NICK WALKER:  Welcome to Manage This, the podcast by project managers for project managers.  Every other week we get together to talk about the things that matter to you as a professional project manager.  And it doesn’t really matter whether you’re a PM veteran or someone simply exploring what the field is all about.  We’re here to offer some ideas, some perspective, and draw on the experiences of others who have been down that road and have realized success. I’m your host, Nick Walker, and with me are two who are still on that road, Andy Crowe and Bill Yates. ANDY CROWE:  Thanks, Nick.  We’ve had so much interest in the topic of artificial intelligence within project management, and we’ve got somebody here who knows a lot about AI who’s going to be processing that with us. NICK WALKER:  Our guest here in the studio is Chris Benson.  He’s an artificial intelligence machine learning strategist, a solution architect, and a keynote speaker who specializes in deep learning.  That’s the computation technology that is driving the artificial intelligence revolution. Chris is the co-host of the Practical AI podcast, produced by Changelog Media, designed to make artificial intelligence practical, productive, and accessible to everyone.  He’s the founder and organizer of the Atlanta Deep Learning Meetup, one of the largest AI communities in the world, with nearly 2,000 members.  Chris, it’s great to have you here on our podcast. CHRIS BENSON:  Thank you very much.  Happy to be here. NICK WALKER:  Could we start off by just defining for our listeners what artificial intelligence is? CHRIS BENSON:  So artificial intelligence means a lot of different things to a lot of different people.  In my view it’s really a marketing word more than it is anything else because over the years the definition of artificial intelligence has changed and evolved.  So what you might have thought of in the 1980s is vastly different from what it is in 2018.  So before I define it, I want to point out I was in a group of artificial intelligence experts that Adobe was hosting about six weeks ago.  And in doing that, they asked us all that same question; and all 10 of us gave 10 different answers. ANDY CROWE:  Well, and the joke is, if you ask two economists for a definition, you get three answers. CHRIS BENSON:  Absolutely. ANDY CROWE:  Same idea, huh. CHRIS BENSON:  Yup.  So it was very much that.  So I wanted to note that.  Take what I say with a grain of salt. ANDY CROWE:  What do you think it is, yeah. CHRIS BENSON:  So what I think it is, is a narrow definition.  I would consider that in 2018 artificial intelligence is synonymous with deep learning, which is the application of deep neural networks. ANDY CROWE:  Interesting.  Well, learning is certainly a part of AI that I think that’s almost a universal component that goes across most definitions.  Most definitions talk about the ability to imitate intelligence and things like that, imitate human intellect.  But that ability to learn and grow as a neural network is an interesting part of it.  So how do machines learn? CHRIS BENSON:  So there’s different techniques.  And those all broadly fall under the definition of machine learning.  The thing that separates deep learning, which is how I’m defining AI, from the rest is that it can take an enormous number of inputs – we call them “features” in data science – and process them in a highly nonlinear manner and give inferences, which are essentially probabilistic predictions on what the answer might be. For instance, to make it real:  If you have machine vision, and you are putting a cat in front of the camera, and it will come back and identify that it thinks it’s a cat.  It might come back 97 percent.  But the difference is these technologies aren’t going to come back with 100 percent.  They’re probabilistic technologies.  But they can make these identifications using a model that is many orders of magnitude ...