Special discussion session: the present and future of generative AI

Weights & Biases · Advanced ·📐 ML Fundamentals ·4mo ago
This special discussion between Mr Fukatsu of THE GUILD and Mr Yamamoto of W&B explores how the rapid advancement of generative AI is reshaping business, organizations, and competitive dynamics. As capital becomes directly convertible into labor and development capacity through AI, the session examines what truly constitutes sustainable competitive advantage in an era where tools are increasingly commoditized. Drawing on examples rooted in Japanese industries and culture, the conversation highlights how non-replicable assets—such as history, domain expertise, and structural positioning—will define success in the future of generative AI. It offers a strategic perspective on how companies and individuals can position themselves for the next phase of the generative AI era. -- 本セッションでは、THE GUILDの深津氏とW&Bエンジニアの山本が生成AIの急速な進化がビジネスや組織、競争戦略をどのように変えつつあるのかを、多角的な視点から議論します。 資本がそのまま労働力や開発力へと変換される時代において、企業や個人はどのような「代替不可能な価値」や競争優位を築くべきかを掘り下げます。 また、日本特有のデータや歴史、文化と生成AIを掛け合わせることで生まれる新たな可能性にも触れ、技術そのものではなく、その先にある本質的な差別化の重要性を提示します。 生成AI時代を生き抜くための視座と中長期的な戦略を提示する特別対談です。
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1 0. What is machine learning?
0. What is machine learning?
Weights & Biases
 2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
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 3 Intro to ML: Course Overview
Intro to ML: Course Overview
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 4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
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 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
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 6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
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 7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
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 8 4. Autoencoders
4. Autoencoders
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 9 5. Sentiment Analysis
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
Weights & Biases
 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
Weights & Biases
 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
Weights & Biases
 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
Weights & Biases
 23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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)
Grading Rubric for AI Applications with Sergey Karayev (2019)
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 25 16. Video Frame Prediction using CNNs and LSTMs (2019)
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)
Weights & Biases
 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)
Weights & Biases
 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
 32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
 34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
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
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)
Organizing ML projects — W&B walkthrough (2020)
Weights & Biases
 41 Brandon Rohrer — Machine Learning in Production for Robots
Brandon Rohrer — Machine Learning in Production for Robots
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 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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 43 My experiments with Reinforcement Learning with Jariullah Safi
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
Weights & Biases
 47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
 48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
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
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
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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 53 Jack Clark — Building Trustworthy AI Systems
Jack Clark — Building Trustworthy AI Systems
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 54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Weights & Biases
 55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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
Antipatterns in open source research code with Jariullah Safi
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 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
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 58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
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 59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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 60 Deep Learning Salon by Weights & Biases
Deep Learning Salon by Weights & Biases
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