Understanding understanding: a renormalization group inspired model of (artificial) intelligence

10/26/2020
by   A. Jakovac, et al.
0

This paper is about the meaning of understanding in scientific and in artificial intelligent systems. We give a mathematical definition of the understanding, where, contrary to the common wisdom, we define the probability space on the input set, and we treat the transformation made by an intelligent actor not as a loss of information, but instead a reorganization of the information in the framework of a new coordinate system. We introduce, following the ideas of physical renormalization group, the notions of relevant and irrelevant parameters, and discuss, how the different AI tasks can be interpreted along these concepts, and how the process of learning can be described. We show, how scientific understanding fits into this framework, and demonstrate, what is the difference between a scientific task and pattern recognition. We also introduce a measure of relevance, which is useful for performing lossy compression.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

12/10/2019

Toward XAI for Intelligent Tutoring Systems: A Case Study

Our research is a step toward understanding when explanations of AI-driv...
04/22/2015

Ascribing Consciousness to Artificial Intelligence

This paper critically assesses the anti-functionalist stance on consciou...
11/28/2017

Digital Encyclopedia of Scientific Results

This study describes a vision, how technology can help improving the eff...
02/01/2022

Quantifying Relevance in Learning and Inference

Learning is a distinctive feature of intelligent behaviour. High-through...
10/14/2015

Mathematical Foundations for Designing and Development of Intelligent Systems of Information Analysis

This article is an attempt to combine different ways of working with set...
03/06/2020

What is "Intelligent" in Intelligent User Interfaces? A Meta-Analysis of 25 Years of IUI

This reflection paper takes the 25th IUI conference milestone as an oppo...
12/12/2012

Understanding (dis)similarity measures

Intuitively, the concept of similarity is the notion to measure an inexa...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.