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And What You Can Take from Each
Pedro Domingos University of Washington
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Evolution Experience
Culture
Evolution Experience
Culture Computers
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
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
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
Tom Mitchell Steve Muggleton Ross Quinlan
Addition Subtraction
2 + 2 ――― = ? ――
2 + ? ――― = 4 ――
Deduction
Socrates is human + Humans are mortal . ――――――――――― = ?
Induction
Socrates is human + ? ――――――――――― = Socrates is mortal
―――――――――― ――――――――――
Yann LeCun Geoff Hinton Yoshua Bengio
John Koza John Holland
Hod Lipson
David Heckerman Judea Pearl Michael Jordan
Peter Hart Vladimir Vapnik Douglas Hofstadter
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
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!
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
Much remains to be done . . . We need your ideas
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