Algorithms, Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, UncategorizedAugust 1, 202434Views0Likes0Comments
Researchers from MIT and the MIT-IBM Watson AI Lab have developed a hardware solution that enhances the security of machine-learning applications on smartphones. Current health-monitoring apps require large amounts of data to be transferred back and forth between the phone and a central server, which can create security vulnerabilities and inefficiency. To counter this, the…
Researchers from MIT and MIT-IBM Watson AI Lab have created a machine-learning accelerator that is resistant to the most common types of cyber attacks. The chip can hold users' sensitive data such as health records and financial information, enabling large AI models to run efficiently on devices while maintaining privacy. The accelerator maintains strong security,…
Roboticists and researchers at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) are working to develop a system that can train robots to perform tasks in specific environments effectively. The ongoing research aims to help robots deal with disturbances, distractions, and changes in their operational environments. For this, they have proposed a method to create…
A recent research paper by the University of Washington and Allen Institute for AI researchers has examined the use of abstention in large language models (LLMs), emphasizing its potential to minimize false results and enhance the safety of AI. The study investigates the current methods of abstention incorporated during the different development stages of LLMs…
Relational databases are fundamental to many digital systems, playing a critical role in data management across a variety of sectors, including e-commerce, healthcare, and social media. Through their table-based structure, they efficiently organize and retrieve data that's crucial to operations in these fields, and yet, the full potential of the valuable relational information within these…
Time series data, used across sectors including finance, healthcare, and sensor networks, is of fundamental importance for tasks including anomaly detection, pattern discovery, and time series classification, informing crucial decision-making and risk management processes. Extracting useful trends and anomalies from this extensive data can be complex and often requires an immense amount of computational resources.…