The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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 in learning more about word2vec. Bruno graciously agreed to come on the show and walk us through an overview of word embeddings, word2vec and related ideas. He provides a great overview of not only word2vec, related NLP concepts such as Skip Gram, Continuous Bag of Words, Node2Vec and TFIDF. Notes for this show can be found at twimlai.com/talk/48.

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. During our talk we discuss some of the things Sentient is working on in the financial services and retail fields, and we dig into the technology behind it, evolutionary algorithms, which is also the focus of Risto’s research at UT. I really enjoyed this interview and learned a ton, and I’m sure you will too! Notes for this show can be found at twimlai.com/talk/47.

My guest this week is Jennifer Prendki. That name might sound familiar, as she was one of the great speakers from my Future of Data Summit back in May. At the time, Jennifer was senior data science manager and principal data scientist at Walmart Labs, but she's since moved on to become head of data science at Atlassian. Back at the summit, Jennifer gave an awesome talk on what she calls Data Mixology, the slides for which you can find on the show notes page. My conversation with Jennifer begins with a recap of that talk. After that, we shift our focus to some of the practices she helped develop and implement at Walmart around the measurement and management of machine learning models in production, and more generally, building agile processes and teams for machine learning. The notes for this show can be found at twimlai.com/talk/46

This week we have a very special interview to share with you! Those of you who’ve been receiving my newsletter for a while might remember that while in Switzerland last month, I had the pleasure of interviewing Jurgen Schmidhuber, in his lab IDSIA, which is the Dalle Molle Institute for Artificial Intelligence Research in Lugano, Switzerland, where he serves as Scientific Director. In addition to his role at IDSIA, Jurgen is also Co-Founder and Chief Scientist of NNaisense, a company that is using AI to build large-scale neural network solutions for “superhuman perception and intelligent automation.” Jurgen is an interesting, accomplished and in some circles controversial figure in the AI community and we covered a lot of very interesting ground in our discussion, so much so that I couldn't truly unpack it all until I had a chance to sit with it after the fact. We talked a bunch about his work on neural networks, especially LSTM’s, or Long Short-Term Memory networks, which are a key innovation behind many of the advances we’ve seen in deep learning and its application over the past few years. Along the way, Jurgen walks us through a deep learning history lesson that spans 50+ years. It was like walking back in time with the 3 eyed raven. I know you’re really going to enjoy this one, and by the way, this is definitely a nerd alert show! For the show notes, visit twimlai.com/talk/44

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! The notes for this show can be found at twimlai.com/talk/43

Recently I had a chance to catch up with a friend and friend of the show, Josh Bloom, vice president of data & analytics at GE Digital. If you’ve been listening for a while, you already know that Josh was on the show around this time last year, just prior to the acquisition of his company Wise.io by GE Digital. It was great to catch up with Josh on his journey within GE, and the work his team is doing around Industrial AI, now that they’re part of the one of the world’s biggest industrial companies. We talk about some really interesting things in this show, including how his team is using autoencoders to create training datasets, and how they incorporate knowledge of physics and physical systems into their machine learning models. The notes for this show can be found at twimlai.com/talk/42.

This show features my interview with Drew Conway, whose Wrangle keynote could have been called “Confessions of a CIA Data Scientist.” 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. If your work involves making decisions or influencing behavior based on data-driven analysis--and it probably does or will--you’re going to want to hear what he has to say. A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at https://twimlai.com/talk/39

The show you’re listening to features my interview with Erin Shellman. Erin is a statistician and data science manager with Zymergen, a company using robots and machine learning to engineer better microbes. If you’re wondering what exactly that means, I was too, and we talk about it in the interview. Our conversation focuses on Zymergen’s use of Apache Airflow, an open-source data management platform originating at Airbnb, that Erin and her team uses to create reliable, repeatable data pipelines for its machine learning applications. A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at https://twimlai.com/talk/41

The show you’re about to listen to features my interview with Sharath Rao, Tech Lead Manager & Machine Learning Engineer at Instacart I reached out to Sharath about being on the show and was blown away when he replied that not only had he heard about the show, but that he was a fan and an avid listener. 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, including the search and recommendation systems at Instacart. We also spend a few minutes discussing our upcoming TWiML Paper Reading Meetup! A quick note before we dive in: As is the case with my other field recordings, there’s a bit of unavoidable background noise in this interview. Sorry about that! The show notes for this episode can be found at https://twimlai.com/talk/40.

This week, I’m happy to bring you my interview with Calvin Seward, a research scientist with Berlin, Germany based Zalando. While our American listeners might not know the name Zalando, they’re one of the largest e-commerce companies in Europe with a focus on fashion and shoes. Calvin is a research scientist there, while also pursuing his doctorate studies at Johannes Kepler University in Linz, Austria. Our discussion, which continues our Industrial AI series, focuses 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. The notes for this show can be found at https://twimlai.com/talk/38.