“Data is the genesis for modern innovation” says Swami Sivasubramanian, Vice President of AWS Data and Machine Learning at #AWS #reInvent
MACHINE LEARNING
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Deep Learning Models vs Complete Systems: The Real Challenge
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Building a Deep Learning model is only a fraction of the work. Building an entire Deep Learning System is much more complex and takes time. Closing this gap is precisely what @abacusai does.
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Train Your Own AI Model with Huggy on Hugging Face
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To train your own good boy: https://t.co/RFMysGNrnP https://t.co/L2VE6rAMSK
— clem 🤗 (@ClementDelangue) 30 novembre 2022To train your own good boy: https://
huggingface.co/spaces/ThomasS
imonini/Huggy
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Google Booth: Functional View of Generative Models Discussion
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Drop by the Google booth today at 10:30am to hear @ziwphd discuss a functional view of generative models — generating functions by learning from functions! pic.twitter.com/8cwm4rJhMv
— Google AI (@GoogleAI) 30 novembre 2022Drop by the Google booth today at 10:30am to hear @ziwphd discuss a functional view of generative models — generating functions by learning from functions!
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Max-Pooling as Spatial ReLU: A Reframing of Network Operations
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I started out with an instinctive dislike of max-pooling in ML networks, favoring avg-pooling based on image filtering concepts. I only later realized that it isn’t really doing image processing, but rather acting as a “spatial ReLU”.
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Active Metadata Management Builds Trust in Data-Driven Decisions
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Explore how "active metadata management" establishes trust in decision-making by cataloging and contextualizing data via #machinelearning and #automation 🤝 ➡️ https://t.co/ByAh5cvv8C
— IBM Data, AI & Automation (@IBMData) 30 novembre 2022
—-#IBM #dataengineering pic.twitter.com/zUo7WMyH0lExplore how "active metadata management" establishes trust in decision-making by cataloging and contextualizing data via #machinelearning and #automation https://
ibm.co/3ir34XH —-
#IBM #dataengineering -
BLOOM: 176B Parameter Open-Access Multilingual Language Model
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model Le Scao et al.: https://
arxiv.org/abs/2211.05100 #ArtificialIntelligence #DeepLearning #MachineLearning -
Text-davinci-002 derived from code-davinci-002 with code training
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News to me that text-davinci-002 derives from code-davinci-002. I imagined the code model had code training the text one lacked. It looks like they both have code training, but instruction tuning interferes with code completion use cases maybe.
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OpenAI retroactively updates training methods for GPT-3.5 models
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OpenAI update on training methods for “GPT‑3.5” models (retroactively including text‑davinci‑002): https://
beta.openai.com/docs/model-ind
ex-for-researchers
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Prescriptive Approach: The Universal Solution for All Problems
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#Opinion: Why We Should Always Prioritize the Prescriptive Approach Regardless of the Problem to Solve by Nikolaj Van Omme https://actuia.com/contribution/nikolajfunartech-com/pourquoi-il-faut-toujours-privilegier-lapproche-prescriptive-quel-que-soit-le-probleme-a-resoudre/ … #AI #artificialintelligence #machinelearning