Microsoft has recently expanded its artificial intelligence capabilities by introducing three sophisticated models: Phi 3.5 Mini Instruct, Phi 3.5 MoE (Mixture of Experts), and Phi 3.5 Vision Instruct. These models represent significant advancements in natural language processing, multimodal AI, and high-performance computing, each designed to address specific challenges and optimize various AI-driven tasks. Let’s examine… →

Neuro-symbolic artificial intelligence (NeSy AI) is a rapidly evolving field that seeks to combine the perceptive abilities of neural networks with the logical reasoning strengths of symbolic systems. This hybrid approach is designed to address complex tasks that require both pattern recognition and deductive reasoning. NeSy systems aim to create more robust and generalizable AI… →

The capacity of platooning technology to precisely control cars, optimize traffic flow, and increase energy economy is well known. Platooning reduces aerodynamic drag, boosts fuel efficiency, and expands road capacity by enabling vehicles to move in close proximity and in unison. However, a number of issues arise when it comes to large-scale mixed platoons, which… →

Process mining is a part of data science concerned with analyzing event logs produced by information systems to learn about business processes. This paper addresses process mining techniques, which involve process discovery. All these are very important in organizations, especially in workflow optimization and enhancing efficiency and potential areas for improvement. One major problem in… →

Logs provide important insights that are frequently the earliest signs of system problems, making them an essential tool for program maintenance and failure diagnostics. These logs must be effectively parsed for automated log analysis tasks like anomaly identification, troubleshooting, and root cause investigation. The act of turning semi-structured log messages into structured templates is known… →

Deep generative models learn continuous data representations from a limited set of training samples, with global metrics like Fréchet Inception Distance (FID) often used to evaluate their performance. However, these models may perform inconsistently across different regions of the learned manifold, especially in foundation models like Stable Diffusion, where generation quality can vary based on… →