| I think this is down to the loudest, most ambitious projects ("AGI! Fully autonomous vehicles!") getting a lot of press. The reality is, production ML is basically everywhere already: - Basically every piece of software that makes recommendations (Netflix, Google, Facebook, YouTube, Instagram, TikTok, etc.) uses machine learning. - Anything that makes time series forecasts (Uber/Maps ETA prediction, Walmart's 2 hour delivery, etc.) uses machine learning. - All the most popular speech-to-text assistants (Alexa, Google Assistant, Siri) use machine learning. - Smartphone cameras use machine learning to enhance picture quality. - A lot of very highly-used security monitoring solutions (Stripe's fraud detection, CloudFlare's bot detection, etc.) rely on machine learning. - A surprising number of physical commerce-type situations rely on machine learning (autonomous filling stations, for example, are pretty common in the trucking industry). - A lot of smart image manipulation tools (Instagram/SnapChat filters, etc.) rely on deep learning. - Email clients, particularly Gmail, use machine learning for spam filtering and for things like Smart Compose. - Some infrastructure products use machine learning, as in the case of EC2's predictive autoscaling. And those are just hyper-scale examples. There's a ton earlier-stage-but-still-in-production projects doing awesome things with ML: - Wildlife Protection Solutions legitimately doubled their detection rate of poachers in nature preserves with ML. - PostEra, Benevolent AI, and a bunch of other ML-based medicine platforms (medicinal chemistry, drug discovery, etc.) have already had exciting results. - There are a bunch of startups building industry-specific APIs out of models, like Glisten.ai, that are already profitable. - A number of computer vision products have been brought to market in the healthcare space—Ezra.ai screens full-body MRIs for cancers, SkinVision detects melanomas. - ML-powered chatbots are a pretty huge market. Olivia (a financial assistant) has something like 500k users. AdmitHub has successfully lowered summer melt (the attrition of college-intending students between spring and fall) at a bunch of colleges. Rasa is an entire platform that helps startups build NLP-powered bots. Sorry that went a bit long, but basically, the production ML space is incredibly deep, and spans most industries/company sizes. Unfortunately, press coverage of ML tends to treat it as if it's this mystic, sci-fi future technology, and as a result, this "Show me AGI or it's snake oil" mindset naturally emerges. |