{"id":237828,"date":"2026-05-27T06:08:55","date_gmt":"2026-05-27T11:08:55","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/05\/paul-vitanyi"},"modified":"2026-05-27T06:08:55","modified_gmt":"2026-05-27T11:08:55","slug":"paul-vitanyi","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/05\/paul-vitanyi","title":{"rendered":"Paul Vit\u00e1nyi"},"content":{"rendered":"

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Consider teaching a computer how to read by giving it billions of books. You don\u2019t teach it grammar rules or logic; you simply ask it to play a game: \u201cLook at these words, and guess what word comes next.\u201d To win this game at a world-class level, the computer can\u2019t just memorize phrases. It has to start figuring out how the world works. If it\u2019s reading a mystery novel, it needs to deduce who the killer is to guess the final sentence. If it\u2019s reading a math textbook, it has to understand addition to predict the answer to a problem. This is the core idea explored in a recent scientific paper titled \u201cAlgorithmic Compression via Pretrained Neural Networks.\u201d*The researchers look under the hood of today\u2019s Large Language Models (LLMs)\u2014like the AI assistants we use every day\u2014to explain a fascinating mystery: Why does a machine trained merely to predict the next word end up looking like it can think, reason, and solve complex problems? Think about how a ZIP file works on your computer. If you have a massive text file filled with the word \u201capple\u201d repeated a million times, a compression program won\u2019t save all million words. It will compress it into a short rule: \u201cRepeat \u2018apple\u2019 1,000,000 times.\u201d It turns a massive mountain of data into a tiny, elegant recipe. (learning how to learn). Because the AI is fed a massive, diverse diet of information, it can\u2019t just memorize everything. Instead, it is forced to find the underlying \u201crecipes\u201d or rules behind the data it sees. When you type a prompt into an AI, it doesn\u2019t just look up an answer in a database. It looks at your text, infers the \u201cgenerative algorithm\u201d (the underlying pattern or logic of what you are asking), and uses that pattern to compress the problem and generate the correct response. In essence, it deduces the hidden rules of the game on the fly. * Discover Complex Logic: When given a sequence of chess moves, the AI doesn\u2019t just guess random moves; it actually reconstructs the abstract rules and evaluations of a chessboard in its digital \u201cmind.\u201d While this framework helps explain why AI is getting so smart, it also opens up big new questions. We know these models are compressing data and finding rules, but we still don\u2019t fully understand the absolute limits of this approach. How close can a practical AI get to that theoretical \u201cperfect\u201d intelligence? What happens when the AI runs out of human data to learn from?<\/p>\n

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Vit\u00e1nyi was appointed professor of computer science at the University of Amsterdam, and researcher at the National Research Institute for Mathematics and Computer Science in the Netherlands (CWI, initially Mathematical Centre [MC]) where he is currently a CWI Fellow. He was guest professor at the University of Copenhagen in 1978; research associate at the Massachusetts Institute of Technology in 1985\/1986; Gaikoku-Jin Kenkyuin (councilor professor) at INCOCSAT at the Tokyo Institute of Technology in 1998; visiting professor at Boston University in 2004, at Monash University in 1996 and at the National ICT of Australia NICTA at University of New South Wales in 2004\/2005; visiting professor at and adjunct professor of computer science at the University of Waterloo from 2005.<\/p>\n","protected":false},"excerpt":{"rendered":"

Consider teaching a computer how to read by giving it billions of books. You don\u2019t teach it grammar rules or logic; you simply ask it to play a game: \u201cLook at these words, and guess what word comes next.\u201d To win this game at a world-class level, the computer can\u2019t just memorize phrases. It has [\u2026]<\/p>\n","protected":false},"author":709,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1509,41,2229,6],"tags":[],"class_list":["post-237828","post","type-post","status-publish","format-standard","hentry","category-entertainment","category-information-science","category-mathematics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/237828","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/709"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=237828"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/237828\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=237828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=237828"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=237828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}