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…
Before the development of PILOT (PIecewise Linear Organic Tree), linear model trees were slow to fit and susceptible to overfitting, notably with large datasets. The traditional regression trees faced challenges capturing linear relationships efficiently. Linear model trees also encountered problems with interpretability when integrating linear models in leaf nodes. The research points out the need…
Multi-target multi-camera tracking (MTMCT) has become indispensable in intelligent transportation systems, yet real-world applications are complex due to a shortage of publicly available data and laborious manual annotation. MTMCT involves tracking vehicles across multiple camera lenses, detecting objects, carrying out multi-object tracking, and finally clustering trajectories to generate a comprehensive image of vehicle movement. MTMCT…
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…
Large Language Models (LLMs) have shown vast potential in various critical sectors, such as finance, healthcare, and self-driving cars. Typically, these LLM agents use external tools and databases to carry out tasks. However, this reliance on external sources has raised concerns about their trustworthiness and vulnerability to attacks. Current methods of attack against LLMs often…
General circulation models (GCMs) are crucial in weather and climate prediction. They work using numerical solvers for big scale dynamics and parameterizations for smaller processes like cloud formation. Despite continuous enhancements, difficulties still persist, including errors, biases, and uncertainties in long-term weather projections and severe weather events. Recently introduced machine-learning models have shown excellent results…