Once upon a time, "reinstalling Windows" was an often recommended remedy for all sorts of computer problems. Windows 95 and XP were notorious for becoming less stable over time, with "crap in the machine" in the form of settings left in the Registry, traces of uninstalled programs that had not been properly removed, and other things that lurked. Indeed, many people chose to reinstall from time to time even if the computer showed no symptoms, as part of regular maintenance. Windows 10 and 11 are much better at keeping order and cleaning up automatically. Today, frequent reinstallations are not something we at PCWorld or any other experts recommend.

Mariza Ferro is a professor at the Federal Fluminense University and a visiting professor at Bordeaux University. She has been working in the field of AI since 2002. She works on AI for good, including human-centric AI, ethical and trustworthy AI, green and sustainable AI, and AI for sustainable development goals. She guides her research based on the principle that AI must benefit humankind. Furthermore, she is also working with public outreach by making science available for all.

A vast amount of data is created every minute, both in the private sector and industry. Whereas it is often easy to get hold of data in the private entertainment sector, in the industrial production environment it is much more difficult due to laws, preservation of intellectual property, and other factors. However, most machine learning methods require a data source that is sufficient in terms of quantity and quality. A suitable way to bring both requirements together is federated learning where learning progress is aggregated, but everyone remains the owner of their data. Federate learning was first proposed by Google researchers in 2016 and is used for example in the improvement of Google's keyboard Gboard. In contrast to billions of android users, comparable machinery is only used by few companies. This paper examines which other constraints prevail in production and which federated learning approaches can be considered as a result.

The world's art on a 4GB USB stick. Download it! Locally generate AI Art You can do this in three easy steps. This may be used on a PC or mobile device.

Since the first Industrial Revolution, mankind has been scared of future technologies. People were afraid of electricity. People were afraid of trains and cars. But it always took just one or two generations to get completely used to these innovations. It's true that most technologies caused harm in some ways, but the net outcome was usually good. This may be true for future technologies too, although there are serious ethical and philosophical reasons to be scared of some of them. Some of them shouldn't really scare us. And some of them are already shaping our world. Before we begin, I have to warn you: some of the things you will read in this story can be VERY controversial. I need you to approach this story with a very open mind, and acknowledge that the ideas I present here are just that, ideas. I hold no extreme or fixed views, nor do I claim to have the exact answers to ethical and philosophical questions. You may have completely different ideas, and that's totally fine. Cryonics may seem very sci-fi (to be fair, everything in this story does), but it already exists. There are companies that freeze you as soon as you die, so you can be brought back to life when technology and medicine will be advanced enough. Seriously, companies like this (I'm NOT affiliated to them).

The other day I had to log in to a service I hadn't used before. Since I was a new user, the website decided that it needed to check that I wasn't a robot and so set me a Captcha (Completely Automated Public Turing test to tell Computers and Humans Apart). This is a challenge-response test to enable a computer to determine whether the user is a person rather than a machine. I was presented with an image of a roadside scene over which was overlaid a grid. My "challenge" was to click on each cell in the grid that contained a traffic sign, or part thereof.

If you want to dabble with machine learning on the $35 Raspberry Pi you've never had more options. Google offers several kits for carrying out speech and image recognition on the Pi and is readying a USB stick that will turbo charge the Pi's machine-learning capabilities. The tech giant recently boosted the Pi's machine-learning credentials even further by officially supporting its machine learning software framework TensorFlow on the board. If you want to get started with machine learning on the Pi, here's everything you need to know. Google's Artificial Intelligence Yourself (AIY) kits provide a great introduction to machine learning on the Pi.

As more carmakers adopt "over the air (OTA)" software updates for their increasingly connected and autonomous cars, is the risk of hacker hijack also increasing? Imagine jumping in your car but being taken somewhere you didn't want to go - into oncoming traffic, say, or even over a cliff. That may seem like an extreme scenario, but the danger is real. Hackers showed two years ago that they could remotely take control of a Chrysler Jeep. And earlier this year, Tesla boss Elon Musk warned about the dangers of hackers potentially taking control of thousands of driverless cars.

Think only a large enterprise has the resources to deploy artificial intelligence technology? Intel aims to make AI more affordable and accessible, especially to smaller companies and entrepreneurs. Last month, the company introduced the Movidius Neural Compute Stick, which it billed as "the world's first USB-based deep learning inference kit and self-contained" AI accelerator. The $79 USB stick delivers "dedicated deep neural network processing capabilities to a wide range of host devices at the edge," Intel says. With the USB stick, Intel suggests that product developers, researchers and makers will be able to add AI capabilities to their devices and develop, tune and deploy AI-based applications far more easily.

Intel, having accepted the inevitable, has dropped out of the wearables and fitness band game, and canned the teams working on that strap-on tech. Now it's shifted its attention to the next thing it will presumably quickly lose interest in: a USB stick for running machine-learning workloads. Having read between the lines over the past six months, you'd have been forgiven for assuming Chipzilla had given up on the wearables market. Since around 2015, Intel has been going bonkers for smartwatches, health trackers, drones, toys, and similar gizmos – yet it hasn't really talked about them in what feels like ages. Last month, Intel killed off a bunch of chips that were supposed to the used in the embedded and low-end space.