A team of researchers has mapped the challenges of AI in software development, and outlined a research agenda to move the field forward.
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Human-computer interaction
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
Read MorePresentations targeted high-impact intersections of AI and other areas, such as health care, business, and education.
Read MoreCombining technology, education, and human connection to improve online learning
Caitlin Morris, a PhD student and 2024 MAD Fellow affiliated with the MIT Media Lab, designs digital learning platforms that make room for the “social magic” that influences curiosity and motivation.
Read MoreUnpacking the bias of large language models
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
Read MoreBringing meaning into technology deployment
The MIT Ethics of Computing Research Symposium showcases projects at the intersection of technology, ethics, and social responsibility.
Read MoreEnvisioning a future where health care tech leaves some behind
The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
Read MoreTeaching AI models the broad strokes to sketch more like humans do
SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.
Read MoreAn anomaly detection framework anyone can use
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
Read MoreStudy shows vision-language models can’t handle queries with negation words
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
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