A few weeks ago, Josh Lovejoy, the head of Design at MS, published a fascinating article on designing for AI [LINK]. This was the first article that I

AI/UX - Interfacing with AI [POST 1]

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2021-06-24 17:30:05

A few weeks ago, Josh Lovejoy, the head of Design at MS, published a fascinating article on designing for AI [LINK]. This was the first article that I’ve read in the last six months that sparked a whole new line of thinking about machine learning. If you have not read the article, I would recommend you do so.

To most ML practitioners, the primary concept that Lovejoy presents is not particularly novel. However, this is partially incomplete and needs further explanation to contextualize it in the machine learning space. ML, or more broadly AI, systems are simply function estimators. In layman's terms they are approximations of the world based on data that they have ‘seen’ and compartmentalized. From the perspective of lay users, ML sometimes seems like black magic. This impression is caused by estimations of very complex state spaces that we as human beings have difficulty conceptualizing leading to a wow factor by anthropomorphizing the ML system.

ML models produce data, that data can be thought of as interpolations or extrapolations (in the case of generative models) of some function. In fact, when you were in high school, you actually learned the basic interpolation/extrapolation method for linear functions, see the visualization below.