Category Physics

The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model. Researchers developed a technique guaranteeing that data remain secure during multiparty, cloud-based computation. This method, which leverages the quantum properties of light, could enable organizations like hospitals or financial companies to use deep learning to securely analyze confidential patient or customer data.

Deep-learning models are being used in many fields, from health care diagnostics to financi...

Tohoku University’s Dr. Le Bin Ho has explored how quantum squeezing can improve measurement precision in complex quantum systems, with potential applications in quantum sensing, imaging, and radar technologies. These findings may lead to advancements in areas like GPS accuracy and early disease detection through more sensitive biosensors.

Quantum squeezing is a concept in quantum physics where the uncertainty in one aspect of a system is reduced while the uncertainty in another related aspect is increased. Imagine squeezing a round balloon filled with air. In its normal state, the balloon is perfectly spherical. When you squeeze one side, it gets flattened and stretched out in the other direction...

A team of AI researchers at Universitat Politècnica de València, in Spain, has found that as popular LLMs (Large Language Models) grow larger and more sophisticated, they become less likely to admit to a user that they do not know an answer.

In their study published in the journal Nature, the group tested the latest version of three of the most popular AI chatbots regarding their responses, accuracy, and how good users are at spotting wrong answers.

As LLMs have become mainstream, users have become accustomed to using them for writing papers, poems or songs and solving math problems and other tasks, and the issue of accuracy has become a bigger...

The LLMs that have increasingly taken over the tech world are not “cheap” in many ways. The most prominent LLMs, such as GPT-4, took some $100 million to build in the form of legal costs of accessing training data, computational power costs for what could be billions or trillions of parameters, the energy and water needed to fuel computation, and the many coders developing the training algorithms that must run cycle after cycle so the machine will “learn.”

But, if a researcher needs to do a specializ...