MIT

Auto Added by WPeMatico

Banner for the AI & Big Data Expo event series.

Motional and MIT AI explains self-driving car decisions

ai, AI (Artificial Intelligence), AI and Us, AI in Action, Artificial Intelligence, Autonomous vehicles, Computer vision, cw-net, Data Engineering & MLOps, Deep Dives, deep learning, driverless cars, explainable AI, Featured News, Governance, Regulation & Policy, How It Works, Human-AI Relationships, Inside AI, MIT, motional, neural networks, physical ai, Road safety, Robotics, self-driving, vehicle safety

Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI. The work, published in Nature, comes from a team at Motional that includes CEO Laura Major, working alongside researchers from MIT’s Computer Science and Artificial Intelligence Laboratory. Their proposed method, […]

Motional and MIT AI explains self-driving car decisions Read More »

MIT AI forecasts extreme weather without historical data

ai, AI (Artificial Intelligence), AI in Action, Artificial Intelligence, Data Engineering & MLOps, Deep Dives, disaster planning, Environment & Sustainability, Featured News, flood prediction, Government & Public Sector AI, infrastructure, Machine Learning, MIT, predictive modelling, risk assessment, Utilities, weather forecasting

MIT engineers have built an AI tool that forecasts extreme weather without training on historical disaster data. Kai Chang, a mechanical engineering graduate student, and Professor Themis Sapsis developed the tool. It produces maps of events that have not appeared in a region’s historical record but remain statistically-possible. Each map also carries estimates of the

MIT AI forecasts extreme weather without historical data Read More »

Why health AI interfaces must adapt to user expertise

ai, AI (Artificial Intelligence), AI in Action, Artificial Intelligence, bias, Computer vision, dermatology, diagnostic tools, Explainability, Features, Health, Healthcare, Healthcare & Wellness AI, Human-AI Relationships, interface, medical, MIT, natural language, Natural Language Processing (NLP), patient safety, Research, study, Trust, Bias & Fairness

MIT researchers and collaborators found that AI explainability tools in the health sector can produce sharply different results depending on who uses them. When applied to skin disease diagnosis, non-experts improved their accuracy with AI assistance, although the improvement largely came from deferring to the model. Primary care providers showed a different pattern: they performed

Why health AI interfaces must adapt to user expertise Read More »

Human Learn

#Programmer, Advanced, Artificial Intelligence, D. Learning, Educación | 🇬🇧 Education, English (Articles), Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, Laboratory, Machine Learning (Article), MIT, news, Not numbered, Open Access, Uncategorized

Machine learning covers a lot of ground but it is also capable of making bad decision. We’ve also reached a stage of hype that folks forget that many classification problems can be handled by natural intelligence too. This package contains scikit-learn compatible tools that should make it easier to construct and benchmark rule based systems

Human Learn Read More »

labml.ai Deep Learning Paper Implementations

#Programmer, Artificial Intelligence, D. Learning, Deep Learning (Article), Deep Learning (Tutorial), Educación | 🇬🇧 Education, English (Articles), English (Tutorial), Formación | 🇬🇧 Training, Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, MIT, news, Normal, Not numbered, Open Access, Research, Tutorials

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.

labml.ai Deep Learning Paper Implementations Read More »