On July 3rd, Tom Cruise will be sixty years old. The fact that he does not look it, at all, even in IMAX closeups so tight you can study the grain of his tooth enamel, adds a note of cognitive dissonance to "Top Gun: Maverick," the long-aborning sequel in which he's called back to mentor a squad of younger stick-jockeys who address him as Pops and Old-Timer until he wins their respect in the air. Even for a physical performer like Cruise, sixty is no longer an expiration date. Mick Jagger blew by that milestone in 2003, as did Sylvester Stallone in 2006, and, thanks presumably to healthy habits and/or medical technology dreamt of only by science fiction, they're both still out there, doing a version of the kind of thing they've always done. But the level of performance expected of a Rolling Stone or an Expendable is one thing, and the work that Tom Cruise appears to demand of himself is something else entirely.

Recommender systems fundamentally address the question – What do people want? Although it is an extensive question, in the context of a consumer application like e-commerce, the answer could be to serve the best products in terms of price and quality for a consumer. For a news aggregator website, it could be to show reliable and relevant content. In a case where a user would have to look through thousands or millions of items to find what they are looking for, a recommendation engine is indispensable. The engine filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. It is so accurate that personalised recommendations from the engine drive 80% of Netflix viewer activity. However, building and evaluating a recommender system is very different compared to a single ML model regarding design decisions, engineering, and metrics. In this article, we will focus on testing a recommendation system. The second and third require a lot of user-item interaction data. If that is not available, one might start with the first type of recommender system.

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At Microsoft Build, ​​programming with OpenAI Codex was demoed. It’s a machine learning model that translates natural language into code.

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"Essentially we trained a machine learning model that can differentiate between the language of people who post to a thread on the topic of depression …