In deep learning, there’s no data like more data. The more examples of a given phenomenon a network is exposed to, the more accurately it can pick out patterns and identify things in the real world. Given much more data, an algorithm designed by a handful of mid-level AI engineers usually outperforms one designed by a world-class deep-learning rese

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As a technology and an industry, AI naturally gravitates toward monopolies. Its reliance on data for improvement creates a self-perpetuating cycle: better products lead to more users, those users lead to more data, and that data leads to even better products, and thus more users and data. Once a company has jumped out to an early lead, this kind of

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They’re using billions of dollars in cash and dizzying stockpiles of data to gobble up available AI talent. They’re also working to construct the “power grids” for the AI age: privately controlled computing networks that distribute machine learning across the economy, with the corporate giants acting as “utilities.” It’s a worrisome phenomenon for

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We also now have a persuasive “so what?” These technologies have enabled a whole new user interface for computers: human language. Just like the graphical user interface made the personal computer accessible to millions of customers in the 1980s, so too have the new natural language interfaces made AI accessible to hundreds of millions of users wor... See more

Cette évolution est déjà visible : au milieu du déluge de contenus générés par l'IA - articles, images, vidéos, codes - émerge une lassitude des discours préformatés, qu'ils viennent des machines ou des idéologues. Les gens commencent à faire la différence entre ce qui est produit pour informer ou vendre, ce qui est produit pour dénoncer, et ce qui... See more