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Discover how the Chinese startup DeepSeek is revolutionizing AI with its groundbreaking models! In this video, we dive into the journey of Liang Wenfeng, the innovative mind behind DeepSeek, and explore how their latest model, DeepSeek-V3, outperforms industry giants using surprisingly basic hardware. Learn about their unique approach to talent acquisition, the significance of open-source development, and how they are democratizing access to advanced AI technology. Join us as we analyze the impact of DeepSeek on the global AI landscape and what it means for the future of artificial intelligence. Don’t forget to like, comment, and subscribe for more insights on AI breakthroughs!

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A team of engineers and roboticists at the University of Hong Kong have designed, built and tested an aerial robot capable of navigating unknown environments safely at high speeds while avoiding obstacles. In their paper published in the journal Science Robotics, the group describes how they overcame problems encountered by others attempting to build similar robots and how well their quadcopter robot, called SUPER, performed during testing.

Roboticists have been trying for several years to build a flying robot that could perform like birds—moving safely at high speeds while adjusting to unknown conditions as they arise, such as encountering gusts of wind, , tree limbs or other objects appearing suddenly in their path.

Most such flying robots have relied on various types of sensors and cameras that had to process massive amounts of video data, slowing the speeds at which they could operate. In this new effort, the researchers in Hong Kong say they have finally overcome these challenges.

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Living matter remains the quintessential puzzle of biological sciences, a question that embodies the intricate complexity and stunning diversity of life forms. A new study suggests that one viable approach to address this extreme complexity is to conceptualize living matter as a cascade of machines producing machines.

This cascade illustrates how cells are composed of smaller submachines, reaching down to the where molecular machines, such as ion pumps and enzymes, operate. In the other direction, it explains how cells self-organize into larger systems, such as tissues, organs, and populations, cumulating into the biosphere.

This new conceptual framework is a fruit of collaboration between Professors Tsvi Tlusty from the Department of Physics at Ulsan National Institute of Science and Technology (UNIST), South Korea, and Albert Libchaber from the Center for Physics and Biology at Rockefeller University, New York. The study was inspired by the seventeenth-century polymath Gottfried Leibniz, who noted that “the machines of nature, that is living bodies, are still machines in their smallest parts, to infinity.”

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The structural design of molecular machines and motors endows them with externally controlled directional motion at the molecular scale. Molecular machines based on both interlocked and non-interlocked molecules and driven by a variety of external stimuli such as light, electrical-or thermal energy, and chemical-or redox processes have been reported. With the field moving forward, they were incorporated into surfaces and interfaces to realize amplified directional molecular motion at the nanoscale which can be applied in the control of macroscopic material properties. More recently, molecular motors and molecular machines based on interlocked molecules have been organized into three dimensional materials to expand their functionality in the solid state and enrich their applicability.

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Cis-trans photoisomerization is a key process for many processes in biology and materials science, but only careful and time-consuming quantum chemistry methods can describe such reaction in detail. Here, a predictive tool is presented requiring few and affordable calculations, evaluating the efficiency of paradigmatic and modified photoswitches.

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Organic photoredox catalysts enable diverse chemical transformations, but predicting their activity is challenging due to complex properties. Now, a two-step data-driven approach is introduced for targeted organic photoredox catalysts synthesis and reaction optimization. Using Bayesian optimization, promising catalysts can be efficiently identified, yielding competitive results with iridium catalysts.

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Computational chemistry has remained largely inaccessible to the experimental chemistry community. Here we report the VIRTUAL CHEMIST, a software suite free for academic use, that enables organic chemists without expertise in computational chemistry to perform virtual screening experiments for asymmetric catalyst discovery and design.

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