big data – Quanta Magazine

Neural Networks Need Data to Learn. Even If It’s Fake.

Amos Zeeberg — Fri, 16 Jun 2023 14:16:49 +0000

On a sunny day in late 1987, a Chevy van drove down a curvy wooded path on the campus of Carnegie Mellon University in Pittsburgh. The hulking vehicle, named Navlab, wasn’t notable for its beauty or speed, but for its brain: It was an experimental version of an autonomous vehicle, guided by four powerful computers (for their time) in the cargo area. At first, the engineers behind Navlab tried to...

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Sparse Networks Come to the Aid of Big Physics

Steve Nadis — Thu, 08 Jun 2023 14:33:06 +0000

Suppose you have a thousand-page book, but each page has only a single line of text. You’re supposed to extract the information contained in the book using a scanner, only this particular scanner systematically goes through each and every page, scanning one square inch at a time. It would take you a long time to get through the whole book with that scanner, and most of that time would be wasted...

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The Computer Scientist Peering Inside AI’s Black Boxes

Allison Parshall — Thu, 27 Apr 2023 13:51:57 +0000

Machine learning models are incredibly powerful tools. They extract deeply hidden patterns in large data sets that our limited human brains can’t parse. These complex algorithms, then, need to be incomprehensible “black boxes,” because a model that we could crack open and understand would be useless. Right? That’s all wrong, at least according to Cynthia Rudin, who studies interpretable machine...

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The Researcher Who Would Teach Machines to Be Fair

Sheon Han — Fri, 10 Mar 2023 15:20:33 +0000

Once in a while, a person can take an abstract concept that’s seemingly too vague for formal study and offer an elegant formal definition. Claude Shannon did it with information, and Andrey Kolmogorov did it with randomness. For the past few years, researchers have been trying to do the same for the concept of fairness in machine learning. Unfortunately, this has been trickier. Not only is the...

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How Big Data Carried Graph Theory Into New Dimensions

Stephen Ornes — Thu, 19 Aug 2021 15:37:40 +0000

Graph theory isn’t enough. The mathematical language for talking about connections, which usually depends on networks — vertices (dots) and edges (lines connecting them) — has been an invaluable way to model real-world phenomena since at least the 18th century. But a few decades ago, the emergence of giant data sets forced researchers to expand their toolboxes and, at the same time...

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