Unlike conventional sustainability audits, which require time-consuming data collection and hardware deployment, this ...

Causal Machine Learning (CML) unites ML techniques with CI in order to take advantage of both approaches’ strengths. CML ...

The difference between AI success and failure often comes down to five strategic choices that leaders make about their data ...

Demand forecasting remains one of the most complex challenges in retail management. As consumer behavior evolves rapidly, ...

Ten years can feel like an eternity in AI, given the current furious pace of change. AI’s adoption has outpaced even the PC ...

Whether it's designing microchips, advancing machine learning, or keeping the national grid stable, each branch of EEE demands a blend of technical mastery, persistence, and curiosity.

Our ability to think deliberately and to make sound decisions is our unique ability in the context of AI automation.

For a long time, the core idea in reinforcement learning (RL) was that AI agents should learn every new task from scratch, like a blank slate. This "tabula rasa" approach led to amazing achievements, ...

Without automation, AI alone risks becoming siloed, hallucinated, incomplete and expensive. Without AI, automation lacks the ...

AI is everywhere—from boardroom agendas to frontline workflows—and the pressure to “do something with AI” has never been higher. Yet many companies are moving fast without a clear strategy, pouring ...