Category Physics

For decades there has been near constant progress in reducing the size, and increasing the performance, of the circuits that power computers and smartphones. But Moore’s Law is ending as physical limitations – such as the number of transistors that can fit on a chip and the heat that results from packing them ever more densely – are slowing the rate of performance increases. Computing capacity is gradually plateauing, even as artificial intelligence, machine learning and other data-intensive applications demand ever greater computational power.

Novel technologies are needed to address this challenge...

A tool developed by Keele University researchers has been shown to help detect fake news with an impressive 99% level of accuracy, offering a vital resource in combating online misinformation

The researchers Dr. Uchenna Ani, Dr. Sangeeta Sangeeta, and Dr. Patricia Asowo-Ayobode from Keele’s School of Computer Science and Mathematics, used a number of different machine learning techniques to develop their model, which can scan news content to give a judgment of whether a news source is trustworthy and genuine or not.

The method developed by the researchers uses an “ensemble voting” technique, which combines the predictions of multiple different machine learning models to give an overall score.

Impressively, this technique was accurate in identi...

The operation and performance of quantum computers relies on the ability to realize and control entanglement between multiple qubits. Yet entanglement between many qubits is inherently susceptible to noise and imperfections in quantum gates.

In recent years, quantum physicists and engineers worldwide have thus been trying to develop more robust protocols to realize and control entanglement. To be most effective for real-world applications, these approaches should reliably support long-range entanglement, or in other words ensure that qubits remain entangled even when they are separated by large distances.

Researchers at IBM Quantum, University of Cologne and Harvard University set out to demonstrate one of these protocols in an experimental setting.

Their paper, published in ...

By conducting tests under an experimental scenario, a team of medical researchers and AI specialists at NYU Langone Health has demonstrated how easy it is to taint the data pool used to train LLMs.

For their study published in the journal Nature Medicine, the group generated thousands of articles containing misinformation and inserted them into an AI training dataset and conducted general LLM queries to see how often the misinformation appeared.

Prior research and anecdotal evidence have shown that the answers given by LLMs such as ChatGPT are not always correct and, in fact, are sometimes wildly off-base...