The popularity of Amazon's Echo smart speakers has helped push the audience for digital radio past that of FM and AM in the UK for the first time. The milestone, which was reached in the first quarter of this year, could prompt the government to launch a review to evaluate whether it should switch off the FM signal. Digital, which covers listening via digital audio broadcasting (DAB) sets in homes and cars, televisions and through services such as Echo, hit a record share of 50.9% of all radio listening in the three months to March. Amazon's smart speakers, powered by the virtual assistant Alexa, have helped reinvent the medium for a new, tech-savvy generation, many of whom have failed to embrace traditional radio listening. Listening online and via apps proved to be by far the fastest-growing segment of digital consumption, with hours of listening in the first quarter surging by 14m (17%) year on year to 95m hours.

Thousands of scientists worldwide tap into CERN's computer networks each day in their quest to better understand the fundamental structure of the universe. Unfortunately, they are not the only ones who want a piece of this vast pool of computing power, which serves the world's largest particle physics laboratory. The hundreds of thousands of computers in CERN's grid are also a prime target for hackers who want to hijack those resources to make money or attack other computer systems. But rather than engaging in a perpetual game of hide-and-seek with these cyber intruders via conventional security systems, CERN scientists are turning to artificial intelligence to help them outsmart their online opponents. Current detection systems typically spot attacks on networks by scanning incoming data for known viruses and other types of malicious code.

There is a persistent myth that bias is something we can "remove" from AI. Bias isn't something that we can, or even should, eliminate from AI. The idea that there could even be a bias-free AI is itself part of a psychological tendency (another bias, in fact) called "naive realism". Naive realism is our common-sense social tendency to think that we see the world objectively, and, worse, that it is the people who disagree with us that are biased. The myth of a bias-free AI comes directly from this bias. I'm not minimizing the appalling biases found in some AI systems (see: the AI beauty contest fail, the MIT/Stanford study of gender and skin-type bias in AI, and the machine learning system that amplified gender biases in images for examples).

Though insect-sized flying robots have been around for a while, none had been able to take untethered fight until now. Engineers at the University of Washington have revealed that the RoboFly has taken its first untethered flaps, marking the first time a wireless flying robotic insect has flown. Previously, the electronics the insects carried to power and control their wings were too heavy for the robots to fly with, meaning they had to remain connected to a wire. RoboFly is only slightly heavier than a toothpick and is powered by an onboard circuit that converts the laser energy into enough electricity to operate its wings. 'Before now, the concept of wireless insect-sized flying robots was science fiction.

Well, time to ask Google Style AI to match your outfit. For a long time, fashion startups and retailers have had their sights set on making fashion apparel recognisable and searchable by AI's image recognition. The idea behind was always to snap a picture of someone walking down the street wearing a super dress, then, a few seconds later have Google telling you about the brand, model, style, material price and where to buy it from. After years of research, development, investments, startups and technology organisations mixing in, finally, Google launched its version of the AI fashion recognition innovation. Called'Style Match,' the feature builds on the existing'Google Lens' technology which lets users take photos of any fashion items and find other similar products.

Audio Analytic's CEO Chris Mitchel smashes window panes with a sledge hammer to help teach machines how to hear the sound of breaking glass as well as humans. Amazon's Echo speaker knows that you only have to say "Alexa" to wake it up. But technically it can't tell the difference between two people murmuring in the corner of the room and the sound of radio static. That would take a deeper dive into the building blocks of sound itself. But one small startup in Cambridge, UK has spent ten years building up an entirely new language of sound which, for the first time, will allow machines to recognize the sound of human speech.

JAXenter: What is the difference between image and text from a machine's point of view? Christoph Henkelmann: Almost all ML methods, especially neural networks, want tensors (multidimensional arrays of numbers) as input. In case of an image the transformation is obvious, we already have a three-dimensional array of pixels (width x height x color channel), i.e. except for smaller pre-processing the image is already "bite-sized". There is no obvious representation for text. Text and words exist at a higher level of meaning, for example, if you simply enter Unicode-encoded letters as numbers in the net, the jump from coding to semantics is too "high".

Medical science continues to progress, allowing doctors to push the boundaries and always achieve more. One such example is the implantation of heart valves via the groin (percutaneous valve replacement /TAVI) – a procedure which has reached full swing in recent years due to it shorter recovery period and less invasive nature. Some questions, however, are still asked. Are costly procedures such as this as successful as scientific studies are? Do these innovative techniques have the effect of reducing mortality?

Nicholas Parry has a Bachelor of Science in genetics from the University of Sheffield and a Master of Research in neuroscience from the University of Nottingham. He has been a featured writer for Health Issues India and Hyderus since 2016. He is based in South Wales.

A team of computer scientists have built the smallest completely autonomous nano-drone that can control itself without the need for a human guidance. Although computer vision has improved rapidly thanks to machine learning and AI, it remains difficult to deploy algorithms on devices like drones due to memory, bandwidth and power constraints. But researchers from ETH Zurich, Switzerland and the University of Bologna, Italy have managed to build a hand-sized drone that can fly autonomously and consumes only about 94 milliWatts (0.094 W) of energy. Their efforts were published in a paper on arXiv earlier this month. At the heart of it all is DroNet, a convolutional neural network that processes incoming images from a camera at 20 frames per second.