Daniel Whitenack
Appears in 283 Episodes
SLICED - will you make the (data science) cut?
SLICED is like the TV Show Chopped but for data science. Competitors get a never-before-seen dataset and two-hours to code a solution to a prediction challenge. Meg an...
AI is creating never before heard sounds! 🎵
AI is being used to transform the most personal instrument we have, our voice, into something that can be “played.” This is fascinating in and of itself, but Yotam Man...
Building a data team
Inspired by a recent article from Erik Bernhardsson titled “Building a data team at a mid-stage startup: a short story”, Chris and Daniel discuss all things AI/data te...
Towards stability and robustness
9 out of 10 AI projects don’t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episo...
From symbols to AI pair programmers đź’»
How did we get from symbolic AI to deep learning models that help you write code (i.e., GitHub and OpenAI’s new Copilot)? That’s what Chris and Daniel discuss in this ...
Multi-GPU training is hard (without PyTorch Lightning)
William Falcon wants AI practitioners to spend more time on model development, and less time on engineering. PyTorch Lightning is a lightweight PyTorch wrapper for hig...
Learning to learn deep learning đź“–
Chris and Daniel sit down to chat about some exciting new AI developments including wav2vec-u (an unsupervised speech recognition model) and meta-learning (a new book ...
The fastest way to build ML-powered apps
Tuhin Srivastava tells Daniel and Chris why BaseTen is the application development toolkit for data scientists. BaseTen’s goal is to make it simple to serve machine le...
Elixir meets machine learning
Today we’re sharing a special crossover episode from The Changelog podcast here on Practical AI. Recently, Daniel Whitenack joined Jerod Santo to talk with José Valim,...
Apache TVM and OctoML
90% of AI / ML applications never make it to market, because fine tuning models for maximum performance across disparate ML software solutions and hardware backends re...
25 years of speech technology innovation
To say that Jeff Adams is a trailblazer when it comes to speech technology is an understatement. Along with many other notable accomplishments, his team at Amazon deve...
Generating "hunches" using smart home data đźŹ
Smart home data is complicated. There are all kinds of devices, and they are in many different combinations, geographies, configurations, etc. This complicated data si...
Mapping the world
Ro Gupta from CARMERA teaches Daniel and Chris all about road intelligence. CARMERA maintains the maps that move the world, from HD maps for automated driving to consu...
Data science for intuitive user experiences
Nhung Ho joins Daniel and Chris to discuss how data science creates insights into financial operations and economic conditions. They delve into topics ranging from pre...
Going full bore with Graphcore!
Dave Lacey takes Daniel and Chris on a journey that connects the user interfaces that we already know - TensorFlow and PyTorch - with the layers that connect to the un...
Next-gen voice assistants
Nikola Mrkšić, CEO & Co-Founder of PolyAI, takes Daniel and Chris on a deep dive into conversational AI, describing the underlying technologies, and teaching them abou...
Recommender systems and high-frequency trading
David Sweet, author of “Tuning Up: From A/B testing to Bayesian optimization”, introduces Dan and Chris to system tuning, and takes them from A/B testing to response s...
Deep learning technology for drug discovery
Our Slack community wanted to hear about AI-driven drug discovery, and we listened. Abraham Heifets from Atomwise joins us for a fascinating deep dive into the interse...
Green AI 🌲
Empirical analysis from Roy Schwartz (Hebrew University of Jerusalem) and Jesse Dodge (AI2) suggests the AI research community has paid relatively little attention to ...
Low code, no code, accelerated code, & failing code
In this Fully-Connected episode, Chris and Daniel discuss low code / no code development, GPU jargon, plus more data leakage issues. They also share some really cool n...
Cooking up synthetic data with Gretel
John Myers of Gretel puts on his apron and rolls up his sleeves to show Dan and Chris how to cook up some synthetic data for automated data labeling, differential priv...
The nose knows
Daniel and Chris sniff out the secret ingredients for collecting, displaying, and analyzing odor data with Terri Jordan and Yanis Caritu of Aryballe. It certainly smel...
Accelerating ML innovation at MLCommons
MLCommons launched in December 2020 as an open engineering consortium that seeks to accelerate machine learning innovation and broaden access to this critical technolo...
The $1 trillion dollar ML model đź’µ
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 i...
Engaging with governments on AI for good
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 o...
From research to product at Azure AI
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...
The world's largest open library dataset
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 ide...
A casual conversation concerning causal inference
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...
Building a deep learning workstation
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 ...
Killer developer tools for machine learning
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 ...