Natural language processing (NLP) has experienced significant growth, largely due to the recent surge in the size and strength of large language models. These models, with their exceptional performance and unique characteristics, are rapidly making a significant impact in real-world applications. These considerations have spurred a great deal of research on interpretability and analysis (IA)… →

Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation. However, their ability to handle different changes in data is still a big concern, especially for use in security-critical applications. Many works evaluated the robustness of CNNs and… →

As artificial intelligence (AI) technology continues to advance and permeate various aspects of society, it poses significant challenges to existing legal frameworks. One recurrent issue is how the law should regulate entities that lack intentions. Traditional legal principles often rely on the concept of mens rea, or the mental state of the actor, to determine… →

Understanding how LLMs comprehend natural language plans, such as instructions and recipes, is crucial for their dependable use in decision-making systems. A critical aspect of plans is their temporal sequencing, which reflects the causal relationships between steps. Planning, integral to decision-making processes, has been extensively studied across domains like robotics and embodied environments. Effective utilization,… →

Large language models (LLMs) face a critical challenge in their training process: the impending scarcity of high-quality internet data. Predictions suggest that by 2026, the available pool of such data will be exhausted, forcing researchers to turn to model-generated or synthetic data for training. This shift presents both opportunities and risks. While some studies have… →

Claude 3.5 Sonnet by Anthropic AI has heralded a new era, surpassing its predecessors and contemporaries with unprecedented capabilities. This iteration of large language models (LLMs) demonstrates versatility and sophistication that exceed expectations and opens doors to applications previously deemed impractical or beyond reach. Let’s delve into ten remarkable examples that showcase Claude 3.5 Sonnet’s… →

Large Language Models (LLMs) have made significant advances in the field of Information Extraction (IE). Information extraction is a task in Natural Language Processing (NLP) that involves identifying and extracting specific pieces of information from text. LLMs have demonstrated great results in IE, especially when combined with instruction tuning. Through instruction tuning, these models are… →

Managing multiple agents in an AI system can be quite challenging. Each agent must communicate effectively, execute tasks reliably, and scale efficiently. This complex process often requires a robust framework to ensure smooth agent interaction and coordination. The available frameworks often fall short regarding ease of use, scalability, and flexibility. Many existing solutions require extensive… →

In recent years, the proliferation of generative AI technologies has led to the development of various user interfaces that harness the power of AI to enhance productivity, creativity, and user interaction. These interfaces are becoming increasingly sophisticated, providing users new ways to engage with digital tools and platforms. Here are seven emerging generative AI user… →

Large Language Models (LLMs) have gained significant prominence in the AI industry, revolutionizing various applications such as chat, programming, and search. However, the efficient serving of multiple LLMs has emerged as a critical challenge for endpoint providers. The primary issue lies in the substantial computational requirements of these models, with a single 175B LLM demanding… →