New embedding models and API updates
We are launching a new generation of embedding models, new GPT-4 Turbo and moderation models, new API usage management tools, and soon, lower pricing on GPT-3.5 Turbo.
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This AI Paper Introduces XAI-AGE: A Groundbreaking Deep Neural Network for Biological Age Prediction and Insight into Epigenetic Mechanisms
Aging involves the gradual accumulation of damage and is an important risk factor for chronic diseases. Epigenetic mechanisms, particularly DNA methylation, play a role in aging, though the specific biological processes remain unclear. Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes must be better understood….
This AI Paper from Meta AI and MIT Introduces In-Context Risk Minimization (ICRM): A Machine Learning Framework to Address Domain Generalization as Next-Token Prediction.
Artificial intelligence is advancing rapidly, but researchers are facing a significant challenge. AI systems struggle to adapt to diverse environments outside their training data, which is critical in areas like self-driving cars, where failures can have catastrophic consequences. Despite efforts by researchers to tackle this problem with algorithms for domain generalization, no algorithm has yet…
Google AI Research Proposes SpatialVLM: A Data Synthesis and Pre-Training Mechanism to Enhance Vision-Language Model VLM Spatial Reasoning Capabilities
Vision-language models (VLMs) are increasingly prevalent, offering substantial advancements in AI-driven tasks. However, one of the most significant limitations of these advanced models, including prominent ones like GPT-4V, is their constrained spatial reasoning capabilities. Spatial reasoning involves understanding objects’ positions in three-dimensional space and their spatial relationships with one another. This limitation is particularly pronounced…
This AI Research Introduces Fast and Expressive LLM Inference with RadixAttention and SGLang
Advanced prompting mechanisms, control flow, contact with external environments, many chained generation calls, and complex activities are expanding the utilization of Large Language Models (LLMs). On the other hand, effective methods for developing and running such programs are severely lacking. LMSYS ORG presents SGLang, a Structured Generation Language for LLMs that collaborates on the architecture…
AIWaves Introduces Weaver: A Family of LLMs Specialized for Writing Endeavors
Large language models (LLMs) have become a prominent force in the rapidly evolving landscape of artificial intelligence. These models, built primarily on Transformer architectures, have expanded AI’s capabilities in understanding and generating human language, leading to diverse applications. Yet, a notable challenge in this realm is enhancing LLMs for creative writing. While proficient in various…
This Paper Reveals The Surprising Influence of Irrelevant Data on Retrieval-Augmented Generation RAG Systems’ Accuracy and Future Directions in AI Information Retrieval
In advanced machine learning, Retrieval-Augmented Generation (RAG) systems have revolutionized how we approach large language models (LLMs). These systems extend the capabilities of LLMs by integrating an Information Retrieval (IR) phase, which allows them to access external data. This integration is crucial, as it enables the RAG systems to overcome the limitations faced by standard…
