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PODCAST

Practical AI

Practical AI LLC

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest

technology

All Episodes

44:20

Open source data labeling tools

Practical AI ·

2019/11/05

What’s the most practical of practical AI things? Data labeling of course! It’s also one of the most time consuming and error prone processes that we deal with in AI development. Michael Malyuk of Heartex and Label Studio joins us to discuss various data labeling challenges and open source tooling to help us overcome those challenges.

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42:45

It's time to talk time series

Practical AI ·

2019/10/28

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

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49:40

AI in the browser

Practical AI ·

2019/10/21

We’ve mentioned ML/AI in the browser and in JS a bunch on this show, but we haven’t done a deep dive on the subject… until now! Victor Dibia helps us understand why people are interested in porting models to the browser and how people are using the functionality. We discuss TensorFlow.js and some applications built using TensorFlow.js

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49:25

Blacklisted facial recognition and surveillance...

Practical AI ·

2019/10/15

The United States has blacklisted several Chinese AI companies working in facial recognition and surveillance. Why? What are these companies doing exactly, and how does this fit into the international politics of AI? We dig into these questions and attempt to do some live fact finding in this episode.

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47:48

Flying high with AI drone racing at AlphaPilot

Practical AI ·

2019/10/07

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

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45:10

AI in the majority world and model distillation

Practical AI ·

2019/09/30

Chris and Daniel take some time to cover recent trends in AI and some noteworthy publications. In particular, they discuss the increasing AI momentum in the majority world (Africa, Asia, South and Central America and the Caribbean), and they dig into Hugging Face’s recent model distillation results.

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45:32

The influence of open source on AI development

Practical AI ·

2019/09/25

The All Things Open conference is happening soon, and we snagged one of their speakers to discuss open source and AI. Samuel Taylor talks about the essential role that open source is playing in AI development and research, and he gives us some tips on choosing AI-related side projects.

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48:24

Worlds are colliding - AI and HPC

Practical AI ·

2019/09/17

In this very special fully-connected episode of Practical AI, Daniel interviews Chris. They discuss High Performance Computing (HPC) and how it is colliding with the world of AI. Chris explains how HPC differs from cloud/on-prem infrastructure, and he highlights some of the challenges of an HPC-based AI strategy.

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58:38

AutoML and AI at Google

Practical AI ·

2019/09/09

We’re talking with Sherol Chen, a machine learning developer, about AI at Google and AutoML methods. Sherol explains how the various AI groups within Google work together and how AutoML fits into that puzzle. She also explains how to get started with AutoML step-by-step (this is “practical” AI after all).

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55:52

On being humAIn

Practical AI ·

2019/08/26

David Yakobovitch joins the show to talk about the evolution of data science tools and techniques, the work he’s doing to teach these things at Galvanize, what his HumAIn Podcast is all about, and more.

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46:17

Serving deep learning models with RedisAI

Practical AI ·

2019/08/12

Redis is a an open source, in-memory data structure store, widely used as a database, cache and message broker. It now also support tensor data types and deep learning models via the RedisAI module. Why did they build this module? Who is or should be using it? We discuss this and much more with Pieter Cailliau.

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54:51

AI-driven studies of the ancient world and good GANs

Practical AI ·

2019/07/30

Chris and Daniel take the opportunity to catch up on some recent AI news. Among other things, they discuss the increasing impact of AI on studies of the ancient world and “good” uses of GANs. They also provide some more learning resources to help you level up your AI and machine learning game.

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53:01

AI code that facilitates good science

Practical AI ·

2019/07/19

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!

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50:54

Celebrating episode 50 and the neural net!

Practical AI ·

2019/07/03

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!

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55:15

Exposing the deception of DeepFakes

Practical AI ·

2019/06/25

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!

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43:44

Model inspection and interpretation at Seldon

Practical AI ·

2019/06/17

Interpreting complicated models is a hot topic. How can we trust and manage AI models that we can’t explain? In this episode, Janis Klaise, a data scientist with Seldon, joins us to talk about model interpretation and Seldon’s new open source project called Alibi. Janis also gives some of his thoughts on production ML/AI and how Seldon addresses related problems.

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51:32

GANs, RL, and transfer learning oh my!

Practical AI ·

2019/06/11

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!

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46:18

Visualizing and understanding RNNs

Practical AI ·

2019/06/04

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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1:02:21

How to get plugged into the AI community

Practical AI ·

2019/05/28

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

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57:10

AI adoption in the enterprise

Practical AI ·

2019/05/21

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.

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323 results

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