Large language models (LLMs) have found applications in diverse industries, automating tasks and enhancing decision-making. However, when applied to specialized domains like chip design, they face unique challenges. Domain-adapted models, such as NVIDIA’s ChipNeMo, often struggle with instruction alignment—the ability to follow precise human commands. This limitation reduces their effectiveness in tasks like generating accurate…
Artificial Intelligence (AI) is now an integral ingredient in automating tasks in various industries, gaining immense efficiency and better decision-making benefits. Autonomy in agents has developed the capability to work independently to achieve specific functionalities, such as controlling smart home appliances or managing data in complex systems. The idea behind these autonomy features is to…
LLMs have demonstrated impressive capabilities in answering medical questions accurately, even outperforming average human scores in some medical examinations. However, their adoption in medical documentation tasks, such as clinical note generation, faces challenges due to the risk of generating incorrect or inconsistent information. Studies reveal that 20% of patients reading clinical notes identified errors, with…
We have established notable milestones in AI understanding over the past decade, especially with rapid research booming in deep learning. However, much of the ocean remains unexplored, and the shore still seems far off for real-world applications. Every moment, researchers from different parts of the world aspire and strive to innovate AI solutions that are…
Generative language models face persistent challenges when transitioning from training to practical application. One significant difficulty lies in aligning these models to perform optimally during inference. Current methods, such as Reinforcement Learning from Human Feedback (RLHF), focus on improving win rates against a baseline model. However, they often overlook the role of inference-time decoding strategies…
AI agents have become an integral part of modern industries, automating tasks and simulating complex systems. Despite their potential, managing multiple AI agents, especially those with diverse roles, can be challenging. Developers often face issues such as inefficient communication protocols, difficulties in maintaining agent states, and limited scalability in large-scale setups. Additionally, generating synthetic data…
Graph Neural Networks GNNs have become a powerful tool for analyzing graph-structured data, with applications ranging from social networks and recommendation systems to bioinformatics and drug discovery. Despite their effectiveness, GNNs face challenges like poor generalization, interpretability issues, oversmoothing, and sensitivity to noise. Noisy or irrelevant node features can propagate through the network, negatively impacting…
Recommendation systems are essential for connecting users with relevant content, products, or services. Dense retrieval methods have been a mainstay in this field, utilizing sequence modeling to compute item and user representations. However, these methods demand substantial computational resources and storage, as they require embeddings for every item. As datasets grow, these requirements become increasingly…
In the ever-evolving landscape of artificial intelligence, the year 2025 has brought forth a treasure trove of educational resources for aspiring AI enthusiasts and professionals. AI agents, with their ability to perform complex tasks autonomously, are at the forefront of this revolution. Here, we highlight 13 free courses that delve into the intricacies of AI…
Spatial-temporal data handling involves the analysis of information gathered over time and space, often through sensors. Such data is crucial in pattern discovery and prediction. However, missing values pose a problem and make it challenging to analyze. Such gaps may often create inconsistencies with the dataset, causing harder analysis. The relationships between features, like environmental…