2026年1月托福机经套卷(第10套) - Read an Academic Passage Q2

Expert Systems Expert systems are a branch of artificial intelligence designed to mimic the decision-making abilities of human experts. These systems use a knowledge base of specialized information and rules to solve specific problems. Their goal is to replicate the expertise and reasoning of professionals in fields like medicine and engineering. An early expert system is MYCIN, developed in the 1970s to diagnose bacterial infections and recommend antibiotics. MYCIN used rules provided by medical experts to analyze patient data, infer diagnoses, and suggest treatments. MYCIN often performed as well as human specialists; however, MYCIN's system needed constant updates, requiring extensive input from medical professionals. Despite their potential, expert systems have limitations; since they rely on static knowledge databases, they can become outdated without ongoing input from human experts. The process of updating the system can be labor-intensive. Advancements in machine learning and natural language processing could address these issues. By integrating these technologies, expert systems could learn from new data and adapt, improving accuracy and relevance. Researchers are optimistic that these advancements will transform expert systems, enabling them to tackle a wider range of challenges and operate more independently.