This article originally appeared on Engadget at https://www.engadget.com/ai/canadian-government-demands-safety-changes-from-openai-204924604.html?src=rss
Anthropic's quotes in an interview with Time sound reasonable enough in a vacuum. "We felt that it wouldn't actually help anyone for us to stop training AI models," Jared Kaplan, Anthropic's chief science officer, told Time. "We didn't really feel, with the rapid advance of AI, that it made sense for us to make unilateral commitments… if competitors are blazing ahead."
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This approach shares a lot in common with the idea of multivariate interpolation over scattered data. Multivariate interpolation attempts to estimate values at unknown points within an existing data set and is often used in fields such as geostatistics or for geophysical analysis like elevation modelling. We can think of our colour palette as the set of variables we want to interpolate from, and our input colour as the unknown we’re trying to estimate. We can borrow some ideas from multivariate interpolation to develop more effective dithering algorithms.
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