Whatโ€™s the path to AGI? A conversation with Turing Co-founder and CEO Jonathan Siddharth

Weights & Biases ยท Beginner ยท๐Ÿ›ก๏ธ AI Safety & Ethics ยท1y ago
In this episode of Gradient Dissent, Jonathan Siddharth, CEO & Co-Founder of Turing, joins host Lukas Biewald to discuss the path to AGI. ๐ŸŽ™ Listen on Apple Podcasts: http://wandb.me/apple-podcasts ๐ŸŽ™ Listen on Spotify: http://wandb.me/spotify 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 Turingโ€™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. โœ… *Subscribe to Weights & Biases* โ†’ https://bit.ly/45BCkYz โณTimestamps: 00:00 - Introduction 01:36 - Turingโ€™s Role in AGI Development 07:09 - The Evolution of Turingโ€™s Developer Cloud 10:53 - Scaling Human Intelligence for AGI 14:09 - Turingโ€™s Approach to Talent Sourcing 17:08 - Market Need for High-Quality Code Data 22:15 - Managed Services vs. SaaS Products for AI 26:59 - Impact of Coding Tokens on Model Training. 35:24 - Complexity of Real-World Coding Tasks 41:01 - Successful Enterprise Use Cases 46:11 - Proof of Concept to Production 48:10 - Measuring Productivity Gains in AI-Driven Coding 51:46 - Future Potential for Automated Coding Assistance ๐ŸŽ™ Get our podcasts on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/gd_google YouTube: http://wandb.me/youtube Connect with Jonathan Siddharth: https://www.linkedin.com/in/jonsid/ Follow Weights & Biases: https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server: https://discord.gg/CkZKRNnaf3
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 15 Weights and Biases - Developer Tools for Deep Learning
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 18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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 23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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 24 Grading Rubric for AI Applications with Sergey Karayev (2019)
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 25 16. Video Frame Prediction using CNNs and LSTMs (2019)
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 26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
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 27 17. Build and Deploy an Emotion Classifier (2019)
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 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
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 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
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 34 Building Machine Learning Teams with Josh Tobin (2019)
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 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
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 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
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 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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 40 Organizing ML projects โ€” W&B walkthrough (2020)
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 41 Brandon Rohrer โ€” Machine Learning in Production for Robots
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 42 Nicolas Koumchatzky โ€” Machine Learning in Production for Self-Driving Cars
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 43 My experiments with Reinforcement Learning with Jariullah Safi
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 Testing Machine Learning Models with Eric Schles
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 46 How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
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 48 Rachael Tatman โ€” Conversational AI and Linguistics
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 49 Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
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 51 GitHub Actions & Machine Learning Workflows with Hamel Husain
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 53 Jack Clark โ€” Building Trustworthy AI Systems
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 54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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 55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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 56 Antipatterns in open source research code with Jariullah Safi
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 59 Angela & Danielle โ€” Designing ML Models for Millions of Consumer Robots
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Chapters (13)

Introduction
1:36 Turingโ€™s Role in AGI Development
7:09 The Evolution of Turingโ€™s Developer Cloud
10:53 Scaling Human Intelligence for AGI
14:09 Turingโ€™s Approach to Talent Sourcing
17:08 Market Need for High-Quality Code Data
22:15 Managed Services vs. SaaS Products for AI
26:59 Impact of Coding Tokens on Model Training.
35:24 Complexity of Real-World Coding Tasks
41:01 Successful Enterprise Use Cases
46:11 Proof of Concept to Production
48:10 Measuring Productivity Gains in AI-Driven Coding
51:46 Future Potential for Automated Coding Assistance
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