ClickHouse started as an internal tool at Yandex. Today it's the database Anthropic, OpenAI, Meta and Tesla all run on.

In this episode, CEO Aaron Katz joins Lukas Biewald to talk about how he turned an open source project into a $15B company, why he acquired LangFuse knowing it could cost him customers, and what he's actually building for the agent era.

Snowflake, Datadog and Databricks all come up. He doesn't shy away.

Connect with us here:

Aaron Katz: https://www.linkedin.com/in/aaron-katz-5762094

ClickHouse: https://www.linkedin.com/company/clickhouseinc/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights and Biases: https://www.linkedin.com/company/wandb/

00:00 Trailer

00:57 The Origin Story: From Yandex to ClickHouse Inc.

04:43 Building ClickHouse Cloud & Raising $300M

10:36 Growing Up Around Xerox PARC

12:51 Salesforce, Mark Benioff & the Dot-Com Bust

15:32 Cloud Skeptics vs. AI Skeptics | History Repeating

18:05 Building a Modern Go-To-Market Playbook

21:57 The SaaS Crash, Agents & the Future of Infrastructure

27:09 The Datadog Love-Hate Story

35:21 Hardest Moments: Russia, SVB & Sleepless Nights

43:16 Outro

In this episode, Lukas Biewald talks with Carina Hong, Founder & CEO of Axiom, about why verification is becoming the real bottleneck in high stakes AI systems.

They discuss how Axiom uses AI to take on the tedious checking that stretches verification cycles across years, starting with formal mathematics and extending to hardware and software.

Carina also explains why Axiom’s approach to auto-formalization mirrors spec driven models like Kiro from AWS.

Connect with us here:

Carina Hong: https://www.linkedin.com/in/carina-hong/

Axiom: https://www.linkedin.com/company/axiommath/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

Atlassian has 10,000+ engineers currently split-testing the world’s top AI coding tools, from GitHub Copilot and Cursor to Claude Code.

In this episode, Co-Founder & CEO Mike Cannon-Brookes joins Lukas Biewald to share what their data reveals about the world's best AI tools today.

Hear how 24 years of building a tech giant and a massive internal study on AI productivity have shaped Mike's vision for the future of dev jobs.

Connect with us here:

Mike Cannon-Brookes: https://www.linkedin.com/in/mcannonbrookes/?originalSubdomain=au

Atlassian: https://www.linkedin.com/company/atlassian/?viewAsMember=true

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

00:00 Trailer

01:08 Introduction

03:11 Connecting Technology and Business Teams

07:22 The Impact of AI on Business Workflows

13:26 Developer Productivity and AI

21:03 Measuring Developer Efficiency

25:41 Future of AI in Development

34:59 Legacy Technology and Code Changes

39:29 AI's Role in Developer Productivity

47:40 AI and Junior Developers

52:30 Product-Led Growth and Business Strategy

01:00:29 Core Metrics for Sustainable Growth

01:06:56 Staying Creative in the Tech Industry

In this episode, Lukas Biewald talks with CoreWeave SVP Corey Sanders about why general-purpose clouds start to break down under large-scale AI workloads.

According to Corey, the industry is shifting toward a "Neo Cloud" model to handle the unique demands of modern models.

They dive into the hardware and software stack required to maximize GPU utilization and achieve high goodput.

Corey’s conclusion is clear: AI demands specialization.

Connect with us here:

Corey Sanders: https://www.linkedin.com/in/corey-sanders-842b72/

CoreWeave: https://www.linkedin.com/company/coreweave/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

(00:00) Trailer

(00:57) Introduction

(02:51) The Evolution of AI Workloads

(06:22) Core Weave's Technological Innovations

(13:58) Customer Engagement and Future Prospects

(28:49) Comparing Cloud Approaches

(33:50) Balancing Executive Roles and Hands-On Projects

(46:44) Product Development and Customer Feedback

In this episode, Lukas Biewald talks to Nikolaus West, CEO of Rerun, about why the breakthrough required to get AI out of the lab and into the messy real world is blocked by poor data tooling. 

Nikolaus explains how Rerun solved this by adopting an Entity Component System (ECS), a data model built for games, to handle complex, multimodal, time-aware sensor data. This is the technology that makes solving previously impossible tasks, like flexible manipulation, suddenly feel "boring." 

Connect with us here: 

Nikolaus West: https://www.linkedin.com/in/nikolauswest/

Rerun: https://www.linkedin.com/company/rerun-io/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

Runway ML founder Cristóbal Valenzuela joins Lukas Biewald just after Gen 4.5 reached the #1 position on the Video Arena Leaderboard, according to community voting on Artificial Analysis. 

Lukas examines how a focused research team at Runway outpaced much larger organizations like Google and Meta in one of the most compute-intensive areas of machine learning.

Cristóbal breaks down the architecture behind Gen 4.5 and explains the role of “taste” in model development. He details the engineering improvements in motion and camera control that solve long-standing issues like the restrictive “tripod look,” and shares why video models are starting to function as simulation engines with applications beyond media generation.

Connect with us here:

• Cristóbal Valenzuela: https://www.linkedin.com/in/cvalenzuelab
• Runway: https://www.linkedin.com/company/runwayml/
• Lukas Biewald: https://www.linkedin.com/in/lbiewald/
• Weights & Biases: https://www.linkedin.com/company/wandb/

They get into the core challenges of modern inference, including why dedicated deployments matter, how runtime and infrastructure bottlenecks stack up, and what makes serving large models fundamentally different from smaller ones.

Tuhin also explains how vLLM, TensorRT-LLM, and SGLang differ in practice, what it takes to tune workloads for new chips like the B200, and why reliability becomes harder as systems scale. 

The conversation dives into company-building, from killing product lines to avoiding premature scaling while navigating a market that shifts every few weeks.

Connect with us here: 

Tuhin Srivastva: https://www.linkedin.com/in/tuhin-srivastava/ 

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

You’ll hear why inter-annotator agreement fails in high-complexity tasks like poetry and math, why synthetic data is often overrated, and how Surge builds rich RL environments to stress-test agentic reasoning. They also go deep on what kinds of data will be critical to future progress in AI—from scientific discovery to multimodal reasoning and personalized alignment.

It’s a rare, behind-the-scenes look into the world of high-quality data generation at scale—straight from the team most frontier labs trust to get it right.

Timestamps:

00:00 – Intro: Who is Edwin Chen?

03:40 – The problem with early data labeling systems

06:20 – Search ranking, clickbait, and product principles

10:05 – Why Surge focused on high-skill, high-quality labeling

13:50 – From Craigslist workers to a billion-dollar business

16:40 – Scaling without funding and avoiding Silicon Valley status games

21:15 – Why most human data platforms lack real tech

25:05 – Detecting cheaters, liars, and low-quality labelers

28:30 – Why inter-annotator agreement is a flawed metric

32:15 – What makes a great poem? Not checkboxes

36:40 – Measuring subjective quality rigorously

40:00 – What types of data are becoming more important

44:15 – Scientific collaboration and frontier research data

47:00 – Multimodal data, Argentinian coding, and hyper-specificity

50:10 – What's wrong with LMSYS and benchmark hacking

53:20 – Personalization and taste in model behavior

56:00 – Synthetic data vs. high-quality human data

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They explore the technical and organizational challenges behind enterprise LLMs—including security, hallucination suppression—and when it makes sense to fine-tune models. Arvind also reflects on his previous startup Rubrik and explains how Glean’s AI platform aims to reshape how teams operate, from personalized agents to ever-fresh internal documentation.

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Timestamps: 

[00:01:00] What Glean is and how it works 

[00:02:39] Starting Glean before the LLM boom 

[00:04:10] Using transformers early in enterprise search 

[00:06:48] Semantic search vs. generative answers 

[00:08:13] When to fine-tune vs. use out-of-box models 

[00:12:38] The value of small, purpose-trained models 

[00:13:04] Enterprise security and embedding risks

[00:16:31] Lessons from Rubrik and starting Glean 

[00:19:31] The contrarian bet on enterprise search 

[00:22:57] Culture and lessons learned from Google 

[00:25:13] Everyone will have their own AI-powered "team" 

[00:28:43] Using AI to keep documentation evergreen 

[00:31:22] AI-generated churn and risk analysis 

[00:33:55] Measuring model improvement with golden sets

[00:36:05] Suppressing hallucinations with citations 

[00:39:22] Agents that can ping humans for help 

[00:40:41] AI as a force multiplier, not a replacement 

[00:42:26] The enduring value of hard work

They dive into what makes translation so difficult for AI, why high-quality translations still require human context, and how DeepL tailors models for enterprise use cases. They also discuss the evolution of speech translation, compute infrastructure, training on curated multilingual datasets, hallucinations in models, and why DeepL avoids fine-tuning for each individual customer. It’s a fascinating behind-the-scenes look at one of the most advanced real-world applications of deep learning.

Timestamps:

[00:00:00] Introducing Jarek and DeepL’s mission

[00:01:46] Competing with Google Translate & LLMs

[00:04:14] Pretraining vs. proprietary model strategy

[00:06:47] Building GPU data centers in 2017

[00:08:09] The value of curated bilingual and monolingual data

[00:09:30] How DeepL measures translation quality

[00:12:27] Personalization and enterprise-specific tuning

[00:14:04] Why translation demand is growing

[00:16:16] ROI of incremental quality gains

[00:18:20] The role of human translators in the future

[00:22:48] Hallucinations in translation models

[00:24:05] DeepL’s work on speech translation

[00:28:22] The broader impact of global communication

[00:30:32] Handling smaller languages and language pairs

[00:32:25] Multi-language model consolidation

[00:35:28] Engineering infrastructure for large-scale inference

[00:39:23] Adapting to evolving LLM landscape & enterprise needs

Thomas shares his experience leading GitHub through its $7.5B acquisition by Microsoft, the unexpected ways it accelerated innovation, and why developer happiness is crucial to productivity. They explore what still makes human engineers irreplaceable and how the next generation of developers might grow up coding alongside AI.

Follow Thomas Dohmke: https://www.linkedin.com/in/ashtom/

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They dive deep into AI and drug discovery, where Shkreli delivers a strong critique of mainstream approaches. He also talks about his latest venture in finance software, building Godel Terminal “a Vim for traders", and why he thinks the AI hype cycle is just beginning. It's a wide-ranging and candid conversation with one of the most controversial figures in tech and biotech.

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Sualeh also discusses scaling AI inference to support hundreds of millions of requests per day, building trust through product quality, and his vision for how programming will evolve in the next few years.

⏳Timestamps:

00:00 How Cursor got started and why it took off

04:50 Switching from Vim to VS Code and the rise of CoPilot

08:10 Why Cursor won among competitors: product philosophy and execution

10:30 How user data and feedback loops drive Cursor’s improvements

12:20 Iterating on AI agents: what made Cursor hold back and wait

13:30 Competitive coding background: advantage or challenge?

16:30 Making coding fun again: latency, flow, and model choices

19:10 Building Cursor’s infrastructure: from GPUs to indexing billions of files

26:00 How Cursor prioritizes compute allocation for indexing

30:00 Running massive ML infrastructure: surprises and scaling lessons

34:50 Why Cursor chose DeepSeek models early

36:00 Where AI agents are heading next

40:07 Debugging and evaluating complex AI agents

42:00 How coding workflows will change over the next 2–3 years

46:20 Dream future projects: AI for reading codebases and papers

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They dive into gripping stories of cyber espionage, including how Recorded Future intercepted a hacker selling access to the U.S. Electoral Assistance Commission. Christopher also explains why the criminal underworld has shifted to platforms like Telegram, how AI is transforming both cyber threats and defenses, and the real-world implications of becoming an "undesirable enemy" of the Russian state.

This episode offers unique insights into cybersecurity, AI-driven intelligence, entrepreneurship lessons from a two-time founder, and what happens when geopolitical tensions intersect with cutting-edge technology. A must-listen for anyone interested in cybersecurity, artificial intelligence, or the complex dynamics shaping global security.

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They explore the challenges of underwater autonomy, multi-agent collaboration, cybersecurity, and the growing role of large language models like Gemini and Claude in the defense space.

Essential listening for anyone curious about military AI, defense tech, and the future of autonomous systems.

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They also discuss:

• What defines a true AI agent versus simple automation
• How AI agents are transforming business processes in industries like finance, insurance, and software
• The evolving business models for APIs as AI agents become the dominant software users
• What the next breakthroughs in agentic AI might look like in 2025 and beyond

If you're curious about the cutting edge of AI automation, enterprise AI adoption, and the real impact of multi-agent systems, this episode is packed with essential insights.

This conversation covers AGI timelines, research breakthroughs, and the future of intelligent systems, making it essential listening for AI enthusiasts, researchers, and entrepreneurs.

Mentioned Show Notes:

https://ndea.com

https://arcprize.org/blog/r1-zero-r1-results-analysis

https://arcprize.org/blog/oai-o3-pub-breakthrough

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Connect with Mike Knoop"

@mikeknoop

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Mentioned Articles:

AI’s $200B Question

AI’s $600B Question

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They dive into how Marimo is enabling developers and researchers to collaborate seamlessly on AI projects, the challenges of scaling AI tools, and the importance of fostering open ecosystems for innovation. Akshay shares insights into building a platform that empowers teams to iterate faster and solve complex AI challenges together.

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They discuss the concept of vibes-based evaluation, which examines not just accuracy but also the style and tone of model responses, and how Chatbot Arena has become a community-driven benchmark for open-source and commercial LLMs. Joseph shares insights on democratizing model evaluation, refining AI-human interactions, and leveraging human preferences to improve model performance. This episode provides a deep dive into the evolving landscape of LLM evaluation and its impact on AI development.

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They explore Brightband's innovative approach to using AI for extreme weather prediction, the shift from physics-based models to AI-driven forecasting, and the potential for democratizing weather data. Julian shares insights into building trust in AI for critical decisions, navigating the challenges of deep tech entrepreneurship, and the broader implications of AI in mitigating climate risks. This episode delves into the intersection of AI and Earth systems, highlighting its transformative impact on weather and climate decision-making.

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They explore how Turing built a "developer cloud" of 3.7 million engineers to power AGI training, providing high-quality code and reasoning data to leading AI labs. Jonathan shares insights on Turing’s journey, from building coding datasets to solving enterprise AI challenges and enabling human-in-the-loop solutions. This episode offers a unique perspective on the intersection of human intelligence and AGI, with an eye on the expansion of new domains beyond coding.

✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz 

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Listen on Apple Podcasts: http://wandb.me/apple-podcasts

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They discuss Sridhar's journey from Google to Snowflake, diving into the evolving role of foundation models, Snowflake’s AI strategy, and the challenges of scaling AI in business. Sridhar also shares his thoughts on leadership, rapid iteration, and creating meaningful AI solutions for enterprise clients. Tune in to discover how Snowflake is driving innovation in the AI and data space.

Connect with Sridhar Ramaswamy:

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Paper Andrew referenced Paul David- Economic historian  

https://www.jstor.org/stable/2006600 

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https://www.linkedin.com/in/kanjun/ 

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General Intelligent Podcast: 

https://imbue.com/podcast/

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Tune in now to explore the evolving landscape of AI hardware and cloud services.

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Discover how Perplexity combines advancements in search engines and LLMs to deliver precise answers. Yarats shares insights on the technical challenges, the importance of speed, and the future of AI in search.

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Learn how Quilter automates the complex, manual process of creating PCBs, making it faster and more efficient. Nesterenko shares insights on the challenges and successes of integrating AI with real-world applications and discusses the future of electronic design.

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They explore how Codeium evolved from GPU virtualization to a widely used AI coding tool and tackled the technical challenges and future prospects of AI-assisted software development. Varun shares insights on overcoming performance and latency issues and how AI can significantly enhance engineering velocity. This episode offers an in-depth look at the intersection of AI and coding, highlighting both technological advancements and the potential for more efficient development processes.

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He shares his development of HELM—a robust framework for evaluating language models. The discussion highlights how this framework improves transparency and effectiveness in AI benchmarks. Additionally, Percy shares insights on the pivotal role of open-source models in democratizing AI development and addresses the challenges of English language bias in global AI applications. This episode offers in-depth insights into how benchmarks are shaping the future of AI, highlighting both technological advancements and the push for more equitable and inclusive technologies.

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Connect with Percy Liang:

• https://www.linkedin.com/in/percy-liang-717b8a/ 
• https://twitter.com/percyliang 

 Anticipatory Music Composer:

• https://stanford.io/3y5VycN 

Blog Post:

• https://crfm.stanford.edu/2024/02/18/helm-instruct.html

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With backgrounds in advanced AI research at DeepMind, Max and Sergei offer their unique insights into the challenges and successes of applying AI in a complex field like biotechnology. They share their journey at Isomorphic Labs, a company dedicated to revolutionizing drug discovery with AI. In this episode, they discuss the transformative impact of deep learning on the drug development process and Isomorphic Labs' strategy to innovate from molecular design to clinical trials.

You’ll come away with valuable insights into the challenges of applying AI in biotech, the role of AI in streamlining the drug discovery pipeline, and peer into the  future of AI-driven solutions in healthcare.

Connect with Sergei Yakneen & Max Jaderberg:

https://www.linkedin.com/in/maxjaderberg/ 

https://www.linkedin.com/in/yakneensergei/ 

https://twitter.com/SergeiIakhnin 

https://twitter.com/maxjaderberg 

Follow Weights & Biases:

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Rodrigo Liang’s journey from Oracle to founding SambaNova is a tale of innovation and determination. In this episode, Rodrigo discusses the importance of specialized hardware in unlocking AI's potential for Enterprise businesses and SambaNova's mission to deliver comprehensive AI solutions from chips to models. 

Explore the critical insights on navigating the challenges of introducing AI to executives and the evolution of AI applications within large enterprises, and get a glimpse into the future of AI in the business world.

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As a former leader at Amazon AI Labs and Yahoo's New York lab, Edo Liberty's extensive background in AI research and development showcases the complexities behind vector databases and their essential role in enhancing AI's capabilities.

Discover the pivotal moments and key decisions that have defined Pinecone's journey, learn about the different embedding strategies that are reshaping AI applications, and understand how Pinecone's success has had a profound impact on the technology landscape.

Connect with Edo Liberty:

https://www.linkedin.com/in/edo-liberty-4380164/ 

https://twitter.com/EdoLiberty 

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Join the Weights & Biases Discord Server:

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Dive into Salesforce AI's cutting-edge approach to customer service through AI, the importance of a trust-first strategy, and the future of AI policies and education. Learn how Salesforce empowers businesses and shapes the future with AI innovations like Prompt Builder and Copilot Studio. Whether you're an AI enthusiast, a business leader, or someone curious about the future of technology, this discussion offers valuable insights into navigating the rapidly evolving world of AI.

✅ Subscribe to Weights & Biases YouTube → https://bit.ly/45BCkYz

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Connect with Clara:

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https://x.com/clarashih?s=20  

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Journey with us down John Halamka's remarkable path from his early tech startup days to leading innovations as the President of the Mayo Clinic Platform, one of the world's most esteemed healthcare institutions. This deep dive into AI's role in modern medicine covers the technology's evolution, its potential to redefine patient care, and the visionary work of Mayo Clinic Platform in harnessing AI responsibly.

Explore the misconceptions surrounding AI in healthcare and discover the ethical and regulatory frameworks guiding its application. Glimpse into the future with Halamka's visionary perspective on AI's potential to democratize and revolutionize healthcare across the globe. Join us for an enlightening discussion on the challenges, triumphs, and the horizon of AI in healthcare through the lens of John Halamka's pioneering experiences.

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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/45BCkYz

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Connect with Chelsea Finn:

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Timestamps:

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:

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Connect with Richard Socher:

https://www.linkedin.com/in/richardsocher/ 

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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/45BCkYz

Timestamps:

0:00 - Introduction 

5:15 - Exploring Fine-Tuning vs RAG in AI

10:30 - Evaluating AI Research for Investment

15:45 - Impact of AI Models on Product Development

20:00 - AI's Role in Evolving Job Markets

25:15 - The Balance Between AI Research and Product Development

30:00 - Code Generation Technologies in Software Engineering

35:00 - AI's Broader Industry Implications

40:00 - Importance of Product-Driven Approaches in AI Startups

45:00 - AI in Various Sectors: Beyond Software Engineering

50:00 - Open Source vs Proprietary AI Models

55:00 - AI's Impact on Traditional Roles and Industries

1:00:00 - 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:

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#OCR #DeepLearning #AI #Modeling #ML

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/jerryjliu0

https://www.linkedin.com/in/jerry-liu-64390071/

Follow Weights & Biases:

YouTube: http://wandb.me/youtube

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Join the Weights & Biases Discord Server:

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#OCR #DeepLearning #AI #Modeling #ML

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 Intro
• 0:32 Joe is Back at Meta
• 3:28 What Does Meta Get Out Of Putting Out LLMs?
• 8:24 Measuring The Quality Of LLMs
• 10:55 How Do You Pick The Sizes Of Models
• 16:45 Advice On Choosing Which Model To Start With
• 24:57 The Secret Sauce In The Training
• 26:17 What Is Being Worked On Now
• 33:00 The Safety Mechanisms In Llama 2
• 37:00 The Datasets Llama 2 Is Trained On
• 38:00 On Multilingual Capabilities & Tone
• 43:30 On The Biggest Applications Of Llama 2
• 47:25 On Why The Best Teams Are Built By Users
• 54:01 The Culture Differences Of Meta vs Open Source
• 57:39 The AI Learning Alliance
• 1:01:34 Where To Learn About Machine Learning
• 1:05:10 Why AI For Science Is Under-rated
• 1:11:36 What Are The Biggest Issues With Real-World Applications

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

We discuss:

• 0:00 - Intro
• 5:59 - Impactful relationships in Chris's life
• 13:15 - Advice for finding co-founders
• 16:25 - Chris's fascination with challenging problems
• 22:30 - Tech stack for AI labs
• 30:50 - Impactful capabilities of AI models
• 36:24 - How this AI era is different
• 47:36 - Advising large enterprises on language model integration
• 51:18 - Using language models for business intelligence and automation
• 52:13 - Closing thoughts and appreciation

Thanks 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

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

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 Chintala

https://www.linkedin.com/in/soumith/

- Meta | LinkedIn

https://www.linkedin.com/company/meta/

- Meta | Website

https://about.meta.com/

- Pytorch

https://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

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

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

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

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

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

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 Networks

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

Shreya 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

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💬 *Host:* Lukas Biewald

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*Subscribe and listen to Gradient Dissent today!*

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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

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⏳ Timestamps:

0:00 Intro

1:10 Lessons learned from previous AI hype cycles

11:46 Maintaining technical knowledge as an investor

19:05 Selling into tech-forward companies vs. traditional enterprises

25:09 Building point solutions vs. end-to-end platforms

36:27 LLMS, new tooling, and commoditization

44:39 Failing fast and how startups can compete with large cloud vendors

52:31 The gap between research and industry, and vice versa

1:00:01 Advice for ML practitioners during hype bubbles

1:03:17 Sarah's thoughts on Rust and bottlenecks in deployment

1:11:23 The importance of aligning technology with people

1:15:58 Outro

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📝 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​​

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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

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⏳ Timestamps:

0:00 Intro

1:06 How Runway uses ML to improve video editing

6:04 A demo of Runway’s video editing capabilities

13:36 How Cris entered the machine learning space

18:55 Cris’ thoughts on the future of ML for creative use cases

28:46 Runway’s tech stack

32:38 Creativity, and keeping humans in the loop

36:15 The potential of audio generation and new mental models

40:01 Outro

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🎥 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 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 Intro

1:06 Diffusion and generative models

14:40 Engaging with large models meaningfully

20:30 Jeremy's thoughts on Stable Diffusion and OpenAI

26:38 Prompt engineering and large language models

32:00 Revisiting Julia vs. Python

40:22 Jeremy's science advocacy during early COVID days

1:01:03 Researching how to improve children's education

1:07:43 The importance of executive buy-in

1:11:34 Outro

1:12:02 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 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 Intro

0:28 Jerome's thought on large language models

12:53 AI applications and challenges at Meta

18:41 The story behind developing PyTorch

26:40 Jerome's experience at IBM Watson

28:53 Drug discovery, AI, and changing the game

36:10 The potential of education and AI

40:10 Meta and AR/VR interfaces

43:43 Why NVIDIA is such a powerhouse

47:08 Jerome's advice to people starting their careers

48:50 Going back to coding, the challenges of scaling

52:11 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. 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 Intro

1:02 Machine learning and technical debt

11:18 MLOps, increased stakes, and realistic expectations

19:12 Evaluating models methodically

25:32 Kaggle's role in the ML world

33:34 Kaggle competitions, datasets, and notebooks

38:49 Why Kaggle is like a rain forest

44:25 Possible future directions for Kaggle

46:50 Healthy competitions and self-growth

48:44 Kaggle's relevance in a compute-heavy future

53:49 AutoML vs. human judgment

56:06 After a model goes into production

1:00:00 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 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 Intro

00:42 How AI fits into the safety/security industry

09:33 Event matching and object detection

14:47 Running models on the right hardware

17:46 Scaling model evaluation

23:58 Monitoring and evaluation challenges

26:30 Identifying and sorting issues

30:27 Bridging vision and language domains

39:25 Challenges and promises of natural language technology

41:35 Production environment

43:15 Using synthetic data

49:59 Working with startups

53:55 Multi-task learning, meta-learning, and user experience

56:44 Optimization and testing across multiple platforms

59:36 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​

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 Intro

00:42 How AI fits into the safety/security industry

09:33 Event matching and object detection

14:47 Running models on the right hardware

17:46 Scaling model evaluation

23:58 Monitoring and evaluation challenges

26:30 Identifying and sorting issues

30:27 Bridging vision and language domains

39:25 Challenges and promises of natural language technology

41:35 Production environment

43:15 Using synthetic data

49:59 Working with startups

53:55 Multi-task learning, meta-learning, and user experience

56:44 Optimization and testing across multiple platforms

59:36 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​

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 Intro

01:00 From SEAL training to FAIR

04:17 Stress-testing Lightning

07:55 Choosing PyTorch over TensorFlow and other frameworks

13:16 Components of the Lightning platform

17:01 Launching Lightning from Facebook

19:09 Similarities between leadership and research

22:08 Lessons from the military

26:56 Scaling PyTorch Lightning to Lightning AI

33:21 Hiring the right people

35:21 The future of Lightning

39:53 Reducing algorithm complexity in self-supervised learning

42:19 A fragmented ML landscape

44:35 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​

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 Intro

00:57 Evolving from surveys to experience management

04:56 Detecting sentiment with ML

10:57 Working with large language models and rule-based systems

14:50 Zero-shot learning, NLP, and low-resource languages

20:11 Letting customers control data

25:13 Deep learning and tabular data

28:40 Hyperscalers and performance monitoring

34:54 Combining deep learning with linguistics

40:03 A sense of accomplishment

42:52 Causality and observational data in healthcare

45:09 Challenges of interdisciplinary collaboration

49:27 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​

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 Intro

00:40 The origins of Standard AI

08:30 Getting Standard into stores

18:00 Supervised learning, the advent of synthetic data, and the manifold hypothesis

24:23 What's important in a MLOps stack

27:32 The merits of AutoML

30:00 Deep learning frameworks

33:02 Python versus Rust

39:32 Raw camera data versus video

42:47 The future of autonomous checkout

48:02 Sharing the StandardSim data set

52:30 Picking the right tools

54:30 Overcoming dynamic data set challenges

57:35 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​

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 Intro

0:45 The story behind the Inception architecture

13:51 Trends and milestones in autonomous vehicles

23:52 The challenges of scalability and simulation

30:19 Why LiDar and mapping are useful

35:31 Waymo Via and autonomous trucking

37:31 Robustness and unsupervised domain adaptation

40:44 Why Waymo released the Waymo Open Dataset

49:02 The domain gap between simulation and the real world

56:40 Finding rare examples

1:04:34 The challenges of production requirements

1:08:36 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 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 Intro

0:46 How investment in AI has changed and developed

7:08 Creating the first MI landscape infographics

10:30 The impact of ML on organizations and management

17:40 Demystifying ML for executives

21:40 Why signals of successful startups change over time

27:07 ML and the emergence of new business models

37:58 New technology vs new consumer goods

39:50 What James considers when investing

44:19 Ethical considerations of accepting that ML models are fallible

50:30 Reflecting on past investment decisions

52:56 Thoughts on consciousness and Theseus' paradox

59:08 Why it's important to increase general ML literacy

1:03:09 Outro

1:03:30 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--VmlldzoyMDE4NDAy

https://wandb.ai/_scott/wandb_example/reports/Collaboration-in-ML-made-easy-with-W-B-Teams--VmlldzoxMjcwMDU5

https://twitter.com/weirddalle

Connect 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​

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 Intro

0:40 How dbt makes data transformation easier

4:52 dbt and avoiding bad data habits

14:23 Agreeing on organizational ground truths

19:04 Staying current while running a company

22:15 The origin story of dbt

26:08 Why dbt is conceptually simple but hard to execute

34:47 The dbt community and the bottom-up mindset

41:50 The future of data and operations

47:41 dbt and machine learning

49:17 Why SQL is so ubiquitous

55:20 Bridging the gap between the ML and data worlds

1:00:22 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 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 Intro

1:04 Quantum computing and ML applications

9:21 Merantix, Ventures, and ML consulting

19:09 Building a cloud-agnostic tech stack

24:40 The open source tooling ecosystem

30:28 Handing off models to customers

31:42 The impact of NLP models on the real world

35:40 Thoughts on AI and regulation

40:10 Statistical physics and optimization problems

42:50 The challenges of getting high-quality data

44:30 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 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 Intro

1:05 Robotic Process Automation (RPA)

4:20 RPA and machine learning at UiPath

8:20 Fine-tuning & PyTorch vs TensorFlow

14:50 Monitoring models in production

16:33 Task mining

22:37 Trade-offs in ML models

29:45 Transitioning from software engineering to ML

34:02 ML teams vs engineering teams

40:41 Spending more time on data

43:55 The organizational machinery behind ML models

45:57 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 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 Intro

0:50 Why NVIDIA moved into the deep learning space

7:33 Balancing the compute needs of different audiences

10:40 Quantum computing, Huang's Law, and the singularity

15:53 Democratizing scientific computing

20:59 How Jensen stays current with technology trends

25:10 The global chip shortage

27:00 Leadership lessons that Jensen has learned

32:32 Keeping a steady vision for NVIDIA

35:48 Omniverse and the next era of AI

42:00 ML topics that Jensen's excited about

45:05 Why MLOps is vital

48:38 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, 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 customizable

They 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 Intro

1:01 Solving real-world problems with GPT-3

6:57 Applying GPT-3 to translation tasks

14:58 Copywriting and other commercial GPT-3 applications

20:22 The OpenAI API and fine-tuning GPT-3

28:22 Logging GPT-3 fine-tuning projects to W&B

38:25 Engineering challenges behind OpenAI's API

43:15 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 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 Intro

0:56 Ray, Anyscale, and making a distributed framework

11:39 How Spark informed the development of Ray

18:53 The story behind Spark and Databricks

33:00 Why TensorFlow and PyTorch haven't monetized

35:35 Picking co-founders and other startup advice

46:04 The early signs of sky computing

49:24 Breaking problems down and prioritizing

53:17 Outro

---

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👉 Apple Podcasts: http://wandb.me/apple-podcasts​​

👉 Google Podcasts: http://wandb.me/google-podcasts​

👉 Spotify: http://wandb.me/spotify​

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 Intro

1:09 NVIDIA Base Command and DGX SuperPOD

10:33 The challenges of multi-node processing at scale

18:35 Why it's hard to use a supercomputer effectively

25:14 The advantages of de-abstracting hardware

29:09 Understanding Base Command's product-market fit

36:59 Data center infrastructure as a value center

42:13 Base Command's role in tech stacks

47:16 Why crowdsourcing is underrated

49:24 The challenges of scaling beyond a POC

51:39 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 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 Intro

1:09 How does See & Spray reduce herbicide usage?

9:15 Classifying weeds and crops in real time

17:45 Insights from deployment and user feedback

29:08 Why weed and crop classification is surprisingly hard

37:33 Improving and updating models in the field

40:55 Blue River's ML stack

44:55 Autonomous tractors and upcoming directions

48:05 Why data pipelines are underrated

52:10 The challenges of scaling software & hardware

54:44 Outro

55:55 Bonus: Transporters and the singularity

---

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👉 Apple Podcasts: http://wandb.me/apple-podcasts​​

👉 Google Podcasts: http://wandb.me/google-podcasts​

👉 Spotify: http://wandb.me/spotify​

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 Intro

0:54 Building a personal finance forecasting model

10:54 Applying RL to trade execution

18:55 Transparent financial models and fairness

26:20 Semantic parsing and building a text-to-SQL interface

29:20 From comparative literature and math to product

37:33 What would Newton and Descartes think about ML?

44:15 On sentient AI and transporters

47:33 Why casual inference is under-appreciated

49:25 The challenges of integrating models into the business

51:45 Outro

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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​

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

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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

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Timestamps:

0:00 Intro

0:53 How Absci merges biology and AI

11:24 Why Absci started investing in ML

19:00 Creating the GPT-3 of DNA

25:34 Investing in data collection and in ML teams

33:14 Clinical trials and Absci's revenue structure

38:17 Combining knowledge from different domains

45:22 The potential of multitask learning

50:43 Why biological data is tricky to work with

55:00 Outro

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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​

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

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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

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Timestamps:

0:00 Intro

1:29 The stories behind Weights & Biases

7:45 The W&B tech stack

9:28 Looking back at the beginning

11:42 Hallmark moments

14:49 Favorite product features

16:49 Rewriting the W&B backend

18:21 The importance of customer feedback

21:18 How Chris and Shawn have changed

22:35 How the ML space has changed

28:24 Staying positive when things look bleak

32:19 Lukas' advice to new entrepreneurs

35:29 Hopes for the next five years

38:09 Making a paintbot & model understanding

41:30 Biggest bottlenecks in deployment

44:08 Outro

44:38 Bonus: Under- vs overrated technologies

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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​

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

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Connect with Pete:

📍 Twitter: https://twitter.com/petewarden

📍 Website: https://petewarden.com/

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Timestamps:

0:00 Intro

1:23 Hacking a Raspberry Pi to run neural nets

13:50 Model and hardware architectures

18:56 Training a magic wand

21:47 Raspberry Pi vs Arduino

27:51 Reducing model size

33:29 Training on the edge

39:47 What it's like to work on TensorFlow

47:45 Improving datasets and model deployment

53:05 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​

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

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Connect with Pieter:

📍 Twitter: https://twitter.com/pabbeel

📍 Website: https://people.eecs.berkeley.edu/~pabbeel/

📍 The Robot Brains Podcast: https://www.therobotbrains.ai/

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Timestamps:

0:00 Intro

1:15 The challenges of robotics

8:10 Progress in robotics

13:34 Imitation learning and reinforcement learning

21:37 Simulated data, real data, and reliability

27:53 The increasing capabilities of robotics

36:23 Entrepreneurship and co-founding Gradescope

44:35 The story behind Covariant

47:50 Pieter's communication tips

52:13 What Pieter's currently excited about

55:08 Focusing on good UI and high reliability

57:01 Outro

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

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Connect with Chris:

- Twitter: https://twitter.com/chrisalbon

- Website: https://chrisalbon.com/

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Timestamps:

0:00 Intro

1:08 How Wikimedia approaches moderation

9:55 Working in the open and embracing humility

16:08 Going down Wikipedia rabbit holes

20:03 How Wikimedia uses machine learning

27:38 Wikimedia's ML infrastructure

42:56 How Chris got into machine learning

46:43 Machine Learning Flashcards and technical interviews

52:10 Low-power models and MLOps

55:58 Outro

Show notes (links to papers and transcript): http://wandb.me/gd-emily-m-bender

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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.

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Timestamps:

0:00 Sneak peek, intro

1:03 Stochastic Parrots

9:57 The societal impact of big language models

16:49 How language models can be harmful

26:00 The important difference between linguistic form and meaning

34:40 The octopus thought experiment

42:11 Language acquisition and the future of language models

49:47 Why benchmarks are limited

54:38 Ways of complementing benchmarks

1:01:20 The #BenderRule

1:03:50 Language diversity and linguistics

1:12:49 Outro

(Read more: http://wandb.me/gd-jeff-hammerbacher)

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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.

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Follow Gradient Dissent on Twitter: https://twitter.com/weights_biases

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0:00 Sneak peek, intro

1:13 The start of Facebook's data science team

6:53 Facebook's early tech stack

14:20 Early growth strategies at Facebook

17:37 The origin story of Cloudera

24:51 Cloudera's success, in retrospect

31:05 Jeff's transition into biomedicine

38:38 Immune checkpoint blockade in cancer therapy

48:55 Data and techniques for biomedicine

53:00 Why Jeff created Related Sciences

56:32 Outro

(Read more: http://wandb.me/gd-josh-bloom)

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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.

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Follow Gradient Dissent on Twitter: https://twitter.com/weights_biases

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0:00 Intro, sneak peek

1:15 How astronomy has informed ML

4:20 The big questions in astronomy today

10:15 On dark matter and dark energy

16:37 Finding life on other planets

19:55 Driving advancements in astronomy

27:05 Putting telescopes in space

31:05 Why Josh started using ML in his research

33:54 Crowdsourcing in astronomy

36:20 How ML has (and hasn't) informed astronomy

47:22 The next generation of cross-functional grad students

50:50 How Josh started coding

56:11 Incentives and maintaining research codebases

1:00:01 ML4Science's tech stack

1:02:11 Uncertainty quantification in a sensor-based world

1:04:28 Why it's not good to always get an answer

1:07:47 Outro