News from the AI & ML world
DeeperML - #mathematics
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Matt Marshall@AI News | VentureBeat
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References:
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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Maximilian Schreiner@THE DECODER
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Google has unveiled Gemini 2.5 Pro, its latest and "most intelligent" AI model to date, showcasing significant advancements in reasoning, coding proficiency, and multimodal functionalities. According to Google, these improvements come from combining a significantly enhanced base model with improved post-training techniques. The model is designed to analyze complex information, incorporate contextual nuances, and draw logical conclusions with unprecedented accuracy. Gemini 2.5 Pro is now available for Gemini Advanced users and on Google's AI Studio.
Google emphasizes the model's "thinking" capabilities, achieved through chain-of-thought reasoning, which allows it to break down complex tasks into multiple steps and reason through them before responding. This new model can handle multimodal input from text, audio, images, videos, and large datasets. Additionally, Gemini 2.5 Pro exhibits strong performance in coding tasks, surpassing Gemini 2.0 in specific benchmarks and excelling at creating visually compelling web apps and agentic code applications. The model also achieved 18.8% on Humanity’s Last Exam, demonstrating its ability to handle complex knowledge-based questions. Recommended read:
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Michael Weiss@Diagonal Argument
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References:
Diagonal Argument
Recent discussions in mathematical concepts and programming tools cover a range of topics, including theoretical foundations and practical applications. Peter Cameron highlighted the Compactness Theorem for first-order logic, explaining its consequences and connections to topology. Also, a beginner's guide to sets has been published to explain how they work and some applications.
Noel Welsh presented a talk at Imperial College on dualities in programming, exploring the relationships between data and codata, calls and returns, and ASTs and stack machines. The use of adjoints in boolean operations was justified, and Daniel Lemire published an overview of parallel programming using Go. These discussions bridge the gap between abstract mathematical principles and their concrete uses in software development and programming paradigms. Recommended read:
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msaul@mathvoices.ams.org
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Researchers at the Technical University of Munich (TUM) and the University of Cologne have developed an AI-based learning system designed to provide individualized support for schoolchildren in mathematics. The system utilizes eye-tracking technology via a standard webcam to identify students’ strengths and weaknesses. By monitoring eye movements, the AI can pinpoint areas where students struggle, displaying the data on a heatmap with red indicating frequent focus and green representing areas glanced over briefly.
This AI-driven approach allows teachers to provide more targeted assistance, improving the efficiency and personalization of math education. The software classifies the eye movement patterns and selects appropriate learning videos and exercises for each pupil. Professor Maike Schindler from the University of Cologne, who has collaborated with TUM Professor Achim Lilienthal for ten years, emphasizes that this system is completely new, tracking eye movements, recognizing learning strategies via patterns, offering individual support, and creating automated support reports for teachers. Recommended read:
References :
@phys.org
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References:
phys.org
, www.sciencedaily.com
Researchers at the Technical University of Munich (TUM) and the University of Cologne have developed an AI-based learning system designed to provide individualized support for schoolchildren in mathematics. The system utilizes eye-tracking technology via a standard webcam to identify students’ strengths and weaknesses. By monitoring eye movements, the AI can pinpoint areas where students struggle, displaying the data on a heatmap with red indicating frequent focus and green representing areas glanced over briefly.
This AI-driven approach allows teachers to provide more targeted assistance, improving the efficiency and personalization of math education. The software classifies the eye movement patterns and selects appropriate learning videos and exercises for each pupil. Professor Maike Schindler from the University of Cologne, who has collaborated with TUM Professor Achim Lilienthal for ten years, emphasizes that this system is completely new, tracking eye movements, recognizing learning strategies via patterns, offering individual support, and creating automated support reports for teachers. Recommended read:
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vishnupriyan@Verdict
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Google's AI mathematics system, known as AlphaGeometry2 (AG2), has surpassed the problem-solving capabilities of International Mathematical Olympiad (IMO) gold medalists in solving complex geometry problems. This second-generation system combines a language model with a symbolic engine, enabling it to solve 84% of IMO geometry problems, compared to the 81.8% solved by human gold medalists. Developed by Google DeepMind, AG2 can engage in both pattern matching and creative problem-solving, marking a significant advancement in AI's ability to mimic human reasoning in mathematics.
This achievement comes shortly after Microsoft released its own advanced AI math reasoning system, rStar-Math, highlighting the growing competition in the AI math domain. While rStar-Math uses smaller language models to solve a broader range of problems, AG2 focuses on advanced geometry problems using a hybrid reasoning model. The improvements in AG2 represent a 30% performance increase over the original AlphaGeometry, particularly in visual reasoning and logic, essential for solving complex geometry challenges. Recommended read:
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@artsci.washington.edu
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References:
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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@techcrunch.com
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DeepMind's artificial intelligence, AlphaGeometry2, has achieved a remarkable feat by solving 84% of the geometry problems from the International Mathematical Olympiad (IMO) over the past 25 years. This performance surpasses the average gold medalist in the prestigious competition for gifted high school students. The AI's success highlights the growing capabilities of AI in handling sophisticated mathematical tasks.
AlphaGeometry2 represents an upgraded system from DeepMind, incorporating advancements such as the integration of Google's Gemini large language model and the ability to reason by manipulating geometric objects. This neuro-symbolic system combines a specialized language model with abstract reasoning coded by humans, enabling it to generate rigorous proofs and avoid common AI pitfalls like hallucinations. This could potentially impact fields that heavily rely on mathematical expertise. Recommended read:
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