Jensen shares the story of NVIDIA and deep learning and talks about his views on the future of machine learning and machine learning development.

In the newest episode of Gradient Dissent, we explore the intersecting worlds of AI and healthcare with John Halamka, President of the Mayo Clinic Platform.

Explore the Future of Investment & Impact in AI with Gradient Dissent Host Lukas Biewald and Guests Elad Gill and Sarah Guo of the No Priors podcast.

Explore the journey of Supabase CEO Paul Copplestone on Gradient Dissent Business Podcast, discussing AI, database challenges, and spatial data innovations with hosts Lavanya Shukla and Caryn Marooney.

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.

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.

Dive into the world of AI with Brandon Houghton, founder of Nomic AI, as he discusses the intricacies of language models, the role of fine-tuning, the concept of prompt engineering, and the importance of AI policy on this enlightening episode of Gradient Dissent.

Discover PyTorch's journey in an episode with Soumith Chintala, its Co-Creator and Meta's VP/Fellow. Learn about TensorFlow's impact, community-guided innovation, and the open vs. closed-source debate.

Revolutionizing AI Processing: Unveiling Cerebras Systems' CEO Andrew Feldman's Insights on Large Chips, Optimal Machines, and Future-proof Chip Design.

On this episode of Gradient Dissent, we’re joined by Stella Biderman, Lead Scientist at Booz Allen Hamilton and Executive Director at ElutherAI. Stella and Lukas discuss EleutherAI's origin and future, LLM similarities and differences, choosing models, reinforcement learning, pre-training/fine-tuning, GPU selection, differences from other LLM companies, interpretability, memorization importance, and public access.

On this episode of Gradient Dissent, 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.

Jonathan Frankle and Lukas Biewald discuss neural network pruning and training, the "Lottery Ticket Hypothesis" and much more on this episode of Gradient Dissent.

Shreya explains the high-level findings of "Operationalizing Machine Learning: An Interview Study", an interview study on deploying and maintaining ML pipelines in production.

Sarah discusses the lessons learned from the "AI renaissance" of the mid 2010s and shares her thoughts on machine learning from her perspective as an investor.

Cris gives a demo of Runway, a new video editing platform that uses machine learning to make content creation easier, and discusses the future of computation and creativity.

Jeremy explains diffusion, shares his thoughts on large models, revisits the debate between Python and Julia, and talks about his scientific advocacy during the early days of COVID-19.

Jerome discusses the current advances around large language models and shares some stories about his time as VP of AI at Meta, including leading the team that developed PyTorch.

D. dives into some of the potential pitfalls of model development and explains the roles that Kaggle plays in the machine learning community.

Emad shares the story and mission behind Stability AI, a startup and network of decentralized developer communities building open AI tools.

Peter and Boris dive into the world of GPT-3: how people are applying OpenAI's flagship language model, why fine-tuning GPT-3 improves performance, and the development of OpenAI's GPT-3 API.description

Jehan discusses applications for AI in public safety and enterprise security

Will explores Lightning's journey from undergrad project to Series B startup

Aaron explains how Qualtrics uses machine learning for the enrichment of experience management, discusses the strength and speed of the current NLP ecosystem, and shares tips and tricks for organizing effective ML projects and teams

Jordan explains how Standard AI uses machine learning to track products and customers in challenging retail environments

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Drago discusses current trends in autonomous driving technology, the challenges of simulation and scalability, and why it's important to find rare examples.

James explains what investing in "the future of work" means, the importance of demystifying ML and making it more accessible, and how new technologies create new business models.

Tristan explains the rise of the modern data stack, how dbt makes data transformation easier, and why SQL is still so popular.

Johannes talks about quantum computing, the state of the ML tools ecosystem today, and the challenges of developing and deploying models for customers.

Mircea explains how machine learning unlocks the next level of potential for robotic process automation (RPA) and how ML teams differ from engineering teams.

Pieter talks about the state of affairs and challenges of robotics in 2021, and shares the stories behind founding Gradescope and Covariant.

Peter Welinder, Robotics lead at OpenAI talks about his love of robotics, the early days of reinforcement learning, and the evolution of the robot hand.

Stephan talks about Base Command Platform, NVIDIA's software platform for its DGX SuperPOD infrastructure.

Ion shares the stories behind developing the distributed computing frameworks Spark and Ray, and commercializing them into Databricks and Anyscale.

Chris explains how Blue River Technology is building smart robots for more sustainable farming by identifying crops and weeds and only spraying the weeds with herbicide.

Kathryn explains how the Royal Bank of Canada is using machine learning, and explores what Descartes and Newton might have thought about ML.

Josh explains how astronomy and machine learning have informed each other, their current limitations, and where their intersection goes from here.

Xavier shares his experience deploying healthcare models, augmenting primary care with AI, the challenges of "ground truth" in medicine, and robustness in ML.

Spence shares his experience creating a product around human-in-the-loop machine translation, and explains how machine translation has evolved over the years.

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.

Amelia and Filip give insights into the recommender systems powering Pandora, from developing models to balancing effectiveness and efficiency in production.

From Apache TVM to OctoML, Luis gives direct insight into the world of ML hardware optimization, and where systems optimization is heading.

Matthew explains how combining machine learning and computational biology can provide mainstream medicine with better diagnostics and insights.

Clem explains the virtuous cycles behind the creation and success of Hugging Face, and shares his thoughts on where NLP is heading.

Wojciech joins us to talk the principles behind OpenAI, the Fermi paradox, and the future stages of developments in AGI.

Phil shares some of the approaches, like sparsity and low precision, behind the breakthrough performance of Graphcore's Intelligence Processing Units (IPUs).

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.

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.

Polly explains how microfluidics allow bioengineering researchers to create high throughput data, and shares her experiences with biology and machine learning.

Adrien shares his approach to building teams and taking state-of-the-art research from conception to production at Toyota Research Institute.

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.

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.

From legged locomotion to drones and autonomous driving, Vladlen explains how simulation and abstraction help us understand embodied intelligence.

Dominik shares the story and principles behind Vega and Vega-Lite, and explains how visualization and machine learning help each other.

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.

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.

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.

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.

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.

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.

Sean and Lukas discuss NLP, working with vast amounts of information, and how crucially it relates to national defense.

Peter Wang talks about his journey of being the CEO of and co-founding Anaconda, his perspective on Python, and its use for scientific computing.

Chris shares his journey starting from playing in R.E.M, becoming interested 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.

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

We're thrilled to have Peter Norvig who 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.

The story of Ray and what lead Robert to go from reinforcement learning researcher to creating open-source tools for machine learning and beyond.

Sofie and Ines walk us through how the new spaCy library helps build end to end SOTA natural language processing workflows.

Supercharging computer vision model performance by generating years of training data in minutes

Joaquin chats about scaling and democratizing AI at Facebook, while understanding fairness and algorithmic bias.

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.

Hear how Zack went from being a musician to professor, where medical applications of Machine Learning are developing, and the challenges of counteracting bias in real world applications.

and which jobs we should be worried about losing to AI in the next few decades.

The story of Machine Learning Tokyo, a nonprofit organization dedicated to democratizing Machine Learning

Jeremy shares his experiences in learning, teaching, developing, and making deep learning more accessible.

Anantha and Lukas dive into the challenges of building deep learning models for self driving cars and deploying them into production.

Lukas and Bharath discuss how ML is being used in the medical and biology research fields

Chip has worked on ML research at Snorkel, NVIDIA, Netflix and Primer. She joins us to shares the biggest challenges of moving machine learning pipelines from research to production.

Machine Learning Executive & Entrepreneur, Peter Skomoroch shares his experience building and running successful data science and machine learning teams

Josh Tobin, a former researcher at OpenAI and creator of Full Stack Deep Learning talks about professionalizing ML workflows for the real world, his work with the Robotics team and FSDL.

Miles Brundage joins us to talk about his work developing methods for rigorous analysis of AI development scenarios and appropriate policy responses at OpenAI.

Hamel Husain, Staff Machine Learning Engineer at Github talks about Github Actions, the CodeSearchNet challenge and the tools they're building to advance progress in AI

As a senior consultant in machine learning, Vicki shares her experiences working with many different companies

Learn how Angela and Danielle make machine learning work at scale at iRobot

Machine learning policy, ethics, and the responsibilities of practitioners and researchers

Learn about the challenges involved in building and deploying conversational models, as well as what it's like to work at Kaggle

Director of AI infrastructure at NVIDIA, Nicolas is responsible for MagLev, the production-grade ML platform

Principal Data Scientist at iRobot, Brandon has an incredibly popular ML course at e2eML

Sean and Greg talk about the challenges of combining two highly specialized fields like biology and ML, and how they think about building cross-functional teams.description

The three Weights & Biases co-founders (Chris, Shawn, and Lukas) share how the company got started, reflect on the highs and lows, and give advice to first-time entrepreneurs.

Pete discusses machine learning for embedded devices, from running neural nets on a Raspberry Pi to wake words and industrial monitoring.

Emily dives into the problems with bigger and bigger language models, the difference between form and meaning, the limits of benchmarks, and more.

Chris talks about machine learning at Wikimedia, from which models they're currently running to where their deployment infrastructure is heading.

Jeff talks about building Facebook's early data team, founding Cloudera, and transitioning into biomedicine with Hammer Lab and Related Sciences.