How artificial intelligence can help combat systemic racism
MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.
MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.
A machine-learning model for image classification that’s trained using synthetic data can rival one trained on the real thing, a study shows.
Students propose solutions to re-imagine the customer experience for Hong Kong’s airport city development.
Researchers create a mathematical framework to examine the genome and detect signatures of natural selection, deciphering the evolutionary past and future of non-coding DNA.
Chemical engineers use neural networks to discover the properties of metal-organic frameworks, for catalysis and other applications.
Empowering a global community of learners in displacement.
New effort empowers MIT researchers to shape real estate’s future and build responsibly and sustainably.
MEng graduate students engage with IBM to develop their research skills and solutions to real-world problems.
A new MIT-wide effort launched by the Institute for Data, Systems, and Society uses social science and computation to address systemic racism.
A new technique boosts models’ ability to reduce bias, even if the dataset used to train the model is unbalanced.
A new machine-learning technique could pinpoint potential power grid failures or cascading traffic bottlenecks in real time.
A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.
A model’s ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report.
Lincoln Laboratory leads a large-scale measurement campaign in New York City to improve air dispersion models and emergency protocols.
Measuring traffic properties requires vast amounts of data. Meshkat Botshekan, a PhD student working with the MIT CSHub, is discovering a more efficient and affordable physics-inspired alternative.