In 1950, Alan Turing<\/strong> proposed 2 assumptions that set the direction of artificial intelligence research for the next 3 quarters of a century. First, that intelligence can exist independently of the body and be recreated in software. Second, that a machine can be regarded as thinking if it successfully imitates a human being in conversation.<\/p>\n\n\n\n Peter J. Denning, a computer scientist and the author of Turing\u2019s Mistake: Escaping the Yoke of Unintelligent Machines<\/em>, believes that these claims sent the development of artificial intelligence<\/strong> down the wrong path:<\/p>\n\n\n\n These 2 claims shaped a large part of research and development in the field of artificial intelligence. In my view, our acceptance of these claims has led to the AI mess in which we now find ourselves.<\/p>\n<\/blockquote>\n\n\n\n According to Denning, both assumptions leave out tacit knowledge<\/strong>. This is the vast store of human understanding that cannot easily be put into words or written in a form computers can process. Denning points to 5 areas that machine learning cannot capture: common sense, everyday interactions with people and the surrounding world, emotions and perception, practical skills, and the social and historical knowledge rooted in culture.<\/p>\n\n\n\n As an example, he invokes the Cyc project<\/strong>: Douglas Lenat\u2019s attempt, begun in the 1980s, to catalogue all common sense in a database. After 4 decades of work, the database contained about 25 million entries. Yet even that enormous collection did not prove enough to make expert systems wise enough to truly deserve the name \u201cexpert.\u201d As Denning notes, Cyc only confirmed that much of the knowledge that makes people experts cannot be expressed in the form of propositions.<\/p>\n\n\n\n Even harder to transmit to machines are practical skills<\/strong>. Denning distinguishes descriptive knowledge<\/a> of \u201cwhat\u201d from embodied knowledge of \u201chow,\u201d the kind needed for skilled action. The first can easily be written down as data. The second cannot be coded. The example of a virtuoso violinist captures this well: the musician can play beautiful music, but cannot explain to a student exactly how to create it.<\/p>\n\n\n\n At the root of the problem lies what Denning calls the representation problem<\/strong>. Computers process only data written in a form they can recognize, and tacit knowledge does not fit into that form.<\/p>\n\n\n\n Behind every word lies a deep layer of tacit knowledge that gives it meaning. Words are only symbolic representations of meanings, not the meanings themselves. Widely used large language models, such as ChatGPT, Claude, and Gemini, merely manipulate words. They cannot know or understand the meaning of what they say,<\/p>\n<\/blockquote>\n\n\n\n Denning emphasizes<\/a>.<\/p>\n\n\n\n A team of researchers from Cardiff Metropolitan University, University College London, and Cardiff University reached similar conclusions. Their paper appeared in AI & Society<\/em>. The authors identified 13 fundamental weaknesses of large language models. On that basis, they argue that human knowledge cannot be reduced to data alone<\/strong>.<\/p>\n\n\n\n As they explain, human knowledge takes shape through socialization, education, and participation in a community. Language models, by contrast, learn from enormous collections of internet text without going through any of these processes. Through education and upbringing, people develop the ability to judge whether information is credible and the ability to admit when they do not know something.<\/p>\n\n\n\n Language models do not have these foundations. They operate on the basis of a kind of response imperative: a tendency to give an answer regardless of whether they have a reliable basis for formulating one.<\/p>\n\n\n\n Large language models are exceptionally good at generating convincing language, but this is not the same as genuine understanding,<\/p>\n<\/blockquote>\n\n\n\n says<\/a><\/strong> Dr. Simon Thorne of Cardiff Metropolitan University.<\/p>\n\n\n\n\n
The knowledge that cannot be written down<\/h2>\n\n\n\n
Words alone are not enough<\/h2>\n\n\n\n
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Human knowledge cannot be reduced to data<\/h2>\n\n\n\n
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