Changelog Media presents
Practical AI: Machine Learning, Data Science
Making artificial intelligence practical, productive, and 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, etc). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!
June 22, 2021
Vector databases for machine learning
Pinecone is the first vector database for machine learning. Edo Liberty explains to Chris how vector similarity search works, and its advantages over traditional database approaches for machine learning. It enables one to search through billions of vector embeddings for similar matches, in milliseconds, and Pinecone is a managed service that puts this capability at the fingertips of machine learning practitioners.
June 15, 2021
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 high-performance AI research that lets you train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code! In this episode, we dig deep into Lightning, how it works, and what it is enabling. William also discusses the Grid AI platform (built on top of PyTorch Lightning). This platform lets you seamlessly train 100s of Machine Learning models on the cloud from your laptop.
June 8, 2021
Learning to learn deep learning 📖
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 about “How To Learn Deep Learning And Thrive In The Digital World”). Along the way they discuss engineering skills for AI developers and strategies for launching AI initiatives in established companies.
June 1, 2021
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 learning models, write custom business logic around them, and expose those through API endpoints without configuring any infrastructure.
May 26, 2021
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, Elixir creator, about Numerical Elixir. This is José’s newest project that’s bringing Elixir into the world of machine learning. They discuss why José chose this as his next direction, the team’s layered approach, influences and collaborators on this effort, and their awesome collaborative notebook that’s built on Phoenix LiveView.
May 18, 2021
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 requires a ton of manual labor and is cost-prohibitive. Luis Ceze and his team created Apache TVM at the University of Washington, then left founded OctoML to bring the project to market.
May 11, 2021
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 developed the Echo, Dash, and Fire TV changing our perception of how we could interact with devices in our home. Jeff now leads Cobalt Speech and Language, and he was kind enough to join us for a discussion about human computer interaction, multimodal AI tasks, the history of language modeling, and AI for social good.
May 4, 2021
Generating "hunches" using smart home data 🏠
Smart home data is complicated. There are all kinds of devices, and they are in many different combinations, geographies, configurations, etc. This complicated data situation is further exacerbated during a pandemic when time series data seems to be filled with anomalies. Evan Welbourne joins us to discuss how Amazon is synthesizing this disparate data into functionality for the next generation of smart homes. He discusses the challenges of working with smart home technology, and he describes how they developed their latest feature called “hunches.”
April 20, 2021
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 predictive forecasting to aid small businesses, to learning about the economic fallout from the COVID-19 Pandemic.
April 13, 2021
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 underlying hardware. Along the way, we learn about Poplar Graph Framework Software. If you are the type of practitioner who values ‘under the hood’ knowledge, then this is the episode for you.
April 6, 2021
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 about the next generation of voice assistants that will be capable of handling true human-level conversations. It’s an episode you’ll be talking about for a long time!
March 30, 2021
Women in Data Science (WiDS)
Chris has the privilege of talking with Stanford Professor Margot Gerritsen, who co-leads the Women in Data Science (WiDS) Worldwide Initiative. This is a conversation that everyone should listen to. Professor Gerritsen’s profound insights into how we can all help the women in our lives succeed - in data science and in life - is a ‘must listen’ episode for everyone, regardless of gender.
March 23, 2021
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 surface methodology, contextual bandit, and finally bayesian optimization. Along the way, we get fascinating insights into recommender systems and high-frequency trading!
March 9, 2021
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 intersection of deep learning models and molecule binding. He describes how these methods work and how they are beginning to help create drugs for “undruggable” diseases!
March 2, 2021
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 computational efficiency. A focus on accuracy rather than efficiency increases the carbon footprint of AI research and increases research inequality. In this episode, Jesse and Roy advocate for increased research activity in Green AI (AI research that is more environmentally friendly and inclusive). They highlight success stories and help us understand the practicalities of making our workflows more efficient.
February 16, 2021
The AI doc will see you now
Elad Walach of Aidoc joins Chris to talk about the use of AI for medical imaging interpretation. Starting with the world’s largest annotated training data set of medical images, Aidoc is the radiologist’s best friend, helping the doctor to interpret imagery faster, more accurately, and improving the imaging workflow along the way. Elad’s vision for the transformative future of AI in medicine clearly soothes Chris’s concern about managing his aging body in the years to come. ;-)
February 2, 2021
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 privacy, and other purposes. His military and intelligence community background give him an interesting perspective that piqued the interest of our intrepid hosts.