Artificial Intelligence Archives - Page 13 of 233

To enhance AI assistant development, begin by emulating the unpredictable conduct of humans.

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, UncategorizedJuly 31, 202461Views 0Likes 0Comments

Artificial Intelligence (AI) that can work effectively with humans requires a robust model of human behaviour. However, humans often behave irrationally, limiting their decision-making abilities. Researchers at MIT and the University of Washington have developed a model for predicting an agent's behaviour (whether human or machine) by considering computational constraints that affect problem-solving, which they refer…

This compact semiconductor provides protection for user information while facilitating streamlined computation on a mobile device.

Artificial Intelligence, Computer science and technology, Cybersecurity, Data, Electrical Engineering & Computer Science (eecs), Electronics, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Privacy, Research, School of Engineering, UncategorizedJuly 31, 202465Views 0Likes 0Comments

Health-monitoring apps powered by advanced machine-learning (ML) models could be more secure and still run efficiently on devices, according to researchers from MIT and the MIT-IBM Watson AI Lab. Though the models require vast amounts of data shuttling between a smartphone and a central memory server, using a machine-learning accelerator can speed up the process…

Julie Shah has been appointed as the lead of the Aeronautics and Astronautics Department.

Administration, Aeronautical and astronautical engineering, Alumni/ae, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Electrical Engineering & Computer Science (eecs), Faculty, Leadership, MIT Schwarzman College of Computing, Robotics, School of Engineering, UncategorizedJuly 31, 202472Views 0Likes 0Comments

Assisting Olympic athletes in enhancing their performance, step by step.

Artificial Intelligence, Community, Computer science and technology, Innovation and Entrepreneurship (I&E), Materials science and engineering, MIT.nano, Nanoscience and nanotechnology, Sports and fitness, Startups, Technology and society, UncategorizedJuly 31, 202465Views 0Likes 0Comments

MIT startup Striv has developed tactile sensing technology that inserts into shoes, effectively tracking force, movement, and form via algorithms that interpret tactile data. The developer, Axl Chen, initially applied his work in a virtual reality gaming context but pivoted to athletics, and several professional athletes, including US marathoner Clayton Young and Olympian Damar Forbes,…

This technique curbs an AI model’s excessive confidence in incorrect responses.

Algorithms, Artificial Intelligence, Data, Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, Research Laboratory of Electronics, School of Engineering, UncategorizedJuly 31, 202464Views 0Likes 0Comments

This AI Study Demonstrates AI Model Breakdown as Consecutive Model Generations are Sequentially Trained on Simulated Data.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 31, 202463Views 0Likes 0Comments

The phenomenon of "model collapse" represents a significant challenge in artificial intelligence (AI) research, particularly impacting large language models (LLMs). When these models are continually trained on data created by earlier versions of similar models, they lose their ability to accurately represent the underlying data distribution, deteriorating in effectiveness over successive generations. Current training methods of…

Enhancing Memory for Extensive NLP Models: An Examination of Mini-Sequence Transformer

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 31, 202458Views 0Likes 0Comments

The rapid development of Transformer models in natural language processing (NLP) has brought about significant challenges, particularly with memory requirements for the training of these large-scale models. A new paper addresses these issues by presenting a new methodology called MINI-SEQUENCE TRANSFORMER (MST) which optimizes memory usage during long-sequence training without compromising performance. Traditional approaches such as…

OuteAI Introduces Innovative Lite-Oute-1 Variants: Lite-Oute-1-300M and Lite-Oute-1-65M as Robust Yet Space-Saving AI Platforms.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Open Source, Small Language Model, Staff, Tech News, Technology, UncategorizedJuly 31, 202473Views 0Likes 0Comments

OuteAI has released two new models of its Lite series, namely Lite-Oute-1-300M and Lite-Oute-1-65M, which are designed to maintain optimum efficiency and performance, making them suitable for deployment across various devices. The Lite-Oute-1-300M model is based on the Mistral architecture and features 300 million parameters, while the Lite-Oute-1-65M, based on the LLaMA architecture, hosts around…

rLLM (relationLLM): A PyTorch library developed for learning Relational Table Learning (RTL) using extensive language models (LLMs).

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedJuly 31, 202466Views 0Likes 0Comments

To enhance an AI assistant’s capabilities, begin by simulating the unpredictable actions of human beings.

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, UncategorizedJuly 31, 202475Views 0Likes 0Comments

Researchers from MIT and the University of Washington have developed a model that predicts human behavior by considering computational constraints that limit an individual's problem-solving ability. This model can be used to estimate a person's ‘inference budget’, or time available for problem-solving, based on their past actions. It can then predict their future behavior. Drawing from…