Only 10% of S&P 500 management teams actually quantified AI’s impact on specific use cases, Walker wrote, and a mere 1% quantified its impact on earnings. Furthermore, broader economic adoption remains sluggish. While half of companies in the broader Russell 3000 discussed AI, U.S. Census survey data indicates that fewer than 20% of establishments are currently utilizing AI for any business functions.
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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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