Novel technique overcomes spurious correlations problem in AI

AI models often rely on “spurious correlations,” making decisions based on unimportant and potentially misleading information. Researchers have now discovered these learned spurious correlations can be traced to a very small subset of the training data and have demonstrated a technique that overcomes the problem. The work has been published on the arXiv preprint server.

Disentangled Representational Learning with the Gromov-Monge Gap

Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretability, or fairness. Although remarkably challenging to solve in theory, disentanglement is often achieved in practice through prior matching. Furthermore, recent works have shown that prior matching approaches can be enhanced by leveraging …

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Palantir’s Blueprint for Early Career Success in Product Design

Editor’s Note: Product Designers are key members of Palantir product teams. This blog post features a banner by Product Designer Sarah, a self-reflection by Product Designer Phoebe on navigating her early career, and a Q&A with design colleagues. Insights from Palantir Product Design Phoebe, Product Designer I’m still figuring out what kind of designer I want to …

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Add Zoom as a data accessor to your Amazon Q index

For many organizations, vast amounts of enterprise knowledge are scattered across diverse data sources and applications. Organizations across industries seek to use this cross-application enterprise data from within their preferred systems while adhering to their established security and governance standards. This post demonstrates how Zoom users can access their Amazon Q Business enterprise data directly …