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Machine learning

The language network of our brain has to exert more effort when dealing with intricate and unfamiliar sentences.

Neuroscientists at MIT, with the aid of an artificial language network, have determined the type of sentences that most likely activate the brain's main language processing centers. The recently published study demonstrates that sentences which are more complex, either due to exceptional grammar or unexpected meanings, stimulate stronger responses in these regions. On the other…

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Intricate and unfamiliar phrases cause the brain’s language system to exert more effort.

In collaboration with an artificial language network, neuroscientists at Massachusetts Institute of Technology (MIT) have revealed what type of sentences most significantly engage the brain’s primary language processing areas. The study indicates that sentences featuring unusual grammar or unexpected meaning trigger a heightened response in these language-oriented regions, as opposed to more straightforward phrases, which…

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Faculty, instructors, and students at MIT engage in trials with generative AI in the field of education and learning.

During the Festival of Learning 2024 at MIT, discussions were held on leveraging generative AI to enhance learning experiences for students both on and off campus. The panelists, comprising MIT faculty, instructors, staff, and students, emphasized that generative AI should be used to enrich, not replace, the educational experience. They highlighted the ongoing experimentation with…

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Generative AI is being experimented with in teaching and learning by students, faculty, and instructors at MIT.

In the MIT Festival of Learning 2024, faculty, students, staff, and alumni explored the role of generative AI in learning and teaching. Some believe that this technology is an essential tool to prepare students for the future of work. Generative AI can be used to support learning experiences, where the student can take ownership. For…

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This machine learning paper, produced by ICMC-USP, NYU, and Capital-One, presents a new AI structure known as T-Explainer, designed to provide consistent and credible explanations of machine learning models.

Machine learning models, as they become more complex, often begin to resemble "black boxes" where the decision-making process is unclear. This lack of transparency can hinder understanding and trust in decision-making, particularly in critical fields such as healthcare and finance. Traditional methods for making these models more transparent have often suffered from inconsistencies. One such…

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MIT researchers have utilized artificial intelligence to discover a new category of potential antibiotics.

MIT researchers, using deep learning techniques, have discovered compounds that can effectively combat methicillin-resistant Staphylococcus aureus (MRSA). This drug-resistant bacterium annually leads to over 10,000 deaths in the United States alone. Detailed in a study published in Nature, the compounds not only successfully killed MRSA in laboratory and mice model tests, but also showed significantly…

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Unfamiliar and complex sentences increase the workload on the brain’s language processing system.

Using an artificial language network, MIT neuroscientists have found that sentences with unusual grammar or unexpected meanings tend to strongly activate the brain's key language processing centers. In contrast, straightforward sentences cause only minimal engagement of these regions, as do nonsensical sequences of words. The researchers discovered this by analyzing how human participants' brain network…

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This AI research unveiled by Google DeepMind presents improved learning abilities through the usage of Many-Shot In-Context Learning.

In-context learning (ICL) in large language models (LLMs) is a cutting-edge subset of machine learning that uses input-output examples to adapt to new tasks without changing the base model architecture. This methodology has revolutionized how these models manage various tasks by learning from example data during the inference process. However, the current setup, referred to…

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