Big Data Now: 2016 Edition

Chapter 6. Deep Learning and AI

We begin this chapter with an overview of the machine intelligence landscape from Shivon Zilis and James Cham. They document the emergence of a clear machine intelligence stack, weigh in on the “great chatbot explosion of 2016,” and argue that implementing machine learning requires companies to make deep organizational and process changes. Then, Aaron Schumacher introduces TensorFlow, the open source software library for machine learning developed by the Google Brain Team, and explains how to build and train TensorFlow graphs. Finally, Song Han explains how deep compression greatly reduces the computation and storage required by neural networks, and also introduces a novel training method—dense-sparse-dense (DSD)—that aims to improve prediction accuracy.

The Current State of Machine Intelligence 3.0

By Shivon Zilis and James Cham

You can read this post on oreilly.com here.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year’s landscape (see Figure 6-1) has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there.

As has been the case for the last couple of years, our fund still obsesses over “problem first” machine intelligence—we’ve invested in 35 machine-intelligence companies solving 35 meaningful problems ...