Miles Brundage — Societal Impacts of Artificial Intelligence
Miles Brundage researches the societal impacts of artificial intelligence and how to make sure they go well. In 2018, he joined OpenAI, as a Research Scientist on the Policy team. Previously, he was a Research Fellow at the University of Oxford's Future of Humanity Institute and served as a member of Axon's AI and Policing Technology Ethics Board.
Keep up with Miles on his website: https://www.milesbrundage.com/
and on Twitter: https://twitter.com/miles_brundage
Topics Covered:
0:00 Sneak peek and intro
1:02 Miles' path into AI policy
4:20 Raising the stakes
9:25 what would you like to see i…
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Chapters (14)
Sneak peek and intro
1:02
Miles' path into AI policy
4:20
Raising the stakes
9:25
what would you like to see in AI?
13:13
ML vs other sectors of tech?
19:00
Mechanism for supporting verifiable claims
26:39
Weights and Biases ad
28:20
effects of using more and more compute
32:00
Reflections on GT-2 Release
41:47
combining policy and building
43:51
where do people disagree in ethics?
51:30
government regulations
58:04
what's the hardest piece of machine learning?
1:00:15
what does tracking research progress look like?
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0. What is machine learning?
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1. Build Your First Machine Learning Model
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Intro to ML: Course Overview
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2. Multi-Layer Perceptrons
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3. Convolutional Neural Networks
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Weights & Biases at OpenAI
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Why Experiment Tracking is Crucial to OpenAI
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4. Autoencoders
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5. Sentiment Analysis
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6. Recurrent Neural Networks [RNNs]
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7. Text Generation using LSTMs and GRUs
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8. Text Classification Using Convolutional Neural Networks
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9. Hybrid LSTMs [Long Short-Term Memory]
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Toyota Research Institute on Experiment Tracking with Weights & Biases
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Weights and Biases - Developer Tools for Deep Learning
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Introducing Weights & Biases
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10. Seq2Seq Models
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11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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12. One-shot learning for teaching neural networks to classify objects never seen before
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13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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14. Data Augmentation | Keras
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15. Batch Size and Learning Rate in CNNs
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Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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Grading Rubric for AI Applications with Sergey Karayev (2019)
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16. Video Frame Prediction using CNNs and LSTMs (2019)
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Image to LaTeX - Applied Deep Learning Fellowship (2019)
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17. Build and Deploy an Emotion Classifier (2019)
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Applied Deep Learning - Data Management with Josh Tobin (2019)
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Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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Troubleshooting and Iterating ML Models with Lee Redden (2019)
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Designing a Machine Learning Project with Neal Khosla (2019)
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Lukas Beiwald on ML Tools and Experiment Management (2019)
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Building Machine Learning Teams with Josh Tobin (2019)
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Pieter Abeel on Potential Deep Learning Research Directions (2019)
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Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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Five Lessons for Team-Oriented Research with Peter Welder (2019)
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Applied Deep Learning - Rosanne Liu on AI Research (2019)
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Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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Organizing ML projects — W&B walkthrough (2020)
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Brandon Rohrer — Machine Learning in Production for Robots
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Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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My experiments with Reinforcement Learning with Jariullah Safi
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Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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VDLS Lavanya Product Walkthrough
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Testing Machine Learning Models with Eric Schles
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How Linear Algebra is not like Algebra with Charles Frye
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Predicting Protein Structures using Deep Learning with Jonathan King
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Rachael Tatman — Conversational AI and Linguistics
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Reformer by Han Lee
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Sequence Models with Pujaa Rajan
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GitHub Actions & Machine Learning Workflows with Hamel Husain
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Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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Jack Clark — Building Trustworthy AI Systems
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Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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Antipatterns in open source research code with Jariullah Safi
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Attention for time series forecasting & COVID predictions - Isaac Godfried
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Made with ML - Goku Mohandas
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Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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DeepCamp AI