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.
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!
Probabilistic logic (e.g., Markov logic networks)
Weighted formulas → Distribution over states
User-defined objective function
Formula discovery: Genetic programming
Weight learning: Backpropagation
Much remains to be done . . .
We need your ideas
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