In the newest episode of Gradient Dissent, Chelsea Finn, Assistant Professor at Stanford's Computer Science Department, discusses the forefront of robotics and machine learning.Discover her groundbreaking work, where two-armed robots learn to cook shrimp (messes included!), and discuss how robotic learning could transform student feedback in education.We'll dive into the challenges of developing humanoid and quadruped robots, explore the limitations of simulated environments and discuss why real-world experience is key for adaptable machines. Plus, Chelsea will offer a glimpse into the future of household robotics and why it may be a few years before a robot is making your bed.Whether you're an AI enthusiast, a robotics professional, or simply curious about the potential and future of the technology, this episode offers unique insights into the evolving world of robotics and where it's headed next.*Subscribe to Weights & Biases* → https://bit.ly/45BCkYzTimestamps:0:00- Introduction13:00 - Reinforcement Learning in Robotics15:00 - Using Simulation vs. Real Data in Robotics17:00 - The Complexity of Grasping and Manipulation Tasks20:00 - Future of Household Robotics23:00 - Humanoids and Quadrupeds in Robotics25:00 - Public Perception and Design of Robots27:00 - Performance of Robot Dogs29:00 - Chelsea's Work on Student Feedback31:00 - Training the Auto-Grading System33:00 - Potential Expansion to Other Classes and Projects35:00 - Impact of AI Coding Tools on Education37:00 - Chelsea's Exciting Research in Robotics39:00 - Cooking Shrimp with a Two-Armed Robot41:00 - Evaluating Robotic Cooking Experiments43:00 - Vision Systems in Robotics50:00 - Conclusion🎙 Get our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleYouTube: http://wandb.me/youtubeConnect with Chelsea Finn:https://www.linkedin.com/in/cbfinn/ https://twitter.com/chelseabfinnFollow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3

In the latest episode of Gradient Dissent, Richard Socher, CEO of You.com, shares his insights on the power of AI in search. The episode focuses on how advanced language models like GPT-4 are transforming search engines and changing the way we interact with digital platforms. The discussion covers the practical applications and challenges of integrating AI into search functionality, as well as the ethical considerations and future implications of AI in our digital lives. Join us for an enlightening conversation on how AI and you.com are reshaping how we access and interact with information online.*Subscribe to Weights & Biases* →  https://bit.ly/45BCkYzTimestamps: 00:00 - Introduction to Gradient Dissent Podcast 00:48 - Richard Socher’s Journey: From Linguistic Computer Science to AI 06:42 - The Genesis and Evolution of MetaMind 13:30 - Exploring You.com's Approach to Enhanced Search 18:15 - Demonstrating You.com's AI in Mortgage Calculations 24:10 - The Power of AI in Search: A Deep Dive with You.com 30:25 - Security Measures in Running AI-Generated Code 35:50 - Building a Robust and Secure AI Tech Stack 42:33 - The Role of AI in Automating and Transforming Digital Work 48:50 - Discussing Ethical Considerations and the Societal Impact of AI 55:15 - Envisioning the Future of AI in Daily Life and Work 01:02:00 - Reflecting on the Evolution of AI and Its Future Prospects 01:05:00 - Closing Remarks and Podcast Wrap-Up🎙 Get our podcasts on these platforms:Apple Podcasts: http://wandb.me/apple-podcastsSpotify: http://wandb.me/spotifyGoogle: http://wandb.me/gd_googleYouTube: http://wandb.me/youtubeConnect with Richard Socher:https://www.linkedin.com/in/richardsocher/ https://twitter.com/RichardSocher Follow Weights & Biases:https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3

Explore the Future of Investment & Impact in AI with Host Lukas Biewald and Guests Elad Gill and Sarah Guo of the No Priors podcast.Sarah is the founder of Conviction VC, an AI-centric $100 million venture fund. Elad, a seasoned entrepreneur and startup investor, boasts an impressive portfolio in over 40 companies, each valued at $1 billion or more, and wrote the influential "High Growth Handbook."Join us for a deep dive into the nuanced world of AI, where we'll explore its broader industry impact, focusing on how startups can seamlessly blend product-centric approaches with a balance of innovation and practical development.*Subscribe to Weights & Biases* →  https://bit.ly/45BCkYzTimestamps:0:00 - Introduction 5:15 - Exploring Fine-Tuning vs RAG in AI10:30 - Evaluating AI Research for Investment15:45 - Impact of AI Models on Product Development20:00 - AI's Role in Evolving Job Markets25:15 - The Balance Between AI Research and Product Development30:00 - Code Generation Technologies in Software Engineering35:00 - AI's Broader Industry Implications40:00 - Importance of Product-Driven Approaches in AI Startups45:00 - AI in Various Sectors: Beyond Software Engineering50:00 - Open Source vs Proprietary AI Models55:00 - AI's Impact on Traditional Roles and Industries1:00:00 - Closing Thoughts Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Follow Weights & Biases:YouTube: http://wandb.me/youtubeTwitter: https://twitter.com/weights_biases LinkedIn: https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3#OCR #DeepLearning #AI #Modeling #ML

In the latest episode of Gradient Dissent, we explore the innovative features and impact of LlamaIndex in AI data management with Jerry Liu, CEO of LlamaIndex. Jerry shares insights on how LlamaIndex integrates diverse data formats with advanced AI technologies, addressing challenges in data retrieval, analysis, and conversational memory. We also delve into the future of AI-driven systems and LlamaIndex's role in this rapidly evolving field. This episode is a must-watch for anyone interested in AI, data science, and the future of technology.Timestamps:0:00 - Introduction 4:46 - Differentiating  LlamaIndex in the AI framework ecosystem.9:00 - Discussing data analysis, search, and retrieval applications.14:17 - Exploring Retrieval Augmented Generation (RAG) and vector databases.19:33 - Implementing and optimizing One Bot in Discord.24:19 - Developing and evaluating datasets for AI systems.28:00 - Community contributions and the growth of LlamaIndex.34:34 - Discussing embedding models and the use of vector databases.39:33 - Addressing AI model hallucinations and fine-tuning.44:51 - Text extraction applications and agent-based systems in AI.49:25 - Community contributions to LlamaIndex and managing refactors.52:00 - Interactions with big tech's corpus and AI context length.54:59 - Final thoughts on underrated aspects of ML and challenges in AI.Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Connect with Jerry:https://twitter.com/jerryjliu0https://www.linkedin.com/in/jerry-liu-64390071/Follow Weights & Biases:YouTube: http://wandb.me/youtubeTwitter: https://twitter.com/weights_biases LinkedIn: https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server:https://discord.gg/CkZKRNnaf3#OCR #DeepLearning #AI #Modeling #ML

Dive into the remarkable journey of Paul Copplestone, CEO of Supabase in this episode of Gradient Dissent Business. Paul recounts his unique experiences, having started with a foundation in web development before venturing into innovative projects in agriculture, blending his rural roots with technological advancements.Throughout our conversation with Paul we uncover his coding and database management skills, and learn how his diverse background was instrumental in shaping his approach to building a thriving tech company.This episode covers everything from challenges in the AI and database industries to the future of spatial data and embeddings. Join us as we explore the fascinating world of innovation, the importance of diverse perspectives, and the future of AI, data storage, and spatial data.We discuss:0:00 What is supabase.com?03:07 Exploration of exciting use cases06:15 Challenges in the AI and Database Industry12:30 Role of Multimodal Models16:45 Innovations in Data Storage22:10 Diverse Perspectives on Technology29:20 The Importance of Intellectual Honesty36:00 Building a Company and Navigating Challenges42:30 The Impact of Global Experience49:20 The Future of AI and Spatial Data54:00 Integrating Spatial Data into Digital SystemsThanks for listening to the Gradient Dissent Business podcast, with hosts Lavanya Shukla and Caryn Marooney, brought to you by Weights & Biases. Be sure to click the subscribe button below, to keep your finger on the pulse of this fast-moving space and hear from other amazing guests.

In the latest episode of Gradient Dissent, we hear from Joseph Spisak, Product Director, Generative AI @Meta, to explore the boundless impacts of AI and its expansive role in reshaping various sectors. We delve into the intricacies of models like GPT and Llama2, their influence on user experiences, and AI's groundbreaking contributions to fields like biology, material science, and green hydrogen production through the Open Catalyst Project. The episode also examines AI's practical business applications, from document summarization to intelligent note-taking, addressing the ethical complexities of AI deployment. We wrap up with a discussion on the significance of open-source AI development, community collaboration, and AI democratization. Tune in for valuable insights into the expansive world of AI, relevant to developers, business leaders, and tech enthusiasts.We discuss:0:00 Intro0:32 Joe is Back at Meta3:28 What Does Meta Get Out Of Putting Out LLMs?8:24 Measuring The Quality Of LLMs10:55 How Do You Pick The Sizes Of Models16:45 Advice On Choosing Which Model To Start With24:57 The Secret Sauce In The Training26:17 What Is Being Worked On Now33:00 The Safety Mechanisms In Llama 237:00 The Datasets Llama 2 Is Trained On38:00 On Multilingual Capabilities & Tone43:30 On The Biggest Applications Of Llama 247:25 On Why The Best Teams Are Built By Users54:01 The Culture Differences Of Meta vs Open Source57:39 The AI Learning Alliance1:01:34 Where To Learn About Machine Learning1:05:10 Why AI For Science Is Under-rated1:11:36 What Are The Biggest Issues With Real-World ApplicationsThanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

In the premiere episode of Gradient Dissent Business, we're joined by Weights & Biases co-founder Chris Van Pelt for a deep dive into the world of large language models like GPT-3.5 and GPT-4. Chris bridges his expertise as both a tech founder and AI expert, offering key strategies for startups seeking to connect with early users, and for enterprises experimenting with AI. He highlights the melding of AI and traditional web development, sharing his insights on product evolution, leadership, and the power of customer conversations—even for the most introverted founders. He shares how personal development and authentic co-founder relationships enrich business dynamics. Join us for a compelling episode brimming with actionable advice for those looking to innovate with language models, all while managing the inherent complexities. Don't miss Chris Van Pelt's invaluable take on the future of AI in this thought-provoking installment of Gradient Dissent Business.We discuss:0:00 - Intro5:59 - Impactful relationships in Chris's life13:15 - Advice for finding co-founders16:25 - Chris's fascination with challenging problems22:30 - Tech stack for AI labs30:50 - Impactful capabilities of AI models36:24 - How this AI era is different47:36 - Advising large enterprises on language model integration51:18 - Using language models for business intelligence and automation52:13 - Closing thoughts and appreciationThanks for listening to the Gradient Dissent Business podcast, with hosts Lavanya Shukla and Caryn Marooney, brought to you by Weights & Biases. Be sure to click the subscribe button below, to keep your finger on the pulse of this fast-moving space and hear from other amazing guests#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Brandon Duderstadt, Co-Founder and CEO of Nomic AI. Both of Nomic AI’s products, Atlas and GPT4All, aim to improve the explainability and accessibility of AI.We discuss:- (0:55) What GPT4All is and its value proposition.- (6:56) The advantages of using smaller LLMs for specific tasks. - (9:42) Brandon’s thoughts on the cost of training LLMs. - (10:50) Details about the current state of fine-tuning LLMs. - (12:20) What quantization is and what it does. - (21:16) What Atlas is and what it allows you to do.- (27:30) Training code models versus language models.- (32:19) Details around evaluating different models.- (38:34) The opportunity for smaller companies to build open-source models. - (42:00) Prompt chaining versus fine-tuning models.Resources mentioned:Brandon Duderstadt - https://www.linkedin.com/in/brandon-duderstadt-a3269112a/Nomic AI - https://www.linkedin.com/company/nomic-ai/Nomic AI Website - https://home.nomic.ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Soumith Chintala, VP/Fellow of Meta and Co-Creator of PyTorch. Soumith and his colleagues’ open-source framework impacted both the development process and the end-user experience of what would become PyTorch.We discuss:- The history of PyTorch’s development and TensorFlow’s impact on development decisions.- How a symbolic execution model affects the implementation speed of an ML compiler.- The strengths of different programming languages in various development stages.- The importance of customer engagement as a measure of success instead of hard metrics.- Why community-guided innovation offers an effective development roadmap.- How PyTorch’s open-source nature cultivates an efficient development ecosystem.- The role of community building in consolidating assets for more creative innovation.- How to protect community values in an open-source development environment.- The value of an intrinsic organizational motivation structure.- The ongoing debate between open-source and closed-source products, especially as it relates to AI and machine learning.Resources:- Soumith Chintalahttps://www.linkedin.com/in/soumith/- Meta | LinkedInhttps://www.linkedin.com/company/meta/- Meta | Websitehttps://about.meta.com/- Pytorchhttps://pytorch.org/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Andrew Feldman, Founder and CEO of Cerebras Systems. Andrew and the Cerebras team are responsible for building the largest-ever computer chip and the fastest AI-specific processor in the industry.We discuss:- The advantages of using large chips for AI work.- Cerebras Systems’ process for building chips optimized for AI.- Why traditional GPUs aren’t the optimal machines for AI work.- Why efficiently distributing computing resources is a significant challenge for AI work.- How much faster Cerebras Systems’ machines are than other processors on the market.- Reasons why some ML-specific chip companies fail and what Cerebras does differently.- Unique challenges for chip makers and hardware companies.- Cooling and heat-transfer techniques for Cerebras machines.- How Cerebras approaches building chips that will fit the needs of customers for years to come.- Why the strategic vision for what data to collect for ML needs more discussion.Resources:Andrew Feldman - https://www.linkedin.com/in/andrewdfeldman/Cerebras Systems - https://www.linkedin.com/company/cerebras-systems/Cerebras Systems | Website - https://www.cerebras.net/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Harrison Chase, Co-Founder and CEO of LangChain. Harrison and his team at LangChain are on a mission to make the process of creating applications powered by LLMs as easy as possible.We discuss:- What LangChain is and examples of how it works. - Why LangChain has gained so much attention. - When LangChain started and what sparked its growth. - Harrison’s approach to community-building around LangChain. - Real-world use cases for LangChain.- What parts of LangChain Harrison is proud of and which parts can be improved.- Details around evaluating effectiveness in the ML space.- Harrison's opinion on fine-tuning LLMs.- The importance of detailed prompt engineering.- Predictions for the future of LLM providers.Resources:Harrison Chase - https://www.linkedin.com/in/harrison-chase-961287118/LangChain | LinkedIn - https://www.linkedin.com/company/langchain/LangChain | Website - https://docs.langchain.com/docs/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Jean Marc Alkazzi, Applied AI at idealworks. Jean focuses his attention on applied AI, leveraging the use of autonomous mobile robots (AMRs) to improve efficiency within factories and more.We discuss:- Use cases for autonomous mobile robots (AMRs) and how to manage a fleet of them. - How AMRs interact with humans working in warehouses.- The challenges of building and deploying autonomous robots.- Computer vision vs. other types of localization technology for robots.- The purpose and types of simulation environments for robotic testing.- The importance of aligning a robotic fleet’s workflow with concrete business objectives.- What the update process looks like for robots.- The importance of avoiding your own biases when developing and testing AMRs.- The challenges associated with troubleshooting ML systems.Resources: Jean Marc Alkazzi - https://www.linkedin.com/in/jeanmarcjeanazzi/idealworks |LinkedIn - https://www.linkedin.com/company/idealworks-gmbh/idealworks | Website - https://idealworks.com/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Stella Biderman, Executive Director at EleutherAI and Lead Scientist - Mathematician at Booz Allen Hamilton.EleutherAI is a grassroots collective that enables open-source AI research and focuses on the development and interpretability of large language models (LLMs).We discuss:- How EleutherAI got its start and where it's headed.- The similarities and differences between various LLMs.- How to decide which model to use for your desired outcome.- The benefits and challenges of reinforcement learning from human feedback.- Details around pre-training and fine-tuning LLMs.- Which types of GPUs are best when training LLMs.- What separates EleutherAI from other companies training LLMs.- Details around mechanistic interpretability.- Why understanding what and how LLMs memorize is important.- The importance of giving researchers and the public access to LLMs.Stella Biderman - https://www.linkedin.com/in/stellabiderman/EleutherAI - https://www.linkedin.com/company/eleutherai/Resources:- https://www.eleuther.ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

On this episode, we’re joined by Aidan Gomez, Co-Founder and CEO at Cohere. Cohere develops and releases a range of innovative AI-powered tools and solutions for a variety of NLP use cases.We discuss:- What “attention” means in the context of ML.- Aidan’s role in the “Attention Is All You Need” paper.- What state-space models (SSMs) are, and how they could be an alternative to transformers. - What it means for an ML architecture to saturate compute.- Details around data constraints for when LLMs scale.- Challenges of measuring LLM performance.- How Cohere is positioned within the LLM development space.- Insights around scaling down an LLM into a more domain-specific one.- Concerns around synthetic content and AI changing public discourse.- The importance of raising money at healthy milestones for AI development.Aidan Gomez - https://www.linkedin.com/in/aidangomez/Cohere - https://www.linkedin.com/company/cohere-ai/Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.Resources:- https://cohere.ai/- “Attention Is All You Need”#OCR #DeepLearning #AI #Modeling #ML

Jonathan Frankle, Chief Scientist at MosaicML and Assistant Professor of Computer Science at Harvard University, joins us on this episode. With comprehensive infrastructure and software tools, MosaicML aims to help businesses train complex machine-learning models using their own proprietary data.We discuss:- Details of Jonathan’s Ph.D. dissertation which explores his “Lottery Ticket Hypothesis.”- The role of neural network pruning and how it impacts the performance of ML models.- Why transformers will be the go-to way to train NLP models for the foreseeable future.- Why the process of speeding up neural net learning is both scientific and artisanal. - What MosaicML does, and how it approaches working with clients.- The challenges for developing AGI.- Details around ML training policy and ethics.- Why data brings the magic to customized ML models.- The many use cases for companies looking to build customized AI models.Jonathan Frankle - https://www.linkedin.com/in/jfrankle/Resources:- https://mosaicml.com/- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural NetworksThanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation.#OCR #DeepLearning #AI #Modeling #ML

About This EpisodeShreya Shankar is a computer scientist, PhD student in databases at UC Berkeley, and co-author of "Operationalizing Machine Learning: An Interview Study", an ethnographic interview study with 18 machine learning engineers across a variety of industries on their experience deploying and maintaining ML pipelines in production.Shreya explains the high-level findings of "Operationalizing Machine Learning"; variables that indicate a successful deployment (velocity, validation, and versioning), common pain points, and a grouping of the MLOps tool stack into four layers. Shreya and Lukas also discuss examples of data challenges in production, Jupyter Notebooks, and reproducibility.Show notes (transcript and links): http://wandb.me/gd-shreya---💬 *Host:* Lukas Biewald---*Subscribe and listen to Gradient Dissent today!*👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Sarah Catanzaro is a General Partner at Amplify Partners, and one of the leading investors in AI and ML. Her investments include RunwayML, OctoML, and Gantry.Sarah and Lukas discuss lessons learned from the "AI renaissance" of the mid 2010s and compare the general perception of ML back then to now. Sarah also provides insights from her perspective as an investor, from selling into tech-forward companies vs. traditional enterprises, to the current state of MLOps/developer tools, to large language models and hype bubbles.Show notes (transcript and links): http://wandb.me/gd-sarah-catanzaro---⏳ Timestamps: 0:00 Intro1:10 Lessons learned from previous AI hype cycles11:46 Maintaining technical knowledge as an investor19:05 Selling into tech-forward companies vs. traditional enterprises25:09 Building point solutions vs. end-to-end platforms36:27 LLMS, new tooling, and commoditization44:39 Failing fast and how startups can compete with large cloud vendors52:31 The gap between research and industry, and vice versa1:00:01 Advice for ML practitioners during hype bubbles1:03:17 Sarah's thoughts on Rust and bottlenecks in deployment1:11:23 The importance of aligning technology with people1:15:58 Outro---📝 Links📍 "Operationalizing Machine Learning: An Interview Study" (Shankar et al., 2022), an interview study on deploying and maintaining ML production pipelines: https://arxiv.org/abs/2209.09125---Connect with Sarah:📍 Sarah on Twitter: https://twitter.com/sarahcat21📍 Sarah's Amplify Partners profile: https://www.amplifypartners.com/investment-team/sarah-catanzaro---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan---Subscribe and listen to Gradient Dissent today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Cristóbal Valenzuela is co-founder and CEO of Runway ML, a startup that's building the future of AI-powered content creation tools. Runway's research areas include diffusion systems for image generation.Cris gives a demo of Runway's video editing platform. Then, he shares how his interest in combining technology with creativity led to Runway, and where he thinks the world of computation and content might be headed to next. Cris and Lukas also discuss Runway's tech stack and research.Show notes (transcript and links): http://wandb.me/gd-cristobal-valenzuela---⏳ Timestamps: 0:00 Intro1:06 How Runway uses ML to improve video editing6:04 A demo of Runway’s video editing capabilities13:36 How Cris entered the machine learning space18:55 Cris’ thoughts on the future of ML for creative use cases28:46 Runway’s tech stack32:38 Creativity, and keeping humans in the loop36:15 The potential of audio generation and new mental models40:01 Outro---🎥 Runway's AI Film Festival is accepting submissions through January 23! 🎥They are looking for art and artists that are at the forefront of AI filmmaking. Submissions should be between 1-10 minutes long, and a core component of the film should include generative content📍 https://aiff.runwayml.com/--📝 Links📍 "High-Resolution Image Synthesis with Latent Diffusion Models" (Rombach et al., 2022)", the research paper behind Stable Diffusion: https://research.runwayml.com/publications/high-resolution-image-synthesis-with-latent-diffusion-models📍 Lexman Artificial, a 100% AI-generated podcast: https://twitter.com/lexman_ai---Connect with Cris and Runway:📍 Cris on Twitter: https://twitter.com/c_valenzuelab📍 Runway on Twitter: https://twitter.com/runwayml📍 Careers at Runway: https://runwayml.com/careers/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan---Subscribe and listen to Gradient Dissent today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Jeremy Howard is a co-founder of fast.ai, the non-profit research group behind the popular massive open online course "Practical Deep Learning for Coders", and the open source deep learning library "fastai".Jeremy is also a co-founder of #Masks4All, a global volunteer organization founded in March 2020 that advocated for the public adoption of homemade face masks in order to help slow the spread of COVID-19. His Washington Post article "Simple DIY masks could help flatten the curve." went viral in late March/early April 2020, and is associated with the U.S CDC's change in guidance a few days later to recommend wearing masks in public.In this episode, Jeremy explains how diffusion works and how individuals with limited compute budgets can engage meaningfully with large, state-of-the-art models. Then, as our first-ever repeat guest on Gradient Dissent, Jeremy revisits a previous conversation with Lukas on Python vs. Julia for machine learning.Finally, Jeremy shares his perspective on the early days of COVID-19, and what his experience as one of the earliest and most high-profile advocates for widespread mask-wearing was like.Show notes (transcript and links): http://wandb.me/gd-jeremy-howard-2---⏳ Timestamps: 0:00 Intro1:06 Diffusion and generative models14:40 Engaging with large models meaningfully20:30 Jeremy's thoughts on Stable Diffusion and OpenAI26:38 Prompt engineering and large language models32:00 Revisiting Julia vs. Python40:22 Jeremy's science advocacy during early COVID days1:01:03 Researching how to improve children's education1:07:43 The importance of executive buy-in1:11:34 Outro1:12:02 Bonus: Weights & Biases---📝 Links📍 Jeremy's previous Gradient Dissent episode (8/25/2022): http://wandb.me/gd-jeremy-howard📍 "Simple DIY masks could help flatten the curve. We should all wear them in public.", Jeremy's viral Washington Post article: https://www.washingtonpost.com/outlook/2020/03/28/masks-all-coronavirus/📍 "An evidence review of face masks against COVID-19" (Howard et al., 2021), one of the first peer-reviewed papers on the effectiveness of wearing masks: https://www.pnas.org/doi/10.1073/pnas.2014564118📍 Jeremy's Twitter thread summary of "An evidence review of face masks against COVID-19": https://twitter.com/jeremyphoward/status/1348771993949151232📍 Read more about Jeremy's mask-wearing advocacy: https://www.smh.com.au/world/north-america/australian-expat-s-push-for-universal-mask-wearing-catches-fire-in-the-us-20200401-p54fu2.html---Connect with Jeremy and fast.ai:📍 Jeremy on Twitter: https://twitter.com/jeremyphoward📍 fast.ai on Twitter: https://twitter.com/FastDotAI📍 Jeremy on LinkedIn: https://www.linkedin.com/in/howardjeremy/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan

Jerome Pesenti is the former VP of AI at Meta, a tech conglomerate that includes Facebook, WhatsApp, and Instagram, and one of the most exciting places where AI research is happening today.Jerome shares his thoughts on Transformers-based large language models, and why he's excited by the progress but skeptical of the term "AGI". Then, he discusses some of the practical applications of ML at Meta (recommender systems and moderation!) and dives into the story behind Meta's development of PyTorch. Jerome and Lukas also chat about Jerome's time at IBM Watson and in drug discovery.Show notes (transcript and links): http://wandb.me/gd-jerome-pesenti---⏳ Timestamps: 0:00 Intro0:28 Jerome's thought on large language models12:53 AI applications and challenges at Meta18:41 The story behind developing PyTorch26:40 Jerome's experience at IBM Watson28:53 Drug discovery, AI, and changing the game36:10 The potential of education and AI40:10 Meta and AR/VR interfaces43:43 Why NVIDIA is such a powerhouse47:08 Jerome's advice to people starting their careers48:50 Going back to coding, the challenges of scaling52:11 Outro---Connect with Jerome:📍 Jerome on Twitter: https://twitter.com/an_open_mind📍 Jerome on LinkedIn: https://www.linkedin.com/in/jpesenti/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

D. Sculley is CEO of Kaggle, the beloved and well-known data science and machine learning community.D. discusses his influential 2015 paper "Machine Learning: The High Interest Credit Card of Technical Debt" and what the current challenges of deploying models in the real world are now, in 2022. Then, D. and Lukas chat about why Kaggle is like a rain forest, and about Kaggle's historic, current, and potential future roles in the broader machine learning community.Show notes (transcript and links): http://wandb.me/gd-d-sculley---⏳ Timestamps: 0:00 Intro1:02 Machine learning and technical debt11:18 MLOps, increased stakes, and realistic expectations19:12 Evaluating models methodically25:32 Kaggle's role in the ML world33:34 Kaggle competitions, datasets, and notebooks38:49 Why Kaggle is like a rain forest44:25 Possible future directions for Kaggle46:50 Healthy competitions and self-growth48:44 Kaggle's relevance in a compute-heavy future53:49 AutoML vs. human judgment56:06 After a model goes into production1:00:00 Outro---Connect with D. and Kaggle:📍 D. on LinkedIn: https://www.linkedin.com/in/d-sculley-90467310/📍 Kaggle on Twitter: https://twitter.com/kaggle---Links:📍 "Machine Learning: The High Interest Credit Card of Technical Debt" (Sculley et al. 2014): https://research.google/pubs/pub43146/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan, Anish Shah, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Emad Mostaque is CEO and co-founder of Stability AI, a startup and network of decentralized developer communities building open AI tools. Stability AI is the company behind Stable Diffusion, the well-known, open source, text-to-image generation model.Emad shares the story and mission behind Stability AI (unlocking humanity's potential with open AI technology), and explains how Stability's role as a community catalyst and compute provider might evolve as the company grows. Then, Emad and Lukas discuss what the future might hold in store: big models vs "optimal" models, better datasets, and more decentralization.-🎶 Special note: This week’s theme music was composed by Weights & Biases’ own Justin Tenuto with help from Harmonai’s Dance Diffusion.-Show notes (transcript and links): http://wandb.me/gd-emad-mostaque-⏳ Timestamps: 00:00 Intro00:42 How AI fits into the safety/security industry 09:33 Event matching and object detection14:47 Running models on the right hardware17:46 Scaling model evaluation23:58 Monitoring and evaluation challenges26:30 Identifying and sorting issues30:27 Bridging vision and language domains39:25 Challenges and promises of natural language technology41:35 Production environment43:15 Using synthetic data49:59 Working with startups53:55 Multi-task learning, meta-learning, and user experience56:44 Optimization and testing across multiple platforms59:36 Outro-Connect with Jehan and Motorola Solutions:📍 Jehan on LinkedIn: https://www.linkedin.com/in/jehanw/📍 Jehan on Twitter: https://twitter.com/jehan/📍 Motorola Solutions on Twitter: https://twitter.com/MotoSolutions/📍 Careers at Motorola Solutions: https://www.motorolasolutions.com/en_us/about/careers.html-💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan, Lavanya Shukla, Anish Shah-Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Jehan Wickramasuriya is the Vice President of AI, Platform & Data Services at Motorola Solutions, a global leader in public safety and enterprise security.In this episode, Jehan discusses how Motorola Solutions uses AI to simplify data streams to help maximize human potential in high-stress situations. He also shares his thoughts on augmenting synthetic data with real data and the challenges posed in partnering with startups.Show notes (transcript and links): http://wandb.me/gd-jehan-wickramasuriya-⏳ Timestamps: 00:00 Intro00:42 How AI fits into the safety/security industry 09:33 Event matching and object detection14:47 Running models on the right hardware17:46 Scaling model evaluation23:58 Monitoring and evaluation challenges26:30 Identifying and sorting issues30:27 Bridging vision and language domains39:25 Challenges and promises of natural language technology41:35 Production environment43:15 Using synthetic data49:59 Working with startups53:55 Multi-task learning, meta-learning, and user experience56:44 Optimization and testing across multiple platforms59:36 Outro-Connect with Jehan and Motorola Solutions:📍 Jehan on LinkedIn: https://www.linkedin.com/in/jehanw/📍 Jehan on Twitter: https://twitter.com/jehan/📍 Motorola Solutions on Twitter: https://twitter.com/MotoSolutions/📍 Careers at Motorola Solutions: https://www.motorolasolutions.com/en_us/about/careers.html-💬 Host: Lukas Biewald📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla-Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Will Falcon is the CEO and co-founder of Lightning AI, a platform that enables users to quickly build and publish ML models.In this episode, Will explains how Lightning addresses the challenges of a fragmented AI ecosystem and reveals which framework PyTorch Lightning was originally built upon (hint: not PyTorch!) He also shares lessons he took from his experience serving in the military and offers a recommendation to veterans who want to work in tech.Show notes (transcript and links): http://wandb.me/gd-will-falcon---⏳ Timestamps: 00:00 Intro01:00 From SEAL training to FAIR04:17 Stress-testing Lightning07:55 Choosing PyTorch over TensorFlow and other frameworks13:16 Components of the Lightning platform17:01 Launching Lightning from Facebook19:09 Similarities between leadership and research22:08 Lessons from the military26:56 Scaling PyTorch Lightning to Lightning AI33:21 Hiring the right people35:21 The future of Lightning39:53 Reducing algorithm complexity in self-supervised learning42:19 A fragmented ML landscape44:35 Outro---Connect with Lightning📍 Website: https://lightning.ai📍 Twitter: https://twitter.com/LightningAI📍 LinkedIn: https://www.linkedin.com/company/pytorch-lightning/📍 Careers: https://boards.greenhouse.io/lightningai---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Anish Shah, Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Aaron Colak is the Leader of Core Machine Learning at Qualtrics, an experiment management company that takes large language models and applies them to real-world, B2B use cases.In this episode, Aaron describes mixing classical linguistic analysis with deep learning models and how Qualtrics organized their machine learning organizations and model to leverage the best of these techniques. He also explains how advances in NLP have invited new opportunities in low-resource languages.Show notes (transcript and links): http://wandb.me/gd-aaron-colak---⏳ Timestamps: 00:00 Intro00:57 Evolving from surveys to experience management04:56 Detecting sentiment with ML10:57 Working with large language models and rule-based systems14:50 Zero-shot learning, NLP, and low-resource languages20:11 Letting customers control data25:13 Deep learning and tabular data28:40 Hyperscalers and performance monitoring34:54 Combining deep learning with linguistics40:03 A sense of accomplishment42:52 Causality and observational data in healthcare45:09 Challenges of interdisciplinary collaboration49:27 Outro---Connect with Aaron and Qualtrics📍 Aaron on LinkedIn: https://www.linkedin.com/in/aaron-r-colak-3522308/📍 Qualtrics on Twitter: https://twitter.com/qualtrics/📍 Careers at Qualtrics: https://www.qualtrics.com/careers/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Jordan Fisher is the CEO and co-founder of Standard AI, an autonomous checkout company that’s pushing the boundaries of computer vision.In this episode, Jordan discusses “the Wild West” of the MLOps stack and tells Lukas why Rust beats Python. He also explains why AutoML shouldn't be overlooked and uses a bag of chips to help explain the Manifold Hypothesis.Show notes (transcript and links): http://wandb.me/gd-jordan-fisher---⏳ Timestamps: 00:00 Intro00:40 The origins of Standard AI08:30 Getting Standard into stores18:00 Supervised learning, the advent of synthetic data, and the manifold hypothesis24:23 What's important in a MLOps stack27:32 The merits of AutoML30:00 Deep learning frameworks33:02 Python versus Rust39:32 Raw camera data versus video42:47 The future of autonomous checkout48:02 Sharing the StandardSim data set52:30 Picking the right tools54:30 Overcoming dynamic data set challenges57:35 Outro---Connect with Jordan and Standard AI📍 Jordan on LinkedIn: https://www.linkedin.com/in/jordan-fisher-81145025/📍 Standard AI on Twitter: https://twitter.com/StandardAi📍 Careers at Standard AI: https://careers.standard.ai/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Drago Anguelov is a Distinguished Scientist and Head of Research at Waymo, an autonomous driving technology company and subsidiary of Alphabet Inc.We begin by discussing Drago's work on the original Inception architecture, winner of the 2014 ImageNet challenge and introduction of the inception module. Then, we explore milestones and current trends in autonomous driving, from Waymo's release of the Open Dataset to the trade-offs between modular and end-to-end systems.Drago also shares his thoughts on finding rare examples, and the challenges of creating scalable and robust systems.Show notes (transcript and links): http://wandb.me/gd-drago-anguelov---⏳ Timestamps: 0:00 Intro0:45 The story behind the Inception architecture13:51 Trends and milestones in autonomous vehicles23:52 The challenges of scalability and simulation30:19 Why LiDar and mapping are useful35:31 Waymo Via and autonomous trucking37:31 Robustness and unsupervised domain adaptation40:44 Why Waymo released the Waymo Open Dataset49:02 The domain gap between simulation and the real world56:40 Finding rare examples1:04:34 The challenges of production requirements1:08:36 Outro---Connect with Drago & Waymo📍 Drago on LinkedIn: https://www.linkedin.com/in/dragomiranguelov/📍 Waymo on Twitter: https://twitter.com/waymo/📍 Careers at Waymo: https://waymo.com/careers/---Links:📍 Inception v1: https://arxiv.org/abs/1409.4842📍 "SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation", Qiangeng Xu et al. (2021), https://arxiv.org/abs/2108.06709📍 "GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting", Zhao Chen et al. (2022), https://arxiv.org/abs/2201.05938---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

James Cham is a co-founder and partner at Bloomberg Beta, an early-stage venture firm that invests in machine learning and the future of work, the intersection between business and technology.James explains how his approach to investing in AI has developed over the last decade, which signals of success he looks for in the ever-adapting world of venture startups (tip: look for the "gradient of admiration"), and why it's so important to demystify ML for executives and decision-makers.Lukas and James also discuss how new technologies create new business models, and what the ethical considerations of a world where machine learning is accepted to be possibly fallible would be like.Show notes (transcript and links): http://wandb.me/gd-james-cham---⏳ Timestamps: 0:00 Intro0:46 How investment in AI has changed and developed7:08 Creating the first MI landscape infographics10:30 The impact of ML on organizations and management17:40 Demystifying ML for executives21:40 Why signals of successful startups change over time27:07 ML and the emergence of new business models37:58 New technology vs new consumer goods39:50 What James considers when investing44:19 Ethical considerations of accepting that ML models are fallible50:30 Reflecting on past investment decisions52:56 Thoughts on consciousness and Theseus' paradox59:08 Why it's important to increase general ML literacy1:03:09 Outro1:03:30 Bonus: How James' faith informs his thoughts on ML---Connect with James:📍 Twitter: https://twitter.com/jamescham📍 Bloomberg Beta: https://github.com/Bloomberg-Beta/Manual---Links:📍 "Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions" by Ali Alkhatib and Michael Bernstein (2019): https://doi.org/10.1145/3290605.3300760---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Check out this report by Boris about DALL-E mini:https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAyhttps://wandb.ai/_scott/wandb_example/reports/Collaboration-in-ML-made-easy-with-W-B-Teams--VmlldzoxMjcwMDU5https://twitter.com/weirddalleConnect with Boris:📍 Twitter: https://twitter.com/borisdayma---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Tristan Handy is CEO and founder of dbt Labs. dbt (data build tool) simplifies the data transformation workflow and helps organizations make better decisions.Lukas and Tristan dive into the history of the modern data stack and the subsequent challenges that dbt was created to address; communities of identity and product-led growth; and thoughts on why SQL has survived and thrived for so long. Tristan also shares his hopes for the future of BI tools and the data stack.Show notes (transcript and links): http://wandb.me/gd-tristan-handy---⏳ Timestamps: 0:00 Intro0:40 How dbt makes data transformation easier4:52 dbt and avoiding bad data habits14:23 Agreeing on organizational ground truths19:04 Staying current while running a company22:15 The origin story of dbt26:08 Why dbt is conceptually simple but hard to execute 34:47 The dbt community and the bottom-up mindset41:50 The future of data and operations47:41 dbt and machine learning49:17 Why SQL is so ubiquitous55:20 Bridging the gap between the ML and data worlds1:00:22 Outro---Connect with Tristan:📍 Twitter: https://twitter.com/jthandy📍 The Analytics Engineering Roundup: https://roundup.getdbt.com/---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Johannes Otterbach is VP of Machine Learning Research at Merantix Momentum, an ML consulting studio that helps their clients build AI solutions.Johannes and Lukas talk about Johannes' background in physics and applications of ML to quantum computing, why Merantix is investing in creating a cloud-agnostic tech stack, and the unique challenges of developing and deploying models for different customers. They also discuss some of Johannes' articles on the impact of NLP models and the future of AI regulations.Show notes (transcript and links): http://wandb.me/gd-johannes-otterbach---⏳ Timestamps: 0:00 Intro1:04 Quantum computing and ML applications9:21 Merantix, Ventures, and ML consulting19:09 Building a cloud-agnostic tech stack24:40 The open source tooling ecosystem 30:28 Handing off models to customers31:42 The impact of NLP models on the real world35:40 Thoughts on AI and regulation40:10 Statistical physics and optimization problems42:50 The challenges of getting high-quality data44:30 Outro---Connect with Johannes:📍 LinkedIn: https://twitter.com/jsotterbach📍 Personal website: http://jotterbach.github.io/📍 Careers at Merantix Momentum: https://merantix-momentum.com/about#jobs---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Mircea Neagovici is VP, AI and Research at UiPath, where his team works on task mining and other ways of combining robotic process automation (RPA) with machine learning for their B2B products.Mircea and Lukas talk about the challenges of allowing customers to fine-tune their models, the trade-offs between traditional ML and more complex deep learning models, and how Mircea transitioned from a more traditional software engineering role to running a machine learning organization.Show notes (transcript and links): http://wandb.me/gd-mircea-neagovici---⏳ Timestamps: 0:00 Intro1:05 Robotic Process Automation (RPA)4:20 RPA and machine learning at UiPath8:20 Fine-tuning & PyTorch vs TensorFlow14:50 Monitoring models in production16:33 Task mining22:37 Trade-offs in ML models29:45 Transitioning from software engineering to ML34:02 ML teams vs engineering teams40:41 Spending more time on data43:55 The organizational machinery behind ML models45:57 Outro---Connect with Mircea:📍 LinkedIn: https://www.linkedin.com/in/mirceaneagovici/📍 Careers at UiPath: https://www.uipath.com/company/careers---💬 Host: Lukas Biewald📹 Producers: Cayla Sharp, Angelica Pan, Sanyam Bhutani, Lavanya Shukla

Jensen Huang is founder and CEO of NVIDIA, whose GPUs sit at the heart of the majority of machine learning models today.Jensen shares the story behind NVIDIA's expansion from gaming to deep learning acceleration, leadership lessons that he's learned over the last few decades, and why we need a virtual world that obeys the laws of physics (aka the Omniverse) in order to take AI to the next era. Jensen and Lukas also talk about the singularity, the slow-but-steady approach to building a new market, and the importance of MLOps.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-jensen-huang---⏳ Timestamps: 0:00 Intro0:50 Why NVIDIA moved into the deep learning space7:33 Balancing the compute needs of different audiences10:40 Quantum computing, Huang's Law, and the singularity15:53 Democratizing scientific computing20:59 How Jensen stays current with technology trends25:10 The global chip shortage27:00 Leadership lessons that Jensen has learned32:32 Keeping a steady vision for NVIDIA35:48 Omniverse and the next era of AI42:00 ML topics that Jensen's excited about45:05 Why MLOps is vital48:38 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Peter Welinder is VP of Product & Partnerships at OpenAI, where he runs product and commercialization efforts of GPT-3, Codex, GitHub Copilot, and more. Boris Dayma is Machine Learning Engineer at Weights & Biases, and works on integrations and large model training.Peter, Boris, and Lukas dive into the world of GPT-3:- How people are applying GPT-3 to translation, copywriting, and other commercial tasks- The performance benefits of fine-tuning GPT-3- - Developing an API on top of GPT-3 that works out of the box, but is also flexible and customizableThey also discuss the new OpenAI and Weights & Biases collaboration, which enables a user to log their GPT-3 fine-tuning projects to W&B with a single line of code.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-peter-and-boris---Connect with Peter & Boris:📍 Peter's Twitter: https://twitter.com/npew📍 Boris' Twitter: https://twitter.com/borisdayma---⏳ Timestamps: 0:00 Intro1:01 Solving real-world problems with GPT-36:57 Applying GPT-3 to translation tasks14:58 Copywriting and other commercial GPT-3 applications20:22 The OpenAI API and fine-tuning GPT-328:22 Logging GPT-3 fine-tuning projects to W&B38:25 Engineering challenges behind OpenAI's API43:15 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Ion Stoica is co-creator of the distributed computing frameworks Spark and Ray, and co-founder and Executive Chairman of Databricks and Anyscale. He is also a Professor of computer science at UC Berkeley and Principal Investigator of RISELab, a five-year research lab that develops technology for low-latency, intelligent decisions.Ion and Lukas chat about the challenges of making a simple (but good!) distributed framework, the similarities and differences between developing Spark and Ray, and how Spark and Ray led to the formation of Databricks and Anyscale. Ion also reflects on the early startup days, from deciding to commercialize to picking co-founders, and shares advice on building a successful company.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-ion-stoica---Timestamps: 0:00 Intro0:56 Ray, Anyscale, and making a distributed framework11:39 How Spark informed the development of Ray18:53 The story behind Spark and Databricks33:00 Why TensorFlow and PyTorch haven't monetized35:35 Picking co-founders and other startup advice46:04 The early signs of sky computing49:24 Breaking problems down and prioritizing53:17 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Stephan Fabel is Senior Director of Infrastructure Systems & Software at NVIDIA, where he works on Base Command, a software platform to coordinate access to NVIDIA's DGX SuperPOD infrastructure.Lukas and Stephan talk about why having a supercomputer is one thing but using it effectively is another, why a deeper understanding of hardware on the practitioner level is becoming more advantageous, and which areas of the ML tech stack NVIDIA is looking to expand into.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-stephan-fabel---Timestamps: 0:00 Intro1:09 NVIDIA Base Command and DGX SuperPOD10:33 The challenges of multi-node processing at scale18:35 Why it's hard to use a supercomputer effectively25:14 The advantages of de-abstracting hardware29:09 Understanding Base Command's product-market fit36:59 Data center infrastructure as a value center42:13 Base Command's role in tech stacks47:16 Why crowdsourcing is underrated49:24 The challenges of scaling beyond a POC51:39 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Chris Padwick is Director of Computer Vision Machine Learning at Blue River Technology, a subsidiary of John Deere. Their core product, See & Spray, is a weeding robot that identifies crops and weeds in order to spray only the weeds with herbicide.Chris and Lukas dive into the challenges of bringing See & Spray to life, from the hard computer vision problem of classifying weeds from crops, to the engineering feat of building and updating embedded systems that can survive on a farming machine in the field. Chris also explains why user feedback is crucial, and shares some of the surprising product insights he's gained from working with farmers.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-chris-padwick---Connect with Chris:📍 LinkedIn: https://www.linkedin.com/in/chris-padwick-75b5761/📍 Blue River on Twitter: https://twitter.com/BlueRiverTech---Timestamps: 0:00 Intro1:09 How does See & Spray reduce herbicide usage?9:15 Classifying weeds and crops in real time17:45 Insights from deployment and user feedback29:08 Why weed and crop classification is surprisingly hard37:33 Improving and updating models in the field40:55 Blue River's ML stack44:55 Autonomous tractors and upcoming directions48:05 Why data pipelines are underrated52:10 The challenges of scaling software & hardware54:44 Outro55:55 Bonus: Transporters and the singularity---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Kathryn Hume is Vice President Digital Investments Technology at the Royal Bank of Canada (RBC). At the time of recording, she was Interim Head of Borealis AI, RBC's research institute for machine learning.Kathryn and Lukas talk about ML applications in finance, from building a personal finance forecasting model to applying reinforcement learning to trade execution, and take a philosophical detour into the 17th century as they speculate on what Newton and Descartes would have thought about machine learning.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-kathryn-hume---Connect with Kathryn:📍 Twitter: https://twitter.com/humekathryn📍 Website: https://quamproxime.com/---Timestamps: 0:00 Intro0:54 Building a personal finance forecasting model10:54 Applying RL to trade execution18:55 Transparent financial models and fairness26:20 Semantic parsing and building a text-to-SQL interface29:20 From comparative literature and math to product37:33 What would Newton and Descartes think about ML?44:15 On sentient AI and transporters47:33 Why casual inference is under-appreciated49:25 The challenges of integrating models into the business51:45 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

Sean McClain is the founder and CEO, and Gregory Hannum is the VP of AI Research at Absci, a biotech company that's using deep learning to expedite drug discovery and development.Lukas, Sean, and Greg talk about why Absci started investing so heavily in ML research (it all comes back to the data), what it'll take to build the GPT-3 of DNA, and where the future of pharma is headed. Sean and Greg also share some of the challenges of building cross-functional teams and combining two highly specialized fields like biology and ML.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-sean-and-greg---Connect with Sean and Greg:📍 Sean's Twitter: https://twitter.com/seanrmcclain📍 Greg's Twitter: https://twitter.com/gregory_hannum📍 Absci's Twitter: https://twitter.com/abscibio---Timestamps: 0:00 Intro0:53 How Absci merges biology and AI11:24 Why Absci started investing in ML19:00 Creating the GPT-3 of DNA25:34 Investing in data collection and in ML teams33:14 Clinical trials and Absci's revenue structure38:17 Combining knowledge from different domains45:22 The potential of multitask learning50:43 Why biological data is tricky to work with55:00 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

You might know him as the host of Gradient Dissent, but Lukas is also the CEO of Weights & Biases, a developer-first ML tools platform!In this special episode, the three W&B co-founders — Chris (CVP), Shawn (CTO), and Lukas (CEO) — sit down to tell the company's origin stories, reflect on the highs and lows, and give advice to engineers looking to start their own business.Chris reveals the W&B server architecture (tl;dr - React + GraphQL), Shawn shares his favorite product feature (it's a hidden frontend layer), and Lukas explains why it's so important to work with customers that inspire you.The complete show notes (transcript and links) can be found here: http://wandb.me/gd-wandb-cofounders---Connect with us:📍 Chris' Twitter: https://twitter.com/vanpelt📍 Shawn's Twitter: https://twitter.com/shawnup📍 Lukas' Twitter: https://twitter.com/l2k📍 W&B's Twitter: https://twitter.com/weights_biases---Timestamps: 0:00 Intro1:29 The stories behind Weights & Biases7:45 The W&B tech stack9:28 Looking back at the beginning11:42 Hallmark moments14:49 Favorite product features16:49 Rewriting the W&B backend18:21 The importance of customer feedback21:18 How Chris and Shawn have changed22:35 How the ML space has changed28:24 Staying positive when things look bleak32:19 Lukas' advice to new entrepreneurs35:29 Hopes for the next five years38:09 Making a paintbot & model understanding41:30 Biggest bottlenecks in deployment44:08 Outro44:38 Bonus: Under- vs overrated technologies---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

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: http://wandb.me/gd-pete-warden---Connect with Pete:📍 Twitter: https://twitter.com/petewarden📍 Website: https://petewarden.com/---Timestamps: 0:00 Intro1:23 Hacking a Raspberry Pi to run neural nets13:50 Model and hardware architectures18:56 Training a magic wand21:47 Raspberry Pi vs Arduino27:51 Reducing model size33:29 Training on the edge39:47 What it's like to work on TensorFlow47:45 Improving datasets and model deployment53:05 Outro---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​

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 some perspective on entrepreneurship, from how he knew it was time to commercialize Gradescope to what he looks for in co-founders to why he started Covariant.Show notes: http://wandb.me/gd-pieter-abbeel---Connect with Pieter:📍 Twitter: https://twitter.com/pabbeel📍 Website: https://people.eecs.berkeley.edu/~pabbeel/📍 The Robot Brains Podcast: https://www.therobotbrains.ai/---Timestamps: 0:00 Intro1:15 The challenges of robotics8:10 Progress in robotics13:34 Imitation learning and reinforcement learning21:37 Simulated data, real data, and reliability27:53 The increasing capabilities of robotics36:23 Entrepreneurship and co-founding Gradescope44:35 The story behind Covariant47:50 Pieter's communication tips52:13 What Pieter's currently excited about55:08 Focusing on good UI and high reliability57:01 Outro

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 interviews.Show notes: http://wandb.me/gd-chris-albon---Connect with Chris:- Twitter: https://twitter.com/chrisalbon- Website: https://chrisalbon.com/---Timestamps: 0:00 Intro1:08 How Wikimedia approaches moderation9:55 Working in the open and embracing humility16:08 Going down Wikipedia rabbit holes20:03 How Wikimedia uses machine learning27:38 Wikimedia's ML infrastructure42:56 How Chris got into machine learning46:43 Machine Learning Flashcards and technical interviews52:10 Low-power models and MLOps55:58 Outro

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 grammar engineering, variation (within and across languages), the relationship between linguistics and computational linguistics, and societal issues in NLP.---Timestamps:0:00 Sneak peek, intro1:03 Stochastic Parrots9:57 The societal impact of big language models16:49 How language models can be harmful26:00 The important difference between linguistic form and meaning34:40 The octopus thought experiment42:11 Language acquisition and the future of language models49:47 Why benchmarks are limited54:38 Ways of complementing benchmarks1:01:20 The #BenderRule1:03:50 Language diversity and linguistics1:12:49 Outro

Jeff talks about building Facebook's early data team, founding Cloudera, and transitioning into biomedicine with Hammer Lab and Related Sciences.(Read more: http://wandb.me/gd-jeff-hammerbacher)---Jeff Hammerbacher is a scientist, software developer, entrepreneur, and investor. Jeff's current work focuses on drug discovery at Related Sciences, a biotech venture creation firm that he co-founded in 2020.Prior to his work at Related Sciences, Jeff was the Principal Investigator of Hammer Lab, a founder and the Chief Scientist of Cloudera, an Entrepreneur-in-Residence at Accel, and the manager of the Data team at Facebook.---Follow Gradient Dissent on Twitter: https://twitter.com/weights_biases---0:00 Sneak peek, intro1:13 The start of Facebook's data science team6:53 Facebook's early tech stack14:20 Early growth strategies at Facebook17:37 The origin story of Cloudera24:51 Cloudera's success, in retrospect31:05 Jeff's transition into biomedicine38:38 Immune checkpoint blockade in cancer therapy48:55 Data and techniques for biomedicine53:00 Why Jeff created Related Sciences56:32 Outro

Josh explains how astronomy and machine learning have informed each other, their current limitations, and where their intersection goes from here. (Read more: http://wandb.me/gd-josh-bloom)---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.---Follow Gradient Dissent on Twitter: https://twitter.com/weights_biases---0:00 Intro, sneak peek1:15 How astronomy has informed ML4:20 The big questions in astronomy today10:15 On dark matter and dark energy16:37 Finding life on other planets19:55 Driving advancements in astronomy27:05 Putting telescopes in space31:05 Why Josh started using ML in his research33:54 Crowdsourcing in astronomy36:20 How ML has (and hasn't) informed astronomy47:22 The next generation of cross-functional grad students50:50 How Josh started coding56:11 Incentives and maintaining research codebases1:00:01 ML4Science's tech stack1:02:11 Uncertainty quantification in a sensor-based world1:04:28 Why it's not good to always get an answer1:07:47 Outro

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. --- ⏳ Timestamps: 0:00 Sneak peak, intro 0:49 What is Curai? 5:48 The role of AI within Curai 8:44 Why Curai keeps humans in the loop 15:00 Measuring diagnostic accuracy 18:53 Patient safety 22:39 Different types of models at Curai 25:42 Using GPT-3 to generate training data 32:13 How Curai monitors and debugs models 35:19 Model explainability 39:27 Robustness in ML 45:52 Connecting metrics to impact 49:32 Outro 🌟 Show notes: - http://wandb.me/gd-xavier-amatriain - Transcription of the episode - Links to papers, projects, and people --- Follow us on Twitter! 📍 https://twitter.com/wandb_gd Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​

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: - http://wandb.me/gd-spence-green - Transcription of the episode - Links to papers, projects, and people ⏳ Timestamps: 0:00 Sneak peak, intro 0:45 The story behind Lilt 3:08 Statistical MT vs neural MT 6:30 Domain adaptation and personalized models 8:00 The emergence of neural MT and development of Lilt 13:09 What success looks like for Lilt 18:20 Models that self-correct for gender bias 19:39 How Lilt runs its models in production 26:33 How far can MT go? 29:55 Why Lilt cares about human-computer interaction 35:04 Bilingual grammatical error correction 37:18 Human parity in MT 39:41 The unexpected challenges of prototype to production --- Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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 seniors. He was also Chief Data Scientist under the Obama administration and the Head of Data Science at LinkedIn. Roger and DJ recently volunteered for the California COVID-19 response, and worked with data to understand case counts, bed capacities and the impact of intervention. Connect with Roger and DJ: 📍 Roger's Twitter: https://twitter.com/rogerm 📍 DJ's Twitter: https://twitter.com/dpatil --- 🌟 Transcript: http://wandb.me/gd-roger-and-dj 🌟 ⏳ Timestamps: 0:00 Sneak peek, intro 1:03 Coining the terms "big data" and "data scientist" 7:12 The rise of data science teams 15:28 Big Data, Hadoop, and Spark 23:10 The importance of using the right tools 29:20 BLUF: Bottom Line Up Front 34:44 California's COVID response 41:21 The human aspects of responding to COVID 48:33 Reflecting on the impact of COVID interventions 57:06 Advice on doing meaningful data science work 1:04:18 Outro 🍀 Links: 1. "MapReduce: Simplified Data Processing on Large Clusters" (Dean and Ghemawat, 2004): https://research.google/pubs/pub62/ 2. "Big Data: Technologies and Techniques for Large-Scale Data" (Magoulas and Lorica, 2009): https://academics.uccs.edu/~ooluwada/courses/datamining/ExtraReading/BigData 3. The O'RLY book covers: https://www.businessinsider.com/these-hilarious-memes-perfectly-capture-what-its-like-to-work-in-tech-2016-4 4. "The Premonition" (Lewis, 2021): https://www.npr.org/2021/05/03/991570372/michael-lewis-the-premonition-is-a-sweeping-indictment-of-the-cdc 5. Why California's beaches are glowing with bioluminescence: https://www.youtube.com/watch?v=AVYSr19ReOs 6. 7. Sturgis Motorcyle Rally: https://en.wikipedia.org/wiki/Sturgis_Motorcycle_Rally --- Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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 musical audio recordings. Connect with Amelia and Filip: 📍 Amelia's LinkedIn: https://www.linkedin.com/in/amelia-nybakke-60bba5107/ 📍 Filip's LinkedIn: https://www.linkedin.com/in/filip-korzeniowski-28b33815a/ --- ⏳ Timestamps: 0:00 Sneak peek, intro 0:42 What type of ML models are at Pandora? 3:39 What makes two songs similar or not similar? 7:33 Improving models and A/B testing 8:52 Chaining, retraining, versioning, and tracking models 13:29 Useful development tools 15:10 Debugging models 18:28 Communicating progress 20:33 Tuning and improving models 23:08 How Pandora puts models into production 29:45 Bias in ML models 36:01 Repetition vs novelty in recommended songs 38:01 The bottlenecks of deployment 🌟 Transcript: http://wandb.me/gd-amelia-and-filip 🌟 Links: 📍 Amelia's "Women's History Month" playlist: https://www.pandora.com/playlist/PL:1407374934299927:100514833 --- Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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: 📍 Twitter: https://twitter.com/luisceze 📍 University of Washington profile: https://homes.cs.washington.edu/~luisceze/ --- ⏳ Timestamps: 0:00 Intro and sneak peek 0:59 What is TVM? 8:57 Freedom of choice in software and hardware stacks 15:53 How new libraries can improve system performance 20:10 Trade-offs between efficiency and complexity 24:35 Specialized instructions 26:34 The future of hardware design and research 30:03 Where does architecture and research go from here? 30:56 The environmental impact of efficiency 32:49 Optimizing and trade-offs 37:54 What is OctoML and the Octomizer? 42:31 Automating systems design with and for ML 44:18 ML and molecular biology 46:09 The challenges of deployment and post-deployment 🌟 Transcript: http://wandb.me/gd-luis-ceze 🌟 Links: 1. OctoML: https://octoml.ai/ 2. Apache TVM: https://tvm.apache.org/ 3. "Scalable and Intelligent Learning Systems" (Chen, 2019): https://digital.lib.washington.edu/researchworks/handle/1773/44766 4. "Principled Optimization Of Dynamic Neural Networks" (Roesch, 2020): https://digital.lib.washington.edu/researchworks/handle/1773/46765 5. "Cross-Stack Co-Design for Efficient and Adaptable Hardware Acceleration" (Moreau, 2018): https://digital.lib.washington.edu/researchworks/handle/1773/43349 6. "TVM: An Automated End-to-End Optimizing Compiler for Deep Learning" (Chen et al., 2018): https://www.usenix.org/system/files/osdi18-chen.pdf 7. Porcupine is a molecular tagging system introduced in "Rapid and robust assembly and decoding of molecular tags with DNA-based nanopore signatures" (Doroschak et al., 2020): https://www.nature.com/articles/s41467-020-19151-8 --- Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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. Connect with Matthew: 📍 Personal website: https://www.linkedin.com/in/matthew-davis-51233386/ 📍 Twitter: https://twitter.com/deadsmiths --- ⏳ Timestamps: 0:00 Sneak peek, intro 1:02 What is Invitae? 2:58 Why genetic testing can help everyone 7:51 How Invitae uses ML techniques 14:02 Modeling molecules and deciding which genes to look at 22:22 NLP applications in bioinformatics 27:10 Team structure at Invitae 36:50 Why reasoning is an underrated topic in ML 40:25 Why having a clear buy-in is important 🌟 Transcript: http://wandb.me/gd-matthew-davis 🌟 Links: 📍 Invitae: https://www.invitae.com/en 📍 Careers at Invitae: https://www.invitae.com/en/careers/ --- Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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: 📍 Twitter: https://twitter.com/ClementDelangue 📍 LinkedIn: https://www.linkedin.com/in/clementdelangue/ --- 🌟 Transcript: http://wandb.me/gd-clement-delangue 🌟 ⏳ Timestamps: 0:00 Sneak peek and intro 0:56 What is Hugging Face? 4:15 The success of Hugging Face Transformers 7:53 Open source and virtuous cycles 10:37 Working with both TensorFlow and PyTorch 13:20 The "Write With Transformer" project 14:36 Transfer learning in NLP 16:43 BERT and DistilBERT 22:33 GPT 26:32 The power of the open source community 29:40 Current applications of NLP 35:15 The Turing Test and conversational AI 41:19 Why speech is an upcoming field within NLP 43:44 The human challenges of machine learning Links Discussed: 📍 Write With Transformer, Hugging Face Transformer's text generation demo: https://transformer.huggingface.co/ 📍 "Attention Is All You Need" (Vaswani et al., 2017): https://arxiv.org/abs/1706.03762 📍 EleutherAI and GPT-Neo: https://github.com/EleutherAI/gpt-neo] 📍 Rasa, open source conversational AI: https://rasa.com/ 📍 Roblox article on BERT: https://blog.roblox.com/2020/05/scaled-bert-serve-1-billion-daily-requests-cpus/ --- Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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. Connect with Wojciech: Personal website: https://wojzaremba.com// Twitter: https://twitter.com/woj_zaremba --- Topics Discussed: 0:00 Sneak peek and intro 1:03 The people and principles behind OpenAI 6:31 The stages of future AI developments 13:42 The Fermi paradox 16:18 What drives Wojciech? 19:17 Thoughts on robotics 24:58 Dota and other projects at OpenAI 33:42 What would make an AI conscious? 41:31 How to be succeed in robotics Transcript: http://wandb.me/gd-wojciech-zaremba Links: Fermi paradox: https://en.wikipedia.org/wiki/Fermi_paradox OpenAI and Dota: https://openai.com/projects/five/ --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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. Connect with Phil: LinkedIn: https://www.linkedin.com/in/philipsbrown/ Twitter: https://twitter.com/phil_s_brown --- 0:00 Sneak peek, intro 1:44 From computational chemistry to Graphcore 5:16 The simulations behind weather prediction 10:54 Measuring improvement in weather prediction systems 15:35 How high performance computing and ML have different needs 19:00 The potential of sparse training 31:08 IPUs and computer architecture for machine learning 39:10 On performance improvements 44:43 The impacts of increasing computing capability 50:24 The ML chicken and egg problem 52:00 The challenges of converging at scale and bringing hardware to market Links Discussed: Rigging the Lottery: Making All Tickets Winners (Evci et al., 2019): https://arxiv.org/abs/1911.11134 Graphcore MK2 Benchmarks: https://www.graphcore.ai/mk2-benchmarks Check out the transcription and discover more awesome ML projects: http://wandb.me/gd-phil-brown --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​​ Spotify: http://wandb.me/spotify​​ Google Podcasts: http://wandb.me/google-podcasts​​​ YouTube: http://wandb.me/youtube​​​ Soundcloud: http://wandb.me/soundcloud​​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​​ Check out our Gallery, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/gallery

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 Appen and Director of Product at IBM Watson. Connect with Sean: Personal website: https://seanjtaylor.com/ Twitter: https://twitter.com/seanjtaylor LinkedIn: https://www.linkedin.com/in/seanjtaylor/ --- Topics Discussed: 0:00 Sneak peak, intro 1:17 Working on COVID-19 vaccine rollout in California 6:50 Real World AI 12:26 Diagnosing bias in models 17:43 Common challenges in ML 21:56 Finding meaningful projects 24:28 ML applications in health insurance 31:21 Longitudinal health records and data cleaning 38:24 Following your interests 40:21 Why teamwork is crucial Transcript: http://wandb.me/gd-alyssa-s-rochwerger Links Discussed: My Turn: https://myturn.ca.gov/ "Turn the Ship Around!": https://www.penguinrandomhouse.com/books/314163/turn-the-ship-around-by-l-david-marquet/ --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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 economics. Connect with Sean: Personal website: https://seanjtaylor.com/ Twitter: https://twitter.com/seanjtaylor LinkedIn: https://www.linkedin.com/in/seanjtaylor/ --- Topics Discussed: 0:00 Sneak peek, intro 0:50 Pricing algorithms at Lyft 07:46 Loss functions and ETAs at Lyft 12:59 Models and tools at Lyft 20:46 Python vs R 25:30 Forecasting time series data with Prophet 33:06 Election forecasting and prediction markets 40:55 Comparing and evaluating models 43:22 Bottlenecks in going from research to production Transcript: http://wandb.me/gd-sean-taylor Links Discussed: "How Lyft predicts a rider’s destination for better in-app experience"": https://eng.lyft.com/how-lyft-predicts-your-destination-with-attention-791146b0a439 Prophet: https://facebook.github.io/prophet/ Andrew Gelman's blog post "Facebook's Prophet uses Stan": https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/ Twitter thread "Election forecasting using prediction markets": https://twitter.com/seanjtaylor/status/1270899371706466304 "An Updated Dynamic Bayesian Forecasting Model for the 2020 Election": https://hdsr.mitpress.mit.edu/pub/nw1dzd02/release/1 --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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. Twitter: https://twitter.com/fordycelab​ Website: http://www.fordycelab.com/​ --- Topics Discussed: 0:00​ Sneak peek, intro 2:11​ Background on protein sequencing 7:38​ How changes to a protein's sequence alters its structure and function 11:07​ Microfluidics and machine learning 19:25​ Why protein folding is important 25:17​ Collaborating with ML practitioners 31:46​ Transfer learning and big data sets in biology 38:42​ Where Polly hopes bioengineering research will go 42:43​ Advice for students Transcript: http://wandb.me/gd-polly-fordyce​ Links Discussed: "The Weather Makers": https://en.wikipedia.org/wiki/The_Wea...​ --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​​ Spotify: http://wandb.me/spotify​​ Google Podcasts: http://wandb.me/google-podcasts​​​ YouTube: http://wandb.me/youtube​​​ Soundcloud: http://wandb.me/soundcloud​​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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. Connect with Adrien: Twitter: https://twitter.com/adnothing LinkedIn: https://www.linkedin.com/in/adrien-gaidon-63ab2358/ Personal website: https://adriengaidon.com/ --- Topics Discussed: 0:00 Sneak peek, intro 0:48 Guitars and other favorite tools 3:55 Why is PyTorch so popular? 11:40 Autonomous vehicle research in the long term 15:10 Game-changing academic advances 20:53 The challenges of bringing autonomous vehicles to market 26:05 Perception and prediction 35:01 Fleet learning and meta learning 41:20 The human aspects of machine learning 44:25 The scalability bottleneck Transcript: http://wandb.me/gd-adrien-gaidon Links Discussed: TRI Global Research: https://www.tri.global/research/ todoist: https://todoist.com/ Contrastive Learning of Structured World Models: https://arxiv.org/abs/2002.05709 SimCLR: https://arxiv.org/abs/2002.05709 --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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: LinkedIn: https://www.linkedin.com/in/nimrod-shabtay-76072840/ --- Links Discussed: Guidelines for building an accurate and robust ML/DL model in production: https://engineering.nanit.com/guideli...​ Careers at Nanit: https://www.nanit.com/jobs​ --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ --- Join our community of ML practitioners where we host AMAs, share interesting projects, and more: http://wandb.me/slack​​ Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

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 Mattmann 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 content detection and analysis framework that was one of the key technologies used to uncover the Panama Papers, and is the author of "Machine Learning with TensorFlow, Second Edition" and "Tika in Action". Connect with Chris: Personal website: https://www.mattmann.ai/ Twitter: https://twitter.com/chrismattmann --- Topics Discussed: 0:00 Sneak peek, intro 0:52 On Perseverance and Ingenuity 8:40 Machine learning applications at NASA JPL 11:51 Innovation in scientific instruments and data formats 18:26 Data processing levels: Level 1 vs Level 2 vs Level 3 22:20 Competitive data processing 27:38 Kerbal Space Program 30:19 The ideas behind "Machine Learning with Tensorflow, Second Edition" 35:37 The future of MLOps and AutoML 38:51 Machine learning at the edge Transcript: http://wandb.me/gd-chris-mattmann Links Discussed: Perseverance and Ingenuity: https://mars.nasa.gov/mars2020/ Data processing levels at NASA: https://earthdata.nasa.gov/collaborate/open-data-services-and-software/data-information-policy/data-levels OCO-2: https://www.jpl.nasa.gov/missions/orbiting-carbon-observatory-2-oco-2 "Machine Learning with TensorFlow, Second Edition" (2020): https://www.manning.com/books/machine-learning-with-tensorflow-second-edition "Tika in Action" (2011): https://www.manning.com/books/tika-in-action Transcript: http://wandb.me/gd-chris-mattmann --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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. Connect with Vladlen: Personal website: http://vladlen.info/ LinkedIn: https://www.linkedin.com/in/vladlenkoltun/ --- 0:00 Sneak peek and intro 1:20 "Intelligent Systems" vs "AI" 3:02 Legged locomotion 9:26 The power of simulation 14:32 Privileged learning 18:19 Drone acrobatics 20:19 Using abstraction to transfer simulations to reality 25:35 Sample Factory for reinforcement learning 34:30 What inspired CARLA and what keeps it going 41:43 The challenges of and for robotics Links Discussed Learning quadrupedal locomotion over challenging terrain (Lee et al., 2020): https://robotics.sciencemag.org/content/5/47/eabc5986.abstract Deep Drone Acrobatics (Kaufmann et al., 2020): https://arxiv.org/abs/2006.05768 Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning (Petrenko et al., 2020): https://arxiv.org/abs/2006.11751 CARLA: https://carla.org/ --- Check out the transcription and discover more awesome ML projects: http://wandb.me/vladlen-koltun​-podcast Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ --- Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

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 Twitter: https://twitter.com/domoritz GitHub: https://github.com/domoritz Personal website: https://www.domoritz.de/ --- 0:00 Sneak peek, intro 1:15 What is Vega-Lite? 5:39 The grammar of graphics 9:00 Using visualizations creatively 11:36 Vega vs Vega-Lite 16:03 ggplot2 and machine learning 18:39 Voyager and the challenges of scale 24:54 Model explainability and visualizations 31:24 Underrated topics: constraints and visualization theory 34:38 The challenge of metrics in deployment 36:54 In between aggregate statistics and individual examples Links Discussed Vega-Lite: https://vega.github.io/vega-lite/ Data analysis and statistics: an expository overview (Tukey and Wilk, 1966): https://dl.acm.org/doi/10.1145/1464291.1464366 Slope chart / slope graph: https://vega.github.io/vega-lite/examples/line_slope.html Voyager: https://github.com/vega/voyager Draco: https://github.com/uwdata/draco Check out the transcription and discover more awesome ML projects: http://wandb.me/gd-domink-moritz --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​ Spotify: http://wandb.me/spotify​ Google: http://wandb.me/google-podcasts​ YouTube: http://wandb.me/youtube​ Soundcloud: http://wandb.me/soundcloud --- Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​ Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

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, "Genius Makers", tells the stories of the pioneers behind AI. Get the book: http://bit.ly/GeniusMakers Follow Cade on Twitter: https://twitter.com/CadeMetz/ And on Linkedin: https://www.linkedin.com/in/cademetz/ Topics discussed: 0:00 sneak peek, intro 3:25 audience and charachters 7:18 *spoiler alert* AGI 11:01 book ends, but story goes on 17:31 overinflated claims in AI 23:12 Deep Mind, OpenAI, building AGI 29:02 neuroscience and psychology, outsiders 34:35 Early adopters of ML 38:34 WojNet, where is credit due? 42:45 press covering AI 46:38 Aligning technology and need Read the transcript and discover awesome ML projects: http://wandb.me/cade-metz Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

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 vision techniques. Prior to founding Deep Sentinel, Dave co-founded RichRelevance, an AI recommendation company. https://www.deepsentinel.com/ https://www.meetup.com/East-Bay-Tri-Valley-Machine-Learning-Meetup/ https://twitter.com/daveselinger Topics covered: 0:00 Sneak peek, smart vs dumb cameras, intro 0:59 What is Deep Sentinel, how does it work? 6:00 Hardware, edge devices 10:40 OpenCV Fork, tinkering 16:18 ML Meetup, Climbing the AI research ladder 20:36 Challenge of Safety critical applications 27:03 New models, re-training, exhibitionists and voyeurs 31:17 How do you prove your cameras are better? 34:24 Angel investing in AI companies 38:00 Social responsibility with data 43:33 Combatting bias with data systems 52:22 Biggest bottlenecks production Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Read the transcript and discover more awesome machine learning material here: http://wandb.me/Dave-selinger-podcast Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

Since reinforcement learning requires hefty compute resources, it can be tough to keep up without a serious budget of your own. Find out how the team at Facebook AI Research (FAIR) is looking to increase access and level the playing field with the help of NetHack, an archaic rogue-like video game from the late 80s. Links discussed: The NetHack Learning Environment: https://ai.facebook.com/blog/nethack-learning-environment-to-advance-deep-reinforcement-learning/ Reinforcement learning, intrinsic motivation: https://arxiv.org/abs/2002.12292 Knowledge transfer: https://arxiv.org/abs/1910.08210 Tim Rocktäschel is a Research Scientist at Facebook AI Research (FAIR) London and a Lecturer in the Department of Computer Science at University College London (UCL). At UCL, he is a member of the UCL Centre for Artificial Intelligence and the UCL Natural Language Processing group. Prior to that, he was a Postdoctoral Researcher in the Whiteson Research Lab, a Stipendiary Lecturer in Computer Science at Hertford College, and a Junior Research Fellow in Computer Science at Jesus College, at the University of Oxford. https://twitter.com/_rockt Heinrich Kuttler is an AI and machine learning researcher at Facebook AI Research (FAIR) and before that was a research engineer and team lead at DeepMind. https://twitter.com/HeinrichKuttler https://www.linkedin.com/in/heinrich-kuttler/ Topics covered: 0:00 a lack of reproducibility in RL 1:05 What is NetHack and how did the idea come to be? 5:46 RL in Go vs NetHack 11:04 performance of vanilla agents, what do you optimize for 18:36 transferring domain knowledge, source diving 22:27 human vs machines intrinsic learning 28:19 ICLR paper - exploration and RL strategies 35:48 the future of reinforcement learning 43:18 going from supervised to reinforcement learning 45:07 reproducibility in RL 50:05 most underrated aspect of ML, biggest challenges? Get our podcast on these other platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

From teaching at Stanford to co-founding Coursera, insitro, and Engageli, Daphne Koller reflects on the importance of education, giving back, and cross-functional research. Daphne Koller is the founder and CEO of insitro, a company using machine learning to rethink drug discovery and development. She is a MacArthur Fellowship recipient, member of the National Academy of Engineering, member of the American Academy of Arts and Science, and has been a Professor in the Department of Computer Science at Stanford University. In 2012, Daphne co-founded Coursera, one of the world's largest online education platforms. She is also a co-founder of Engageli, a digital platform designed to optimize student success. https://www.insitro.com/ https://www.insitro.com/jobs https://www.engageli.com/ https://www.coursera.org/ Follow Daphne on Twitter: https://twitter.com/DaphneKoller https://www.linkedin.com/in/daphne-koller-4053a820/ Topics covered: 0:00​ Giving back and intro 2:10​ insitro's mission statement and Eroom's Law 3:21​ The drug discovery process and how ML helps 10:05​ Protein folding 15:48​ From 2004 to now, what's changed? 22:09​ On the availability of biology and vision datasets 26:17​ Cross-functional collaboration at insitro 28:18​ On teaching and founding Coursera 31:56​ The origins of Engageli 36:38​ Probabilistic graphic models 39:33​ Most underrated topic in ML 43:43​ Biggest day-to-day challenges Get our podcast on these other platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

Piero shares the story of how Ludwig was created, as well as the ins and outs of how Ludwig works and the future of machine learning with no code. Piero is a Staff Research Scientist in the Hazy Research group at Stanford University. He is a former founding member of Uber AI, where he created Ludwig, worked on applied projects (COTA, Graph Learning for Uber Eats, Uber’s Dialogue System), and published research on NLP, Dialogue, Visualization, Graph Learning, Reinforcement Learning, and Computer Vision. Topics covered: 0:00 Sneak peek and intro 1:24 What is Ludwig, at a high level? 4:42 What is Ludwig doing under the hood? 7:11 No-code machine learning and data types 14:15 How Ludwig started 17:33 Model performance and underlying architecture 21:52 On Python in ML 24:44 Defaults and W&B integration 28:26 Perspective on NLP after 10 years in the field 31:49 Most underrated aspect of ML 33:30 Hardest part of deploying ML models in the real world Learn more about Ludwig: https://ludwig-ai.github.io/ludwig-docs/ Piero's Twitter: https://twitter.com/w4nderlus7 Follow Piero on Linkedin: https://www.linkedin.com/in/pieromolino/?locale=en_US Get our podcast on these other platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

How Rosanne is working to democratize AI research and improve diversity and fairness in the field through starting a non-profit after being a founding member of Uber AI Labs, doing lots of amazing research, and publishing papers at top conferences. Rosanne is a machine learning researcher, and co-founder of ML Collective, a nonprofit organization for open collaboration and mentorship. Before that, she was a founding member of Uber AI. She has published research at NeurIPS, ICLR, ICML, Science, and other top venues. While at school she used neural networks to help discover novel materials and to optimize fuel efficiency in hybrid vehicles. ML Collective: http://mlcollective.org/ Controlling Text Generation with Plug and Play Language Models: https://eng.uber.com/pplm/ LCA: Loss Change Allocation for Neural Network Training: https://eng.uber.com/research/lca-loss-change-allocation-for-neural-network-training/ Topics covered 0:00 Sneak peek, Intro 1:53 The origin of ML Collective 5:31 Why a non-profit and who is MLC for? 14:30 LCA, Loss Change Allocation 18:20 Running an org, research vs admin work 20:10 Advice for people trying to get published 24:15 on reading papers and Intrinsic Dimension paper 36:25 NeurIPS - Open Collaboration 40:20 What is your reward function? 44:44 Underrated aspect of ML 47:22 How to get involved with MLC Get our podcast on these other platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts YouTube: http://wandb.me/youtube Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices: https://wandb.ai/gallery

In this episode of Gradient Dissent, Primer CEO Sean Gourley and Lukas Biewald sit down to talk about NLP, working with vast amounts of information, and how crucially it relates to national defense. They also chat about their experience of being second-time founders coming from a data science background and how it affects the way they run their companies. We hope you enjoy this episode! Sean Gourley is the founder and CEO Primer, a natural language processing startup in San Francisco. Previously, he was CTO of Quid an augmented intelligence company that he cofounded back in 2009. And prior to that, he worked on self-repairing nano circuits at NASA Ames. Sean has a PhD in physics from Oxford, where his research as a road scholar focused on graph theory, complex systems, and the mathematical patterns underlying modern war. Follow Sean on Twitter: https://primer.ai/ https://twitter.com/sgourley Topics Covered: 0:00 Sneak peek, intro 1:42 Primer's mission and purpose 4:29 The Diamond Age – How do we train machines to observe the world and help us understand it 7:44 a self-writing Wikipedia 9:30 second-time founder 11:26 being a founder as a data scientist 15:44 commercializing algorithms 17:54 Is GPT-3 worth the hype? The mind-blowing scale of transformers 23:00 AI Safety, military/defense 29:20 disinformation, does ML play a role? 34:55 Establishing ground truth and informational provenance 39:10 COVID misinformation, Masks, division 44:07 most underrated aspect of ML 45:09 biggest bottlenecks in ML? Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on these other platforms: YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. https://wandb.ai/gallery

Peter Wang talks about his journey of being the CEO of and co-founding Anaconda, his perspective on the Python programming language, and its use for scientific computing. Peter Wang has been developing commercial scientific computing and visualization software for over 15 years. He has extensive experience in software design and development across a broad range of areas, including 3D graphics, geophysics, large data simulation and visualization, financial risk modeling, and medical imaging. Peter’s interests in the fundamentals of vector computing and interactive visualization led him to co-found Anaconda (formerly Continuum Analytics). Peter leads the open source and community innovation group. As a creator of the PyData community and conferences, he devotes time and energy to growing the Python data science community and advocating and teaching Python at conferences around the world. Peter holds a BA in Physics from Cornell University. Follow peter on Twitter: https://twitter.com/pwang​ https://www.anaconda.com/​ Intake: https://www.anaconda.com/blog/intake-...​ https://pydata.org/​ Scientific Data Management in the Coming Decade paper: https://arxiv.org/pdf/cs/0502008.pdf Topics covered: 0:00​ (intro) Technology is not value neutral; Don't punt on ethics 1:30​ What is Conda? 2:57​ Peter's Story and Anaconda's beginning 6:45​ Do you ever regret choosing Python? 9:39​ On other programming languages 17:13​ Scientific Data Management in the Coming Decade 21:48​ Who are your customers? 26:24​ The ML hierarchy of needs 30:02​ The cybernetic era and Conway's Law 34:31​ R vs python 42:19​ Most underrated: Ethics - Don't Punt 46:50​ biggest bottlenecks: open-source, python Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on these other platforms: YouTube: http://wandb.me/youtube Soundcloud: http://wandb.me/soundcloud Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. https://wandb.ai/gallery

Chris shares his journey starting from playing in R.E.M, becoming interested in physics to leading WIRED Magazine for 11 years. His robot fascination lead to starting a company that manufactures drones, and creating a community democratizing self-driving cars. Chris Anderson is the CEO of 3D Robotics, founder of the Linux Foundation Dronecode Project and founder of the DIY Drones and DIY Robocars communities. From 2001 through 2012 he was the Editor in Chief of Wired Magazine. He's also the author of the New York Times bestsellers `The Long Tail` and `Free` and `Makers: The New Industrial Revolution`. In 2007 he was named to "Time 100," most influential men and women in the world. Links discussed in this episode: DIY Robocars: diyrobocars.com Getting Started with Robocars: https://diyrobocars.com/2020/10/31/getting-started-with-robocars/ DIY Robotics Meet Up: https://www.meetup.com/DIYRobocars Other Works 3DRobotics: https://www.3dr.com/ The Long Tail by Chris Anderson: https://www.amazon.com/Long-Tail-Future-Business-Selling/dp/1401309666/ref=sr_1_1?dchild=1&keywords=The+Long+Tail&qid=1610580178&s=books&sr=1-1 Interesting links Chris shared OpenMV: https://openmv.io/ Intel Tracking Camera: https://www.intelrealsense.com/tracking-camera-t265/ Zumi Self-Driving Car Kit: https://www.robolink.com/zumi/ Possible Minds: Twenty-Five Ways of Looking at AI: https://www.amazon.com/Possible-Minds-Twenty-Five-Ways-Looking/dp/0525557997 Topics discussed: 0:00 sneak peek and intro 1:03 Battle of the REM's 3:35 A brief stint with Physics 5:09 Becoming a journalist and the woes of being a modern physicis 9:25 WIRED in the aughts 12:13 perspectives on "The Long Tail" 20:47 getting into drones 25:08 "Take a smartphone, add wings" 28:07 How did you get to autonomous racing cars? 33:30 COVID and virtual environments 38:40 Chris's hope for Robocars 40:54 Robocar hardware, software, sensors 53:49 path to Singularity/ regulations on drones 58:50 "the golden age of simulation" 1:00:22 biggest challenge in deploying ML models Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on these other platforms: YouTube: http://wandb.me/youtube Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. https://wandb.ai/gallery

Adrien shares his journey from making games that advance science (Eterna, Foldit) to creating a Streamlit, an open-source app framework enabling ML/Data practitioners to easily build powerful and interactive apps in a few hours. Adrien is co-founder and CEO of Streamlit, an open-source app framework that helps create beautiful data apps in hours in pure Python. Dr. Treuille has been a Zoox VP, Google X project lead, and Computer Science faculty at Carnegie Mellon. He has won numerous scientific awards, including the MIT TR35. Adrien has been featured in the documentaries What Will the Future Be Like by PBS/NOVA, and Lo and Behold by Werner Herzog. https://twitter.com/myelbows https://www.linkedin.com/in/adrien-treuille-52215718/ https://www.streamlit.io/ https://eternagame.org/ https://fold.it/ Topics covered: 0:00 sneak peek/Streamlit 0:47 intro 1:21 from aspiring guitar player to machine learning 4:16 Foldit - games that train humans 10:08 Eterna - another game and its relation to ML 16:15 Research areas as a professor at Carnegie Mellon 18:07 the origin of Streamlit 23:53 evolution of Streamlit: data science-ing a pivot 30:20 on programming languages 32:20 what’s next for Streamlit 37:34 On meditation and work/life 41:40 Underrated aspect of Machine Learning 443:07 Biggest challenge in deploying ML in the real world Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on YouTube, Apple, Spotify, and Google! YouTube: http://wandb.me/youtube Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/google-podcasts Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work: http://wandb.me/salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.

We're thrilled to have Peter Norvig join us to talk about the evolution of deep learning, his industry-defining book, his work at Google, and what he thinks the future holds for machine learning research. Peter Norvig is a Director of Research at Google Inc; previously he directed Google's core search algorithms group. He is co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and co-teacher of an Artificial Intelligence class that signed up 160,000. Prior to his work at Google, Norvig was NASA's chief computer scientist. Peter's website: https://norvig.com/ Topics covered: 0:00 singularity is in the eye of the beholder 0:32 introduction 1:09 project Euler 2:42 advent of code/pytudes 4:55 new sections in the new version of his book 10:32 unreasonable effectiveness of data Paper 15 years later 14:44 what advice would you give to a young researcher? 16:03 computing power in the evolution of deep learning 19:19 what's been surprising in the development of AI? 24:21 from alpha go to human-like intelligence 28:46 What in AI has been surprisingly hard or easy? 32:11 synthetic data and language 35:16 singularity is in the eye of the beholder 38:43 the future of python in ML and why he used it in his book 43:00 underrated topic in ML and bottlenecks in production Visit our podcasts homepage for transcripts and more episodes! https://www.wandb.com/podcast Get our podcast on Apple, Spotify, and Google! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF Google: https://tiny.cc/GD_Google We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: https://tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: https://bit.ly/wb-slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. https://wandb.ai/gallery

The story of Ray and what lead Robert to go from reinforcement learning researcher to creating open-source tools for machine learning and beyond Robert is currently working on Ray, a high-performance distributed execution framework for AI applications. He studied mathematics at Harvard. He’s broadly interested in applied math, machine learning, and optimization, and was a member of the Statistical AI Lab, the AMPLab/RISELab, and the Berkeley AI Research Lab at UC Berkeley. robertnishihara.com https://anyscale.com/ https://github.com/ray-project/ray https://twitter.com/robertnishihara https://www.linkedin.com/in/robert-nishihara-b6465444/ Topics covered: 0:00 sneak peak + intro 1:09 what is Ray? 3:07 Spark and Ray 5:48 reinforcement learning 8:15 non-ml use cases of ray 10:00 RL in the real world and and common uses of Ray 13:49 Ppython in ML 16:38 from grad school to ML tools company 20:40 pulling product requirements in surprising directions 23:25 how to manage a large open source community 27:05 Ray Tune 29:35 where do you see bottlenecks in production? 31:39 An underrated aspect of Machine Learning Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Apple, Spotify, and Google! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF Google: http://tiny.cc/GD_Google Subscribe to our YouTube channel for videos of these podcasts and more Machine learning-related videos: https://www.youtube.com/c/WeightsBiases We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://bit.ly/wb-slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. https://app.wandb.ai/gallery

Sofie and Ines walk us through how the new spaCy library helps build end to end SOTA natural language processing workflows. Ines Montani is the co-founder of Explosion AI, a digital studio specializing in tools for AI technology. She's a core developer of spaCy, one of the leading open-source libraries for Natural Language Processing in Python and Prodigy, a new data annotation tool powered by active learning. Before founding Explosion AI, she was a freelance front-end developer and strategist. https://twitter.com/_inesmontani Sofie Van Landeghem is a Natural Language Processing and Machine Learning engineer at Explosion.ai. She is a Software Engineer at heart, with an absurd love for quality assurance and testing, introducing proper levels of abstraction, and ensuring code robustness and modularity. She has more than 12 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry. https://twitter.com/oxykodit https://spacy.io/ https://prodi.gy/ https://thinc.ai/ https://explosion.ai/ Topics covered: 0:00 Sneak peek 0:35 intro 2:29 How spaCy was started 6:11 Business model, open source 9:55 What was spaCy designed to solve? 12:23 advances in NLP and modern practices in industry 17:19 what differentiates spaCy from a more research focused NLP library? 19:28 Multi-lingual/domain specific support 23:52 spaCy V3 configuration 28:16 Thoughts on Python, Syphon, other programming languages for ML 33:45 Making things clear and reproducible 37:30 prodigy and getting good training data 44:09 most underrated aspect of ML 51:00 hardest part of putting models into production Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Apple, Spotify, and Google! Apple Podcasts: bit.ly/2WdrUvI Spotify: bit.ly/2SqtadF Google:tiny.cc/GD_Google We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: bit.ly/wb-slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. app.wandb.ai/gallery

Supercharging computer vision model performance by generating years of training data in minutes. Daeil Kim is the co-founder and CEO of AI.Reverie(https://aireverie.com/), a startup that specializes in creating high quality synthetic training data for computer vision algorithms. Before that, he was a senior data scientist at the New York Times. And before that he got his PhD in computer science from Brown University, focusing on machine learning and Bayesian statistics. He's going to talk about tools that will advance machine learning progress, and he's going to talk about synthetic data. https://twitter.com/daeil Topics covered: 0:00 Diversifying content 0:23 Intro+bio 1:00 From liberal arts to synthetic data 8:48 What is synthetic data? 11:24 Real world examples of synthetic data 16:16 Understanding performance gains using synthetic data 21:32 The future of Synthetic data and AI.Reverie 23:21 The composition of people at AI.reverie and ML 28:28 The evolution of ML tools and systems that Daeil uses 33:16 Most underrated aspect of ML and common misconceptions 34:42 Biggest challenge in making synthetic data work in the real world Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Apple, Spotify, and Google! Apple Podcasts: bit.ly/2WdrUvI Spotify: bit.ly/2SqtadF Google:tiny.cc/GD_Google We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: bit.ly/wb-slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. app.wandb.ai/gallery

Joaquin chats about scaling and democratizing AI at Facebook, while understanding fairness and algorithmic bias. --- Joaquin Quiñonero Candela is Distinguished Tech Lead for Responsible AI at Facebook, where he aims to understand and mitigate the risks and unintended consequences of the widespread use of AI across Facebook. He was previously Director of Society and AI Lab and Director of Engineering for Applied ML. Before joining Facebook, Joaquin taught at the University of Cambridge, and worked at Microsoft Research. Connect with Joaquin: Personal website: https://quinonero.net/ Twitter: https://twitter.com/jquinonero LinkedIn: https://www.linkedin.com/in/joaquin-qui%C3%B1onero-candela-440844/ --- Topics Discussed: 0:00 Intro, sneak peak 0:53 Looking back at building and scaling AI at Facebook 10:31 How do you ship a model every week? 15:36 Getting buy-in to use a system 19:36 More on ML tools 24:01 Responsible AI at Facebook 38:33 How to engage with those effected by ML decisions 41:54 Approaches to fairness 53:10 How to know things are built right 59:34 Diversity, inclusion, and AI 1:14:21 Underrated aspect of AI 1:16:43 Hardest thing when putting models into production Transcript: http://wandb.me/gd-joaquin-candela Links Discussed: Race and Gender (2019): https://arxiv.org/pdf/1908.06165.pdf Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning (2019): https://arxiv.org/abs/1912.10389 Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018): http://proceedings.mlr.press/v81/buolamwini18a.html --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wandb.me/google-podcasts​​ YouTube: http://wandb.me/youtube​​ Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

Richard Socher, ex-Chief Scientist at Salesforce, joins us to talk about The AI Economist, NLP protein generation and biggest challenge in making ML work in the real world. Richard Socher was the Chief scientist (EVP) at Salesforce where he lead teams working on fundamental research(einstein.ai/), applied research, product incubation, CRM search, customer service automation and a cross-product AI platform for unstructured and structured data. Previously, he was an adjunct professor at Stanford’s computer science department and the founder and CEO/CTO of MetaMind(www.metamind.io/) which was acquired by Salesforce in 2016. In 2014, he got my PhD in the [CS Department](www.cs.stanford.edu/) at Stanford. He likes paramotoring and water adventures, traveling and photography. More info: - Forbes article: https://www.forbes.com/sites/gilpress/2017/05/01/emerging-artificial-intelligence-ai-leaders-richard-socher-salesforce/) with more info about Richard's bio. - CS224n - NLP with Deep Learning(http://cs224n.stanford.edu/) the class Richard used to teach. - TEDx talk(https://www.youtube.com/watch?v=8cmx7V4oIR8) about where AI is today and where it's going. Research: Google Scholar Link(https://scholar.google.com/citations?user=FaOcyfMAAAAJ&hl=en) The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies Arxiv link(https://arxiv.org/abs/2004.13332), blog(https://blog.einstein.ai/the-ai-economist/), short video(https://www.youtube.com/watch?v=4iQUcGyQhdA), Q&A(https://salesforce.com/company/news-press/stories/2020/4/salesforce-ai-economist/), Press: VentureBeat(https://venturebeat.com/2020/04/29/salesforces-ai-economist-taps-reinforcement-learning-to-generate-optimal-tax-policies/), TechCrunch(https://techcrunch.com/2020/04/29/salesforce-researchers-are-working-on-an-ai-economist-for-more-equitable-tax-policy/) ProGen: Language Modeling for Protein Generation: bioRxiv link(https://www.biorxiv.org/content/10.1101/2020.03.07.982272v2), [blog](https://blog.einstein.ai/progen/) ] Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things Issue11, (**Chemical Science 2020**). paper link(https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc06145b#!divAbstract) CTRL: A Conditional Transformer Language Model for Controllable Generation: Arxiv link(https://arxiv.org/abs/1909.05858), code pre-trained and fine-tuning(https://github.com/salesforce/ctrl), blog(https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/) Genie: a generator of natural language semantic parsers for virtual assistant commands: PLDI 2019 pdf link(https://almond-static.stanford.edu/papers/genie-pldi19.pdf), https://almond.stanford.edu Topics Covered: 0:00 intro 0:42 the AI economist 7:08 the objective function and Gini Coefficient 12:13 on growing up in Eastern Germany and cultural differences 15:02 Language models for protein generation (ProGen) 27:53 CTRL: conditional transformer language model for controllable generation 37:52 Businesses vs Academia 40:00 What ML applications are important to salesforce 44:57 an underrated aspect of machine learning 48:13 Biggest challenge in making ML work in the real world Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Soundcloud, Apple, Spotify, and Google! Soundcloud: https://bit.ly/2YnGjIq Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF Google: http://tiny.cc/GD_Google Weights and Biases makes developer tools for deep learning. Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://tiny.cc/wb-salon Join our community of ML practitioners: http://bit.ly/wb-slack Our gallery features curated machine learning reports by ML researchers. https://app.wandb.ai/gallery

How Zack went from being a musician to professor, how medical applications of Machine Learning are developing, and the challenges of counteracting bias in real world applications. Zachary Chase Lipton is an assistant professor of Operations Research and Machine Learning at Carnegie Mellon University. His research spans core machine learning methods and their social impact and addresses diverse application areas, including clinical medicine and natural language processing. Current research focuses include robustness under distribution shift, breast cancer screening, the effective and equitable allocation of organs, and the intersection of causal thinking with messy data. He is the founder of the Approximately Correct (approximatelycorrect.com) blog and the creator of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. Zack’s blog - http://approximatelycorrect.com/ Detecting and Correcting for Label Shift with Black Box Predictors: https://arxiv.org/pdf/1802.03916.pdf Algorithmic Fairness from a Non-Ideal Perspective https://www.datascience.columbia.edu/data-good-zachary-lipton-lecture Jonas Peter’s lectures on causality: https://youtu.be/zvrcyqcN9Wo 0:00 Sneak peek: Is this a problem worth solving? 0:38 Intro 1:23 Zack’s journey from being a musician to a professor at CMU 4:45 Applying machine learning to medical imaging 10:14 Exploring new frontiers: the most impressive deep learning applications for healthcare 12:45 Evaluating the models – Are they ready to be deployed in hospitals for use by doctors? 19:16 Capturing the signals in evolving representations of healthcare data 27:00 How does the data we capture affect the predictions we make 30:40 Distinguishing between associations and correlations in data – Horror vs romance movies 34:20 The positive effects of augmenting datasets with counterfactually flipped data 39:25 Algorithmic fairness in the real world 41:03 What does it mean to say your model isn’t biased? 43:40 Real world implications of decisions to counteract model bias 49:10 The pragmatic approach to counteracting bias in a non-ideal world 51:24 An underrated aspect of machine learning 55:11 Why defining the problem is the biggest challenge for machine learning in the real world Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on YouTube, Apple, and Spotify! YouTube: https://www.youtube.com/c/WeightsBiases Soundcloud: https://bit.ly/2YnGjIq Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: http://tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: http://bit.ly/wandb-forum Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. https://app.wandb.ai/gallery

Anthony Goldbloom is the founder and CEO of Kaggle. In 2011 & 2012, Forbes Magazine named Anthony as one of the 30 under 30 in technology. In 2011, Fast Company featured him as one of the innovative thinkers who are changing the future of business. He and Lukas discuss the differences in strategies that do well in Kaggle competitions vs academia vs in production. They discuss his 2016 Ted talk through the lens of 2020, frameworks, and languages. Topics Discussed: 0:00 Sneak Peek 0:20 Introduction 0:45 methods used in kaggle competitions vs mainstream academia 2:30 Feature engineering 3:55 Kaggle Competitions now vs 10 years ago 8:35 Data augmentation strategies 10:06 Overfitting in Kaggle Competitions 12:53 How to not overfit 14:11 Kaggle competitions vs the real world 18:15 Getting into ML through Kaggle 22:03 Other Kaggle products 25:48 Favorite under appreciated kernel or dataset 28:27 Python & R 32:03 Frameworks 35:15 2016 Ted talk though the lens of 2020 37:54 Reinforcement Learning 38:43 What’s the topic in ML that people don’t talk about enough? 42:02 Where are the biggest bottlenecks in deploying ML software? Check out Kaggle: https://www.kaggle.com/ Follow Anthony on Twitter: https://twitter.com/antgoldbloom Watch his 2016 Ted Talk: https://www.ted.com/talks/anthony_goldbloom_the_jobs_we_ll_lose_to_machines_and_the_ones_we_won_t Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Soundcloud, Apple, and Spotify! Soundcloud: https://bit.ly/2YnGjIq Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. * Blog: https://www.wandb.com/articles * Gallery: See what you can create with W&B - https://app.wandb.ai/gallery * Join our community of ML practitioners working on interesting problems - https://www.wandb.com/ml-community Host: Lukas Biewald - https://twitter.com/l2k Producer: Lavanya Shukla - https://twitter.com/lavanyaai Editor: Cayla Sharp - http://caylasharp.com/

👩‍💻Today our guest is Suzanah Ilić! Suzanah is a founder of Machine Learning Tokyo which is a nonprofit organization dedicated to democratizing Machine Learning. They are a team of ML Engineers and Researchers and a community of more than 3000 people. Machine Learning Tokyo: https://mltokyo.ai/ Follow Suzanah on twitter: https://twitter.com/suzatweet Check out our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

Jeremy Howard is a founding researcher at fast.ai, a research institute dedicated to making Deep Learning more accessible. Previously, he was the CEO and Founder at Enlitic, an advanced machine learning company in San Francisco, California. Howard is a faculty member at Singularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on "Jobs For The Machines." Howard advised Khosla Ventures as their Data Strategist, identifying the biggest opportunities for investing in data-driven startups and mentoring their portfolio companies to build data-driven businesses. Howard was the founding CEO of two successful Australian startups, FastMail and Optimal Decisions Group. Before that, he spent eight years in management consulting, at McKinsey & Company and AT Kearney. TOPICS COVERED: 0:00 Introduction 0:52 Dad things 2:40 The story of Fast.ai 4:57 How the courses have evolved over time 9:24 Jeremy’s top down approach to teaching 13:02 From Fast.ai the course to Fast.ai the library 15:08 Designing V2 of the library from the ground up 21:44 The ingenious type dispatch system that powers Fast.ai 25:52 Were you able to realize the vision behind v2 of the library 28:05 Is it important to you that Fast.ai is used by everyone in the world, beyond the context of learning 29:37 Real world applications of Fast.ai, including animal husbandry 35:08 Staying ahead of the new developments in the field 38:50 A bias towards learning by doing 40:02 What’s next for Fast.ai 40.35 Python is not the future of Machine Learning 43:58 One underrated aspect of machine learning 45:25 Biggest challenge of machine learning in the real world Follow Jeremy on Twitter: https://twitter.com/jeremyphoward Links: Deep learning R&D & education: http://fast.ai Software: http://docs.fast.ai Book: http://up.fm/book Course: http://course.fast.ai Papers: The business impact of deep learning https://dl.acm.org/doi/10.1145/2487575.2491127 De-identification Methods for Open Health Data https://www.jmir.org/2012/1/e33/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Soundcloud, Apple, and Spotify! YouTube: https://www.youtube.com/c/WeightsBiases Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

As Lyft’s VP of Engineering, Software at Level 5, Autonomous Vehicle Program, Anantha Kancherla has a birds-eye view on what it takes to make self-driving cars work in the real world. He previously worked on Windows at Microsoft focusing on DirectX, Graphics and UI; Facebook’s mobile Newsfeed and core mobile experiences; and led the Collaboration efforts at Dropbox involving launching Dropbox Paper as well as improving core collaboration functionality in Dropbox. He and Lukas dive into the challenges of working on large projects and how to approach breaking down a major project into pieces, tracking progress and addressing bugs. Check out Lyft’s Self-Driving Website: https://self-driving.lyft.com/ And this article on building the self-driving team at Lyft: https://medium.com/lyftlevel5/going-from-zero-to-sixty-building-lyfts-self-driving-software-team-1ac693800588 Follow Lyft Level 5 on Twitter: https://twitter.com/LyftLevel5 Topics covered: 0:00 Sharp Knives 0:44 Introduction 1:07 Breaking down a big goal 8:15 Breaking down Metrics 10:50 Allocating Resources 12:40 Interventions 13:27 What part still has lots ofroom for improvement? 14:25 Various ways of deploying models 15:30 Rideshare 15:57 Infrastructure, updates 17:28 Model versioning 19:16 Model improvement goals 22:42 Unit testing 25:12 Interactions of models 26:30 Improvements in data vs models 29:50 finding the right data 30:38 Deploying models into production 32:17 Feature drift 34:20 When to file bug tickets 37:25 Processes and growth 40:56 Underrated aspect 42:34 Biggest challenges Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF

Bharath created the deepchem.io open-source project to grow the deep drug discovery open source community, co-created the moleculenet.ai benchmark suite to facilitate development of molecular algorithms, and more. Bharath’s graduate education was supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences. Bharath is the lead author of “TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning”, a developer’s introduction to modern machine learning, with O’Reilly Media. Today, Bharath is focused on designing the decentralized protocols that will unlock data and AI to create the next stage of the internet. He received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He did his PhD in computer science at Stanford University where he studied the application of deep-learning to problems in drug-discovery. Follow Bharath on Twitter and Github https://twitter.com/rbhar90 rbharath.github.io Check out some of his projects: https://deepchem.io/ https://moleculenet.ai/ https://scholar.google.com/citations?user=LOdVDNYAAAAJ&hl=en&oi=ao Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

Chip Huyen is a writer and computer scientist currently working at a startup that focuses on machine learning production pipelines. Previously, she’s worked at NVIDIA, Netflix, and Primer. She helped launch Coc Coc - Vietnam’s second most popular web browser with 20+ million monthly active users. Before all of that, she was a best selling author and traveled the world. Chip graduated from Stanford, where she created and taught the course on TensorFlow for Deep Learning Research. Check out Chip's recent article on ML Tools: https://huyenchip.com/2020/06/22/mlops.html Follow Chip on Twitter: https://twitter.com/chipro And on her Website: https://huyenchip.com/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

👨🏻‍💻Our guest on this episode of Gradient Dissent is Peter Skomoroch! Peter is the former head of data products at Workday and LinkedIn. Previously, he was the cofounder and CEO of venture-backed deep learning startup SkipFlag, which was acquired by Workday, and a principal data scientist at LinkedIn. Check out his recent publication: What you need to know about product management for AI https://www.oreilly.com/radar/what-you-need-to-know-about-product-management-for-ai/ Follow Peter on Twitter: https://twitter.com/peteskomoroch And read some of his other work: Pangloss: Fast Entity Linking in Noisy Text Environments Large-Scale Hierarchical Topic Models Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Soundcloud, Apple, and Spotify! YouTube: https://bit.ly/32NzZvI Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

Josh Tobin is a researcher working at the intersection of machine learning and robotics. His research focuses on applying deep reinforcement learning, generative models, and synthetic data to problems in robotic perception and control. Additionally, he co-organizes a machine learning training program for engineers to learn about production-ready deep learning called Full Stack Deep Learning. https://fullstackdeeplearning.com/ Josh did his PhD in Computer Science at UC Berkeley advised by Pieter Abbeel and was a research scientist at OpenAI for 3 years during his PhD. Finally, Josh created this amazing field guide on troubleshooting deep neural networks: http://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf Follow Josh on twitter: https://twitter.com/josh_tobin And on his website:http://josh-tobin.com/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Youtube, Apple, and Spotify! Youtube: https://www.youtube.com/playlist?list=PLD80i8An1OEEb1jP0sjEyiLG8ULRXFob_ Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

Miles Brundage researches the societal impacts of artificial intelligence and how to make sure they go well. In 2018, he joined OpenAI, as a Research Scientist on the Policy team. Previously, he was a Research Fellow at the University of Oxford's Future of Humanity Institute and served as a member of Axon's AI and Policing Technology Ethics Board. Keep up with Miles on his website: https://www.milesbrundage.com/ and on Twitter: https://twitter.com/miles_brundage Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Soundcloud, Apple, and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

Hamel Husain is a Staff Machine Learning Engineer at Github. He has extensive experience building data analytics and predictive modeling solutions for a wide range of industries, including: hospitality, telecom, retail, restaurant, entertainment and finance. He has built large data science teams (50+) from the ground up and have extensive experience building solutions as an individual contributor. Follow Hamel on Twitter: https://twitter.com/HamelHusain And on his website: http://hamel.io/ Learn more about Github Actions: https://github.com/features/actions and the CodeSearchNet Challenge: https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/ Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple, and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

Peter Welinder is a research scientist and roboticist at OpenAI. Before that, he was an engineer at Dropbox and ran the machine learning team, and before that, he co-founded Anchovi Labs a startup using Computer Vision to organize photos that was acquired by Dropbox in 2012. In this episode of our podcast, Peter shares his experiences and the challenges associated with building a robotic hand that can solve a rubix cube. Read some of Peter’s Articles: https://openai.com/blog/authors/peter/ Follow Peter on Twitter: https://twitter.com/npew Check out our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple, and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

👩‍💻Today our guest is Vicki Boykis! Vicki is a senior consultant in machine learning and engineering and works with clients to build holistic data products used for decision-making. She's previously spoken at PyData, taught SQL for GirlDevelopIt, and blogs about data pipelines and open internet. Follow her on her website: vickiboykis.com On twitter: https://twitter.com/vboykis and subscribe to her newsletter: vicki.substack.com Check out our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

👩‍💻👩‍💻On this episode of Gradient Dissent our guests are Angela Bassa and Danielle Dean! Angela is an expert in building and leading data teams. An MIT-trained and Edelman-award-winning mathematician, she has over 15 years of experience across industries—spanning finance, life sciences, agriculture, marketing, energy, software, and robotics. Angela heads Data Science and Machine Learning at iRobot, where her teams help bring intelligence to a global fleet of millions of consumer robots. She is also a renowned keynote speaker and author, with credits including the Wall Street Journal and Harvard Business Review. Follow Angela on twitter: https://twitter.com/angebassa And on her website: https://www.angelabassa.com/ Danielle Dean, PhD is the Technical Director of Machine Learning at iRobot where she is helping lead the intelligence revolution for robots. She leads a team that leverages machine learning, reinforcement learning, and software engineering to build algorithms that will result in massive improvements in our robots. Before iRobot, Danielle was a Principal Data Scientist Lead at Microsoft Corp. in AzureCAT Engineering within the Cloud AI Platform division. Follow Danielle on Twitter: https://twitter.com/danielleodean Check out our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast 🔊 Get our podcast on Apple and Spotify! Apple Podcasts: https://bit.ly/2WdrUvI Spotify: https://bit.ly/2SqtadF We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

Jack Clark is the Strategy and Communications Director at OpenAI and formerly worked as the world’s only neural network reporter at Bloomberg. Lukas and Jack discuss AI policy, ethics, and the responsibilities of AI researchers. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims by OpenAI: https://arxiv.org/abs/2004.07213 Follow Jack Clark on Twitter: twitter.com/jackclarkSF Read more posts by Jack on his website: https://jack-clark.net/ Get our podcast on Apple and Spotify! https://podcasts.apple.com/us/podcast/gradient-dissent-weights-biases/id1504567418 https://open.spotify.com/show/7o9r3fFig3MhTJwehXDbXm 🤖Gradient Dissent by Weights and Biases Get a behind-the-scenes look at how industry leaders are using machine learning in the real world. While building experiment tracking tools, we’ve had the opportunity to learn about how different teams are building and deploying models. In this podcast, we share some of the insights and stories we’ve heard along the way. Follow Gradient Dissent for weekly machine learning updates, and be part of the conversation. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

🏅 See how W&B is your secret weapon to make it onto the Kaggle leaderboards - https://www.wandb.com/kaggle 👩‍💻Rachael Tatman is a developer advocate for Rasa, where she helps developers build and deploy conversational AI applications using their open source framework. 🤖💬 She has a PhD in Linguistics from the University of Washington where she researched computational sociolinguistics, or how our social identity affects the way we use language in computational contexts. Previously she was a data scientist at Kaggle where she’s still a Grandmaster. 💻Keep up with Rachael on her website: http://www.rctatman.com/ 🐦Follow Rachael on twitter: https://twitter.com/rctatman Get our podcast on Apple and Spotify! https://podcasts.apple.com/us/podcast/gradient-dissent-weights-biases/id1504567418 https://open.spotify.com/show/7o9r3fFig3MhTJwehXDbXm 🤖Gradient Dissent by Weights and Biases We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. - Blog: https://www.wandb.com/articles - Gallery: See what you can create with W&B - https://app.wandb.ai/gallery - Continue the conversation on our slack community - http://bit.ly/wandb-forum 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

👨🏻‍💻Nicolas Koumchatzky is the Director of AI infrastructure at NVIDIA, where he's responsible for MagLev, the production-grade machine learning platform by NVIDIA. His team supports diverse ML use cases: autonomous vehicles, medical imaging, super resolution, predictive analytics, cyber security, robotics. He started as a Quant in Paris, then joined Madbits, a startup specialized on using deep learning for content understanding. When Madbits was acquired by Twitter in 2014, he joined as a deep learning expert and led a few projects in Cortex, include a real-time live video classification product for Periscope. In 2016, he focused on building an scalable AI platform for the company. Early 2017, he became the lead for the Cortex team. He joined NVIDIA in 2018. 🐦Follow Nicolas on twitter: https://twitter.com/nkoumchatzky 🛠Maglev: https://blogs.nvidia.com/blog/2018/09/13/how-maglev-speeds-autonomous-vehicles-to-superhuman-levels-of-safety/ ✍️Scalable Active Learning for Autonomous Driving: https://medium.com/nvidia-ai/scalable-active-learning-for-autonomous-driving-a-practical-implementation-and-a-b-test-4d315ed04b5f ✍️Active Learning – Finding the right self-driving training data doesn’t have to take a swarm of human labelers: https://blogs.nvidia.com/blog/2020/01/16/what-is-active-learning/ 👫Continue the conversation on our slack community - http://bit.ly/wandb-forum 🤖Gradient Dissent by Weights and Biases We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. * Visualize your Scikit model performance with W&B - https://app.wandb.ai/lavanyashukla/visualize-sklearn/reports/Visualizing-Sklearn-With-Weights-and-Biases--Vmlldzo0ODIzNg * Blog: https://www.wandb.com/articles * Gallery: See what you can create with W&B - https://app.wandb.ai/gallery 🎙Host: Lukas Biewald - https://twitter.com/l2k 👩🏼‍💻Producer: Lavanya Shukla - https://twitter.com/lavanyaai 📹Editor: Cayla Sharp - http://caylasharp.com/

👨🏻‍💻Brandon Rohrer is a Mechanical Engineer turned Data Scientist. He’s currently a Principal Data Scientist at iRobot and has an incredibly popular Machine Learning course at e2eML where he’s made some wildly popular videos on convolutional neural networks and deep learning. His fascination with robots began after watching Luke Skywalker’s prosthetic hand in the Empire Strikes Back. He turned this fascination into a PhD from MIT and subsequently found his way to building some incredible data science products at Facebook, Microsoft and now at iRobot. ✍️Brandon’s brilliant machine learning course: http://e2eml.school/ 🐦Follow Brandon on twitter: https://twitter.com/_brohrer_ 👫Continue the conversation on our slack community - http://bit.ly/wandb-forum 🤖Gradient Dissent by Weights and Biases - http://wandb.com We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast. We hope you have as much fun listening to it as we had making it. Today our guest is Brandon Rohrer. 👩🏼‍🚀Weights and Biases: We’re always free for academics and open source projects. Email carey@wandb.com with any questions or feature suggestions. • Visualize your Scikit model performance with W&B - https://app.wandb.ai/lavanyashukla/visualize-sklearn/reports/Visualizing-Sklearn-With-Weights-and-Biases--Vmlldzo0ODIzNg • Blog: https://www.wandb.com/articles • Gallery: See what you can create with W&B - https://app.wandb.ai/gallery