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Media coverage of MIT: A summary of the year 2023

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Media Coverage of MIT: A Recap of 2023

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Start developing a more efficient AI assistant by first understanding and replicating the unpredictable actions of humans.

Artificial Intelligence (AI) researchers at MIT and the University of Washington have created a model that can predict a human's decision-making behaviour by learning from their past actions. The model incorporates the understanding that humans can behave sub-optimally due to computational constraints — essentially the idea that humans can't spend indefinitely long periods considering the…

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The computational model accurately captures the hard-to-detect transition stages of chemical reactions.

Researchers from MIT have used machine learning to expedite the calculation of transient molecular states that occur during chemical reactions. The team's innovative new model streamlines the process, from a previously time-consuming task, performed using quantum chemistry techniques, to a few seconds. Applied, it could assist chemists to design new reactions and catalysts to create…

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MIT scientists utilize AI to discover a fresh category of potential antibiotics.

Using artificial intelligence (AI) technology called deep learning, MIT researchers have identified compounds capable of defeating methicillin-resistant Staphylococcus aureus (MRSA), a drug-resistant bacterium causing over 10,000 deaths annually in the US. The compounds, which exhibit low toxicity to human cells, were found to effectively kill MRSA in lab and mouse models, making them potential drug…

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Employing deep learning for imaging the Earth’s atmospheric boundary layer.

The planetary boundary layer (PBL), the lowest layer of the troposphere, significantly influences weather near the Earth's surface and holds the potential to enhance storm forecasting and improve climate projections. A research team from Lincoln Laboratory's Applied Space Systems Group has been studying the PBL with a focus on deploying machine learning for creating 3-D…

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There is potential in deep neural networks as they could effectively serve as models for human auditory perception.

A new MIT study has found that computational models derived from machine learning that mimic the structure and function of the human auditory system could help improve the design of hearing aids, cochlear implants, and brain-machine interfaces. Its research explored deep neural networks trained to perform auditory tasks and showed that these models generate internal…

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