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.
At this year’s Government & Public Sector R Conference (or R|Gov) our very own Daniel Whitenack moderated a panel on how AI practitioners can engage with governments on AI for good projects. That discussion is being republished in this episode for all our listeners to enjoy!
The panelists were Danya Murali from Arcadia Power and Emily Martinez from the NYC Department of Health and Mental Hygiene. Danya and Emily gave some great perspectives on sources of government data, ethical uses of data, and privacy.
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!
Everyone working in data science and AI knows about Anaconda and has probably “conda” installed something. But how did Anaconda get started and what are they working on now? Peter Wang, CEO of Anaconda and creator of PyData and popular packages like Bokeh and DataShader, joins us to discuss that and much more. Peter gives some great insights on the Python AI ecosystem and very practical advice for scaling up your data science operation.
We made it to 100 episodes of Practical AI! It has been a privilege to have 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 Adam from The Changelog helped us 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!)
Come hang with the bad boys of natural language processing (NLP)! Jack Morris joins Daniel and Chris to talk about TextAttack, a Python framework for adversarial attacks, data augmentation, and model training in NLP. TextAttack will improve your understanding of your NLP models, so come prepared to rumble with your own adversarial attacks!
Sash Rush, of Cornell Tech and Hugging Face, catches us up on all the things happening with Hugging Face and transformers. Last time we had Clem from Hugging Face on the show (episode 35), their transformers library wasn’t even a thing yet. Oh how things have changed! This time Sasha tells us all about Hugging Face’s open source NLP work, gives us an intro to the key components of transformers, and shares his perspective on the future of AI research conferences.
DevOps for deep learning is well… different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro’s CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.
The multidisciplinary field of AI Ethics is brand new, and is currently being pioneered by a relatively small number of leading AI organizations and academic institutions around the world. AI Ethics focuses on ensuring that unexpected outcomes from AI technology implementations occur as rarely as possible. Daniel and Chris discuss strategies for how to arrive at AI ethical principles suitable for your own organization, and what is involved in implementing those strategies in the real world. Tune in for a practical AI primer on AI Ethics!
Daniel and Chris get you Fully-Connected with open source software for artificial intelligence.
In addition to defining what open source is, they discuss where to find open source tools and data, and how you can contribute back to the open source AI community.
A lot of effort is put into the training of AI models, but, for those of us that actually want to run AI models in production, performance and scaling quickly become blockers. Nikita from MemSQL joins us to talk about how people are integrating ML/AI inference at scale into existing SQL-based workflows. He also touches on how model features and raw files can be managed and integrated with distributed databases.
This full connected has it all: news, updates on AI/ML tooling, discussions about AI workflow, and learning resources. Chris and Daniel breakdown the various roles to be played in AI development including scoping out a solution, finding AI value, experimentation, and more technical engineering tasks. They also point out some good resources for exploring bias in your data/model and monitoring for fairness.
Daniel and Chris go beyond the current state of the art in deep learning to explore the next evolutions in artificial intelligence. From Yoshua Bengio’s NeurIPS keynote, which urges us forward towards System 2 deep learning, to DARPA’s vision of a 3rd Wave of AI, Chris and Daniel investigate the incremental steps between today’s AI and possible future manifestations of artificial general intelligence (AGI).
The CEO of Darwin AI, Sheldon Fernandez, joins Daniel to discuss generative synthesis and its connection to explainability. You might have heard of AutoML and meta-learning. Well, generative synthesis tackles similar problems from a different angle and results in compact, explainable networks. This episode is fascinating and very timely.
On the heels of NVIDIA’s latest announcements, Daniel and Chris explore how the new NVIDIA Ampere architecture evolves the high-performance computing (HPC) landscape for artificial intelligence. After investigating the new specifications of the NVIDIA A100 Tensor Core GPU, Chris and Daniel turn their attention to the data center with the NVIDIA DGX A100, and then finish their journey at “the edge” with the NVIDIA EGX A100 and the NVIDIA Jetson Xavier NX.
Chandler McCann tells Daniel and Chris about how DataRobot engaged in a project to develop sustainable water solutions with the Global Water Challenge (GWC). They analyzed over 500,000 data points to predict future water point breaks. This enabled African governments to make data-driven decisions related to budgeting, preventative maintenance, and policy in order to promote and protect people’s access to safe water for drinking and washing. From this effort sprang DataRobot’s larger AI for Good initiative.