The AI field is plagued by irrational optimism and irrational despair. In 1973, Sir James Lighthill was asked to compile a report on the then-present state of artificial intelligence. His report criticized the hype surrounding artificial intelligence research, suggesting that AI's best algorithms would always fail at solving real world problems and could really only work for solving "baby" problems. His report followed almost twenty-five years of fervent research into human-like algorithms. The AI "summer" between the 1950s and 1970s saw DARPA investing millions into undirected research that touched on natural language processing.

Photo: Robotic arm made for MIT AI lab, 1972. In 1973, the burgeoning field of artificial intelligence (AI) was at a crossroads: Governments had grown skeptical of the research and were hesitant to continue pumping millions of dollars into projects with few actionable results. So, to examine what AI had done, the United Kingdom solicited a report from a famous mathematician named Sir James Lighthill. What happened next was controlled chaos, the sort of polite brawling that only PhD's in neat suits are capable of. AI researchers erupted with anger, and possibly for good reason: It turned out that Lighthill, whose report demolished AI, did not fully understand the field he was trying to crush.

The area of computation called artificial intelligence (AI) is falsified by describing a previous 1972 falsification of AI by British applied mathematician James Lighthill. It is explained how Lighthill's arguments continue to apply to current AI. It is argued that AI should use the Popperian scientific method in which it is the duty of every scientist to attempt to falsify theories and if theories are falsified to replace or modify them. The paper describes the Popperian method in detail and discusses Paul Nurse's application of the method to cell biology that also involves questions of mechanism and behavior. Arguments used by Lighthill in his original 1972 report that falsified AI are discussed. The Lighthill arguments are then shown to apply to current AI. The argument uses recent scholarship to explain Lighthill's assumptions and to show how the arguments based on those assumptions continue to falsify modern AI. An important focus of the argument involves Hilbert's philosophical programme that defined knowledge and truth as provable formal sentences. Current AI takes the Hilbert programme as dogma beyond criticism while Lighthill as a mid 20th century applied mathematician had abandoned it. The paper uses recent scholarship to explain John von Neumann's criticism of AI that I claim was assumed by Lighthill. The paper discusses computer chess programs to show Lighthill's combinatorial explosion still applies to AI but not humans. An argument showing that Turing Machines (TM) are not the correct description of computation is given. The paper concludes by advocating studying computation as Peter Naur's Dataology.