Category Technology/Electronics

The rapid technological advancements of our world have been enabled by our capacity to design and fabricate ever smaller electronic chips. These underpin computers, mobile phones and every smart device deployed to date.

One of the many challenges is that electronic components generate increasingly more heat as they are miniaturized. A significant issue lies in making the wires which connect the transistors on the chip thinner while ensuring that the minimum amount of heat is released.

These interconnects are typically made from copper, and as we start to scale them down to nano-scale thicknesses, their electrical resistance increases rapidly because the electrons moving along the wires have a higher probability of colliding into the surface of th...

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...

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...