John Myers of Gretel puts on his apron and rolls up his sleeves to show Dan and Chris how to cook up some synthetic data for automated data labeling, differential privacy, and other purposes. His military and intelligence community background give him an interesting perspective that piqued the interest of our intrepid hosts.
Daniel and Chris sniff out the secret ingredients for collecting, displaying, and analyzing odor data with Terri Jordan and Yanis Caritu of Aryballe. It certainly smells like a good time, so join them for this scent-illating episode!
MLCommons launched in December 2020 as an open engineering consortium that seeks to accelerate machine learning innovation and broaden access to this critical technology for the public good. David Kanter, the executive director of MLCommons, joins us to discuss the launch and the ambitions of the organization.
In particular we discuss the three pillars of the organization: Benchmarks and Metrics (e.g. MLPerf), Datasets and Models (e.g. People’s Speech), and Best Practices (e.g. MLCube).
American Express is running what is perhaps the largest commercial ML model in the world; a model that automates over 8 billion decisions, ingests data from over $1T in transactions, and generates decisions in mere milliseconds or less globally. Madhurima Khandelwal, head of AMEX AI Labs, joins us for a fascinating discussion about scaling research and building robust and ethical AI-driven financial applications.
Bharat Sandhu, Director of Azure AI and Mixed Reality at Microsoft, joins Chris and Daniel to talk about how Microsoft is making AI accessible and productive for users, and how AI solutions can address real world challenges that customers face. He also shares Microsoft’s research-to-product process, along with the advances they have made in computer vision, image captioning, and how researchers were able to make AI that can describe images as well as people do.
Unsplash has released the world’s largest open library dataset, which includes 2M+ high-quality Unsplash photos, 5M keywords, and over 250M searches. They have big ideas about how the dataset might be used by ML/AI folks, and there have already been some interesting applications. In this episode, Luke and Tim discuss why they released this data and what it take to maintain a dataset of this size.
Lucy D’Agostino McGowan, cohost of the Casual Inference Podcast and a professor at Wake Forest University, joins Daniel and Chris for a deep dive into causal inference. Referring to current events (e.g. misreporting of COVID-19 data in Georgia) as examples, they explore how we interact with, analyze, trust, and interpret data - addressing underlying assumptions, counterfactual frameworks, and unmeasured confounders (Chris’s next Halloween costume).
What’s it like to try and build your own deep learning workstation? Is it worth it in terms of money, effort, and maintenance? Then once built, what’s the best way to utilize it? Chris and Daniel dig into questions today as they talk about Daniel’s recent workstation build. He built a workstation for his NLP and Speech work with two GPUs, and it has been serving him well (minus a few things he would change if he did it again).
Weights & Biases is coming up with some awesome developer tools for AI practitioners! In this episode, Lukas Biewald describes how these tools were a direct result of pain points that he uncovered while working as an AI intern at OpenAI. He also shares his vision for the future of machine learning tooling and where he would like to see people level up tool-wise.
Hamish from Sajari blows our mind with a great discussion about AI in search. In particular, he talks about Sajari’s quest for performant AI implementations and extensive use of Reinforcement Learning (RL). We’ve been wanting to make this one happen for a while, and it was well worth the wait.
Rajiv Shah teaches Daniel and Chris about data leakage, and its major impact upon machine learning models. It’s the kind of topic that we don’t often think about, but which can ruin our results. Raj discusses how to use activation maps and image embedding to find leakage, so that leaking information in our test set does not find its way into our training set.
Suju Rajan from LinkedIn joined us to talk about how they are operationalizing state-of-the-art AI at LinkedIn. She sheds light on how AI can and is being used in recruiting, and she weaves in some great explanations of how graph-structured data, personalization, and representation learning can be applied to LinkedIn’s candidate search problem. Suju is passionate about helping people deal with machine learning technical debt, and that gives this episode a good dose of practicality.
We’re partnering with the upcoming R Conference, because the R Conference is well… amazing! Tons of great AI content, and they were nice enough to connect us to Daniel Chen for this episode. He discusses data science in Computational Biology and his perspective on data science project organization.
In anticipation of the upcoming NVIDIA GPU Technology Conference (GTC), Will Ramey joins Daniel and Chris to talk about education for artificial intelligence practitioners, and specifically the role that the NVIDIA Deep Learning Institute plays in the industry. Will’s insights from long experience are shaping how we all stay on top of AI, so don’t miss this ‘must learn’ episode.
So, you trained a great AI model and deployed it in your app? It’s smooth sailing from there right? Well, not in most people’s experience. Sometimes things goes wrong, and you need to know how to respond to a real life AI incident. In this episode, Andrew and Patrick from BNH.ai join us to discuss an AI incident response plan along with some general discussion of debugging models, discrimination, privacy, and security.
Many people are excited about creating usable speech technology. However, most of the audio data used by large companies isn’t available to the majority of people, and that data is often biased in terms of language, accent, and gender. Jenny, Josh, and Remy from Mozilla join us to discuss how Mozilla is building an open-source voice database that anyone can use to make innovative apps for devices and the web (Common Voice). They also discuss efforts through Mozilla fellowship program to develop speech tech for African languages and understand bias in data sets.
Waymo’s mission is to make it safe and easy for people and things to get where they’re going.
After describing the state of the industry, Drago Anguelov - Principal Scientist and Head of Research at Waymo - takes us on a deep dive into the world of AI-powered autonomous driving. Starting with Waymo’s approach to autonomous driving, Drago then delights Daniel and Chris with a tour of the algorithmic tools in the autonomy toolbox.
Hilary Mason is building a new way for kids and families to create stories with AI. It’s called Hidden Door, and in her first interview since founding it, Hilary reveals to Chris and Daniel what the experience will be like for kids. It’s the first Practical AI episode in which some of the questions came from Chris’s 8yo daughter Athena.
Hilary also shares her insights into various topics, like how to build data science communities during the COVID-19 Pandemic, reasons why data science goes wrong, and how to build great data-based products. Don’t miss this episode packed with hard-won wisdom!
We’re so excited to see Chris and Daniel take this show to 100 episodes, and that’s exactly why we’re rebroadcasting Practical AI #100 here on The Changelog. They’ve had so many great guests and discussions about everything from AGI to GPUs to AI for good. In this episode, we circle back to the beginning when Jerod and I joined the first episode to help kick off the podcast. We discuss how our perspectives have changed over time, what it has been like to host an AI podcast, and what the future of AI might look like. (GIVEAWAY!)