An Essay concerning machine understanding
This work tackles the fundamental problem of machine understanding for AI researchers, but it is incremental as it builds on existing philosophical and psychological paradigms without presenting new empirical results.
The essay addresses the lack of understanding in AI systems by proposing a framework for constructing machines that can understand, based on the idea that understanding involves recovering concepts from words, as inspired by historical and cognitive psychology insights.
Artificial intelligence systems exhibit many useful capabilities, but they appear to lack understanding. This essay describes how we could go about constructing a machine capable of understanding. As John Locke (1689) pointed out words are signs for ideas, which we can paraphrase as thoughts and concepts. To understand a word is to know and be able to work with the underlying concepts for which it is an indicator. Understanding between a speaker and a listener occurs when the speaker casts his or her concepts into words and the listener recovers approximately those same concepts. Current models rely on the listener to construct any potential meaning. The diminution of behaviorism as a psychological paradigm and the rise of cognitivism provide examples of many experimental methods that can be used to determine whether and to what extent a machine might understand and to make suggestions about how that understanding might be instantiated.