AI Video Generation has become increasingly popular in many industries due to its efficacy, cost-effectiveness, and ease of use. However, most state-of-the-art video generators rely on bidirectional models that consider both forward and backward temporal information to create each video part. This approach yields high-quality videos but presents a heavy computational load and is not… →

The advancement of AI model capabilities raises significant concerns about potential misuse and security risks. As artificial intelligence systems become more sophisticated and support diverse input modalities, the need for robust safeguards has become paramount. Researchers have identified critical threats, including the potential for cybercrime, biological weapon development, and the spread of harmful misinformation. Multiple… →

Large language models (LLMs) trained on vast datasets of human language simulate logical and problem-solving abilities by following structured approaches. However, existing methods predominantly operate within a language space, where textual chains explicitly express reasoning processes. While effective for clarity, this reliance on language introduces inefficiencies, as natural language is inherently optimized for communication rather… →

Designing accurate all-atom protein structures is a critical challenge in bioengineering, as it involves generating both 3D structural data and 1D sequence information to define side-chain atom placements. Most approaches currently rely heavily on resolved experimentally determined structural datasets, which are scarce and biased, thereby limiting exploration of the natural protein space. Moreover, these approaches… →

Artificial intelligence systems are becoming integral to various aspects of society, yet understanding their real-world impact presents significant challenges. While user data offers valuable insights into how these systems are used, ethical and privacy concerns hinder its analysis. Manual examination of raw conversations raises risks of privacy breaches and exposure to sensitive content. Moreover, the… →

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. These layers include an input, multiple hidden, and output layers. Each neuron processes input data by applying weights, biases, and an activation function to generate an output. The “deep” aspect of DNNs comes from… →

The growing reliance on video data in machine learning applications has exposed several challenges in video decoding. Extracting meaningful frames or sequences efficiently and in formats suitable for model training often requires complex workflows. Traditional pipelines can be slow, resource-intensive, and cumbersome to integrate into machine learning frameworks. Furthermore, the lack of streamlined APIs complicates… →