The field of artificial intelligence is evolving rapidly, with increasing efforts to develop more capable and efficient language models. However, scaling these models comes with challenges, particularly regarding computational resources and the complexity of training. The research community is still exploring best practices for scaling extremely large models, whether they use a dense or Mixture-of-Experts… →

In the evolving landscape of artificial intelligence, integrating vision and language capabilities remains a complex challenge. Traditional models often struggle with tasks requiring a nuanced understanding of both visual and textual data, leading to limitations in applications such as image analysis, video comprehension, and interactive tool use. These challenges underscore the need for more sophisticated… →

With the current conversation about widespread LLMs in AI, it is crucial to understand some of the basics involved. Despite their general-purpose pretraining in developing LLMs, most require fine-tuning to excel in specific tasks, domains, or applications. Fine-tuning tailors a model’s performance, making it efficient and precise for specialized use cases. Today, let’s examine the… →

Multimodal large language models (MLLMs) have emerged as a promising approach towards artificial general intelligence, integrating diverse sensing signals into a unified framework. However, MLLMs face substantial challenges in fundamental vision-related tasks, significantly underperforming compared to human capabilities. Critical limitations persist in object recognition, localization, and motion recall, presenting obstacles to comprehensive visual understanding. Despite… →

GUI agents seek to perform real tasks in digital environments by understanding and interacting with graphical interfaces such as buttons and text boxes. The biggest open challenges lie in enabling agents to process complex, evolving interfaces, plan effective actions, and execute precision tasks that include finding clickable areas or filling text boxes. These agents also… →

Retrieval-Augmented Generation (RAG) is a key technique in enterprise applications that combines large foundation models with external retrieval systems to generate responses that are both accurate and grounded in factual information. Unlike traditional foundation models, which are trained on massive datasets and remain static post-deployment, RAG enhances reliability by incorporating real-time or domain-specific information during… →