An Essay concerning machine understanding

Herbert L. Roitblat ABSTRACT 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. I know there are not words enough in any language to answer all the variety of ideas that enter into men's discourses and reasonings. But this hinders not but that when any one uses any term, he may have in his mind a determined idea, which he makes it the sign of, and to which he should keep it steadily annexed during that present discourse. John Locke 1689 Artificial intelligence systems exhibit many useful capabilities, but as has often been said, they lack "understanding," which would be a critical capability for general intelligence. The transformer architecture on which current systems are based takes one string of tokens and produces another string of tokens (one token at a time) based on the aggregated statistics of the associations among tokens. The representations mediating between the inputs (e.g., prompts) and their production is one purely of the statistical relations among the word tokens. In the case of large language models, we know these facts to be true because this is how the models were designed and they were trained on a kind of fill-in-the-blank test to guess the next word. What exactly would it mean for an artificial intelligence system to understand? How would we know that it does?