knowledge acquisition bottleneck

The difficulty, cost and slowness of eliciting, formalizing and maintaining the knowledge that expert systemsexpert system, n.: A program that stores a human specialist's know-how as many if–then rules and uses them to give advice or make decisions in a narrow field, such as diagnosing infections or configuring computers. and other knowledge-based programs require, long regarded as the main limit on their scope. Experts often cannot articulate tacit knowledge, rules interact unpredictably as a knowledge baseknowledge base, n.: A store of facts and rules that a program can reason with: like a database, but organized so that new conclusions can be worked out from it, not just looked up. grows, and knowledge must be revised as the domain changes, so building a major system took years of knowledge engineeringknowledge engineering, n.: The craft of collecting what human experts know — through interviews, observation and documents — and turning it into rules or structured data that a computer system can use.. Responses included specialized elicitation tools, reusable ontologiesontology, n.: A carefully defined vocabulary for a subject such as medicine, spelling out each kind of thing, how kinds nest inside one another, and which connections between them are allowed., case-based reasoningcase-based reasoning, n.: Handling a new situation by finding the most similar one dealt with before and adjusting what worked then, much as a cook adapts a remembered recipe to the ingredients at hand. and, above all, machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats., which extracts knowledge from data — first as learned rules and decision treesdecision tree, n.: A model that makes a prediction by asking a series of yes-or-no questions about the input, like a flowchart, with each answer leading to the next question until it reaches a final answer., later as the implicit knowledge of large language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code.. Some researchers now describe an inverted bottleneck: models absorb vast knowledge cheaply, but it is hard to inspect, verify or correct.