Saturday, November 28, 2015

Monetizing Intellectual Property and Google's Machine Learning

From stratechery:

TensorFlow and Monetizing Intellectual Property
Ten years ago Bill Gates suggested that open source software was the province of “modern-day sort of communists” whose views on intellectual property were hopelessly outdated:
The idea that the United States has led in creating companies, creating jobs, because we’ve had the best intellectual property system — there’s no doubt about that in my mind, and when people say they want to be the most competitive economy, they’ve got to have the incentive system. Intellectual property is the incentive system for the products of the future.
Gates’ perspective was understandable: he had built Microsoft into the biggest company in technology and one of the biggest in the world by, for all intents and purposes, selling licenses to text. Sure, that’s a dramatic over-simplification of Windows and the other software Microsoft sold, but that didn’t change what a seachange the Redmond-based company seemed to represent: one where the pure expression of ideas could make you the richest person in the world. Yet those antediluvian open-source zealots wanted to simply give it all away.
The Open-Sourcing of TensorFlow
Microsoft is still a big company — their market cap was $427 billion at yesterday’s market close — but an even bigger company today is Alphabet ($506 billion), which has a decidedly different approach:
earlier this week its Google subsidiary announced it was open-sourcing TensorFlow, its formerly proprietary machine learning system. From the official Google blog:
Just a couple of years ago, you couldn’t talk to the Google app through the noise of a city sidewalk, or read a sign in Russian using Google Translate, or instantly find pictures of your Labradoodle in Google Photos. Our apps just weren’t smart enough. But in a short amount of time they’ve gotten much, much smarter. Now, thanks to machine learning, you can do all those things pretty easily, and a lot more. But even with all the progress we’ve made with machine learning, it could still work much better.
So we’ve built an entirely new machine learning system, which we call “TensorFlow.” TensorFlow is faster, smarter, and more flexible than our old system, so it can be adapted much more easily to new products and research. It’s a highly scalable machine learning system — it can run on a single smartphone or across thousands of computers in datacenters. We use TensorFlow for everything from speech recognition in the Google app, to Smart Reply in Inbox, to search in Google Photos. It allows us to build and train neural nets up to five times faster than our first-generation system, so we can use it to improve our products much more quickly.
We’ve seen firsthand what TensorFlow can do, and we think it could make an even bigger impact outside Google. So today we’re also open-sourcing TensorFlow. We hope this will let the machine learning community — everyone from academic researchers, to engineers, to hobbyists — exchange ideas much more quickly, through working code rather than just research papers. And that, in turn, will accelerate research on machine learning, in the end making technology work better for everyone.
Machine learning is super important to Google; just a couple of weeks ago, on Alphabet’s Q3 earnings call, Google CEO Sundar Pichai stated in his opening remarks, “I also want to point out that our investments in machine learning and artificial intelligence are a priority for us”, and followed that up with a series of examples where machine learning was serving as a differentiator for Google. Pichai later added, in response to a question:
Machine learning is a core transformative way by which we are rethinking everything we are doing. We’ve been investing in this area for a while. We believe we are state-of-the-art here. And the progress particularly in the last two years has been pretty dramatic. And so we are thoughtfully applying it across all our products, be it search, be it ads, be it YouTube and Play et cetera. And we are in early days, but you will see us in a systematic manner, think about how we can apply machine learning to all these areas.
At a superficial level, this doesn’t make sense: if machine learning is core to Google’s future, then what is the point of giving it away? Does the company not care about making money?...

Previously on the Puny Earthlings channel:

How Google Aims To Dominate Artificial Intelligence
"Why Is Machine Learning (CS 229) The Most Popular Course At Stanford?"
'Deep Learning' as Applied to Investing
Deep Learning is VC Worthy
Zuckerberg, Musk Invest in Machine Learning Artificial-Intelligence Company, Vicarious
Google's Plan To Make Your Brain Irrelevant (GOOG; EVIL;)
MIT's Technology Review: "10 technologies we think most likely to change the world"
"As Machines Get Smarter, Evidence They Learn Like Us"
Researcher Dreams Up Machines That Learn Without Humans
Google Launches the Quantum Artificial Intelligence Lab (GOOG)
Baidu Artificial Intelligence Beats Google, Microsoft In Image Recognition
"Inside Google’s Massive Effort in Deep Learning" (GOOG)
Artificial Intelligence Company Sentient Raises Another $103 Million, Emerges From Stealth 
Deep Learning: "A Common Logic to Seeing Cats and Cosmos"
Federal Reserve Board FEDS Notes: "Using big data in finance: Example of sentiment-extraction from news articles"
DeepMind: Google's Artificial Intelligence Guy (GOOG)

And quite a few more, use the search blog box if interested.

Additionally, here's the GOOG's Research blog.