This Week in Machine Learning & AI This Week in Machine Learning & AI This Week in Machine Learning & AI This Week in Machine Learning & AI This Week in Machine Learning & AI

    Shows

    Human Factors in Machine Intelligence with James Guszcza
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    I sat down with James Guszcza, US Chief Data Scientist at Deloitte Consulting to talk about human factors in machine intelligence. 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.

    Intel Nervana Devcloud with Naveen Rao & Scott Apeland
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    In this episode, I talk to Naveen Rao, VP and GM of Intel’s AI Products Group, and Scott Apeland, director of Intel’s Developer Network. It’s been a few months since we last spoke to Naveen, so he gives us a quick update on what Intel’s been up to and we discuss his perspective on some…

    AI-Powered Conversational Interfaces with Paul Tepper
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    My guest for this show is Paul Tepper, worldwide head of cognitive innovation and product manager for machine learning & AI at Nuance Communications. We covered a bunch of topics, like voice UI design, behavioral biometrics and a ton of other interesting things that Nuance has in the works.

    Topological Data Analysis with Gunnar Carlsson
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    My guest for this show is Gunnar Carlsson, professor emeritus of mathematics at Stanford University and president and co-founder of machine learning startup Ayasdi.

    ML Use Cases at Think Big Analytics with Mo Patel and Laura Frølich
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    This time around, I speak with Mo Patel, practice director of AI & deep learning and Laura Frølich, data scientist, of Think Big Analytics. Mo and Laura joined me at the AI conference after their session on “Training vision models with public transportation datasets.”

    Ray: A Distributed Computing Platform for Reinforcement Learning with Ion Stoica
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    I talk with Ion Stoica, professor of computer science & director of the RISE Lab at UC Berkeley. We dive into Ray, a new distributed computing platform for RL, as well as RL generally, along with some of the other projects RISE Lab is working on, like Clipper & Tegra.

    Bayesian Optimization for Hyperparameter Tuning with Scott Clark
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    This week I catch up with Scott Clark, Co-Founder and CEO of Sigopt, a company whose software is focused on automatically tuning your model’s parameters through Bayesian optimization.

    Symbolic and Sub-Symbolic Natural Language Processing with Jonathan Mugan
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    Like last week’s interview with Bruno Goncalves, this week’s interview was also recorded at the last O’Reilly AI Conference back in New York in June. Also like last week’s show, this week’s is also focused on Natural Language Processing and I think you’ll enjoy it. I’m joined by Jonathan Mugan, co-founder and CEO of Deep…

    Word2Vec & Friends with Bruno Goncalves
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    This week i’m bringing you an interview from Bruno Goncalves, a Moore-Sloan Data Science Fellow at NYU. As you’ll hear in the interview, Bruno is a longtime listener of the podcast. We were able to connect at the NY AI conference back in June after I noted on a previous show that I was interested…

    Evolutionary Algorithms in Machine Learning with Risto Miikkulainen
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    My guest this week is Risto Miikkulainen, professor of computer science at UT-Austin and vice president of Research at Sentient Technologies. Risto came locked and loaded to discuss a topic that we’ve received a ton of requests for — evolutionary algorithms.

    Agile Machine Learning at Walmart with Jennifer Prendki
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    My guest this week is Jennifer Prendki, former senior data science manager and principal data scientist at Walmart Labs, since moved on to become head of data science at Atlassian. We focus on some of the practices she helped develop and implement at Walmart around the measurement and management of and more generally, building agile processes and teams for, machine learning.

    LSTM’s, plus a Deep Learning History Lesson with Jürgen Schmidhuber
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    This week’s guest is Jürgen Schmidhuber, Scientific Director of IDSIA in Switzerland. We talked a bunch about his work on neural networks, especially LSTM’s and along the way, Jurgen walks us through a deep learning history lesson that spans 50+ years.

    Machine Teaching for Better Machine Learning with Mark Hammond
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    Today’s show, which concludes the first season of the Industrial AI Series, features my interview with Bonsai co-founder and CEO Mark Hammond. Mark describes the role of what he calls “machine teaching” in delivering practical machine learning solutions, particularly for enterprise or industrial AI use cases.

    Marrying Physics-Based and Data Driven ML Models with Josh Bloom
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    This week we catch up with friend of the show, and first return guest, Josh Bloom, vice president of data & analytics at GE Digital. We catch up with Josh on his journey within GE and the work his team is doing around Industrial AI.

    Cognitive Biases in Data Science with Drew Conway
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    This show features my interview with Drew Conway, Founder and CEO of Alluvium. The focus of our interview, and of Drew’s presentation, is an interesting set of observations he makes about the role of cognitive biases in data science.

    Data Pipelines at Zymergen with Airflow with Erin Shellman
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    This show, TWiML Talk 41, features my interview with Erin Shellman, a statistician and data science manager with Zymergen, a company using robots and machine learning to engineer better microbes.

    Web Scale Engineering for Machine Learning with Sharath Rao
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    The show you’re about to listen to features my interview with Sharath Rao, Tech Lead Manager & Machine Learning Engineer at Instacart. My conversation with him digs into some of the practical lessons and patterns he’s learned by building production-ready, web-scale data products based on machine learning models.

    Deep Learning for Warehouse Operations with Calvin Seward
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    This week we continue our Industrial AI series with Calvin Seward, Research Scientist with Zalando in Berlin, Germany. We focus on how Calvin’s team tackled an interesting warehouse optimization problem using deep learning. Calvin also gives his thoughts on the distinction between AI and ML, and the four P’s that he focuses on: Prestige, Products, Paper, and Patents.

    Deep Robotic Learning with Sergey Levine
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    This week we continue our Industrial AI series with Sergey Levine, an Assistant Professor at UC Berkeley whose research focus is Deep Robotic Learning. Sergey is part of the same research team as a couple of our previous guests in this series, Chelsea Finn and Pieter Abbeel, and if the response we’ve seen to those shows is any indication, you’re going to love this episode!

    Smart Buildings & IoT with Yodit Stanton
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    Our guest this week is Yodit Stanton, a self-described Data Nerd, and the Founder & CEO of Opensensors.io. Our discussion focuses on Smart Buildings and how they’re enabled by IoT and machine learning techniques.