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This AI study by Tenyx investigates the logical capabilities of Large Language Models (LLMs) based on their understanding of geometric concepts.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 9, 202433Views0Likes0Comments
Large language models (LLMs) have made remarkable strides in many tasks, with their capacity to reason forming a vital aspect of their development. However, the main drivers behind these advancements remain unclear. Current measures to boost reasoning primarily involve increasing the model's size and extending the context length with methods such as the chain of…

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This article proposes Neural Operators as a solution to the generalization challenge by suggesting their use in the modeling of Constitutive Laws.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 9, 202431Views0Likes0Comments
Accurate magnetic hysteresis modeling remains a challenging task that is crucial for optimizing the performance of magnetic devices. Traditional methods, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs), have limitations when it comes to generalizing novel magnetic fields. This generalization is vital for real-world applications. A team of…

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Introducing DRLQ: A New Approach Utilizing Deep Reinforcement Learning (DRL) for Task Allocation within Quantum Cloud Computing Settings.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 9, 202429Views0Likes0Comments
In the rapidly advancing field of quantum computing, managing tasks efficiently and effectively is a complex challenge. Traditional models often struggle due to their heuristic approach, which fails to adapt to the intricacies of quantum computing and can lead to inefficient system performance. Task scheduling, therefore, is critical to minimizing time wastage and optimizing resource…

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Progress in Protein Sequence Design: Utilizing Reinforcement Learning and Language Models

AI Paper Summary, AI Shorts, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 9, 202428Views0Likes0Comments
Protein sequence design is a significant part of protein engineering for drug discovery, involving the exploration of vast amino acid sequence combinations. To overcome the limitations of traditional methods like evolutionary strategies, researchers have proposed utilizing reinforcement learning (RL) techniques to facilitate the creation of new protein sequences. This progress comes as advancements in protein…

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“They have the ability to envision influencing the world they reside in.”

academics, Algorithms, Analytics, Apps, Artificial Intelligence, astronomy and planetary science, Climate change, Collaboration, Computer science and technology, Data, Digital technology, Education, Environment, Global, Human-computer interaction, Internet, K-12 education, Machine learning, Media Lab, MIT Schwarzman College of Computing, nasa, Office of Open Learning, Open access, Programming, Social Media, Space, Special events and guest speakers, STEM education, teaching, Technology and society, UncategorizedJuly 9, 202451Views0Likes0Comments
A group of New England Innovation Academy students have developed a mobile app that highlights deforestation trends in Massachusetts as part of a project for the Day of AI, a curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative. The TreeSavers app aims to educate users about the effects of…

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Scientists from the IT University in Copenhagen suggest using self-regulating neural networks to improve adaptability.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Deep Learning, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 8, 202425Views0Likes0Comments
Artificial Neural Networks (ANNs) have long been used in artificial intelligence but are often criticized for their static structure which struggles to adapt to changing circumstances. This has restricted their use in areas such as real-time adaptive systems or robotics. In response to this, researchers from the IT University of Copenhagen have designed an innovative…

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Copenhagen’s IT University scientists suggest using self-adjusting neural networks for improved adaptability.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Deep Learning, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 8, 202422Views0Likes0Comments
Artificial Neural Networks (ANNs), while transformative, have traditional shortcomings in terms of adaptability and plasticity. This lack of flexibility poses a significant challenge for their applicability in dynamic and unpredictable environments. It also inhibits their effectiveness in real-time applications like robotics and adaptive systems, making real-time learning and adaptation a crucial achievement for artificial intelligence…

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Google researchers have put forth a novel machine learning algorithm, formally boosting an algorithm that applies to any loss function whose set of discontinuities bears no Lebesgue measure.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 8, 202430Views0Likes0Comments
Google's research team is working on developing an optimized machine learning (ML) method known as "boosting." Boosting involves creating high performing models using a "weak learner oracle" which gives classifiers a performance slightly better than random guessing. Over the years, boosting has evolved into a first-order optimization setting. However, some in the industry erroneously define…

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Google scientists suggest a precise enhancing system for machine learning algorithms that can work with any loss function, provided its set of discontinuities possesses no Lebesgue measurement.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 8, 202432Views0Likes0Comments
Boosting, a highly effective machine learning (ML) optimization setting, has evolved from a model that did not require first-order loss information into a method that necessitates this knowledge. Despite this transformation, few investigations have been made into boosting, even as machine learning witnesses a surge in zeroth-order optimization - methods that bypass the use of…

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Scientists at the University College London have deciphered the shared mechanics of representation learning in deep neural networks.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 8, 202430Views0Likes0Comments
Deep Neural Networks (DNNs) represent a great promise in current machine learning approaches. Yet a key challenge facing their implementation is scalability, which becomes more complicated as networks become more sizeable and intricate. New research from the University College London presents a novel understanding of common learning patterns across different neural network structures. The researchers behind…

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