An optical fibre technology can help chips communicate with each other at the speed of light, enabling them to transmit 80 times as much information as they could using traditional electrical connections. That could significantly speed up the training times required for large artificial intelligence models – from months to weeks – while also reducing the energy and emissions costs for data centres. Most advanced computer chips still communicate using electrical signals carried over copper wires. But as the tech industry races to train large AI models – a process that requires networks of AI superchips to transfer huge amounts of data – companies are eager to link chips using the light-speed communication of fibre optics. This technology isn't new: the internet already relies on undersea fibre-optic cables stretching thousands of kilometres between continents.

American innovation, from smartphones to search engines to gene sequencing, is built on a foundation of impossibly intricate, perfectly etched silicon. But few of those semiconductors are actually made in the US. Only 12 percent of chips sold worldwide were made in the US in 2019, down from 37 percent in 1990. For decades, that wasn't seen as a problem. US companies were world leaders in designing cutting-edge chips, the most valuable and important part of the process.

Amid the shift towards more complex chips at advanced nodes, many chipmakers are exploring or turning to advanced forms of machine learning to help solve some big challenges in IC production. A subset of artificial intelligence (AI), machine learning, uses advanced algorithms in systems to recognize patterns in data as well as to learn and make predictions about the information. In the fab, machine learning promises to provide faster and more accurate results in select areas, such as finding and classifying defects in chips. Machine learning also is used in other process steps, but there are still some challenges to deploy it. It has been used in computing and other fields for decades. It first appeared in semiconductor production in the 1990s. Some saw it as a way to help automate the steps for some manually-driven fab equipment. Over time, machine learning has made staggering progress in computing and elsewhere.

The limits of silicon have not been reached quite yet. Today, an IBM-led group of researchers have detailed a breakthrough transistor design, one that will enable processors to continue their Moore's Law march toward smaller, more affordable iterations. They achieved it not with carbon nanotubes or some other theoretical solution, but with an inventive new process that actually works, and should scale up to the demands of mass manufacturing within several years. That should also, conveniently enough, be just in time to power the self-driving cars, on-board artificial intelligence, and 5G sensors that comprise the ambitions of nearly every major tech player today--which was no sure thing. For decades, the semiconductor industry has obsessed over smallness, and for good reason.

Gordon Ramsay's father-in-law and two of his brothers-in-law have admitted hacking computers at the celebrity chef's restaurant and business empire during a time of bitter dispute in the family. Chris Hutcheson and two of his sons - Adam and Chris Jr – pleaded guilty at the Old Bailey on Tuesday to charges of conspiring to cause a computer to access programs and data without authority. Hutcheson's daughter, Orlanda Butland, denied the charge but the prosecution offered no evidence in her case, effectively withdrawing proceedings. In October 2010, Hutcheson was sacked by Ramsay as chief executive of Gordon Ramsay Holdings. The following year the pair ended up in a high court battle, with Ramsay alleging his computers had been hacked and blaming Hutcheson amid claims that emails between Ramsay and Tana had been read by a third party.