Daniel Whitenack
CEO @Prediction Guard & cohost @Practical AI podcast
Appears in 339 Episodes
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 rapid...
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 o...
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 generate...
Open source data labeling tools
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 d...
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,...
AI in the browser
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 pe...
Blacklisted facial recognition and surveillance companies
The United States has blacklisted several Chinese AI companies working in facial recognition and surveillance. Why? What are these companies doing exactly, and how doe...
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 fo...
AI in the majority world and model distillation
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 w...
The influence of open source on AI development
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...
Worlds are colliding - AI and HPC
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 w...
AutoML and AI at Google
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 toget...
On being humAIn
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 Hum...
Serving deep learning models with RedisAI
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...
AI-driven studies of the ancient world and good GANs
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 ...
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 AllenNL...
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 prime...
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...
Model inspection and interpretation at Seldon
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 Seldo...
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 the...