Synthetic data are computer-generated examples that can augment or replace real data to speed up the training of #AI. Here’s how @IBMResearch is using this “fake data”
MACHINE LEARNING
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YOLOv6 Underwater Trash Detection Training Experiment
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Hundreds of aquatic species are being negatively impacted by the increased under water trash deposits. This is a real problem. We recently ran an underwater trash detection training experiment using YOLOv6.
— Satya Mallick (@LearnOpenCV) 16 novembre 2022
Head over to our https://t.co/tSVje68CSR for the full read.#yolov6 #ai pic.twitter.com/vS6Ug105WJHundreds of aquatic species are being negatively impacted by the increased under water trash deposits. This is a real problem. We recently ran an underwater trash detection training experiment using YOLOv6.
Head over to our https://
learnopencv.com/yolov6-custom-
dataset-training/
… for the full read. #yolov6 #ai -
Hugging Face Releases 1.3T Parameter Mixture-of-Experts Model
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We’ve heard mixture-of-experts (MoE) were in the air (GPT4??) so we’ve just added the first one in the transformers library for you to play with 🙂 With for nothing less than a 1.3 trillion parameters checkpoint model on the hub! The largest model on the hub at the moment
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Hugging Face Hub and Gradio Democratizing Machine Learning Ecosystem
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Will be live in an hour to talk about @huggingface hub, Gradio and how the entire ecosystem comes together to democratise ML.
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Technical risks of prolonged machine learning model training
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Nice ! Mais attention a l’incompréhension que tu suggère , c’est pas parce que on entraîne plus longtemps que c’est forcément mieux. Ton gradient peut diverger et aller dans les cactus . Ou juste stagner et là tu as juste gaspiller de l’électricité et un GPU.
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Self-examination in AI science models for error detection
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what would be cool with science models like Galactica would be to add self examination of the inputs like the coming Codex model, where the model can inspect it’s theorem proofs or essays and spot errors
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Galactica: 120B Scientific Language Model for Research
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"Galactica" is a large scientific language model with 120B parameters, that can be used for a lot of tasks including
⦿ Predicting citations
⦿ Generating literature review
⦿ Generating molecules ⦿ Generating Jupiter notebooks etc., -
Galactica LLM Performance with XGBoost Model Implementation
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Amazed by the performance of Galactica by @paperswithcode
— SRK (@sudalairajkumar) 16 novembre 2022
Tried with prompt "jupyter notebook on how to use xgboost model" @tunguz might like it 🙂 #LLM #NLP #AI pic.twitter.com/7ohrREfiltAmazed by the performance of Galactica by @paperswithcode Tried with prompt "jupyter notebook on how to use xgboost model" @tunguz might like it 🙂 #LLM #NLP #AI
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Large Language Models Are Not Zero-Shot Communicators
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Large language models are not zero-shot communicators Ruis et al.: https://
arxiv.org/abs/2210.14986 #Artificialintelligence #DeepLearning #MachineLearning -
Iterative Denoising: Simple Code, Core Idea
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like! tiny idea tiny code, strips away the formalism except the high level idea (iterative denoising on a schedule)