Google & UC Berkeley’s ‘Self-Debugging’ Framework Teaches LLMs to Debug Their Own Code https://
syncedreview.com/2023/04/17/goo
gle-uc-berkeleys-self-debugging-framework-teaches-llms-to-debug-their-own-code/
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MACHINE LEARNING
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Google and UC Berkeley’s Self-Debugging Framework for LLMs
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SAS Innovate 2023 Event Registration for Data Analytics and AI
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At #SASInnovate 2023 (a complimentary event), you’ll have opportunities to learn, be inspired by, and guide the future of Data #Analytics and #AI. Register now and build your Agenda here: https://
sas.com/gms/redirect.j
sp?detail=PLN2755_1799220624
… by @SASsoftware ———
#DataScience #MachineLearning #ML #SASVisionary -
MLOps Environment Requirements and Abacus AI Solutions
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Building #MachineLearning Systems is hard. Here are 30 requirements for an #MLOps environment. @abacusai handles all that for you. You bring the data and the use case. They deliver the #ML environment: https://
abacus.ai/mlops
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#BigData #DataScience #AI #DataScientists #ML -
Emergence and Reasoning in Large Language Models Talk
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Emergence and reasoning in large language models – A talk This is a great talk by @_jasonwei on the emergent abilities of large language models(LLMs), chain-of-thought prompting, and other related topics. Video(YT): https://
youtube.com/watch?v=0Z1ZwY
2K2-M
… Slides: https://
self-supervised.cs.jhu.edu/fa2022/files/j
asonwei-CoT-emergence-talk-JHU.pdf
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DINOv2 Open-Source Release with Interactive Demo Available
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Want to explore DINOv2 for yourself? Today we're releasing both the open-source code and an interactive demo. GitHub https://
bit.ly/40eMPxk
Demo https://
bit.ly/3KHBnVa -
Meta Releases DINOv2: Self-Supervised Computer Vision Model
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Announced by Mark Zuckerberg this morning — today we're releasing DINOv2, the first method for training computer vision models that uses self-supervised learning to achieve results matching or exceeding industry standards.
— AI at Meta (@AIatMeta) 17 avril 2023
More on this new work ➡️ https://t.co/h5exzLJsFt pic.twitter.com/2pdxdTyxC4Announced by Mark Zuckerberg this morning — today we're releasing DINOv2, the first method for training computer vision models that uses self-supervised learning to achieve results matching or exceeding industry standards. More on this new work https://
bit.ly/3GQnIKf -
DINOv2 Complements Computer Vision Work Beyond Segmentation
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DINOv2 complements our other computer vision work, such as Segment Anything. While SAM is a promptable system focused on zero-shot generalization to diverse segmentation tasks, DINOv2 uses simple linear classifiers to achieve strong results across tasks beyond segmentation.
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AI Models Map Forests Tree-by-Tree Across Continents
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Models like this will be useful in a wide variety of applications. For example, we recently collaborated with @RestoreForward to use AI to map forests, tree-by-tree, across areas the size of continents. pic.twitter.com/T2we4cqTa4
— AI at Meta (@AIatMeta) 17 avril 2023Models like this will be useful in a wide variety of applications. For example, we recently collaborated with @RestoreForward to use AI to map forests, tree-by-tree, across areas the size of continents.
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Groq Celebrates Entanglement AI CEO for Quantum Computing Innovation
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Happy to see our customer @entanglement_ai CEO Jason Turner getting some well deserved attention for the space they are innovating in. #QuantumComputing #AI #machinelearning #inference #cybersecurity https://
reuters.com/article/quantu
m-software/waiting-for-quantum-computers-to-arrive-software-engineers-get-creative-idUSL1N36I0KA
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Graph Neural Networks: Essential Resource Guide
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If you're interested in graph neural networks, I highly recommend checking it out. I'm confident that you'll find it to be an invaluable resource as you explore this exciting and rapidly growing field. Buy it now at