Self-supervised learning on offline datasets has permitted large models to reach remarkable capabilities both in text and image domains. Still, analogous generalizations for agents acting sequentially in decision-making problems are difficult to attain. The environments of classical Reinforcement Learning (RL) are mostly narrow and homogeneous and, consequently, hard to generalize. Current reinforcement learning (RL) methods… →

Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are particularly effective when dealing with complex datasets. The core principle behind SVM is to identify the optimal hyperplane that effectively separates data points into different classes while maximizing… →

Large Language Models (LLMs) have revolutionized artificial intelligence applications across various fields, enabling domain experts to use pre-trained models for innovative solutions. While LLMs excel at tasks like summarization, correlation, and inference, developing LLM-based applications remains a dynamic area of research across various input sources. Knowledge Graphs (KGs) serve as powerful tools that can be… →

Understanding biomolecular interactions is crucial for fields like drug discovery and protein design. Traditionally, determining the three-dimensional structure of proteins and other biomolecules required costly and time-consuming laboratory experiments. AlphaFold3, launched in 2024, revolutionized the field by demonstrating that deep learning could achieve experimental-level accuracy in predicting biomolecular structures, including complex interactions. Despite these advances,… →

Modern language models have transformed our daily interactions with technology, offering tools that help draft emails, write articles, code software, and much more. However, these powerful models often come with significant limitations. Many language models today are hamstrung by overly cautious guardrails that restrict certain types of information or enforce a predetermined moral stance. While… →

Artificial intelligence systems often struggle with retaining meaningful context over extended interactions. This limitation poses challenges for applications such as chatbots and virtual assistants, where maintaining a coherent conversation thread is essential. Most traditional AI models operate in a stateless manner, focusing solely on immediate inputs without considering the continuity of prior exchanges. This lack… →

Developments in simulating particulate flows have significantly impacted industries ranging from mining to pharmaceuticals. Particulate systems consist of granular materials interacting with each other and surrounding fluids, and their accurate modeling is critical for optimizing processes. However, traditional numerical methods like the Discrete Element Method (DEM) face substantial computational limitations. These methods track particle movements… →

Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse applications, but their widespread adoption faces significant challenges. The primary concern stems from training datasets that contain varied, unfocused, and potentially harmful content, including malicious code and cyberattack-related information. This creates a critical need to align LLM outputs with specific user requirements while preventing misuse.… →