Can machines learn the laws of physics? Sounds cool, but it’s not as easy as it seems! In this episode of Simply Science, we dive into the wild world of AI trying to grasp the universe's rules. From transfer learning giving machines a head start, to multi-task learning making them multitask like pros (sometimes), to reinforcement learning teaching them through trial and error—it’s a rollercoaster of breakthroughs and roadblocks. But physics is no pushover, and blending AI with nature’s laws comes with plenty of challenges.

Tune in for a fun, mind-bending journey through the intersection of AI and the ultimate rulebook: the laws of physics!

In this episode of Simply Science, we explore how data-driven evolutionary optimization is reshaping the way we solve complex problems. Unlike traditional methods relying on straightforward objective functions, this cutting-edge approach uses data from simulations, experiments, and real-world observations to evaluate solutions.

However, real-world data often comes with challenges like noise and heterogeneity, making optimization more complicated. Enter physics-informed models—AI-inspired frameworks that integrate physical knowledge to reduce computational costs and improve generalization. Coupled with knowledge-driven AI, which condenses and interprets data for greater efficiency, these advancements are driving a shift toward smarter, more interpretable optimization methods.

We discuss the exciting potential of combining knowledge- and data-driven optimization strategies to tackle some of AI’s toughest challenges. If you’re curious about the future of AI in solving real-world problems with efficiency and precision, this episode is a must-listen!

In this episode of Simply Science, we explore the fascinating parallels between the brain's memory system and artificial intelligence. The brain acts as an information filter, much like a computer clearing disk space—it deletes outdated or irrelevant data to make room for new insights. This process, known as “catastrophic forgetting” in neuroscience, involves the weakening or resetting of neural connections, allowing the brain to rewire itself and adapt. While this may seem like a flaw, it’s a feature that enhances flexibility, helps us let go of negative experiences, and improves decision-making in dynamic environments.

For artificial intelligence, forgetting is just as essential. To achieve artificial general intelligence, AI systems must learn, adapt, and selectively forget outdated information. This ability prevents overfitting, enhances algorithm generalization, and enables AI to tackle real-world problems effectively.

Join us as we dive into how selective forgetting, both in humans and machines, holds the key to adaptability, better learning, and the pursuit of artificial general intelligence. If you’ve ever wondered why forgetting is just as important as remembering, this episode is for you!