Lifeboat Foundation

Big discovery on the patterns of evolution and how it’ll change medicine and even potentially climate change and synthetic biology.

The experts meticulously analyzed the pangenome — a complete set of genes within a species. By deploying a machine learning technique known as Random Forest, and processing data from 2,500 complete genomes of a single bacterial species, the team embarked on a journey to unravel the mysteries of evolutionary predictability.

“The implications of this research are nothing short of revolutionary,” said Professor McInerney, the lead author of the study.

Continue reading “Paradigm shift: Evolution is not as random as we thought” »

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Students at Ferris State University in Michigan will soon be sharing the classroom with AI-powered freshman “students” who will enroll in classes alongside them, MLive reports.

And no, they won’t have humanoid robot bodies — they’ll be interacting with students via computers, microphones, and speakers.

Continue reading “University Enrolling AI-Powered ‘Students’ Who Will Turn in Assignments, Participate in Class Discussions” »

Soft robots, medical devices, and wearable devices have permeated our daily lives. KAIST (Korea Advanced Institute of Science and Technology) researchers have developed a fluid switch using ionic polymer artificial muscles that operates at ultra-low power and produces a force 34 times greater than its weight. Fluid switches control fluid flow, causing the fluid to flow in a specific direction to invoke various movements.

KAIST announced on the 4th of January that a research team under Professor IlKwon Oh from the Department of Mechanical Engineering has developed a soft fluidic switch that operates at ultra-low voltage and can be used in narrow spaces.

The results have been published in Science Advances (“Polysulfonated Covalent Organic Framework as Active Electrode Host for Mobile Cation Guests in Electrochemical Soft Actuator”).

In a significant breakthrough, Microsoft and the Pacific Northwest National Laboratory have utilised artificial intelligence and supercomputing to discover a new material that could dramatically reduce lithium use in batteries by up to 70%. This discovery, potentially revolutionising the battery industry, was achieved by narrowing down from 32 million inorganic materials to 18 candidates in just a week, a process that could have taken over 20 years traditionally.

#microsoft #ai #gravitas.

Continue reading “Gravitas | Artificial Intelligence discovers material to cut Lithium use | WION” »

Adversaries can deliberately confuse or even “poison” artificial intelligence (AI) systems to make them malfunction—and there’s no foolproof defense that their developers can employ. Computer scientists from the National Institute of Standards and Technology (NIST) and their collaborators identify these and other vulnerabilities of AI and machine learning (ML) in a new publication.

Their work, titled Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, is part of NIST’s broader effort to support the development of trustworthy AI, and it can help put NIST’s AI Risk Management Framework into practice. The publication, a collaboration among government, academia, and industry, is intended to help AI developers and users get a handle on the types of attacks they might expect along with approaches to mitigate them—with the understanding that there is no silver bullet.

“We are providing an overview of attack techniques and methodologies that consider all types of AI systems,” said NIST computer scientist Apostol Vassilev, one of the publication’s authors. “We also describe current mitigation strategies reported in the literature, but these available defenses currently lack robust assurances that they fully mitigate the risks. We are encouraging the community to come up with better defenses.”

Hummingbirds use two distinct sensory strategies to control their flight, depending on whether they’re hovering or in forward motion, according to new research by University of British Columbia (UBC) zoologists.

“When in forward fight, hummingbirds rely on what we call an ‘internal forward model’—almost an ingrained, intuitive autopilot—to gauge speed,” says Dr. Vikram B. Baliga, lead author of a new study on hummingbird locomotion published in Proceedings of the Royal Society B. “There’s just too much information coming in to rely directly on every visual cue from your surroundings.”

“But when hovering or dealing with cues that might require a change in altitude, we found they rely much more on real-time, direct visual feedback from their environment.”

The union’s agreement with AI voiceover studio Replica is being called out by some of the industry’s top voice talent.

Staff at the Anheuser-Busch InBev (AB InBev) brewery in Leuven, Belgium took Boston Dynamics’ Spot robot on a test run in 2022 to see how many mechanical issues or air leaks it could find in the sprawling facility. Less than two hours later, they were ready to offer the robot a full-time job.

In the year since, Spot has become a key part of AB InBev’s “Brewery of the Future” program, which invests in emerging technology to support the company’s ambition of achieving net-zero operations at the Leuven facility by 2028. Spot conducts 1,800 individual inspections each week across ten packaging lines that churn out over 50,000 containers of Stella Artois, Budweiser, and Corona beer every hour. In its first six months of deployment, Spot discovered nearly 150 anomalies and slashed average repair times from a few months to a mere 13 days.

Continue reading “Energy Savings & Predictive Maintenance at AB InBev’s Largest European Brewery” »

It’s a well-accepted fact in the forensics community that fingerprints of different fingers of the same person— intra-person fingerprints—are unique and, therefore, unmatchable.

A team led by Columbia Engineering undergraduate senior Gabe Guo challenged this widely held presumption. Guo, who had no prior knowledge of forensics, found a public U.S. government database of some 60,000 fingerprints and fed them in pairs into an artificial intelligence-based system known as a deep contrastive network. Sometimes the pairs belonged to the same person (but different fingers), and sometimes they belonged to different people.