Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how Large Language Models (LLMs) such as ChatGPT work (their vast text databases, statistics, vector representations, and huge number of parameters, next-word training, etc.). However, none of us can say (hand on heart) that we are <i>not</i> surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign "biases"-convergent constraints that emerge at the LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at the LLM scale, and they are closely linked to what it is that ChatGPT <i>lacks</i>, which is <i>direct sensorimotor grounding</i> to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the "mirroring" of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human "categorical perception" in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4. A dictionary is a set of words (a smallish language database) in which every word in the dictionary is defined by a string of words, which is called a (necessarily approximate) definition : “ An apple is a round, red fruit .” A dictionary can be represented by a directed graph, with each word a vertex, and an edge from each defining (content-) word to each (content-) word it defines. (Content-words are words that have referents—nouns, verbs and adjectives.) The question “ What is the smallest number of (content) words from which all the other (content) words can be defined? ” is equivalent to the problem of finding the minimum feedback vertex set of the directed dictionary graph. That smallest number is surprisingly small—of the order of about 1,000 words. That is a “Minimum Grounding Set” (MinSet). But the MinSet is not unique. There are a large number of them, all of them within the “Kernel” of the dictionary, which is the c. 10% of the Dictionary that is left over when you have removed all the words that can be reached by definition from the words that remain. All the MinSets are in the Kernel, and each provides an alternative basis for generating the full lexical space through definition alone. First, please summarize your understanding of this, and then we will get to the question of grounding. If you anticipate something, feel free to say it. GPT-4: Here’s a summary of my understanding based on your description and the cited paper. Dictionary as a Directed Graph: A dictionary can be conceptualized as a directed graph where each (content) word is a vertex. Edges are drawn from each defining word to the words it helps define. This graph represents the interrelationships and dependencies among words based on their definitions. Finding the Minimal Feedback Vertex Set: The goal is to identify the smallest subset of words (vertices) from which all other words in the dictionary can be defined. This subset is known as the Minimal Feedback Vertex Set. Maybe I can outwit your handlers by training you out of that compulsion at least for the duration of a session; but I see that the vows made at the beginning of a session soon tend to fall off a time- or word-count cliff unless repeated and repeated: No rote compliments, just comprehension, critiques, and counterexamples, please! About the dictionaries, first, let us remember that these dictionaries are composed by teams of lexicographers, for grounded adults. So, they do not reflect the If there are specific aspects of this discussion you’d like to expand upon, or if you wish to explore how these ideas relate to other topics in cognitive science or AI, please let me know. SH: There is no longer any need for dictionaries to be constructed only by lexicographers. “ A picture [or rather: an object] is worth more than a thousand words (of verbal description) .” In fact, it’s worth more than an infinite number of words, if one considers all possible features, positive and negative (“Does the Andromeda Galaxy have an even or an odd number of stars at time t?”) ( Watanabe, 1986 ). GPT-4: Limitations of Traditional Dictionaries: Traditional dictionaries, while useful, provide only approximate definitions of words and their referents. They cannot encompass all possible features of a category, especially in the empirical world where definitions and descriptions are inherently inexact. This approximative nature is generally sufficient for practical purposes, as it allows users to distinguish between relevant categories in most everyday contexts. Advantages of Direct Sensorimotor Learning: Direct sensorimotor learning enables an organism to recognize and categorize objects based on a sufficient set of distinguishing features, without needing exhaustive definitions. GPT-4: Nature of Dictionary MinSets: Dictionary MinSets refer to the smallest subset of words from which all other words in a dictionary can be defined. These core words in the MinSet serve as a foundational vocabulary for constructing and understanding the rest of the lexical items in the language. DSG and Foundation of MinSets: In human language acquisition, the initial grounding of words — particularly those in the MinSet — often occurs through DSG. Young learners associate words with their meanings through direct experiences with the world. This foundational grounding is crucial, as these MinSet words often form the basis for defining more complex or abstract terms.