3min chapter

Machine Learning Street Talk (MLST) cover image

#54 Gary Marcus and Luis Lamb - Neurosymbolic models

Machine Learning Street Talk (MLST)

CHAPTER

Introduction

Machine learning systems typically learn to approximate functions by relating input variables to output variables in a process that Judea Pearl has likened to curve-fitting. Programmers on the other hand define their algorithms independently of training data purely in terms of operations over variables. Humans can pick up new skills and assimilate new knowledge with a small amount of new information. This is the so-called kaleidoscope effect, which is to say being able to cast previous experience into many new types of situations in experienced space. We do this by building new models on the fly by extrapolating from abstract prior knowledge.

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