In brief An AI system has bested nearly 1,300 human competitors in the annual American Crossword Puzzle Tournament to achieve the top score. The computer, named Dr Fill, is the brainchild of computer scientist Matt Ginsberg, who designed its software to automatically fill out crosswords using a mixture of "good old-fashioned AI" and more modern machine-learning techniques, according to Slate. It was able to solve multiple word conundrums fast with fewer errors than its opponents. Dr Fill, however, was not eligible for the $3,000 cash prize, which instead went to the best human player, a man named Tyler Hinman, who presumably isn't feeling somewhat redundant. Ginsberg's machine contained a computer running a 64-core CPU and two GPUs, and was trained on tons of text scraped from Wikipedia to learn words, and a database of crossword clues and their answers to parse the competition questions.

Until now, most processor chips used for AI applications have been adaptations of devices, like GPUs, that were initially developed for other purposes. This is partly because those existing devices have proven effective enough to be useful, and partly because most companies have seen development of AI-specific processors as prohibitively costly, complex, and/or risky. But, with the AI market poised for intense expansion, diverse new applications at the edge and in endpoints will require silicon that's more closely tailored for specific tasks and situations, removing the need to go to the cloud for AI processing. We are now starting to see indications of how the semiconductor sector is evolving to meet those needs, and what it will mean for the evolution of the AI marketplace and those who seek to enter it. A useful case study is provided by Edge AI startup Blaize, which is pursuing customers in industrial, smart city, automotive sensor fusion, last-mile delivery, and retail applications with its recently launched Pathfinder and Xplorer system-on-module platforms and accompanying software tools.