It’s funny you say “about to start” - I feel like I’m in a minority here but I’m kind of suspicious that we’re at the end of the S-curve of ML, and the real-wor

It’s funny you say “about to start” - I feel like I’m in a minority here but I’m kind of suspicious that we’re at the end of the S-curve of ML, and the real-world applications are well-known: - A/B testing for UI design - Ml modeling for ad targeting and optimization - fraud detection - image classification, object recognition and segmentation (lots of neat Snap filters, many niche CV apps with drones, industrial sensors, medical diagnostic tools, self-driving cars???) - text classification (sentiment analysis) - text generation (translation, document summaries, writing assistants) - niche academic/scientific applications (simulation acceleration) Empirical modeling has been around for more than a century, there’s just been an explosion of image and text data that have demanded a new generation of empirical models (ML) - it’s resulted in a vast array of decision-acceleration applications, but aside from ChatGPT it feels like the boundaries are starting to be felt more concretely these days.