Here are a few of my talks & panels:
On what must have been the hottest NYC summer day of 2017, Columbia University’s Tow Center for Digital Journalism and Brown Institute for Media Innovation presented Artificial Intelligence: Practice and Implications for Journalism. The conference featured leading journalists, technologists, legal scholars and academics in conversation around the current and near future applications, challenges, and legal implications of AI implementation in products and newsrooms.
In May 2017 I gave a talk on building data science teams in industry and setting them up for success at csv,conf—a non-profit community conference run by some folks who really love data and sharing knowledge.
Here are a few of my writings and appearances:
GE Industrial Machine Learning Workshop, October 2017
I gave a keynote presentation at the first GE Industrial Machine Learning Workshop on October 24, 2017 in San Francisco, CA during GE's Minds + Machines 2017 Event. In it I discussed how, in the field of data science, we are constantly bombarded with innovative approaches and methods. These fresh new tools promise to yield impressively accurate results—and, like all Faustian bargains, can come at a cost that all too often stays hidden until it’s too late. Those of us who gravitate towards this discipline can get easily seduced by the promise of cutting-edge precision. We can even fall prey to our impulses of following the latest and coolest at the expense of our objectives. When does it make sense to deploy complex solutions into production environments? And how should we assess the pros and cons of doing so?
Hanselminutes Podcast, June 2017
In this episode I sat down with Scott and talked about the major concepts around data science: Is this a new science and an old one? What’s the traditional path for a Data Scientist - and is that the only path?
Links from the show:
Thinking Poker Podcast, February 2017
I am an occasional poker player with more than a passing interest in Libratus and its implications for artificial intelligence. In this interview (starting 41 minutes into the recording), we talked about poker’s intersections with data science and artificial intelligence, as well as what’s going through a data scientist’s mind when they sit down for beer and poker with friends.
Wall Street Journal, December 2016
Artificial intelligence is having a moment. Startups that claim to be using AI are attracting record levels of investment. Big tech companies are going all-in, draining universities of entire departments. But wringing measurable utility from these new AI toys can be hard. I chat with Chris Mims about my thoughts on what's next for AI in the near- and mid-term.
WTB Medium, July 2016
Since 2014, AI startups have experienced an explosion of funding, giving even more incentive to the keen computer scientist. In essence, programming computers to think for themselves may just be the ultimate goal. I did a Q&A in which I discussed the challenges is making sense out of the unbelievable complexity of our data: weather models, consumption tracking, tariff structures, billing details, geographic nuances, local and global regulations, sector and industry benchmarks, macro trends, and much more. I review how the data science team turns all of that “messy data” into information that is useful, valuable, and actionable for our customers.
Slice of MIT, November 2015
Over 90 percent of all of the data in the world was created in the last two years and it's our job to figure out what that might mean. In this interview I
talk about my path to data science, and how “I’ve always used data to try and explain what is happening, why it’s happening, and when it will happen again.”
Payette, March 2015
Whether we are trying to convey building program or energy performance, we utilize graphic representations of data to tell complex stories. Data visualization is a universal tool. The key is to remember the intent and decide the narrative before you create the end product because not all data is actually telling you what you think, you have to dig a little deeper.