In this age of algorithms, personalized insurance is encountering legal limits, especially the ban on discrimination. Can we balance accurate pricing, solidarity, and fairness?

AI adoption is a test of values. For nonprofits, the decision carries implications far beyond operational efficiency.

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Ramya Krishnamoorthy shares a detailed case ...

Will the application of AI reduce staff in pursuit of efficiency, or can we design systems that preserve human dignity, agency and shared meaning? This is the tension driving cognitive migration. But ...

Image generators are designed to mimic their training data, so where does their apparent creativity come from? A recent study suggests that it's an inevitable by-product of their architecture.

Delphi-2M reads your medical history like a language model reads text—and forecasts health problems 20 years out with surprising accuracy.

Abstract: This study explores the development of AI-powered adaptive systems for English language learning, focusing on leveraging Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP ...

Expertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. Artificial intelligence has officially crossed the hype threshold. From boardrooms to ...

Ah yes, the future we were promised: flying cars, universal basic income, robots doing our chores. The future we got? Rents going up because Chad from Stanford learned how to fine-tune a chatbot.

Compliance, compute and cross-border rules are becoming the true arbiters of A.I. advantage. Unsplash+ The contest for A.I. leadership has shifted from lab breakthroughs to law books. Over the next ...

It can take weeks to write a grant proposal, so how would it feel to have a machine reject it in seconds? Researchers in Spain have been finding out, after a major funding foundation in the country ...

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms—addressing a ...