Pete is the Technical Lead of the TensorFlow Micro team, which works on deep learning for mobile and embedded devices.
Lukas and Pete talk about hacking a Raspberry Pi to run AlexNet, the power and size constraints of embedded devices, and techniques to reduce model size. Pete also explains real world applications of TensorFlow Lite Micro and shares what it's been like to work on TensorFlow from the beginning.
The complete show notes (transcript and links) can be found here:...
Pieter is the Chief Scientist and Co-founder at Covariant, where his team is building universal AI for robotic manipulation. Pieter also hosts The Robot Brains Podcast, in which he explores how far humanity has come in its mission to create conscious computers, mindful machines, and rational robots.
Lukas and Pieter explore the state of affairs of robotics in 2021, the challenges of achieving consistency and reliability, and what it'll take to make robotics more ubiquitous. Pieter also shares...
In this episode we're joined by Chris Albon, Director of Machine Learning at the Wikimedia Foundation.
Lukas and Chris talk about Wikimedia's approach to content moderation, what it's like to work in a place so transparent that even internal chats are public, how Wikimedia uses machine learning (spoiler: they do a lot of models to help editors), and why they're switching to Kubeflow and Docker. Chris also shares how his focus on outcomes has shaped his career and his approach to technical...
In this episode, Emily and Lukas dive into the problems with bigger and bigger language models, the difference between form and meaning, the limits of benchmarks, and why it's important to name the languages we study.
Show notes (links to papers and transcript): http://wandb.me/gd-emily-m-bender
Emily M. Bender is a Professor of Linguistics at and Faculty Director of the Master's Program in Computational Linguistics at University of Washington. Her research areas include multilingual...
Jeff talks about building Facebook's early data team, founding Cloudera, and transitioning into biomedicine with Hammer Lab and Related Sciences.
Josh explains how astronomy and machine learning have informed each other, their current limitations, and where their intersection goes from here.
Josh is a Professor of Astronomy and Chair of the Astronomy Department at UC Berkeley. His research interests include the intersection of machine learning and physics, time-domain transients events, artificial intelligence, and optical/infared instrumentation.
0:00 Intro, sneak peek
1:15 How astronomy has informed ML
Xavier shares his experience deploying healthcare models, augmenting primary care with AI, the challenges of "ground truth" in medicine, and robustness in ML.
Xavier Amatriain is co-founder and CTO of Curai, an ML-based primary care chat system. Previously, he was VP of Engineering at Quora, and Research/Engineering Director at Neflix, where he started and led the Algorithms team responsible for Netflix's recommendation systems.
0:00 Sneak peak, intro
0:49 What is...
Spence shares his experience creating a product around human-in-the-loop machine translation, and explains how machine translation has evolved over the years.
Spence Green is co-founder and CEO of Lilt, an AI-powered language translation platform. Lilt combines human translators and machine translation in order to produce high-quality translations more efficiently.
🌟 Show notes:
- Transcription of the episode
- Links to papers, projects, and...
Roger and DJ share some of the history behind data science as we know it today, and reflect on their experiences working on California's COVID-19 response.
Roger Magoulas is Senior Director of Data Strategy at Astronomer, where he works on data infrastructure, analytics, and community development. Previously, he was VP of Research at O'Reilly and co-chair of O'Reilly's Strata Data and AI Conference.
DJ Patil is a board member and former CTO of Devoted Health, a healthcare company for...
Amelia and Filip give insights into the recommender systems powering Pandora, from developing models to balancing effectiveness and efficiency in production.
Amelia Nybakke is a Software Engineer at Pandora. Her team is responsible for the production system that serves models to listeners.
Filip Korzeniowski is a Senior Scientist at Pandora working on recommender systems. Before that, he was a PhD student working on deep neural networks for acoustic and language modeling applied to...
From Apache TVM to OctoML, Luis gives direct insight into the world of ML hardware optimization, and where systems optimization is heading.
Luis Ceze is co-founder and CEO of OctoML, co-author of the Apache TVM Project, and Professor of Computer Science and Engineering at the University of Washington. His research focuses on the intersection of computer architecture, programming languages, machine learning, and molecular biology.
Connect with Luis:
Matthew explains how combining machine learning and computational biology can provide mainstream medicine with better diagnostics and insights.
Matthew Davis is Head of AI at Invitae, the largest and fastest growing genetic testing company in the world. His research includes bioinformatics, computational biology, NLP, reinforcement learning, and information retrieval. Matthew was previously at IBM Research AI, where he led a research team focused on improving AI systems.
Clem explains the virtuous cycles behind the creation and success of Hugging Face, and shares his thoughts on where NLP is heading.
Clément Delangue is co-founder and CEO of Hugging Face, the AI community building the future. Hugging Face started as an open source NLP library and has quickly grown into a commercial product used by over 5,000 companies.
Connect with Clem:
Wojciech joins us to talk the principles behind OpenAI, the Fermi Paradox, and the future stages of developments in AGI.
Wojciech Zaremba is a co-founder of OpenAI, a research company dedicated to discovering and enacting the path to safe artificial general intelligence. He was also Head of Robotics, where his team developed general-purpose robots through new approaches to transfer learning, and taught robots complex behaviors.
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Phil shares some of the approaches, like sparsity and low precision, behind the breakthrough performance of Graphcore's Intelligence Processing Units (IPUs).
Phil Brown leads the Applications team at Graphcore, where they're building high-performance machine learning applications for their Intelligence Processing Units (IPUs), new processors specifically designed for AI compute.
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From working on COVID-19 vaccine rollout to writing a book on responsible ML, Alyssa shares her thoughts on meaningful projects and the importance of teamwork.
Alyssa Simpson Rochwerger is as a Director of Product at Blue Shield of California, pursuing her dream of using technology to improve healthcare. She has over a decade of experience in building technical data-driven products and has held numerous leadership roles for machine learning organizations, including VP of AI and Data at...
Sean joins us to chat about ML models and tools at Lyft Rideshare Labs, Python vs R, time series forecasting with Prophet, and election forecasting.
Sean Taylor is a Data Scientist at (and former Head of) Lyft Rideshare Labs, and specializes in methods for solving causal inference and business decision problems. Previously, he was a Research Scientist on Facebook's Core Data Science team. His interests include experiments, causal inference, statistics, machine learning, and...
Polly explains how microfluidics allow bioengineering researchers to create high throughput data, and shares her experiences with biology and machine learning.
Polly Fordyce is an Assistant Professor of Genetics and Bioengineering and fellow of the ChEM-H Institute at Stanford. She is the Principal Investigator of The Fordyce Lab, which focuses on developing and applying new microfluidic platforms for quantitative, high-throughput biophysics and biochemistry.
Adrien Gaidon shares his approach to building teams and taking state-of-the-art research from conception to production at Toyota Research Institute.
Adrien Gaidon is the Head of Machine Learning Research at the Toyota Research Institute (TRI). His research focuses on scaling up ML for robot autonomy, spanning Scene and Behavior Understanding, Simulation for Deep Learning, 3D Computer Vision, and Self-Supervised Learning.
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A look at how Nimrod and the team at Nanit are building smart baby monitor systems, from data collection to model deployment and production monitoring.
Nimrod Shabtay is a Senior Computer Vision Algorithm Developer at Nanit, a New York-based company that's developing better baby monitoring devices.
Connect with Nimrod:
Guidelines for building an accurate and robust ML/DL model in production:...
Chris shares some of the incredible work and innovations behind deep space exploration at NASA JPL and reflects on the past, present, and future of machine learning.
Chris Mattman is the Chief Technology and Innovation Officer at NASA Jet Propulsion Laboratory, where he focuses on organizational innovation through technology. He's worked on space missions such as the Orbiting Carbon Observatory 2 and Soil Moisture Active Passive satellites.
Chris is also a co-creator of Apache Tika, a...
From legged locomotion to autonomous driving, Vladlen explains how simulation and abstraction help us understand embodied intelligence.
Vladlen Koltun is the Chief Scientist for Intelligent Systems at Intel, where he leads an international lab of researchers working in machine learning, robotics, computer vision, computational science, and related areas.
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Personal website: http://vladlen.info/
Dominik shares the story and principles behind Vega and Vega-Lite, and explains how visualization and machine learning help each other.
Dominik is a co-author of Vega-Lite, a high-level visualization grammar for building interactive plots. He's also a professor at the Human-Computer Interaction Institute Institute at Carnegie Mellon University and an ML researcher at Apple.
Connect with Dominik
How Cade got access to the stories behind some of the biggest advancements in AI, and the dynamic playing out between leaders at companies like Google, Microsoft, and Facebook.
Cade Metz is a New York Times reporter covering artificial intelligence, driverless cars, robotics, virtual reality, and other emerging areas. Previously, he was a senior staff writer with Wired magazine and the U.S. editor of The Register, one of Britain’s leading science and technology news sites. His first book,...
Learn why traditional home security systems tend to fail and how Dave’s love of tinkering and deep learning are helping him and the team at Deep Sentinel avoid those same pitfalls. He also discusses the importance of combatting racial bias by designing race-agnostic systems and what their approach is to solving that problem.
Dave Selinger is the co-founder and CEO of Deep Sentinel, an intelligent crime prediction and prevention system that stops crime before it happens using deep learning...