Researchers
AI companions unsafe for teens under 18, researchers say
As the popularity of artificial intelligence companions surges amongst teens, critics point to warning signs that the risks of use are not worth the potential benefits. Now, in-depth testing of three well-known platforms — Character.AI, Nomi, and Replika — has led researchers at Common Sense Media to an unequivocal conclusion: AI social companions are not…
Researchers teach LLMs to solve complex planning challenges
Imagine a coffee company trying to optimize its supply chain. The company sources beans from three suppliers, roasts them at two facilities into either dark or light coffee, and then ships the roasted coffee to three retail locations. The suppliers have different fixed capacity, and roasting costs and shipping costs vary from place to place….
NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized
Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating layers of attention mechanisms and feed-forward networks (FFNs) sequentially process tokenized input. However, with an increase in size and complexity, the computational burden required…
Researchers from FutureHouse and ScienceMachine Introduce BixBench: A Benchmark Designed to Evaluate AI Agents on Real-World Bioinformatics Task
Modern bioinformatics research is characterized by the constant emergence of complex data sources and analytical challenges. Researchers routinely confront tasks that require the synthesis of diverse datasets, the execution of iterative analyses, and the interpretation of subtle biological signals. High-throughput sequencing, multi-dimensional imaging, and other advanced data collection techniques contribute to an environment where traditional,…
Google DeepMind researchers introduce new benchmark to improve LLM factuality, reduce hallucinations
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Hallucinations, or factually inaccurate responses, continue to plague large language models (LLMs). Models falter particularly when they are given more complex tasks and when users are looking for specific and highly detailed responses. It’s a challenge…
Can AI Models Scale Knowledge Storage Efficiently? Meta Researchers Advance Memory Layer Capabilities at Scale
The field of neural network architectures has witnessed rapid advancements as researchers explore innovative ways to enhance computational efficiency while maintaining or improving model performance. Traditional dense networks rely heavily on computationally expensive matrix operations to encode and store information. This reliance poses challenges when scaling these models for real-world applications that demand extensive knowledge…