Artificial Intelligence is undergoing rapid evolution, especially regarding the training of massive language models (LLMs) with parameters exceeding 70 billion. These models have become indispensable for various tasks, including creative text generation, translation, and content creation. However, effectively harnessing the power of such advanced LLMs requires human input through a technique known as Reinforcement Learning… →

Large language models (LLMs) have recently become highly valuable tools in complicated reasoning tasks, language production, and human language interpretation. Since then, there has been a dramatic increase in funding for studies in this area, and both the number of models used and the amount of data used for training have grown substantially. This also… →

Vision-language models (VLMs), capable of processing both images and text, have gained immense popularity due to their versatility in solving a wide range of tasks, from information retrieval in scanned documents to code generation from screenshots. However, the development of these powerful models has been hindered by a lack of understanding regarding the critical design… →

The field of AI involves the development of systems that can do tasks requiring human intelligence. These tasks encompass a broad range, including language translation, speech recognition, and decision-making processes. Researchers in this domain are dedicated to creating advanced models and tools to process and analyze vast datasets efficiently.  A significant challenge in AI is… →

CRISPR-based genome editing technologies have revolutionized gene study and medical treatment by enabling precise DNA alterations. AI integration has enhanced these technologies’ precision, efficiency, and affordability, particularly for diseases like Sickle Cell Anemia and Thalassemia. AI models such as DeepCRISPR, CRISTA, and DeepHF optimize guide RNA (gRNA) design for CRISPR-Cas systems by considering factors like… →

Learning in simulation and applying the learned policy to the real world is a potential approach to enable generalist robots, and solve complex decision-making tasks. However, the challenge to this approach is to address simulation-to-reality (sim-to-real) gaps. Also, a huge amount of data is needed while learning to solve these tasks, and the load of… →

Reinforcement Learning (RL) has gained substantial traction over recent years, driven by its successes in complex tasks such as game playing, robotics, & autonomous systems. However, deploying RL in real-world applications necessitates addressing safety concerns, which has led to the emergence of Safe Reinforcement Learning (Safe RL). Safe RL aims to ensure that RL algorithms… →

In recent years, computer vision has made significant strides by leveraging advanced neural network architectures to tackle complex tasks such as image classification, object detection, and semantic segmentation. Transformative models like Transformers and Convolutional Neural Networks (CNNs) have become fundamental tools, driving substantial improvements in visual recognition performance. These advancements have paved the way for… →

Large language models (LLMs), particularly Generative Pre-trained Transformer (GPT) models, have demonstrated strong performance across various language tasks. However, challenges persist in their decoder architecture, Specifically in time-to-first-token (TTFT) and time-per-output token (TPOT). TTFT, reliant on extensive user context, and TPOT, for rapid subsequent token generation, have spurred research into memory-bound solutions like sparsification and… →