Handling personally identifiable information (PII) in large language models (LLMs) is especially difficult for privacy. Such models are trained on enormous datasets with sensitive data, resulting in ...

With researchers aiming to unify visual generation and understanding into a single framework, multimodal artificial intelligence is evolving rapidly. Traditionally, these two domains have been treated ...

Learning useful features from large amounts of unlabeled images is important, and models like DINO and DINOv2 are designed for this. These models work well for tasks like image classification and ...

Modern software development faces a multitude of challenges that extend beyond simple code generation or bug detection. Developers must navigate complex codebases, manage legacy systems, and address ...

In the rapidly evolving field of digital communication, traditional text-to-speech (TTS) systems have often struggled to capture the full range of human emotion and nuance. Conventional systems tend ...

DeepSeek’s recent update on its DeepSeek-V3/R1 inference system is generating buzz, yet for those who value genuine transparency, the announcement leaves much to be desired. While the company ...

Large language models (LLMs) have progressed beyond basic natural language processing to tackle complex problem-solving tasks. While scaling model size, data, and compute has enabled the development ...

The evolution of robotics has long been constrained by slow and costly training methods, requiring engineers to manually teleoperate robots to collect task-specific training data. But with the launch ...

In today’s rapidly evolving technological landscape, developers and organizations often grapple with a series of practical challenges. One of the most significant hurdles is the efficient processing ...

Pre-trained LLMs require instruction tuning to align with human preferences. Still, the vast data collection and rapid model iteration often lead to oversaturation, making efficient data selection a ...

Large language models (LLMs) are limited by complex reasoning tasks that require multiple steps, domain-specific knowledge, or external tool integration. To address these challenges, researchers have ...

While LLMs have shown remarkable advancements in general-purpose applications, their development for specialized fields like medicine remains limited. The complexity of medical knowledge and the ...