# Difference between revisions of "from Machine Learning to Machine Reasoning"

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It turns out that deep learning and multi-task learning show that we can leverage auxiliary tasks to help solve a task of interest. This idea can be interpreted as a rudimentary form of reasoning. | It turns out that deep learning and multi-task learning show that we can leverage auxiliary tasks to help solve a task of interest. This idea can be interpreted as a rudimentary form of reasoning. | ||

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## Revision as of 19:38, 5 November 2015

## Introduction

Learning and reasoning are both essential abilities associated with intelligence and machine learning and machine reasoning have received considerable attention given the short history of computer science. The statistical nature of machine learning is now understood but the ideas behind machine reasoning is much more elusive. Converting ordinary data into a set of logical rules proves to be very challenging: searching the discrete space of symbolic formulas leads to combinatorial explosion (cite). Algorithms for probabilistic inference (cite) still suffer from unfavourable computational properties (cite). Algorithms for inference do exist but they do however, come at a price of reduced expressive capabilities in logical inference and probabilistic inference.

Humans display neither of these limitations.

The ability to reason is the not the same as the ability to make logical inferences. The way that humans reason provides evidence to suggest the existence of a middle layer, already a form of reasoning, but not yet formal or logical. Informal logic is attractive because we hope to avoid the computational complexity that is associated with combinatorial searches in the vast space of discrete logic propositions.

It turns out that deep learning and multi-task learning show that we can leverage auxiliary tasks to help solve a task of interest. This idea can be interpreted as a rudimentary form of reasoning.