Jensen Huang is founder and CEO of NVIDIA, whose GPUs sit at the heart of the majority of machine learning models today.

Jensen shares the story behind NVIDIA's expansion from gaming to deep learning acceleration, leadership lessons that he's learned over the last few decades, and why we need a virtual world that obeys the laws of physics (aka the Omniverse) in order to take AI to the next era. Jensen and Lukas also talk about the singularity, the slow-but-steady approach to building a new market, and the importance of MLOps.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-jensen-huang

---

⏳ Timestamps:

0:00 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 Welinder is VP of Product & Partnerships at OpenAI, where he runs product and commercialization efforts of GPT-3, Codex, GitHub Copilot, and more. Boris Dayma is Machine Learning Engineer at Weights & Biases, and works on integrations and large model training.

Peter, Boris, and Lukas dive into the world of GPT-3:

- How people are applying GPT-3 to translation, copywriting, and other commercial tasks

- The performance benefits of fine-tuning GPT-3-

- Developing an API on top of GPT-3 that works out of the box, but is also flexible and 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 Stoica is co-creator of the distributed computing frameworks Spark and Ray, and co-founder and Executive Chairman of Databricks and Anyscale. He is also a Professor of computer science at UC Berkeley and Principal Investigator of RISELab, a five-year research lab that develops technology for low-latency, intelligent decisions.

Ion and Lukas chat about the challenges of making a simple (but good!) distributed framework, the similarities and differences between developing Spark and Ray, and how Spark and Ray led to the formation of Databricks and Anyscale. Ion also reflects on the early startup days, from deciding to commercialize to picking co-founders, and shares advice on building a successful company.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-ion-stoica

---

Timestamps:

0:00 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

---

Subscribe and listen to our podcast today!

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

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

👉 Spotify: http://wandb.me/spotify​

Stephan Fabel is Senior Director of Infrastructure Systems & Software at NVIDIA, where he works on Base Command, a software platform to coordinate access to NVIDIA's DGX SuperPOD infrastructure.

Lukas and Stephan talk about why having a supercomputer is one thing but using it effectively is another, why a deeper understanding of hardware on the practitioner level is becoming more advantageous, and which areas of the ML tech stack NVIDIA is looking to expand into.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-stephan-fabel

---

Timestamps:

0:00 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 Padwick is Director of Computer Vision Machine Learning at Blue River Technology, a subsidiary of John Deere. Their core product, See & Spray, is a weeding robot that identifies crops and weeds in order to spray only the weeds with herbicide.

Chris and Lukas dive into the challenges of bringing See & Spray to life, from the hard computer vision problem of classifying weeds from crops, to the engineering feat of building and updating embedded systems that can survive on a farming machine in the field. Chris also explains why user feedback is crucial, and shares some of the surprising product insights he's gained from working with farmers.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-chris-padwick

---

Connect with Chris:

📍 LinkedIn: https://www.linkedin.com/in/chris-padwick-75b5761/

📍 Blue River on Twitter: https://twitter.com/BlueRiverTech

---

Timestamps:

0:00 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

---

Subscribe and listen to our podcast today!

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

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

👉 Spotify: http://wandb.me/spotify​

Kathryn Hume is Vice President Digital Investments Technology at the Royal Bank of Canada (RBC). At the time of recording, she was Interim Head of Borealis AI, RBC's research institute for machine learning.

Kathryn and Lukas talk about ML applications in finance, from building a personal finance forecasting model to applying reinforcement learning to trade execution, and take a philosophical detour into the 17th century as they speculate on what Newton and Descartes would have thought about machine learning.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-kathryn-hume

---

Connect with Kathryn:

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

📍 Website: https://quamproxime.com/

---

Timestamps:

0:00 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

---

Subscribe and listen to our podcast today!

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

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

👉 Spotify: http://wandb.me/spotify​

Sean McClain is the founder and CEO, and Gregory Hannum is the VP of AI Research at Absci, a biotech company that's using deep learning to expedite drug discovery and development.

Lukas, Sean, and Greg talk about why Absci started investing so heavily in ML research (it all comes back to the data), what it'll take to build the GPT-3 of DNA, and where the future of pharma is headed. Sean and Greg also share some of the challenges of building cross-functional teams and combining two highly specialized fields like biology and ML.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-sean-and-greg

---

Connect with Sean and Greg:

📍 Sean's Twitter: https://twitter.com/seanrmcclain

📍 Greg's Twitter: https://twitter.com/gregory_hannum

📍 Absci's Twitter: https://twitter.com/abscibio

---

Timestamps:

0:00 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

---

Subscribe and listen to our podcast today!

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

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

👉 Spotify: http://wandb.me/spotify​

You might know him as the host of Gradient Dissent, but Lukas is also the CEO of Weights & Biases, a developer-first ML tools platform!

In this special episode, the three W&B co-founders — Chris (CVP), Shawn (CTO), and Lukas (CEO) — sit down to tell the company's origin stories, reflect on the highs and lows, and give advice to engineers looking to start their own business.

Chris reveals the W&B server architecture (tl;dr - React + GraphQL), Shawn shares his favorite product feature (it's a hidden frontend layer), and Lukas explains why it's so important to work with customers that inspire you.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-wandb-cofounders

---

Connect with us:

📍 Chris' Twitter: https://twitter.com/vanpelt

📍 Shawn's Twitter: https://twitter.com/shawnup

📍 Lukas' Twitter: https://twitter.com/l2k

📍 W&B's Twitter: https://twitter.com/weights_biases

---

Timestamps:

0:00 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

---

Subscribe and listen to our podcast today!

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

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

👉 Spotify: http://wandb.me/spotify​

Pete is the Technical Lead of the TensorFlow Micro team, which works on deep learning for mobile and embedded devices.

Lukas and Pete talk about hacking a Raspberry Pi to run AlexNet, the power and size constraints of embedded devices, and techniques to reduce model size. Pete also explains real world applications of TensorFlow Lite Micro and shares what it's been like to work on TensorFlow from the beginning.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-pete-warden

---

Connect with Pete:

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

📍 Website: https://petewarden.com/

---

Timestamps:

0:00 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​

Pieter is the Chief Scientist and Co-founder at Covariant, where his team is building universal AI for robotic manipulation. Pieter also hosts The Robot Brains Podcast, in which he explores how far humanity has come in its mission to create conscious computers, mindful machines, and rational robots.

Lukas and Pieter explore the state of affairs of robotics in 2021, the challenges of achieving consistency and reliability, and what it'll take to make robotics more ubiquitous. Pieter also shares some perspective on entrepreneurship, from how he knew it was time to commercialize Gradescope to what he looks for in co-founders to why he started Covariant.

Show notes: http://wandb.me/gd-pieter-abbeel

---

Connect with Pieter:

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

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

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

---

Timestamps:

0:00 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