What people call "ML" is actually several bundled phenomena. Unbundling them is profitable exercise that can help prevent alot of heartburn
* 1 -> the discovery of specific families of non-linear classification algorithms (with image and language patterns being examples succesful new domains). the domain where these approaches are productive might be significantly smaller than what all the hyperventilation and obfuscation suggests.
* 2 -> the ability to deploy algorithms "at scale". this cannot be overemphasized. Statistics used to be dark art practiced by scienty types in white lab coats locked in ivory towers. With open source libraries, linux, etc to a large degree ML means "statistics as understood and practiced by recently graduated computer scientists"
* 3 -> business models and regulatory environments that enabled the collection of massive amounts of personal data and the application of algorithms in "live" human contexts without much regard for consent, implications, risks etc. Compare that wild west with the hoops that medical, insurance or banking algorithms are supposed to pass
Conclusion, ML is here to stay in some shape or form, but ML hype has an expiration date
* 1 -> the discovery of specific families of non-linear classification algorithms (with image and language patterns being examples succesful new domains). the domain where these approaches are productive might be significantly smaller than what all the hyperventilation and obfuscation suggests.
* 2 -> the ability to deploy algorithms "at scale". this cannot be overemphasized. Statistics used to be dark art practiced by scienty types in white lab coats locked in ivory towers. With open source libraries, linux, etc to a large degree ML means "statistics as understood and practiced by recently graduated computer scientists"
* 3 -> business models and regulatory environments that enabled the collection of massive amounts of personal data and the application of algorithms in "live" human contexts without much regard for consent, implications, risks etc. Compare that wild west with the hoops that medical, insurance or banking algorithms are supposed to pass
Conclusion, ML is here to stay in some shape or form, but ML hype has an expiration date