In the ever-evolving landscape of technology, the challenge of dealing with legacy systems has become more pronounced. Generative AI is emerging as a transformative force, providing business technology leaders with innovative solutions for modernizing their aging information-technology systems. This article explores the dynamic intersection of artificial intelligence and legacy tech, focusing on how companies are leveraging generative AI to tackle the perennial problem of outdated code.
In a groundbreaking leap for robotics, a team at the Technical University of Munich (TUM) has propelled the field forward by integrating sensitive synthetic skin into humanoid robots. This innovative system, inspired by human skin, marries artificial skin with advanced control algorithms, giving birth to the world’s first autonomous humanoid robot adorned with full-body artificial skin.
The Intergovernmental Panel on Climate Change (IPCC) has issued a chilling report: a “code red for humanity.” It’s not just scientists ringing alarm bells anymore; this is a global emergency demanding immediate action. Dive into the IPCC’s findings and unlock the power within each of us to be part of the solution.
Our planet is simmering, but it’s not just natural processes turning up the heat. Human activity is stoking the flames of climate change, jeopardizing not only our environment but also our health. Can we turn down the dial before it’s too late? Join us as we explore five keyways, we’re “cooking the Earth” and discover AI-powered solutions for a sustainable future.
In a pioneering move that redefines the landscape of robotics, scientists at the University of Tokyo have unveiled a two-legged biohybrid robot that integrates muscle tissues with artificial materials. This innovative creation, published in the journal Matter on January 26, marks a significant advancement in the field. Shoji Takeuchi, the corresponding author, emphasizes the growing interest in biohybrid robots—a fusion of biology and mechanics—showcasing their unique potential in achieving efficient, silent movements with a soft touch.
In recent years, diffusion models have undergone a transformative evolution, extending far beyond their initial applications in AI art and synthetic image generation. While these models were initially recognized for their visually captivating outputs, they have now transcended into a realm of practical utility, making significant strides in fields such as drug design and continuous control. Traditionally, these models were trained through the maximization of likelihood estimation and aligning with training data, but a paradigm shift is occurring. This post introduces a groundbreaking approach – the direct training of diffusion models on downstream objectives through reinforcement learning (RL).