Lifeboat Foundation

Honesty is the best policy… most of the time. Social norms help humans understand when we need to tell the truth and when we shouldn’t, to spare someone’s feelings or avoid harm. But how do these norms apply to robots, which are increasingly working with humans? To understand whether humans can accept robots telling lies, scientists asked almost 500 participants to rate and justify different types of robot deception.

“I wanted to explore an understudied facet of robot ethics, to contribute to our understanding of mistrust towards emerging technologies and their developers,” said Andres Rosero, Ph.D. candidate at George Mason University and lead author of the study in Frontiers in Robotics and AI. “With the advent of generative AI, I felt it was important to begin examining possible cases in which anthropomorphic design and behavior sets could be utilized to manipulate users.”

The widespread use of artificial intelligence (AI) tools designed to process large amounts of data has increased the need for better performing memory devices. The data storage solutions that could help to meet the computational demands of AI include so-called high-bandwidth memories, technologies that can increase the memory bandwidth of computer processors, speeding up the transfer of data and reducing power consumption.

Currently, flash memories are the most prominent memory solutions capable of storing information when a device is turned off (i.e., non-volatile memories). Despite their widespread use, the speed of most existing flash memories is limited and does not best support the operation of AI.

In recent years, some engineers have thus been trying to develop ultrafast flash memories that could transfer data faster and more efficiently. Two-dimensional (2D) materials have shown promise for fabricating these better performing memory devices.

Irrespective of their personal, professional and social circumstances, different individuals can experience varying levels of life satisfaction, fulfillment and happiness. This general measure of life satisfaction, broadly referred to as “well-being,” has been the key focus of numerous psychological studies.

Better understanding the many factors contributing to well-being could help to devise personalized and targeted interventions aimed at improving people’s levels of fulfillment. While many past studies have tried to delineate these factors, few have done so leveraging the advanced machine learning models available today.

Machine learning models are designed to analyze large amounts of data, unveiling hidden patterns and making accurate predictions. Using these tools to analyze data collected in previous studies in neuroscience and psychology could help to shed light on the environmental and genetic factors influencing well-being.

Shut up and take my money.

Summary: A new study using artificial intelligence has provided novel insights into how the brain predicts future events and processes information. Researchers discovered that the brain’s spontaneous activity, even without external stimuli, plays a critical role in how we think and feel.

By analyzing local field potentials (LFPs), they uncovered how the brain remains active in anticipating possible scenarios, even in a resting state. These findings could lead to better diagnostic tools and treatments for neurological diseases.

Marfan syndrome is a genetic disorder that leads to a greater risk of aneurysms developing in a patient’s aorta. Early detection is key to survival. Researchers at Yale School of Medicine studied the use of artificial intelligence in the diagnostic process. The results could eventually lead to an easier and more accessible test.

Related: Future moon astronauts may 3D-print their supplies using lunar minerals

“With the printing of the first metal 3D shape in space, ESA Exploration teams have achieved a significant milestone in establishing in-orbit manufacturing capabilities. This accomplishment, made possible by an international and multidisciplinary team, paves the way for long-distance and long-duration missions where creating spare parts, construction components, and tools on demand will be essential,” said Daniel Neuenschwander, director of Human and Robotic Exploration at ESA, in a statement.

This groundbreaking technology continues to expand its applications on Earth, revolutionizing fields such as medicine, fashion, art, construction, food production and manufacturing. In space, as long-duration missions to the moon and potentially Mars take shape, astronauts will need a means of independently repairing or creating tools or parts for machinery or structures that would be difficult to carry onboard a spacecraft, which have limited capacity.

Physicalism implies a form of panpsychism panprotopsychism solves the combination problem and the existence ofvprotophenomonal properties which are the basis of mental properties this will lead to artificial intelligence and conscious machines.

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A team of researchers from Texas A&M University, Sandia National Lab — Livermore, and Stanford University are taking lessons from the brain to design materials for more efficient computing. The new class of materials discovered is the first of their kind – mimicking the behavior of an axon by spontaneously propagating an electrical signal as it travels along a transmission line. These findings could be critical to the future of computing and artificial intelligence.

This study was published in Nature (“Axon-like active signal transmission”).

Any electrical signal propagating in a metallic conductor loses amplitude due to the metal’s natural resistance. Modern computer processing (CPU) and graphic processing units can contain around 30 miles of fine copper wires moving electrical signals around within the chip. These losses quickly add up, requiring amplifiers to maintain the pulse integrity. These design constraints impact the performance of current interconnect-dense chips.