Catalog of Bias | The Catalogue of Bias https://
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#AI #MachineLearning #DeepLearning #LLMs #DataScience
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Catalog of Bias in AI and Machine Learning Systems
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Building Transformers: A Crash Course in Machine Learning
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A Crash Course for Building Transformers. #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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Machine Learning for HealthTech on AWS Platform
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#MachineLearning for #HealthTech and Life Sciences Using AWS Platform! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
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Essential Books for Data Science and Programming Learning
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Best Books to Read! #BigData #Analytics #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Mathematics #Coding #100DaysofCode https://
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GPT-4 Vision: Image Processing in Large Language Models
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They've been using the image as raw input to the model since GPT-4 vision back in late 2023 – here's a decent explanation of how that kind of model works
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Deep Learning Limitations: Researcher Demands Apology from Sama
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Dear @sama,
— Gary Marcus (@GaryMarcus) 15 mars 2026
You owe me an apology. You have relentlessly, publicly and privately, attacked my integrity and wisdom since my 2022 paper “Deep Learning is a Hitting a Wall”.
But in your own way you have just come around to conceding *exactly* what I was arguing in that paper:… https://t.co/qww1bGjPbdDear @sama
, You owe me an apology. You have relentlessly, publicly and privately, attacked my integrity and wisdom since my 2022 paper “Deep Learning is a Hitting a Wall”. But in your own way you have just come around to conceding *exactly* what I was arguing in that paper: -
Scaling limitations in AI: experiments settle the debate
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integrity and intellectual honesty are vital. and experiments, at the cost of tens of billions, have settled it. scaling isn’t working, and that’s why Sam is saying what he is saying. and it confirms that my intuitions were correct i am sorry you missed all that context.
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Memory Gaps in AI: Compression, Association, and Forgetting
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Well said. Compression, association, and forgetting—these three are indeed core capability gaps. In EverMemBench, we measured the cross-group accuracy dropping from 54.5% to 19.7%, which essentially means the "association" link is completely missing. I'm also optimistic about the
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OpenAI’s Culture of Risk-Taking and Continuous Innovation at Codex
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Working at OpenAI is fun because questioning everything and taking risks is part of the culture. Within Codex, the team asks itself how we could make it an order of magnitude better every few months and then sets most things aside to go and do it across the entire stack. Some examples were the Codex App and our first deployment of Cerebras inference with WebSockets. We are now well under way on the next bet and it’s making even our best engineers nervous as it’s at the edge of what’s possible today.
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Deep Hitting Is Hitting: Research Paper Discussion
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hilariously that was literally the point of my much-hated 2022 paper “Deep Hitting Is Hitting”