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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
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The Changelog The Changelog #380

Productionising real-world ML data pipelines

Yetunde Dada from QuantumBlack joins Jerod for a deep dive on Kedro, a workflow tool that helps structure reproducible, scaleable, deployable, robust, and versioned data pipelines. They discuss what Kedro’s all about and how it’s “changing the landscape of data pipelines in Python”, the ins/outs of open sourcing Kedro, and how they found early success by sweating the details. Finally, Jerod asks Yetunde about her passion project: a virtual reality film which debuted at the Sundance Film Festival in January.

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 #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 #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 #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 #45

How to get plugged into the AI community

Chris and Daniel take you on a tour of local and global AI events, and discuss how to get the most out of your experiences. From access to experts to developing new industry relationships, learn how to get your foot in the door and make connections that help you grow as an AI practitioner.

Then drawing from their own wealth of experience as speakers, they dive into what it takes to give a memorable world-class talk that your audience will love. They break down how to select the topic, write the abstract, put the presentation together, and deliver the narrative with impact!

Practical AI Practical AI #44

AI adoption in the enterprise

At the recent O’Reilly AI Conference in New York City, Chris met up with O’Reilly Chief Data Scientist Ben Lorica, the Program Chair for Strata Data, the AI Conference, and TensorFlow World.

O’Reilly’s ‘AI Adoption in the Enterprise’ report had just been released, so naturally Ben and Chris wanted to do a deep dive into enterprise AI adoption to discuss strategy, execution, and implications.

Practical AI Practical AI #42

TensorFlow Dev Summit 2019

This week Daniel and Chris discuss the announcements made recently at TensorFlow Dev Summit 2019. They kick it off with the alpha release of TensorFlow 2.0, which features eager execution and an improved user experience through Keras, which has been integrated into TensorFlow itself. They round out the list with TensorFlow Datasets, TensorFlow Addons, TensorFlow Extended (TFX), and the upcoming inaugural O’Reilly TensorFlow World conference.

Practical AI Practical AI #40

Deep Reinforcement Learning

While attending the NVIDIA GPU Technology Conference in Silicon Valley, Chris met up with Adam Stooke, a speaker and PhD student at UC Berkeley who is doing groundbreaking work in large-scale deep reinforcement learning and robotics. Adam took Chris on a tour of deep reinforcement learning - explaining what it is, how it works, and why it’s one of the hottest technologies in artificial intelligence!

Practical AI Practical AI #39

Making the world a better place at the AI for Good Foundation

Longtime listeners know that we’re always advocating for ‘AI for good’, but this week we have taken it to a whole new level. We had the privilege of chatting with James Hodson, Director of the AI for Good Foundation, about ways they have used artificial intelligence to positively-impact the world - from food production to climate change. James inspired us to find our own ways to use AI for good, and we challenge our listeners to get out there and do some good!

Practical AI Practical AI #36

Growing up to become a world-class AI expert

While at the NVIDIA GPU Technology Conference 2019 in Silicon Valley, Chris enjoyed an inspiring conversation with Anima Anandkumar. Clearly a role model - not only for women - but for anyone in the world of AI, Anima relayed how her lifelong passion for mathematics and engineering started when she was only 3 years old in India, and ultimately led to her pioneering deep learning research at Amazon Web Services, CalTech, and NVIDIA.

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