In an age overwhelmed by data, how do we ensure that AI doesn’t just recognize patterns but also understands the deeper relationships that drive those patterns? Sree Charanreddy Pothireddi addresses this challenge in his article, Blending Data and Expert Knowledge in Causal AI: A New Paradigm for Intelligent Systems. His work

GenAI isn’t just revolutionizing software—it’s poised to reshape the physical world in ways that solve some of society’s most urgent challenges. One of the most pressing is agriculture. As the global population grows and climate conditions become more erratic, the ability to plant, tend and harvest crops with speed and

AI integration promises efficiency, but unchecked adoption risks fragility, vendor lock-in, and data exposure. To unlock true value, enterprises must prioritize resilience, open architectures, and strong governance—choosing strategic, secure, and sustainable paths over speed-driven deployment. The allure of AI in enterprises is undeniable — seamless workflows, hyper-automation, and intelligence woven across CRM,

As artificial intelligence continues to evolve, its ability to create highly realistic visual representations has grown, bringing opportunities and challenges. AI can now generate sophisticated deepfakes and synthetic media that closely mimic human faces. While these advancements push technological boundaries, they also introduce serious security risks, especially as facial recognition becomes a

While AI promises efficiency and scale, it also brings unprecedented challenges. Algorithms can mirror human biases, automate inequalities, and make decisions without context, writes Ganesh Natarajan Artificial Intelligence (AI) has evolved far beyond the initial waves of hype and the equally misplaced fears of machines taking over human jobs completely.