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Category: robotics/AI – Page 25

A small team of AI engineers at Zoom Communications has developed a new approach to training AI systems that uses far fewer resources than the standard approach now in use. The team has published their results on the arXiv preprint server.

The new approach developed at Zoom is called Chain of Draft (CoD), an update of the traditional approach now in use called Chain of Thought (CoT). CoT uses a step-by-step approach to solving a problem, similar in many ways to human problem-solving. The research team noted that CoT tends to generate more steps than are needed to solve a problem and found a way to reduce them.

Humans do not usually think about every step involved in solving a problem, especially if they are writing them down, because some steps are seen as basic knowledge. Instead, they jump over or combine some of them. The result is a list of essential steps.

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Artificial Intelligence (AI) has made significant strides in recent years, transforming various aspects of our lives. From self-driving cars to personalized recommendations on streaming platforms, AI has become an integral part of our daily existence. However, the fear that AI will replace humans entirely is unfounded. Instead, a more nuanced perspective emerges: AI will augment human capabilities, leading to the emergence of “AI-powered humans.”

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Artificial intelligence in various forms has been used in medicine for decades — but not like this. Experts predict that the adoption of large language models will reshape medicine. Some compare the potential impact with the decoding of the human genome, even the rise of the internet. The impact is expected to show up in doctor-patient interactions, physicians’ paperwork load, hospital and physician practice administration, medical research, and medical education.

Most of these effects are likely to be positive, increasing efficiency, reducing mistakes, easing the nationwide crunch in primary care, bringing data to bear more fully on decision-making, reducing administrative burdens, and creating space for longer, deeper person-to-person interactions.

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Interstellar objects are among the last unexplored classes of solar system objects, holding tantalizing information about primitive materials from exoplanetary star systems. They pass through our solar system only once in their lifetime at speeds of tens of kilometers per second, making them elusive.

Hiroyasu Tsukamoto, a faculty member in the Department of Aerospace Engineering in the Grainger College of Engineering, University of Illinois Urbana-Champaign, has developed Neural-Rendezvous—a -driven guidance and control framework to autonomously encounter these extremely fast-moving objects.

The research is published in the Journal of Guidance, Control, and Dynamics and on the arXiv preprint server.

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Complex materials such as organic semiconductors or the microporous metal-organic frameworks known as MOFs are already being used for numerous applications such as OLED displays, solar cells, gas storage and water extraction. Nevertheless, they still harbor a few secrets. One of these has so far been a detailed understanding of how they transport thermal energy.

Egbert Zojer’s research team at the Institute of Solid State Physics at Graz University of Technology (TU Graz), in collaboration with colleagues from TU Vienna and the University of Cambridge, has now cracked this secret using the example of organic semiconductors, opening up new perspectives for the development of innovative materials with customized thermal properties.

The team has published its findings in npj Computational Materials.

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Imagine fiber optic cables acting as vast sensor networks, detecting vibrations for everything from earthquake warnings to railway monitoring. The challenge? Processing the enormous data flow in real-time. Traditional electronic computing struggles, but researchers have merged machine learning wi

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Here Harkos et al. review the role of continuous models and discrete models in predicting and understanding therapy delivery and efficacy in solid tumours. They propose ways to integrate mechanistic and AI-based models to further improve patient outcomes.

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