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Category: robotics/AI – Page 1255

Energy, mass, velocity. These three variables make up Einstein’s iconic equation E=MC2. But how did Einstein know about these concepts in the first place? A precursor step to understanding physics is identifying relevant variables. Without the concept of energy, mass, and velocity, not even Einstein could discover relativity. But can such variables be discovered automatically? Doing so could greatly accelerate scientific discovery.

This is the question that researchers at Columbia Engineering posed to a new AI program. The program was designed to observe through a , then try to search for the minimal set of fundamental variables that fully describe the observed dynamics. The study was published on July 25 in Nature Computational Science.

The researchers began by feeding the system raw video footage of phenomena for which they already knew the answer. For example, they fed a video of a swinging double pendulum known to have exactly four “state variables”—the angle and of each of the two arms. After a few hours of analysis, the AI produced the answer: 4.7.

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A chess-playing robot fractured the finger of its 7-year-old opponent during a tournament in Moscow last week.

The incident happened after the boy hurried the artificial intelligence-powered robot, the president of the Moscow Chess Federation told the Russian state news agency Tass. “The robot broke the child’s finger — this, of course, is bad,” Sergey Lazarev said.

Video of the incident, which occurred at the Moscow Chess Open competition Tuesday, went viral on social media after a post by the local outlet Baza News.

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Researchers at Ulm University in Germany have recently developed a new framework that could help to make self-driving cars safer in urban and highly dynamic environments. This framework, presented in a paper pre-published on arXiv, is designed to identify potential threats around the vehicle in real-time.

The team’s paper builds on one of their previous studies, featured in IEEE Transactions on Intelligent Vehicles earlier this year. This previous work was aimed at providing autonomous vehicles with situation-aware environment perception capabilities, thus making them more responsive in complex and dynamic unknown environments.

“The core idea behind our work is to allocate perception resources only to areas around an automated that are relevant in its current situation (e.g., its current driving task) instead of the naive 360° perception field,” Matti Henning, one of the researchers who carried out the study, told TechXplore. “In this way, computational resources can be saved to increase the efficiency of automated vehicles.”

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This week our guest academic philosopher, Susan Schneider, who is the founding director for the Center for the Future Mind at Florida Atlantic University, as well as the author of the 2019 book, Artificial You: AI and the Future of Your Mind. In this episode we focus heavily on Susan’s thoughts, hopes, and concerns surrounding the current conversations regarding artificial intelligence. This includes, but is certainly not limited to, the philosophical and ethical questions that AI presents in general, the feasibility of mind uploading and machine consciousness, the ways we may end up outsourcing our decision making to machines, how we might merge with machines, and how these potential tech futures might impact identity and sense of self. You can learn more about Susan at schneiderwebsite.com, and find out how to get involved with her work at fau.edu/future-mind ** Host: Steven Parton — LinkedIn / Twitter Music by: Amine el Filali.

41 MINS

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Molecular computing is a promising area of study aimed at using biological molecules to create programmable devices. This idea was first introduced in the mid-1990s and has since been realized by several computer scientists and physicists worldwide.

Researchers at East China Normal University and Shanghai Jiao Tong University have recently developed molecular convolutional (CNNs) based on synthetic DNA regulatory circuits. Their approach, introduced in a paper published in Nature Machine Intelligence, overcomes some of the challenges typically encountered when creating efficient artificial neural networks based on molecular components.

“The intersection of computer science and is a fertile ground for new and exciting science, especially the design of intelligent systems is a longstanding goal for scientists,” Hao Pei, one of the researchers who carried out the study, told TechXplore. “Compared to the brain, the scale and computing power of developed DNA neural networks are severely limited, due to the size limitations. The primary objective of our work was to scale up the computing power of DNA circuits by introducing a suitable model for DNA molecular systems.”

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