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…
Large Language Models (LLMs) and Vision Language Models (VLMs) have revolutionized the automation of mobile device control through natural language commands, offering solutions for complex user tasks. The conventional approach, “Step-wise GUI agents,” operates by querying the LLM at each GUI state for dynamic decision-making and reflection, continuously processing the user’s task, and observing the…