Turns out the ancient Chinese knew a lot about modern neural networks
MACHINE LEARNING
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AI Agent Assist Technology Enhances Customer Service Operations
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Agent assist technology uses AI and machine learning to provide facts and make real-time suggestions that help human agents across retail, telecom and other industries conduct conversations with customers. Learn more: https://
nvda.ws/3nx7O0W -
ChromaDB Self-Querying Retriever with LLM Integration
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ChromaDB Self-Querying Retriever Last week we introduced the self-querying retriever Basic idea is to use an LLM to turn a user query into a "query" and a "filter" We now implemented to work with @trychroma
! Docs: https://
github.com/hwchase17/lang
chain/blob/master/docs/modules/indexes/retrievers/examples/chroma_self_query_retriever.ipynb
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SSL Cookbook: Practical Guide for Self-Supervised Learning Research
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Self-supervised learning is a key ingredient in recent AI breakthroughs. To lower barriers + help democratize access to this research, we compiled The SSL Cookbook: a practical guide for researchers navigating the intricacies of this research space.
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Dr. Sahin Defends Ph.D. on Learning Control Systems
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So proud of Dr. @SahinLale for his successful Ph.D. defense on "learning and control". He has derived regret guarantees for partially observed systems and deployed them on turbulent airfoil stabilization. His slides: http://
tensorlab.cms.caltech.edu/users/anima/sl
ides/sahin_defense_slides.pptx
… @caltech -
AI Interpretability: Systems Are Increasingly Understandable, Not Black Boxes
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people who say "nobody knows" how ai does what it does are, at this point, obfuscating (and/or *they* don't know). systems are still commonly called "black boxes" etc but it's increasingly clear how they work (which can vary based on things like the architecture of the model).
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RNN Cells: A PhD Research Retrospective on Recurrent Architecture
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Ah, RNN cells. I seldom share the work I did during my PhD (pre-Google) but I thought maybe people would find this work fun and amusing. https://
arxiv.org/abs/1811.09786 Throwback to the fond memories of drawing recurrent cells in papers. If you look into that paper you will see that -
JAX Omission Raises Questions About AI Training Technology Strategy
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There’s no mention of JAX. That was a little weird as they own an extremely powerful next-generation training technology in the same way they owned TensorFlow at first.
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Google’s Strategic Missteps in AI and Open Source Innovation
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Google keeps falling asleep at the wheel here. It didn’t productize MapReduce, allowing Hadoop to happen. It let PyTorch blossom and take over the DS/DL community. And now it has basically let competitors run off with the Transformers technology.
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Meta’s FAIR Outpaces Google in Open Source AI Leadership
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Meta comes out looking so good from this. Meta’s AI division FAIR has consistently outmaneuvered Google in the open source deep learning community, starting with PyTorch and now with LLaMA.