Chapter 8 - Simplification without Falsification: The Problem of Relevance in Logic and AI

Summary

Introduction

The problem I discuss in this chapter, with reference to Ludwig Wittgenstein's later philosophy of logic, its development and AI, concerns the issue of how to simplify complex information without falsification. Two relevant modes of simplification are abstraction and idealization. When we abstract, we leave out some features of the actual cases. When we idealize, we make things neater, for example, more uniform or exact than they are. As I will explain, the problem of how to simplify without falsifying quickly brings us to the notion of relevance. What can be abstracted or idealized away without falsification is that which is not relevant (essential, important or significant), and in general simplification without falsification requires that whatever is relevant (essential, important or significant) is taken into account. However, the notion of relevance in turn assumes or involves the perception of things being significant; it presupposes that the acting or thinking agent or entity has goals, purposes or interests. Thus, to get an AI system to simplify without falsifying, and to make it able to handle complex information in an intelligent way in this sense, seems to require that its behaviour is informed by goals, purposes or interests.

The assumption I am making about intelligent behaviour is worth making explicit: In what follows, I assume that simplification without falsification is an essential aspect of intelligent behaviour, even though it does not exhaust it. This is what enables an intelligent agent or entity to pick out what is relevant from a wealth of information and to update its perception of what is relevant when needed. Accordingly, I assume that to create an AI system whose behaviour could be described as intelligent (whatever intelligence in general is or means) requires that the system is capable of simplification without falsification. This seems important also for AI systems that are specialized rather than generally intelligent and might be used, for example, for diagnosing illnesses, examining images to find a certain kind of object, and so on for a great number of possible tasks. Before an AI system can be relied on in performing such tasks without a human being checking whether it might have ignored something relevant, we must be confident that it can simplify without falsifying.