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… →

As artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) become central to innovation across industries, they also bring challenges that cannot be ignored. These workloads demand powerful computing resources, efficient memory management, and well-optimized software to make the most of the hardware. For developers, migrating legacy code to GPU-based frameworks can feel like… →

Large language models have made impressive strides in understanding natural language, solving programming tasks, and tackling reasoning challenges. However, their high computational costs and dependence on large-scale datasets bring their own set of problems. Many of these datasets lack the variety and depth needed for complex reasoning, while issues like data contamination can compromise evaluation… →

Diffusion models are closely linked to imitation learning because they generate samples by gradually refining random noise into meaningful data. This process is guided by behavioral cloning, a common imitation learning approach where the model learns to copy an expert’s actions step by step. For diffusion models, the predefined process transforms noise into a final… →