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A collaborative team from MIT and Stanford introduced the Search of Stream (SoS), a machine learning structure that allows language models to learn problem-solving skills through linguistic searching without the need for external assistance.

Language models (LMs) are a crucial segment of artificial intelligence and can play a key role in complex decision-making, planning, and reasoning. However, despite LMs having the capacity to learn and improve, their training often lacks exposure to effective learning from mistakes. Several models also face difficulties in planning and anticipating the consequences of their…

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This AI Research Presents ReasonEval: An Innovative Machine Learning Approach for Assessing Mathematical Logic Beyond Precision

The complexity of mathematical reasoning in large language models (LLMs) often exceed the capabilities of existing evaluation methods. These models are crucial for problem-solving and decision-making, particularly in the field of artificial intelligence (AI). Yet the primary method of evaluation – comparing the final LLM result to a ground truth and then calculating overall accuracy…

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The University of Cambridge’s researchers have suggested AnchorAL: an innovative method of machine learning for active learning in tasks involving unbalanced classification.

Generative language models in the field of natural language processing (NLP) have fuelled significant progression, largely due to the availability of a vast amount of web-scale textual data. Such models can analyze and learn complex linguistic structures and patterns, which are subsequently used for various tasks. However, successful implementation of these models depends heavily on…

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Meta has unveiled a machine learning (ML) method that enables holistic solutions to networking issues across various layers, including Bandwidth Expansion (BWE).

Meta has developed a machine-learning (ML) model to improve the efficiency and reliability of real-time communication (RTC) across its various apps. Developing this ML-based solution is an answer to the limitations of existing bandwidth estimation (BWE) and congestion control methods, such as the Google Congestion Controller (GCC) used in WebRTC, which relies on hand-tuned parameters…

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The team from MIT has published research papers on the management of Artificial Intelligence.

A committee formed by MIT scholars and leaders has released a series of policy briefs that propose a framework for artificial intelligence (AI) governance in the United States. The proposed approach extends existing regulatory and liability procedures to manage AI effectively. The committee believes this could boost the country's leadership position in AI while minimizing…

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A computational expert advances the frontiers of geometric studies.

Over 2,000 years ago, Euclid, the Greek mathematician, laid the foundation of geometry and altered our perception of shapes. Justin Solomon, inspired by Euclid's work, applies modern geometric techniques to resolve challenging problems that may not appear related to shapes. As an associate professor at the MIT Department of Electrical Engineering and Computer Science and…

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Deep neural networks demonstrate potential as representations of human auditory mechanisms.

An MIT study has been making strides towards developing computational models that mimic the human auditory system, which could enhance the design of hearing aids, cochlear implants, and brain-machine interfaces. These computational models stem from advances in machine learning. The study found that internal representations generated by deep neural networks often mirror those within the…

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A computational model successfully depicts the hard-to-capture transitional stages of chemical reactions.

A group of MIT researchers has developed a new machine learning model which rapidly calculates the structure of transition states during chemical reactions. This fleeting moment is a crucial "point of no return" in reactions. Although this transition state is vital to understanding the pathway of the reaction, it has been notoriously difficult to observe…

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