Data

Researchers from MIT and the MIT-IBM Watson AI Lab have designed a machine-learning accelerator that is impervious to the two most common types of cyberattacks. Currently, healthcare apps that monitor chronic diseases or fitness goals are relying on machine learning to operate. However, the voluminous machine-learning models utilized need to be transferred between a smartphone…

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a machine-learning accelerator that enhances the security of health-tracking apps. These apps can be slow and consume a lot of energy due to the data exchange requirements between the phone and a central server. “Machine-learning accelerators” are used to speed up such apps but…

Researchers from MIT and the MIT-IBM Watson AI Lab have developed a novel machine-learning accelerator that can protect sensitive data like health records from two common types of cybersecurity threats while efficiently running large AI models. This advancement could make an noticable impact on challenging AI applications, such as augmented and virtual reality, autonomous driving…

Researchers from the Massachusetts Institute of Technology (MIT) and the MIT-IBM Watson AI Lab have developed a machine-learning accelerator designed to be resistant to cyber-attacks, offering a secure platform for health-monitoring applications. The chip secures users' data whilst running large artificial intelligence (AI) models efficiently, protecting sensitive health and financial information. The technology is capable of…

Health-monitoring applications have become pivotal in managing chronic diseases and tracking fitness goals, largely due to the advent of machine-learning powered tools. However, these applications are often slow and energy-inefficient, largely due to the massive machine-learning models that require transfer between a smartphone and a central memory server. Despite the development of machine-learning accelerators that…

A team of researchers from MIT and the MIT-IBM Watson AI Lab has developed a machine-learning accelerator that is resistant to the two most common types of cyberattacks. This ensures that sensitive information such as finance and health records remain private while still enabling large AI models to run efficiently on devices. The researchers targeted…

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…

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…

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…