| I work for a data science consulting company and previously in advertising, so I can say that there are (1) a number of very real problems that cannot realistically be solved _without_ "AI" (though what does that term even mean these days?), and (2) endless piles of "AI"-branded nonsense. First of all, what do you consider AI? Deep learning? Hierarchical bayesian models? Linear regression? I would call all of these "AI" simply because I have seen all of these labeled as such at one point or another. Does your business revolve around computer vision tasks ("how can I track people/cars/things in this image/video?")? You almost definitely need "AI" even if you have almost no historical data. Does your business revolve around optimizing decisions based on large amounts of historical and relevant data (e.g. user interactions on your website, etc.)? You _probably_ need "AI" (but it depends). There are an infinite number of ways to incompetently apply AI and upsell what you're doing to people who don't know the difference. It happens all the time and is a natural consequence of any hype cycle. But underneath all of the hype there is obvious substance (just maybe not at your company). Concrete and very public examples of competent AI: * Tesla's "auto"-pilot (computer vision/SLAM) * Recommender systems that you see everywhere (Facebook Watch/News Feed, YouTube, Netflix, etc.) -- can be reinforcement learning based, factorization based, etc. * Real-time bidding markets in advertising (DSP's like Google's DV360, Trade Desk, Xandr run optimized bidding "AI") |