Learning, Fast and Slow: 10 Years Plan - Memory Soup, Hier. Planning, Emotions, Knowledge Sharing
35How can agents learn and adapt fast in changing environments?
Neural network weights alone are too slow, we need memory.
How can memory be encoded and retrieved, how can goals be decided and abstracted? What role will emotions play? How does memory soup, where all levels of abstraction go into one common soup, fit in?
This is a follow-up to my ICDL 2023 paper "Learning, Fast and Slow", which was awarded Best Paper Finalist.
There are more questions than answers, but solving them will ultimately lead us closer to the end goal of more adaptive systems, which could have numerous use-cases we have yet to even think of yet.
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Slides: https://github.com/tanchongmin/TensorFlow-Implementations/blob/main/Paper_Reviews/Learning%2C%20Fast%20and%20Slow%20(10%20years%20plan).pdf
Base Learning, Fast and Slow Model (15 min recap): https://www.youtube.com/watch?v=DSVFA7nmwHQ
Code: https://github.com/tanchongmin/learning-fast-and-slow
Paper: https://arxiv.org/abs/2301.13758
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0:00 Introduction
1:10 Base Model Recap
42:24 10 Year Plan Overview
50:43 Goal-Directed Decision Transformer
52:10 Hierarchical Prediction (Yann LeCun)
54:48 Hierarchical Prediction and Multiple Abstraction Spaces (Fast and Slow)
1:15:42 Curriculum Learning to add to Self-Learning
1:18:33 How to Define Goals
1:25:40 Building Abstracted Goals by Proximity
1:34:20 Different Embedding Spaces in Same Memory Soup
1:37:13 Memory Soup
1:41:56 How to Learn Different Abstraction Spaces by Self-Supervised Learning
1:52:10 Emotion-Guided Memory Retrieval
2:02:02 Knowledge Sharing Between Agents
2:14:50 Multimodal Memory Search
2:21:53 Can everything be modelled as a graph traversal problem?
2:28:13 Fast and Slow 10 Year Plan Overall Recap and Conclusion
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AI and ML enthusiast. Likes to think about the essences behind breakthroughs of AI and explain it in a simple and relatable way. Also, I am an avid game creator.
Discord: https://discord.gg/bzp87AHJy5
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