Riffing on the idea that “productionizing a cool research result into a tool/product/feature that a substantial number of users find better than their next best alternative is actually a lot of work”: it’s a lot less work in larger organizations with existing users numbering in the millions (or billions). But, as noted, larger orgs have their own overhead.
I think this predicts that most of the useful products built around deep learning which come out of larger orgs will have certain characteristics, like “is a feature that integrates/enhances an existing product with lots of users” rather than “is a totally new product that was spun up incubator-style within the organization”. It plays to the strengths of those orgs—having both datasets and users, playing better with the existing org structure and processes, more incentive-aligned with the people who “make things happen”, etc.
A couple examples of what I’m thinking of:
substantial improvements in speech recognition—productionized as voice assistant technology, it’s now good enough that it’s sometimes easier to use one than to do something by hand, like setting a timer/alarm/reminder/etc while your hands are occupied with something else,
substantial improvements in image recognition—productionized as image search. I can search for “documents” in Google Photos, and it’ll pull up everything that looks like a document. I can more narrowly search “passport” and it’ll pull up pictures I took of my passport. I can search for “license plate” and it’ll pull up a picture I took of my license plate. I just tried searching for “animal” and it pulled up:
An animated gif of a dog with large glasses on it
Statues of men on horseback, as well as some sculptures of eagles
A bunch of fish in tanks
For structural reasons I’d expect “totally novel, standalone products” to come out of startups rather than larger organizations, but because they’re startups they lack many of the “hard things are easy” buttons that some larger orgs have.
Riffing on the idea that “productionizing a cool research result into a tool/product/feature that a substantial number of users find better than their next best alternative is actually a lot of work”: it’s a lot less work in larger organizations with existing users numbering in the millions (or billions). But, as noted, larger orgs have their own overhead.
I think this predicts that most of the useful products built around deep learning which come out of larger orgs will have certain characteristics, like “is a feature that integrates/enhances an existing product with lots of users” rather than “is a totally new product that was spun up incubator-style within the organization”. It plays to the strengths of those orgs—having both datasets and users, playing better with the existing org structure and processes, more incentive-aligned with the people who “make things happen”, etc.
A couple examples of what I’m thinking of:
substantial improvements in speech recognition—productionized as voice assistant technology, it’s now good enough that it’s sometimes easier to use one than to do something by hand, like setting a timer/alarm/reminder/etc while your hands are occupied with something else,
substantial improvements in image recognition—productionized as image search. I can search for “documents” in Google Photos, and it’ll pull up everything that looks like a document. I can more narrowly search “passport” and it’ll pull up pictures I took of my passport. I can search for “license plate” and it’ll pull up a picture I took of my license plate. I just tried searching for “animal” and it pulled up:
An animated gif of a dog with large glasses on it
Statues of men on horseback, as well as some sculptures of eagles
A bunch of fish in tanks
For structural reasons I’d expect “totally novel, standalone products” to come out of startups rather than larger organizations, but because they’re startups they lack many of the “hard things are easy” buttons that some larger orgs have.