Learning Machines

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  1. The Master AlgorithmHow the Quest for the Ultimate Learning Machine Will Remake Our World Pedro Domingos University of Washington

  2. Machine Learning

  3. Computer Computer Traditional Programming Machine Learning Data Output Algorithm Data Algorithm Output

  4. Master Algorithm Computer Traditional Programming Machine Learning Data Output Algorithm Data Algorithm Output

  5. The Five Tribes of Machine Learning

  6. Symbolists Tom Mitchell Steve Muggleton Ross Quinlan

  7. Inverse Deduction Addition Subtraction 2 + 2 ――― = ? 2 + ? ――― = 4 ―― ――

  8. Inverse Deduction Deduction Induction Socrates is human + ? ――――――――――― = Socrates is mortal Socrates is human + Humans are mortal . ――――――――――― = ? ―――――――――― ――――――――――

  9. Spot the Biologist in this Picture

  10. Connectionists Yann LeCun Geoff Hinton Yoshua Bengio

  11. A Neuron

  12. An Artificial Neuron

  13. Backpropagation

  14. The Google Cat Network

  15. Evolutionaries John Holland Hod Lipson John Koza

  16. Genetic Algorithms

  17. Genetic Programming

  18. Evolving Robots

  19. Bayesians David Heckerman Judea Pearl Michael Jordan

  20. Probabilistic Inference

  21. Probabilistic Inference

  22. Spam Filters

  23. Analogizers Peter Hart Vladimir Vapnik Douglas Hofstadter

  24. Nearest Neighbor

  25. Kernel Machines

  26. Recommender Systems

  27. The Big Picture

  28. The Big Picture But what we really need is a single algorithm that solves all five!

  29. Putting the Pieces Together • 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

  30. Toward a Universal Learner • Much remains to be done . . . • We need your ideas

  31. What a Universal Learner Will Enable Home Robots World Wide Brains Cancer Cures 360o Recommenders

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