News from the AI & ML world
DeeperML - #research
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@sciencedaily.com
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The Quantum Insider
, www.sciencedaily.com
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Recent advancements in quantum computing research have yielded promising results. Researchers at the University of the Witwatersrand in Johannesburg, along with collaborators from Huzhou University in China, have discovered a method to shield quantum information from environmental disruptions, potentially leading to more reliable quantum technologies. This breakthrough involves manipulating quantum wave functions to preserve quantum information, which could enhance medical imaging, improve AI diagnostics, and strengthen data security by providing ultra-secure communication.
UK startup Phasecraft has announced a new algorithm, THRIFT, that improves the ability of quantum computers to model new materials and chemicals by a factor of 10. By optimizing quantum simulation, THRIFT enables scientists to model new materials and chemicals faster and more accurately, even on today’s slower machines. Furthermore, Oxford researchers have demonstrated a 25-nanosecond controlled-Z gate with 99.8% fidelity, combining high speed and accuracy in a simplified superconducting circuit. This achievement advances fault-tolerant quantum computing by improving raw gate performance without relying heavily on error correction or added hardware. Recommended read:
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Matt Marshall@AI News | VentureBeat
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Microsoft Security Blog
, www.zdnet.com
Microsoft is enhancing its Copilot Studio platform with AI-driven improvements, introducing deep reasoning capabilities that enable agents to tackle intricate problems through methodical thinking and combining AI flexibility with deterministic business process automation. The company has also unveiled specialized deep reasoning agents for Microsoft 365 Copilot, named Researcher and Analyst, to help users achieve tasks more efficiently. These agents are designed to function like personal data scientists, processing diverse data sources and generating insights through code execution and visualization.
Microsoft's focus includes securing AI and using it to bolster security measures, as demonstrated by the upcoming Microsoft Security Copilot agents and new security features. Microsoft aims to provide an AI-first, end-to-end security platform that helps organizations secure their future, one example being the AI agents designed to autonomously assist with phishing, data security, and identity management. The Security Copilot tool will automate routine tasks, allowing IT and security staff to focus on more complex issues, aiding in defense against cyberattacks. Recommended read:
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@phys.org
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mathoverflow.net
, medium.com
Recent mathematical research is pushing the boundaries of theoretical understanding across various domains. One area of focus involves solving the least squares problem, particularly with rank constraints. A specific problem involves minimizing a function with a rank constraint and the quest for efficient solutions to these constrained optimization challenges remains a significant area of investigation.
This also involves a three-level exploration into a "mathematics-driven universe," questioning whether math is discovered or invented, and delving into the philosophical implications of mathematics in modern physics. Furthermore, mathematicians are employing topology to investigate the shape of the universe. This includes exploring possible 2D and 3D spaces to better understand the cosmos we inhabit, hinting at intriguing and surprising possibilities that could change our understanding of reality. Recommended read:
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@artsci.washington.edu
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Recent News
, artsci.washington.edu
University of Washington professors Xiaodong Xu, Cynthia Vinzant, and Shayan Oveis Gharan have been honored by the National Academy of Sciences (NAS) for their outstanding research achievements. The NAS awards program has been recognizing outstanding achievement in the physical, biological, and social sciences since 1866. The annual awards ceremony will honor the major contributions made by 20 researchers.
Xu received the NAS Award for Scientific Discovery for his experimental observation of the fractional quantum anomalous Hall effect. This award, presented every two years, recognizes an accomplishment or discovery in basic research within the previous five years that is expected to have a significant impact on astronomy, biochemistry, biophysics, chemistry, materials science, or physics. Xu's research explores new quantum phenomena in layered two-dimensional materials and engineered quantum systems. Vinzant and Oveis Gharan, along with Nima Anari and Kuikui Liu, will receive the Michael and Sheila Held Prize for breakthrough work advancing the theory of matroids and mixing rates of Markov chains. The Michael and Sheila Held Prize is presented annually to honor outstanding, innovative, creative, and influential research in the areas of combinatorial and discrete optimization, or related parts of computer science, such as the design and analysis of algorithms and complexity theory. This $100,000 prize is intended to recognize recent work. Recommended read:
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@the-decoder.com
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pub.towardsai.net
, THE DECODER
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AI research is rapidly advancing, with new tools and techniques emerging regularly. Johns Hopkins University and AMD have introduced 'Agent Laboratory', an open-source framework designed to accelerate scientific research by enabling AI agents to collaborate in a virtual lab setting. These agents can automate tasks from literature review to report generation, allowing researchers to focus more on creative ideation. The system uses specialized tools, including mle-solver and paper-solver, to streamline the research process. This approach aims to make research more efficient by pairing human researchers with AI-powered workflows.
Carnegie Mellon University and Meta have unveiled a new method called Content-Adaptive Tokenization (CAT) for image processing. This technique dynamically adjusts token count based on image complexity, offering flexible compression levels like 8x, 16x, or 32x. CAT aims to address the limitations of static compression ratios, which can lead to information loss in complex images or wasted computational resources in simpler ones. By analyzing content complexity, CAT enables large language models to adaptively represent images, leading to better performance in downstream tasks. Recommended read:
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