From the astrology software of the 1970s to the Co-Star app, spirituality has proliferated online. Now, large language models can find overlooked ways to connect with a higher plane.

Robot arms yield powerful jets of water and sand.

Do you know how heavy container ships are decommissioned? The process takes place in South Asia with the use of fossil fuel-powered torches to tear the ships apart and cut them into smaller pieces.

This results in much pollution both from emissions and the waste that runs down to the sea from the broken ships. It is also quite dangerous for the operators who have no protective clothing and often suffer many fatalities.

Researchers from the Singapore University of Technology and Design (SUTD) have successfully applied reinforcement learning to a video game problem. The research team created a new complicated movement design software based on an approach that has proven effective in board games like Chess and Go. In a single testing, the movements from the new approach appeared to be superior to those of top human players.

These findings could possibly impact robotics and automation, ushering in a new era of movement design. The team’s article in Advanced Intelligence Systems is titled “A Phase-Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street Fighter Players.”

“Our findings demonstrate that reinforcement learning can do more than just master simple board games. The program excelled in creating more complex movements when trained to address long-standing challenges in movement science,” said principal investigator Desmond Loke, Associate Professor, SUTD.