The "event" that Geoff Hinton concludes "X" should, rationally, update your posterior probability of X, unless you put zero weight on that evidence.
MACHINE LEARNING
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Few-Shot Domain Adaptation with Polymorphic Transformers
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Few-Shot Domain Adaptation with Polymorphic Transformers Li et al.: https://
arxiv.org/abs/2107.04805 #Transformers #GPT #ChatGPT -
Reinforcement Learning Applied to Encrypted Data Security
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Reinforcement Learning on Encrypted Data Jesu et al.: https://
arxiv.org/abs/2109.08236 #ReinforcementLearning #CyberSecurity #QLearning -
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
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Deep Transformer Q-Networks for Partially Observable Reinforcement Learning Kevin Esslinger, Robert Platt, Christopher Amato : https://
arxiv.org/abs/2206.01078 #Transformers #QNetworks #ReinforcementLearning -
GPT as Turing Machine: Programming Capabilities Explained
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GPT is becoming a Turing machine: Here are some ways to program it Ana Jojic, Zhen Wang, Nebojsa Jojic: https://
arxiv.org/abs/2303.14310 #GPT #ChatGPT #TuringMachine -
Chess AI Evolution: From Alpha-Beta to Monte-Carlo Tree Search
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Chess players are all about planning.
It used to be Alpha-Beta tree search with a fixed, handcrafted evaluation function.
Nowadays, it's Monte-Carlo Tree Search with a policy network to pick good candidate branches and a Value network that learns to evaluate nodes.
But planning -
Math Updates Required for Next Generation AI
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Seems the math needs to be updated for nextgen https://
x.com/choldgraf/stat
us/1727819994640289935
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Self-Attention Mechanism in Large Language Models Explained
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Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch https://
bit.ly/3YL821l #AI #DeepLearning #MachineLearning #DataScience -
Self-Supervised Learning for Vision and World Models
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Self-Supervised learning.
Particularly for vision and world models. -
Planning with Learned World Models in Robotics
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Many people had the idea.
Planning is a centerpiece of control engineering, robotics, and "classical" AI.
The question is how to plan with a *learned* world model.
Many people have worked on this since the late 1980s (e.g. Widrow's "truck backer-upper").
There have been many