News from the AI & ML world
DeeperML - #learning
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@medium.com
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References:
medium.com
, medium.com
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AI is making significant strides in revolutionizing math learning and education. AI Math Master is a new mobile app designed to provide students and educators with a tool to solve math problems quickly, offering step-by-step solutions. It covers a range of subjects from basic arithmetic to advanced topics like calculus.
Researchers at the Technical University of Munich (TUM) and the University of Cologne have developed an AI-based learning system to recognize strengths and weaknesses in mathematics. This system uses a webcam to track eye movements, generating problem-solving hints and enabling teachers to provide more individualized support to students. The AI system analyzes patterns in eye movements displayed on a heatmap to select learning videos and exercises tailored to the pupil. 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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@Communications of the ACM
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Andrew G. Barto and Richard S. Sutton have been awarded the 2024 ACM A.M. Turing Award for their foundational work in reinforcement learning (RL). The ACM recognized Barto and Sutton for developing the conceptual and algorithmic foundations of reinforcement learning, one of the most important approaches for creating intelligent systems. The researchers took principles from psychology and transformed them into a mathematical framework now used across AI applications. Their 1998 textbook "Reinforcement Learning: An Introduction" has become a cornerstone of the field, cited more than 75,000 times.
Their work, beginning in the 1980s, has enabled machines to learn independently through reward signals. This technology later enabled achievements like AlphaGo and today's large reasoning models (LRMs). Combining RL with deep learning has led to major advances, from AlphaGo defeating Lee Sedol to ChatGPT's training through human feedback. Their algorithms are used in various areas such as game playing, robotics, chip design and online advertising. Recommended read:
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