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Based on the mathematical similarity between chemical pathway networks and artificial neural networks, a new construct to achieve artificial intelligence running on a matter is developed. A general theory is derived followed by an evaluation of its fitting capability and an example where a low\u2010temperature plasma is trained to play the board game Tic\u2013Tac\u2013Toe. The plasma can emit a spectrum representing its next move when we are feeding a gas combination carrying the board information. Finally, the fourth state of matter shows a significantly high winning rate against a random\u2010move player, reflecting its own strategies. This work reveals that any matter, with substantial chemical complexity, can process information based on particle collisions, like a programmable analog computer at the molecular level.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200157","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T19:46:02Z","timestamp":1657223162000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial Intelligence without Digital Computers: Programming Matter at a Molecular Scale"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0176-8858","authenticated-orcid":false,"given":"Li","family":"Lin","sequence":"first","affiliation":[{"name":"Mechanical and Aerospace Engineering School of Engineering and Applied Science The George Washington University  800 22nd Street, NW. 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