A new AI weather prediction system, developed by a team of researchers from the University of Cambridge, can deliver accurate forecasts which use less computing power than current AI and physics-based forecasting systems. The system, Aardvark Weather, has been supported by the Alan Turing Institute, Microsoft Research and the European Centre for Medium Range Weather Forecasts. It provides a blueprint for a new approach to weather forecasting with the potential to improve current practices. The results are reported in the journal Nature. "Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries," said Professor Richard Turner from Cambridge's Department of Engineering, who led the research.

You might think your desktop computer is old, but that's nothing compared to these ancient relics. Two of the world's very first desktop computers have been discovered during a house clearance in London. The chance discovery revealed two of only three surviving Q1 computers anywhere in the world. Although it is often now overlooked, the Q1 paved the way for the computers we have today when it was launched more than 50 years ago. Brendan O'Shea, head of Just Clear which discovered the items, says: 'Never did I imagine that we'd find something so important to the field of technology and the history of computing.'

On Sunday, 22 January 1984, the Los Angeles Raiders defeated the Washington (then) Redskins 38-9 in Super Bowl XVIII. With the exception of a few aging Raiders' fans, what we all remember better from that evening 40 years ago was one advertisement that set the tone for a techno-optimism that would dominate the 21st century. The ad showed an auditorium full of zombie-like figures watching a projection of an elderly leader who resembled the Emperor from 1980's The Empire Strikes Back. A young, athletic woman in red and white (the colors of the flag of Poland, which had been engaging in a massive labor uprising against the Soviet-controlled communist state) twirls a hammer and throws it through the screen framing the leader's face, just as armored police rush in to try to stop her. The ad explicitly invoked George Orwell's dystopian novel Nineteen Eighty-Four.

It's sensible not to take everything ChatGPT told you at face value, not least because the software admitted itself that you shouldn't take everything it says as gospel. Oxford University's Mike Wooldridge, who spoke to us when this story was breaking, is with us now to provide the human touch, and hopefully help us understand how all this is possible… Mike - What's happened is that people have realised that scale matters in artificial intelligence. And what scale means for these systems is three things. Firstly, it means how big are your neural networks? Literally the larger your neural networks are, the more elements that they have. The amount of training data that you use to train your system - modern artificial intelligence absolutely relies on training data so that matters.

A new school season is upon us – cue the rolling eyes, students – and so you might be in the market for a new computer. Whether you're back in the classroom or continuing to learn online, it's simply the most important piece of tech to help you stay on your game. Problem is, how do you decide what kind of computer is for you? Not only are there varying prices, competing operating systems and countless brands to choose from, but the student – or the parent(s) footing the bill – must decide on an ideal form factor (or type of computer), such as a laptop, desktop, 2-in-1 or all-in-one. And you might think you need a degree in computer science just to understand today's specifications ("specs").

The open-source Brain Simulator II neuron engine has been successfully tested with one billion neurons on a desktop computer comprised completely of off-the-shelf components. From a performance perspective, this system processes more than 2.5 billion synapses per second. For this test, the network included 10 million neurons with 500 synapses per neuron, for a total of 5 billion neurons which could be fully processed in 1,900 milliseconds. For comparison, the identical program on a four-year-old, four-core CPU can only achieve 0.7 billion synapses per second. The computer in this example included an AMD Ryzen Threadripper 3990X CPU running at 2.9Ghz (not overclocked) and 128 Gigabytes of RAM.

In a significant advance in the development of Artificial General Intelligence (AGI), the Brain Simulator II neural simulator successfully tested one billion neurons on a desktop computer comprised completely of off-the-shelf components. From a performance perspective, the system processed three billion synapses per second. Brain Simulator II is an open-source software platform for proving the evolution of artificial intelligence (AI) to AGI. Seen as another step toward creating brain-level functionality on computers, the spiking neural models used by the Brain Simulator II are more like biological neurons than traditional AI models and contribute immensely to the efficiency of the program. The computer used for this achievement included an AMD Ryzen Threadripper 3990X CPU running at 2.9Ghz (not overclocked) and 128 Gigabytes of RAM.

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research.[1] The term was coined by analogy to the idea of a nuclear winter.[2] The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.[2] At the meeting, Roger Schank and Marvin Minsky--two leading AI researchers who had survived the "winter" of the 1970s--warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.[2] Hype is common in many emerging technologies, such as the railway mania or the dot-com bubble. The AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists.

Covenant Eyes is not the only tech firm to play on these concerns, however. California-based X3watch, for example, offers a similar tracking and reporting feature, albeit one that works by creating a categorised list of the sites users visit that is then shared with their accountability partners. 'This is an opportunity to know and be known,' the X3watch website argues. 'Whether your chosen partner is a friend or a spouse, or you've come across explicit activity on your children's devices, the true goal is liberation that blossoms from open and honest relationships with others who are dedicated to your well-being.' An annual subscription to X3watch is currently priced at $70 (£54) per year.

Artificial intelligence has been around since 1956 when the term was first coined. Those in the industry know that there has been previous hype and then disillusionment around AI. The period of decline of interest in AI is known in the industry as an AI winter, and has happened twice before. An AI winter is a point at which research, investment, and funding for AI goes into a period of decline and it's hard to get funding for research or other projects that have to do with artificial intelligence, and talent and companies focus their efforts elsewhere. Today, there is a lot of hype surrounding artificial intelligence, but is AI around to stay or will it see its period of interest peak and wane as it has in the past?