Revolutionizing AI Data Management with Jerry Liu, CEO of LlamaIndex

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

1 Juni 202351min

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

18 Maj 202358min

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

4 Maj 202357min

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

20 Apr 202351min

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

4 Apr 20231h 2min

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​

3 Mars 202354min

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​

2 Feb 20231h 16min

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​

19 Jan 202340min