Self-supervised machine learning adapts to new tasks without retraining

The field of machine learning is traditionally divided into two main categories: “supervised” and “unsupervised” learning. In supervised learning, algorithms are trained on labeled data, where each input is paired with its corresponding output, providing the algorithm with clear guidance. In contrast, unsupervised learning relies solely on input data, requiring the algorithm to uncover patterns …

Can “Safe AI” Companies Survive in an Unrestrained AI Landscape?

TL;DR A conversation with 4o about the potential demise of companies like Anthropic. As artificial intelligence (AI) continues to advance, the landscape is becoming increasingly competitive and ethically fraught. Companies like Anthropic, which have missions centered on developing “safe AI,” face unique challenges in an ecosystem where speed, innovation, and unconstrained power are often prioritized …

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AI Systems Governance through the Palantir Platform

Editor’s note: This is the second post in a series that explores a range of topics about upcoming AI regulation, including an overview of the the EU AI Act and Palantir solutions that foster and support regulatory compliance when using AI. This blog post provides an overview on how Palantir AIP empowers organizations to meet …