This tells me that the page does not exist (apparently because I'm in a GDPR region that doesn't allow this behavior from LinkedIn, I understand now from reading this thread).
Physical systems that can learn by themselves
Andrea Liu
Brains learn and perform an enormous variety of tasks on their own, using relatively little energy. Brains are able to accomplish this without an external computer because their analog constituent parts (neurons) update their connections without knowing what all the other neurons are doing using local rules. We have developed an approach to learning that shares the property that analog constituent parts update their properties via a local rule, but does not otherwise emulate the brain. Instead, we exploit physics to learn in a far simpler way. Our collaborators have implemented this approach in the lab, developing physical systems that learn and perform machine learning tasks on their own with little energy cost. These systems should open up the opportunity to study how many more is different within a new paradigm for scalable learning.
The fallout from the hack of a little-known but pivotal health-care company is inflicting pain on hospitals, doctor offices, pharmacies and millions of patients across the nation, with government and industry officials calling it one of the most serious attacks on the health-care system in U.S. history.
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Change Healthcare is a juggernaut in the health-care world, processing 15 billion claims totaling more than $1.5 trillion a year, the company says. It operates the largest electronic “clearinghouse” in the business, acting as a pipeline that connects health-care providers with insurance companies who pay for their services and determine what patients owe. It supported tens of thousands of physicians, dentists, pharmacies and hospitals, handling 50 percent of all medical claims in the United States, the Justice Department wrote in a 2022 lawsuit that unsuccessfully tried to block UnitedHealth from acquiring the company.
Wednesday, January 31, 2024
The Future of Artificial Intelligence
AI is all around us—recognizing our faces in photos, transcribing our speech, constructing our news feeds, navigating our driving routes, answering our search queries, and much more. But rapidly improving AI is poised to play a much bigger role in all of our lives. In this lecture, AI expert Melanie Mitchell will demystify how current-day AI works, how “intelligent” it really is, and what our expectations—and concerns—about its near-term and long-term prospects should be.
I'm surprised by the results (from what I've heard so far on the podcast) and a bit skeptical given the involvement of Meta. Looking forward to reactions from various scientific and technical communities.
> The AI Text Classifier is a fine-tuned GPT model that predicts how likely it is that a piece of text was generated by AI from a variety of sources, such as ChatGPT.
> This classifier is available as a free tool to spark discussions on AI literacy.
