Category: Uncategorized

Back in 2021, Morgan Neville thought using AI to recreate the late Anthony Bourdain’s voice would be an interesting Easter egg in his documentary. He ended up being a canary in Hollywood’s AI coal mine. Read more

Long known as a messenger within cells, RNA is increasingly seen as life’s molecular communication system—even between organisms widely separated by evolution. Read more

High-quality imitations of luxury products are rising, and people aren’t ashamed to buy them anymore. How do designer brands retain their appeal? Read more

The On Running Cloudboom Strike LS marathon shoes are sprayed together by robots. Read more

Large language models (LLMs) have demonstrated impressive capabilities in in-context learning (ICL), a form of supervised learning that doesn’t require parameter updates. However, researchers are now exploring whether this ability extends to reinforcement learning (RL), introducing the concept of in-context reinforcement learning (ICRL). The challenge lies in adapting the ICL approach, which relies on input-output… Read more

High latency in time-to-first-token (TTFT) is a significant challenge for retrieval-augmented generation (RAG) systems. Existing RAG systems, which concatenate and process multiple retrieved document chunks to create responses, require substantial computation, leading to delays. Repeated computation of key-value (KV) caches for retrieved documents further exacerbates this inefficiency. As a result, RAG systems struggle to meet… Read more

Scaling state-of-the-art models for real-world deployment often requires training different model sizes to adapt to various computing environments. However, training multiple versions independently is computationally expensive and leads to inefficiencies in deployment when intermediate-sized models are optimal. Current solutions like model compression and distillation have limitations, often requiring additional data and retraining, which may degrade… Read more

Large Language Models (LLMs) have gained significant attention for their versatility in various tasks, from natural language processing to complex reasoning. A promising application of these models is the development of autonomous multi-agent systems (MAS), which aim to utilize the collective intelligence of multiple LLM-based agents for collaborative problem-solving. However, LLM-based MAS faces two critical… Read more

Retrieval-augmented generation (RAG) is a method that integrates external knowledge sources into large language models (LLMs) to provide accurate and contextually relevant responses. These systems enhance the ability of LLMs to offer detailed and specific answers to user queries by utilizing up-to-date information from various domains. The field is particularly important in applications such as… Read more

Ego-centric searches are essential in many applications, from financial fraud detection to social network research, because they concentrate on a single vertex and its immediate neighbors. These queries offer insights into direct connections by analyzing interconnections around a key node. Enabling such searches without jeopardizing privacy becomes a major difficulty when graphs are dispersed over… Read more