AI Nexus Lab Cohort 2 - Mt. Cleverest - TWiML Talk #63
The podcast you’re about to hear is the first of a series of shows recorded at the NYU Future Labs AI Summit last week in New York City. My guests this time around are James Villarrubia and Bernie Prat, CEO and COO respectively, of Mt. Cleverest, an online service for teachers and students, that can take any text via the web, and generate a quiz along with answers based on the content supplied. To do this, Bernie and James employ a pretty sophisticated natural language understanding pipeline, which we discuss in this interview. We also touch on the challenges they face in generating correct question answers, how they fine tune their ML models to improve those answers over time, and more. The notes for this show can be found at twimlai.com/talk/63 For Series information, visit twimlai.com/nexuslabs2
6 Nov 201732min
Learning to Learn, and other Opportunities in Machine Learning with Graham Taylor - TWiML Talk #62
The podcast you’re about to hear is the third of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest this time is Graham Taylor, professor of engineering at the University of Guelph, who keynoted day two of the conference. Graham leads the Machine Learning Research Group at Guelph, and is affiliated with Toronto’s recently formed Vector Institute for Artificial Intelligence. Graham and I discussed a number of the most important trends and challenges in artificial intelligence, including the move from predictive to creative systems, the rise of human-in-the-loop AI, and how modern AI is accelerating with our ability to teach computers how to learn-to-learn. The notes for this show can be found at twimlai.com/talk/62. For series info, visit twimlai.com/GPPC2017
3 Nov 201737min
Building Conversational Application for Financial Services with Kenneth Conroy - TWiML Talk #61
The podcast you’re about to hear is the second of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest for this interview is Kenneth Conroy, VP of data science at Vancouver, Canada-based Finn.ai, a company building a chatbot system for banks. Kenneth and I spoke about how Finn.AI built its core conversational platform. We spoke in depth about the requirements and challenges of conversational applications, and how and why they transitioned off of a commercial chatbot platform--in their case API.ai--and built their own custom platform based on deep learning, word2vec and other natural language understanding technologies. The notes for this show can be found at https://twimlai.com/talk/61
1 Nov 201737min
Fighting Fraud with Machine Learning at Shopify with Solmaz Shahalizadeh - TWiML Talk #60
The podcast you’re about to hear is the first of a series of shows recorded at the Georgian Partners Portfolio Conference last week in Toronto. My guest for this show is Solmaz Shahalizadeh, Director of Merchant Services Algorithms at Shopify. Solmaz gave a great talk at the GPPC focused on her team’s experiences applying machine learning to fight fraud and improve merchant satisfaction. Solmaz and I dig into, step-by-step, the process they used to transition from a legacy, rules-based fraud detection system system to a more scalable, flexible one based on machine learning models. We discuss the importance of well-defined project scope; tips and traps when selecting features to train your models; and the various models, transformations and pipelines the Shopify team selected; and how they use PMML to make their Python models available to their Ruby-on-Rails web application. The notes for this show can be found at twimlai.com/talk/60 For Series info, visit twimlai.com/GPPC2017
30 Okt 201735min
Modeling Human Drivers for Autonomous Vehicles with Katie Driggs-Campbell - TWiML Talk #59
We are back with our third show this week, episode 3 of our Autonomous Vehicles Series. My guest this time is Katie Driggs-Campbell, PostDoc in the Intelligent Systems Lab at Stanford University’s Department of Aeronautics and Astronautics. Katie joins us to discuss her research into human behavioral modeling and control systems for self-driving vehicles. Katie also gives us some insight into her process for collecting training data, how social nuances come into play for self-driving cars, and more. The notes for this show can be found at twimlai.com/talk/59 For Series info, visit twimlai.com/av2017
27 Okt 201733min
Perception Models for Self-Driving Cars with Jianxiong Xiao - TWiML Talk #58
We are back with our second show this week, episode 2 of our Autonomous Vehicles Series. This time around we are joined by Jianxiong Xiao of AutoX, a company building computer vision centric solutions for autonomous vehicles. Jianxiong, a PhD graduate of MIT’s CSAIL Lab, joins me to discuss the different layers of the autonomous vehicle stack and the models for machine perception currently used in self-driving cars. If you’re new to the autonomous vehicles space I’m confident you’ll learn a ton, and even if you know the space in general, you’ll get a glimpse into why Jianxiong thinks AutoX’s direct perception approach is superior to end-to-end processing or mediated perception. The notes for this show can be found at twimlai.com/talk/58 For Series info, visit twimlai.com/av2017
25 Okt 201741min
Training Data for Autonomous Vehicles - Daryn Nakhuda - TWiML Talk #57
The episode you are about to hear is the first of a new series of shows on Autonomous Vehicles. We all know that self-driving cars is one of the hottest topics in ML & AI, so we had to dig a little deeper into the space. To get us started on this journey, I’m excited to present this interview with Daryn Nakhuda, CEO and Co-Founder of MightyAI. Daryn and I discuss the many challenges of collecting training data for autonomous vehicles, along with some thoughts on human-powered insights and annotation, semantic segmentation, and a ton more great stuff. For the notes for this show, Visit twimlai.com/talk/57. For series info, visit twimlai.com/AV2017
23 Okt 201747min
Human Factors in Machine Intelligence with James Guszcza - TWiML Talk #56
As you all know, a few weeks ago, I spent some time in SF at the Artificial Intelligence Conference. I sat down with James Guszcza, US Chief Data Scientist at Deloitte Consulting to talk about human factors in machine intelligence. James was in San Francisco to give a talk at the O’Reilly AI Conference on “Why AI needs human-centered design.” We had an amazing chat, in which we explored the many reasons why the human element is so important in ML and AI, along with useful ways to build algorithms and models that reflect this human element, while avoiding out problems like group-think and bias. This was a very interesting conversation. I enjoyed it a ton, and I’m sure you will too! The notes for this episode can be found at twimlai.com/talk/56
16 Okt 201742min
