A major challenge in computer vision and graphics is the ability to reconstruct 3D scenes from sparse 2D images. Traditional Neural Radiance Fields (NeRFs), while effective for rendering photorealistic views from novel perspectives, are inherently limited to forward rendering tasks and cannot invert to deduce the 3D structure from 2D projections. This limitation hinders the… →
Mental illness is a critical global public health issue, with one in eight people affected and many lacking access to adequate treatment. A significant challenge in mental health professional training is the disconnect between formal education and real-world patient interactions. A study interviewed 12 experts to address this, revealing that traditional role-playing is often unrealistic… →
Robotic process automation (RPA) and browser automation (UA) are becoming more important to startups for data scraping and RPA. Nevertheless, several obstacles exist when developing, deploying, and maintaining such automation. For example, constructing and delivering automation for browsers necessitates specific infrastructure, which can be difficult to establish and keep up to date. On top of… →
Artificial intelligence (AI) has seen significant advancements through game-playing agents like AlphaGo, which achieved superhuman performance via self-play techniques. Self-play allows models to improve by training on data generated from games played against themselves, proving effective in competitive environments like Go and chess. This technique, which pits identical copies of a model against each other,… →
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… →