MIT and University of Washington researchers have created a method to model both human and machine behaviours, taking into account unknown computational constraints which can limited problem-solving skills. The model infers an "inference budget" from previous actions. The inference budget can then predict the agent's future behaviour. Their technique can be used to predict navigation…
Health-monitoring apps can help individuals manage chronic diseases and keep up with their fitness goals. However, these apps can often be slow and energy-inefficient due to the machine-learning models they use, which need a significant amount of data shuffling between the smartphone and a central memory server. Engineers typically use hardware (machine-learning accelerators) to streamline…
Researchers at MIT and the University of Washington have developed a model that accounts for the sub-optimal decision-making processes in humans, potentially improving the way artificial intelligence can predict human behavior.
Named 'inference budget,' the model infers an agent's computational constraints, whether human or machine, after observing a few traces of their past actions. It…
Researchers from MIT and the University of Washington have developed a method to model the behaviour of an agent, including its computational limitations, predicting future behaviours by examining prior actions. The method applies to both humans and AI, and has a wide range of potential applications, including predicting navigation goals from past routes and forecasting…
Health-monitoring apps that assist people in managing chronic diseases or tracking fitness goals work with the help of large machine-learning models, which are often shuttled between a user's smartphone and a central memory server. This process can slow down the app's performance and drain the energy of the device. While machine-learning accelerators can help to…
In an effort to improve AI systems and their ability to collaborate with humans, scientists are trying to better understand human decision-making, including its suboptimal aspects, and model it in AI. A model for human or AI agent behaviour, developed by researchers at MIT and the University of Washington, takes into account an agent’s unknown…
Researchers at the Massachusetts Institute of Technology (MIT) and the MIT-IBM Watson AI Lab have developed a machine learning accelerator chip that is resistant to the most common types of cyberattacks, ensuring data privacy while supporting efficient AI model operations on devices. The chip can be used in demanding AI applications like augmented and virtual…
Machine learning (ML) models are increasingly used by organizations to allocate scarce resources or opportunities, such as for job screening or determining priority for kidney transplant patients. To avoid bias in a model's predictions, users may adjust the data features or calibrate the model's scores to ensure fairness. However, researchers at MIT and Northeastern University…
MIT engineers have identified new materials that could be more efficient conductors of protons – the nucleus of a hydrogen atom – which could pave the way for a number of climate-protecting technologies. Today's proton-conducting materials require very high temperatures, but lower-temperature alternatives could boost new technologies such as fuel cells that produce clean electricity…
As AI models become increasingly integrated into various sectors, understanding how they function is crucial. By interpreting the mechanisms underlying these models, we can audit them for safety and biases, potentially deepening our understanding of intelligence. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have been working to automate this interpretation process, specifically…