“MouthIO” is an in-mouth device that users can digitally design and 3D print with integrated sensors and actuators to capture health data and interact with a computer or phone.
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.
The program will invite students to investigate new vistas at the intersection of music, computing, and technology.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
The three-day, hands-on conference hosted by the MIT RAISE Initiative welcomed youths and adults from nearly 30 countries.
The approach can detect anomalies in data recorded over time, without the need for any training.
More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.
Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.
A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.
A new technique enables users to compare several large models and choose the one that works best for their task.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
Developed by MIT RAISE, the Day of AI curriculum empowers K-12 students to collaborate on local and global challenges using AI.