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AI Will Not Replace Humans, but “AI-Powered Humans” Are the Future

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.”

How AI is transforming medicine

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.

Concept for interstellar object encounters developed, then simulated using a spacecraft swarm

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.

Machine learning uncovers hidden heat transport mechanisms in organic semiconductors

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.

This AI Uses Light Instead of Electricity and It’s Mind-Blowingly Fast

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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