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Researchers from MIT and elsewhere designed a communication framework that enables academics to ask for research help on social media using meronymous communication, in which the asker only reveals certain verified aspects of their identity. They found that meronymous communication encouraged people to ask questions they otherwise might not have for fear of judgment from more senior scientists (Credits: MIT News; iStock).
CSAIL article

Have you ever felt reluctant to share ideas during a meeting because you feared judgment from senior colleagues? You’re not alone. Research has shown this pervasive issue can lead to a lack of diversity in public discourse, especially when junior members of a community don’t speak up because they feel intimidated.

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alt="Situated in the heart of campus on Vassar Street, the central location of the MIT Schwarzman College of Computing building will help form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence at MIT (Photo: Dave Burk/SOM)."
CSAIL article

On Vassar Street, in the heart of MIT’s campus, the MIT Stephen A. Schwarzman College of Computing recently opened the doors to its new headquarters in Building 45. The building’s central location and welcoming design will help form a new cluster of connectivity at MIT and enable the space to have a multifaceted role. 

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To close the gap with classical computers, researchers created the quantum control machine — an instruction set for a quantum computer that works like the classical idea of a virtual machine (Credits: Alex Shipps/MIT CSAIL).
CSAIL article

When MIT professor and now Computer Science and Artificial Intelligence Laboratory (CSAIL) member Peter Shor first demonstrated the potential of quantum computers to solve problems faster than classical ones, he inspired scientists to imagine countless possibilities for the emerging technology. Thirty years later, though, the quantum edge remains a peak not yet reached.

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CSAIL article

From students crafting essays and engineers writing code to call center operators responding to customers, generative artificial intelligence tools have prompted a wave of experimentation over the past year. At MIT, these experiments have raised questions — some new, some ages old — about how these tools can change the way we live and work.

 

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Researchers from MIT and elsewhere found that complex large language machine-learning models use a simple mechanism to retrieve stored knowledge when they respond to a user prompt. The researchers can leverage these simple mechanisms to see what the model knows about different subjects, and also possibly correct false information that it has stored (Credits: iStock).
CSAIL article

Large language models, such as those that power popular artificial intelligence chatbots like ChatGPT, are incredibly complex. Even though these models are being used as tools in many areas, such as customer support, code generation, and language translation, scientists still don’t fully grasp how they work.