Articles for tag: AI, Artificial Intelligence, benchmarks, Ethics, Explainability, generalization, metrics, reasoning, robustness

June 3, 2025

Mathew

Measuring True AI Progress Beyond Benchmarks (Future Metrics)

Measuring True AI Progress Beyond Benchmarks (Future Metrics)

Measuring True AI Progress Beyond Benchmarks Artificial intelligence is rapidly evolving, transforming industries and redefining what’s possible. While benchmarks like ImageNet and GLUE have been instrumental in tracking AI’s advancement, relying solely on them provides an incomplete picture of true progress. This article delves into the limitations of current AI benchmarks and explores future metrics needed to comprehensively assess AI capabilities. The Problem with Current Benchmarks Traditional benchmarks often focus on narrow tasks within controlled environments. AI models excel at these tasks through intensive training on specific datasets. However, their performance often fails to generalize to real-world scenarios due to

May 16, 2025

Mathew

Explainable AI (XAI): Will We Ever Truly Understand AI Decisions? (2025+)

Explainable AI (XAI): Will We Ever Truly Understand AI Decisions? (2025+)

Explainable AI (XAI): Will We Ever Truly Understand AI Decisions? (2025+) Artificial Intelligence (AI) is rapidly transforming industries, powering everything from self-driving cars to medical diagnoses. However, as AI systems become more complex, their decision-making processes become increasingly opaque. This lack of transparency raises concerns about bias, accountability, and trust. Enter Explainable AI (XAI), a field dedicated to making AI decisions more understandable to humans. The Need for Explainable AI The ‘black box’ nature of many AI algorithms, particularly deep learning models, makes it difficult to understand why a particular decision was made. This lack of transparency can have serious