The Future of Content Creation and AI: Insights from Cristóbal Valenzuela"

Weights & Biases · Beginner ·🎨 Image & Video AI ·3y ago
Cristóbal Valenzuela is co-founder and CEO of Runway ML, a startup that's building the future of AI-powered content creation tools. Runway's research areas include diffusion systems for image generation. Cris gives a demo of Runway's video editing platform. Then, he shares how his interest in combining technology with creativity led to Runway, and where he thinks the world of computation and content might be headed to next. Cris and Lukas also discuss Runway's tech stack and research. Show notes (transcript and links): http://wandb.me/gd-cristobal-valenzuela --- ⏳ Timestamps: 0:00 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 --- 🎥 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:
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1 0. What is machine learning?
0. What is machine learning?
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 2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
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Weights & Biases at OpenAI
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 8 4. Autoencoders
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5. Sentiment Analysis
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 10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
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 11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
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 12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
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 13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
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 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
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 15 Weights and Biases - Developer Tools for Deep Learning
Weights and Biases - Developer Tools for Deep Learning
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 16 Introducing Weights & Biases
Introducing Weights & Biases
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 17 10. Seq2Seq Models
10. Seq2Seq Models
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 18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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 19 12. One-shot learning for teaching neural networks to classify objects never seen before
12. One-shot learning for teaching neural networks to classify objects never seen before
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 20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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 21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
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 22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
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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)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
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 27 17. Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
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 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
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)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
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)
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 — W&B walkthrough (2020)
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 41 Brandon Rohrer — Machine Learning in Production for Robots
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 42 Nicolas Koumchatzky — 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
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
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 46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
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 48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
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 51 GitHub Actions & Machine Learning Workflows with Hamel Husain
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 52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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 53 Jack Clark — 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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 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
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Chapters (9)

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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