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Making artificial intelligence practical, productive, and accessible to everyone

Practical AI Practical AI #75

Insights from the AI Index 2019 Annual Report

Daniel and Chris do a deep dive into The AI Index 2019 Annual Report, which provides unbiased rigorously-vetted data that one can use “to develop intuitions about the complex field of AI”. Analyzing everything from R&D and technical advancements to education, the economy, and societal considerations, Chris and Daniel lay out this comprehensive report’s key insights about artificial intelligence.

Practical AI Practical AI #74

Testing ML systems

Production ML systems include more than just the model. In these complicated systems, how do you ensure quality over time, especially when you are constantly updating your infrastructure, data and models? Tania Allard joins us to discuss the ins and outs of testing ML systems. Among other things, she presents a simple formula that helps you score your progress towards a robust system and identify problem areas.

Practical AI Practical AI #72

How the U.S. military thinks about AI

Chris and Daniel talk with Greg Allen, Chief of Strategy and Communications at the U.S. Department of Defense (DoD) Joint Artificial Intelligence Center (JAIC). The mission of the JAIC is “to seize upon the transformative potential of artificial intelligence technology for the benefit of America’s national security… The JAIC is the official focal point of the DoD AI Strategy.” So if you want to understand how the U.S. military thinks about artificial intelligence, then this is the episode for you!

Practical AI Practical AI #68

Modern NLP with spaCy

SpaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!

Practical AI Practical AI #66

Build custom ML tools with Streamlit

Streamlit recently burst onto the scene with their intuitive, open source solution for building custom ML/AI tools. It allows data scientists and ML engineers to rapidly build internal or external UIs without spending time on frontend development. In this episode, Adrien Treuille joins us to discuss ML/AI app development in general and Streamlit. We talk about the practicalities of working with Streamlit along with its seemingly instant adoption by AI2, Stripe, Stitch Fix, Uber, and Twitter.

Practical AI Practical AI #65

Intelligent systems and knowledge graphs

There’s a lot of hype about knowledge graphs and AI-methods for building or using them, but what exactly is a knowledge graph? How is it different from a database or other data store? How can I build my own knowledge graph? James Fletcher from Grakn Labs helps us understand knowledge graphs in general and some practical steps towards creating your own. He also discusses graph neural networks and the future of graph-augmented methods.

Practical AI Practical AI #64

Robot hands solving Rubik's cubes

Everyone is talking about it. OpenAI trained a pair of neural nets that enable a robot hand to solve a Rubik’s cube. That is super dope! The results have also generated a lot of commentary and controversy, mainly related to the way in which the results were represented on OpenAI’s blog. We dig into all of this in on today’s Fully Connected episode, and we point you to a few places where you can learn more about reinforcement learning.

robot hand

Practical AI Practical AI #62

It's time to talk time series

Times series data is everywhere! I mean, seriously, try to think of some data that isn’t a time series. You have stock prices and weather data, which are the classics, but you also have a time series of images on your phone, time series log data coming off of your servers, and much more. In this episode, Anais from InfluxData helps us understand the range of methods and problems related to time series data. She also gives her perspective on when statistical methods might perform better than neural nets or at least be a more reasonable choice.

time series

Practical AI Practical AI #59

Flying high with AI drone racing at AlphaPilot

Chris and Daniel talk with Keith Lynn, AlphaPilot Program Manager at Lockheed Martin. AlphaPilot is an open innovation challenge, developing artificial intelligence for high-speed racing drones, created through a partnership between Lockheed Martin and The Drone Racing League (DRL).

AlphaPilot challenged university teams from around the world to design AI capable of flying a drone without any human intervention or navigational pre-programming. Autonomous drones will race head-to-head through complex, three-dimensional tracks in DRL’s new Artificial Intelligence Robotic Racing (AIRR) Circuit. The winning team could win up to $2 million in prizes.

Keith shares the incredible story of how AlphaPilot got started, just prior to its debut race in Orlando, which will be broadcast on NBC Sports.

Practical AI Practical AI #51

AI code that facilitates good science

We’re talking with Joel Grus, author of Data Science from Scratch, 2nd Edition, senior research engineer at the Allen Institute for AI (AI2), and maintainer of AllenNLP. We discussed Joel’s book, which has become a personal favorite of the hosts, and why he decided to approach data science and AI “from scratch.” Joel also gives us a glimpse into AI2, an introduction to AllenNLP, and some tips for writing good research code. This episode is packed full of reproducible AI goodness!

Practical AI Practical AI #50

Celebrating episode 50 and the neural net!

Woo hoo! As we celebrate reaching episode 50, we come full circle to discuss the basics of neural networks. If you are just jumping into AI, then this is a great primer discussion with which to take that leap.

Our commitment to making artificial intelligence practical, productive, and accessible to everyone has never been stronger, so we invite you to join us for the next 50 episodes!

Practical AI Practical AI #49

Exposing the deception of DeepFakes

This week we bend reality to expose the deceptions of deepfake videos. We talk about what they are, why they are so dangerous, and what you can do to detect and resist their insidious influence. In a political environment rife with distrust, disinformation, and conspiracy theories, deepfakes are being weaponized and proliferated as the latest form of state-sponsored information warfare. Join us for an episode scarier than your favorite horror movie, because this AI bogeyman is real!

Practical AI Practical AI #47

GANs, RL, and transfer learning oh my!

Daniel and Chris explore three potentially confusing topics - generative adversarial networks (GANs), deep reinforcement learning (DRL), and transfer learning. Are these types of neural network architectures? Are they something different? How are they used? Well, If you have ever wondered how AI can be creative, wished you understood how robots get their smarts, or were impressed at how some AI practitioners conquer big challenges quickly, then this is your episode!

Practical AI Practical AI #46

Visualizing and understanding RNNs

Andreas Madsen, a freelance ML/AI engineer and Distill.pub author, joins us to discuss his work visualizing neural networks and recurrent neural units. Andreas discusses various neural unites, RNNs in general, and the “why” of neural network visualization. He also gives us his perspective on ML/AI freelancing and moving from web development to AI research.

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