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Meta and NVIDIA collaborated to accelerate vector search on GPUs by integrating NVIDIA cuVS into Faiss v1.10, Meta’s open source library for similarity search. This new...
Meta and Quansight have improved key libraries in the Python Ecosystem. There is plenty more to do and we invite the community to help with our...
Reinforcement learning with human feedback (RLHF) is the standard method for aligning large language models (LLMs) with human preferences — such as the preferences for nontoxic...
Meta develops infrastructure all across the globe to transport information and content for the billions of people using our services around the world. At the core...
When a large language model (LLM) is prompted with a request such as Which medications are likely to interact with St. John’s wort?, it doesn’t search...
Developing AI-powered predictive models for real-world data typically requires expertise in data science, familiarity with machine learning (ML) algorithms, and a solid understanding of the model’s...
Large language models (LLMs) go through several stages of training on mixed datasets with different distributions, stages that include pretraining, instruction tuning, and reinforcement learning from...
As climate change intensifies, our ability to predict and respond to cascading and compounding disasters grows increasingly critical. Floods, droughts, wildfires, and extreme storms are no...
At Amazon, responsible AI development includes partnering with leading universities to foster breakthrough research. Recognizing that many academic institutions lack the resources for large-scale studies, we’re...
Quantum computing (QC) is a new computational paradigm that promises significant speedup over classical computing on some problems. Quantum computations are often represented as complex circuits...