Page 10: Research news on Trustworthy machine learning

Trustworthy machine learning addresses methods for training and deploying models that are secure, privacy-preserving, and robust to manipulation. Work in this area develops federated and decentralized learning schemes, cryptographic and homomorphic encryption frameworks, and privacy-preserving compression to protect data and models. It also studies adversarial example generation and defenses, certified unlearning, bias and spurious correlation mitigation, and the use of synthetic and filtered data. Applications span fraud and cyberattack detection, fake news and deception detection, and secure automation systems.

Computer Sciences

New AI tool learns to read medical images with far less data

A new artificial intelligence (AI) tool could make it much easier—and cheaper—for doctors and researchers to train medical imaging software, even when only a small number of patient scans are available.

Business

Palantir, the AI giant that preaches US dominance

Palantir, an American data analysis and artificial intelligence company, has emerged as Silicon Valley's latest tech darling—one that makes no secret of its macho, America-first ethos now ascendant in Trump-era tech culture.

Computer Sciences

Platform can make machine learning more transparent and accessible

What began as a Ph.D. project has grown into a website with 120,000 unique visitors each year. With the platform OpenML, researcher Jan van Rijn is contributing to open science, aiming to make machine learning more transparent, ...

Machine learning & AI

Democratizing AI-powered sentiment analysis

Artificial intelligence is accelerating at breakneck speed, with larger models dominating the scene—more parameters, more data, more power. But here is the real question: Do we really need bigger to be better? We challenged ...

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