Gradient Dissent: Conversations on AI

Explore the revolutionary intersection of AI and Healthcare with John Halamka of Mayo Clinic. Delve into AI's evolution in modern medicine, Mayo Clinic's visionary work, and the ethical frameworks guiding its application. Discover AI's potential to democratize healthcare globally and the challenges and triumphs on the horizon.

In this episode of Gradient Dissent, Chelsea Finn, Assistant Professor at Stanford's Computer Science Department, discusses the forefront of robotics and machine learning. Topics include two-armed robots learning to cook shrimp, the challenges of developing humanoid and quadruped robots, the limitations of simulated environments, and the future of household robotics. She also talks about her work on student feedback in education and the impact of AI coding tools on computer science education.

Richard Socher, CEO of You.com, discusses the power of AI in search and the transformation of search engines with advanced language models like GPT-4. He explores the practical applications and challenges of integrating AI into search functionality and the ethical considerations and future implications of AI in our digital lives. They also touch on the role of AI in automating and transforming digital work, as well as envisioning the future of AI in daily life and work.

Join Sarah Guo and Elad Gil on a deep dive into the world of AI, exploring topics such as fine-tuning versus RAG in AI, evaluating AI research for investment, the impact of AI models on product development, AI's role in evolving job markets, code generation technologies in software engineering, AI's broader industry implications, importance of product-driven approaches in AI startups, AI's impact on traditional roles and industries.

Jerry Liu, CEO of LlamaIndex, discusses the integration of diverse data formats with AI technologies, addressing challenges in data retrieval, analysis, and conversational memory. They explore topics such as Retrieval Augmented Generation (RAG), vector databases, AI model hallucinations, text extraction applications, and the role of LlamaIndex in the rapidly evolving field of AI-driven systems.

Joe Spisak, Product Director, Generative AI @Meta, discusses the influence of AI models like GPT and Llama2 on user experiences, as well as their contributions to fields like biology and material science. The podcast also explores AI's practical business applications and the significance of open-source AI development and collaboration.

AI expert and co-founder of Weights & Biases, Chris Van Pelt, shares insights on startups connecting with early users, melding AI with web development, AI model capabilities, advising enterprises on language model integration, and the future of AI.

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