A Mathematical Theory of Communication By C. E. SHANNON
MACHINE LEARNING
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Physics Laws as Master Algorithm: Intelligence Emergence
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The laws of physics are the master algorithm: run them long enough and intelligence emerges.
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Inductive Bias, Complete Data, and Causal Theories Recap
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To recap:
– We always need prior assumptions/inductive biases, unless we have complete data (no free lunch theorem).
– If we have complete data, we don't need anything else for any rung.
– Causal theories are a type of inductive bias.
I guess that's a No answer to your question. -
Incomplete Models: Action’s Role in Learning and Decision-Making
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Those models are incomplete, because they don't include your actions. Convergence aside, and tautologically, if you know everything there's nothing left to learn. You can then make optimal decisions without causal assumptions.
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Priors and inductive biases are equivalent concepts
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Yes. (Again, priors and inductive biases are really the same thing.)
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MrsFormer: Multiresolution Attention Cuts Transformer Costs
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‘MrsFormer’ Employs a Nove Multiresolution-Head Attention Mechanism to Cut Transformers’ Compute and Memory Costs https://
syncedreview.com/2022/11/14/mrs
former-employs-a-nove-multiresolution-head-attention-mechanism-to-cut-transformers-compute-and-memory-costs/
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YOLOv7 Multi-Person vs MediaPipe Single-Person Pose Estimation
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How does the multi-person pose estimation of YOLOv7 stack up against the single-person pose estimation of Mediapipe?
— Satya Mallick (@LearnOpenCV) 14 novembre 2022
Check out the video to learn more: https://t.co/VjrVCBDDoB#yolov7 #mediapipe #poseestimation #ai pic.twitter.com/NnKahYtwfCHow does the multi-person pose estimation of YOLOv7 stack up against the single-person pose estimation of Mediapipe? Check out the video to learn more: https://
youtube.com/watch?v=hCJIU0
pOl5g
… #yolov7 #mediapipe #poseestimation #ai -
AI and Supercomputing Convergence Reshapes Industry Landscape
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The world of artificial intelligence drifts ever more into the domain of supercomputing, and both fields will be shaped by the interaction. // @cerebras // #artificialintelligence #deeplearning #machinelearning // $NVDA
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Free Webinar on Deep Learning Techniques and Model Operationalization
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A review of the state-of-the-art deep learning techniques. Nov 21.
You can attend for FREE! Here is what you'll learn: • Experiment and iterate on models
• Evaluate results
• Operationalize them https://
eventbrite.com/e/ai-assisted-
data-science-tony-stark-and-jarvis-tickets-458646302437
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CRINGE Loss: Learning What Language Not to Model
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The CRINGE Loss: Learning what language not to model Adolphs et al.: https://
arxiv.org/abs/2211.05826 #ArtificialIntelligence #DeepLearning #MachineLearning