Why can’t computers directly understand words and sentences?

Why can’t computers directly understand words and sentences?

A computer can store and display a sentence without knowing what the sentence means. Text encodings assign numbers to characters, while programs decide what to do with those numbers. Recognizing the characters in “The glass is full” does not automatically establish what a glass is or whether the statement is true.

Character encoding is only a starting point

Consider the word “bat.” Its letters can be represented using a standard character encoding, but the same spelling may refer to an animal or sports equipment. The character codes remain the same in both cases. A useful language system therefore needs more than a reliable way to store letters.

An LLM first tokenizes the text and maps token IDs to vectors. Its later layers use surrounding information to produce representations that can distinguish “a bat flew overhead” from “he swung the bat.” These are learned computational relationships, rather than a dictionary definition embedded in each character.

Meaning depends on relationships

Language also involves word order, negation, references, and background assumptions. “The dog chased the cat” differs from “The cat chased the dog,” even though the vocabulary is identical. A model needs mechanisms for using sequence information and combining relevant context.

What learning makes possible

Training allows a neural network to improve how it processes these patterns. It can then classify a message, answer a question, or continue a passage. Its success should be judged through behavior and testing: producing a fluent sentence is different from demonstrating a dependable interpretation in every situation.