Daniel and Chris cover the AI news of the day in this wide-ranging discussion. They start with Truss from Baseten while addressing how to categorize AI infrastructure and tools. Then they move on to transformers (again!), and somehow arrive at an AI pilot model from CMU that can navigate crowded airspace (much to Chris’s delight).
AI is increasingly being applied in creative and artistic ways, especially with recent tools integrating models like Stable Diffusion. This is making some artists mad. How should we be thinking about these trends more generally, and how can we as practitioners release and license models anticipating human impacts? We explore this along with other topics (like AI models detecting swimming pools 😊) in this fully connected episode.
Every year Mozilla releases an Internet Health Report that combines research and stories exploring what it means for the internet to be healthy. This year’s report is focused on AI. In this episode, Solana and Bridget from Mozilla join us to discuss the power dynamics of AI and the current state of AI worldwide. They highlight concerning trends in the application of this transformational technology along with positive signs of change.
We’re back with another Fully Connected episode – Daniel and Chris dive into a series of articles called ‘A New AI Lexicon’ that collectively explore alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI. The fun begins early as they discuss and debate ‘An Electric Brain’ with strong opinions, and consider viewpoints that aren’t always popular.
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 team building. They share some stories from their experiences kick starting AI efforts at various organizations and weight the pro and cons of things like centralized data management, prototype development, and a focus on engineering skills.
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 Mann from Never Before Heard Sounds is doing so much more! In this episode, he describes how he is using neural nets to process audio in real time for musicians and how AI is poised to change the music industry forever.
In Kenya, 33% of maternal deaths are caused by delays in seeking care, and 55% of maternal deaths are caused by delays in action or inadequate care by providers. Jacaranda Health is employing NLP and dialogue system techniques to help mothers experience childbirth safely and with respect and to help newborns get a safe start in life. Jay and Sathy from Jacaranda join us in this episode to discuss how they are using AI to prioritize incoming SMS messages from mothers and help them get the care they need.
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 and Nick, the SLICED show hosts, join us in this episode to discuss how the show is creating much needed data science community. They give us a behind the scenes look at all the datasets, memes, contestants, scores, and chat of SLICED.
Yonatan Geifman of Deci makes Daniel and Chris buckle up, and takes them on a tour of the ideas behind his amazing new inference platform. It enables AI developers to build, optimize, and deploy blazing-fast deep learning models on any hardware. Don’t blink or you’ll miss it!
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.
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 episode, Roey from BeyondMinds gives us some insights on how to filter garbage input, detect risky output, and generally develop more robust AI systems.
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 episode about the history and future of deep learning (with some help from an article recently published in ACM and written by the luminaries of deep learning).
In this inaugural episode of Practical AI — Adam Stacoviak and Jerod Santo sit down with Daniel Whitenack and Chris Benson to discuss their experiences in Artificial Intelligence, Machine Learning, and Data Science and what they hope to accomplish as hosts of this podcast.
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.
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.
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.
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!
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!
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 in transactions, and generates decisions in mere milliseconds or less globally. Madhurima Khandelwal, head of AMEX AI Labs, joins us for a fascinating discussion about scaling research and building robust and ethical AI-driven financial applications.
Rajiv Shah teaches Daniel and Chris about data leakage, and its major impact upon machine learning models. It’s the kind of topic that we don’t often think about, but which can ruin our results. Raj discusses how to use activation maps and image embedding to find leakage, so that leaking information in our test set does not find its way into our training set.
In anticipation of the upcoming NVIDIA GPU Technology Conference (GTC), Will Ramey joins Daniel and Chris to talk about education for artificial intelligence practitioners, and specifically the role that the NVIDIA Deep Learning Institute plays in the industry. Will’s insights from long experience are shaping how we all stay on top of AI, so don’t miss this ‘must learn’ episode.
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 pain points that he uncovered while working as an AI intern at OpenAI. He also shares his vision for the future of machine learning tooling and where he would like to see people level up tool-wise.
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.
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 new learning opportunities for leveling up your AI/ML game!
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.
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!
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.
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.
Hamish from Sajari blows our mind with a great discussion about AI in search. In particular, he talks about Sajari’s quest for performant AI implementations and extensive use of Reinforcement Learning (RL). We’ve been wanting to make this one happen for a while, and it was well worth the wait.
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.”
