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Does this artificial intelligence think like a human?

MIT researchers developed a method that helps a user to better understand a machine-learning model’s reasoning, and how that reasoning compares to that of a human. The technique enables a user to rapidly identify and analyze patterns in a model’s behavior. ....

United States , Arvind Satyanarayan , Angie Boggust , Hendrik Strobelt , Benjamin Hoover , Artificial Intelligence Laboratory , Watson Ai Lab , United States Air Force Artificial Intelligence Accelerator , Visualization Group , Visualization Group Of The Computer Science , United States Air Force Research Laboratory , Shared Interest , Computer Science , Human Factors , United States Air Force Artificial Intelligence , Arvind Satyanarayan , Machine Learning , Neural Networks , Aliency Methods , Image Classification , Explainable Ai , Mit Ibm Watson Ai Lab ,

Legal AI Series [Chapter Six]: AI Transcription & Translation: The Audio to Text Superpowers of AI Legal Software | Association of Certified E-Discovery Specialists (ACEDS)

From jail house calls to body-camera footage, voicemail to video depositions, modern technologies have introduced a new type of evidence to legal professionals: unstructured data.. ....

Association Of Certified Specialists , Certifiede Discovery Specialists , Image Classification ,

Developing an AI Algorithm to Monitor Infant Sleep Positions

A team of researchers has developed an optimal AI algorithm for infant sleep posture classification to help prevent sudden infant death syndrome. ....

United States , United Kingdom , British Columbia , City Of , Megan Craig , Feng Yu Shutterstock , Las Vegas , Movement Tracking Of Infants , Transactions On Neural Networks , International Journal Of Computer Vision , Journal Of Information Technology In Construction , Network Training , Integers In Deep Neural Networks , Network Collected Intelligence , Convolutional Networks , Proceedings Of The International Conference On Machine , International Conference On Power , Convolutional Neural Network , Information System Hardware Co , Wearable Movement Sensors , Wearable Technology For Baby Monitoring , Journal Of The American Medical Association , Training Of Neural Networks , Journal Of Textile Engineering Fash Technology , International Journal Of Epidemiology , Southern Illinois University Carbondale ,

Demystifying machine-learning systems

MIT researchers created a technique that can automatically describe the roles of individual neurons in a neural network with natural language, helping machine learning practitioners better understand how their model will behave in the real world. ....

Jacob Andreas , David Bau , Antonio Torralba , Nevan Hernandez , Sarah Schwettmann , Teona Bagashvili , International Conference On Learning Representations , Artificial Intelligence Laboratory , Delta Electronics Professor Of Electrical Engineering , Watson Ai Lab , Northeastern University , X Consortium Assistant Professor , Delta Electronics Professor , Electrical Engineering , Computer Science , International Conference , Image Classification , Explainable Ai ,

How well do explanation methods for machine-learning models work?

Feature-attribution methods are used to determine if a neural network is working correctly when completing a task like image classification. MIT researchers developed a way to evaluate whether these feature-attribution methods are correctly identifying the features of an image that are important to a neural network’s prediction. ....

Yilun Zhou , Julie Shah , Marco Tulio Ribeiro , Microsoft Research , Artificial Intelligence Laboratory , Interactive Robotics Group , National Science Foundation , Computer Science , Serena Booth , National Science , Explainable Artificial Intelligence , Eature Attribution , Image Classification ,