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Silicon Valley Doesn't Seem to Be Buying Sam Altman's NDA Pleas

Some tech workers aren't convinced Sam Altman didn't know OpenAI had non-disparagement clauses in its exit agreements, following Vox reporting. ....

Jacob Hilton , Soumith Chintala , Neel Nanda , Sam Altman , Naveen Rao , Nils Reimers , Google Deepmind , Business Insider ,

Beyond chatbots: The wide world of embeddings

Embedding models are set to revolutionize AI in 2024, providing advanced data representation and retrieval capabilities for cutting-edge enterprise applications and large language model enhancements. ....

Nils Reimers , Jerry Liu , Andre Zayarni , University Of Illinois At Urbana Champaign , Tsinghua University , Embeddings Lead ,

Vector databases (Part 4): Analyzing the trade-offs

A deeper dive into some of the trade-offs involved when choosing a vector DB ....

Jo Kristian Bergum , Colin Harman , Erika Cardenas , Milvus Zilliz , Nils Reimers , Nirant Kasliwal , Product Quantization , Sparse Lexical And Expansion , Elasticsearch Learned Sparse Encoder , Rank Fusion , Data Engineering ,

Building LLM applications for production

A question that I’ve been asked a lot recently is how large language models (LLMs) will change machine learning workflows. After working with several compani. ....

United States , Wyatt Cheng , Niklas Muennighoff , Nils Reimers , Lilian Weng , Matt Ross , Michelle Huang , How Many Data Points , Stanford Alpaca , Alpaca Github , Prompt Engineering , Google Search , Google Assistant , Hub Copilot , Designing Machine Learning , Google Drive , Duo Security ,

Contrastive Representation Learning

The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.
Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved. ....

Prannay Khosla , Wang Isola , Salakhutdinov Hinton , Jason Wei , Ekind Cubuk , Lajanugen Logeswaran , Phillip Isola , Dmitry Kalenichenko , Tianyu Gao , Yazhe Li Oriol Vinyals , Joshua Robinson , Logeswaran Lee , Ching Yao Chuang , Monte Carlo , Geoffrey Hinton , Mathilde Caron , Florian Schroff , Iryna Gurevych , Geoff Hinton , Cutmix Yun , Nicolas Papernot , Aapo Hyv , Bohan Li , Dinghan Shen , Sumit Chopra , Wei Zou ,