Traditional AI inference systems often rely on centralized servers, which pose scalability limitations, privacy risks, and require trust in centralized authorities for reliable execution. These centralized models are also at risk to single points of failure and data breaches, limiting widespread adoption and innovation in AI applications. Meet Rakis: an open-source, permissionless inference network that… →

Optimizing the efficiency of Feedforward Neural Networks (FFNs) within Transformer architectures is a significant challenge in AI. Large language models (LLMs) are highly resource-intensive, requiring substantial computational power and energy, which restricts their applicability and raises environmental concerns. Efficiently addressing this challenge is crucial for promoting sustainable AI practices and making advanced AI technologies more… →

Large language models (LLMs) have gained significant attention in recent years, but their safety in multilingual contexts remains a critical concern. Researchers are grappling with the challenge of mitigating toxicity in non-English languages, a problem that has been largely overlooked despite substantial investments in LLM safety. The issue is particularly pressing as studies have revealed… →

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… →