On July 9, the second day of the summit, Alex Zhavoronkov PhD, founder and CEO of Insilico Medicine, will be delivering a keynote titled “Generative AI for Drug Discovery, Longevity, and Sustainability: From Theory to Applications” from 14:20-14:40 local time, before participating in the panel discussion “Beyond human: AI, superhumans, and the quest for limitless performance & longevity” with David Sinclair, Professor of Genetics from Harvard Medical School.

Gut bacteria are known to be a key factor in many health-related concerns. However, the number and variety of them is vast, as are the ways in which they interact with the body’s chemistry and each other. For the first time, researchers from the University of Tokyo used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify.

This review covers recent advances in artificial intelligence (AI) for aquatic species identification and conservation, systematically summarizing the current applications and challenges of machine learning algorithms in image analysis, acoustic identification, and ecological threat detection. It highlights that AI models such as convolutional neural network (CNN) and recurrent neural network (RNN) have demonstrated significantly higher accuracy and scalability in species classification, habitat monitoring, and environmental threat detection compared to traditional ecological survey methods. Additionally, through bibliometric analysis, the study identifies global research trends and interdisciplinary collaboration patterns, emphasizing the importance of strengthening cross-disciplinary cooperation, establishing standardized protocols, and developing open data platforms to support sustainable aquatic ecosystem conservation.

Researchers at Tohoku University used machine learning potential to create large-scale models of tin (Sn) catalysts under different conditions. The results may help design high-performance catalysts that can take harmful carbon dioxide and convert it into green, carbon-based fuels.

New research shows that the more we interact with robots, the more human we perceive them to become.

The team carried out a series of experiments with a box-shaped robot called Cozmo. They found that playing games with this little robot to ‘break the ice’ helped bring out its human side.

The implications are significant for the future of robotics. As robots take on roles from care-giving to customer service, designing interactions that promote social engagement could make them more acceptable to humans.