https://cdn.twimlai.com/wp-content/uploads/2017/08/17172256/TWIMLAI_Background_800x800_MH_43.jpg 800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) https://cdn.twimlai.com/wp-content/uploads/2017/08/17172256/TWIMLAI_Background_800x800_MH_43.jpg August 21, 2017 November 1, 2018

Today’s show, which concludes the first season of the Industrial AI Series, features my interview with Bonsai co-founder and CEO Mark Hammond. I sat down with Mark at Bonsai HQ a few weeks ago and we had a great discussion while I was there. We touched on a ton of subjects throughout this talk, including his starting point in Artificial intelligence, how Bonsai came about & more.

Mark also describes the role of what he calls “machine teaching” in delivering practical machine learning solutions, particularly for enterprise or industrial AI use cases. This was one of my favorite conversations, I know you’ll enjoy it!

TWIML Online Meetup

The first TWIML Online Meetup was last week and it was wonderful. The focus of the meetup was the CVPR best-paper-award-winner “Learning From Simulated and and Unsupervised Images through Adversarial Training” by researchers from Apple. The idea behind this paper is this: Consider a problem like eye gaze detection. You’ve got a picture from, for example a cell phone camera, and you want to determine which way the user is looking. Generating labeled eye gaze training data is hard and expensive. Generating simulated eye gaze training data sets, is much easier and cheaper, and can be done for example using something like a video game engine. The problem is that the simulated eye gaze images don’t look close enough to real images to train a model to work effectively on real data. This paper proposes using a Generative Adversarial Network to train a “refiner” model that can make simulated eye gaze images look like real eye gaze images while preserving the gaze direction. Thanks again to community members Josh Manela who did a great job presenting this paper and to Kevin Mader for walking through a TensorFlow implementation of the model that he created! You guys are just awesome!

We’re working on getting the recording posted for those who weren’t able to join us live. If you’re signed up for the meetup or the newsletter, you’ll be notified when it’s available. If you’d like to join the Meetup, head over to the meetup page to register. Next month’s meetup will be held on Wednesday September 13th at 11 am Pacific Time, and we’ll post details of the program shortly.

Thanks to Our Sponsors

If you’re trying to build AI-powered applications focused on optimizing and controlling the physical systems in your enterprise, whether robots or HVAC systems or vehicle fleets, you should take a look at what Bonsai up to. They’ve got a unique approach to building AI models that lets you model the real-world concepts in your application; automatically generate, train and evaluate low level models for your project–using technologies like reinforcement learning; and easily integrate those models into your applications and systems using APIs. You can check them out at bons.ai/twimlai, and definitely let them know you appreciate their support of the podcast.

I’m also excited to announce Wise.io and GE Digital as a sponsor. Wise.io was among the first companies I began following in what I called the Machine Learning platforms space, back in 2012/2013. I’ve since interviewed co-founder Josh Bloom here on the show and mentioned the company’s subsequent acquisition by GE Digital. At GE Digital, the Wise.io team is focused on creating technology and solutions to enable advanced capabilities for the Industrial Internet of things, making infrastructure more intelligent and advancing the industries critical to the world we live in. I want to give a hearty thanks and shout-out to the team at Wise.io and GE Digital for supporting my Industrial AI research and this podcast series. Of course you can check them out at Wise.io.

The Artificial Intelligence Conference

If you weren’t one of the lucky winners of our AI conference SF giveaway, but are still interested in attending the Conference, register using the code PCTWIML for 20% off registration! Links to the conference can be found below!

About Mark

Mentioned in the Interview