"Any sufficiently advanced technology is indistinguishable from magic." – Arthur C. Clarke (1961) This quote couldn't apply better to general search engines and web ranking algorithms. You can ask Bing about mostly anything and you'll get the best 10 results out of billions of webpages within a couple of seconds. If that's not magic, I don't know what is! Sometimes the query is about an obscure hobby. Sometimes it's about a news event that nobody could have predicted yesterday. Sometimes it's even unclear what the query is about!

Ever since Amazon turned its gaze to healthcare, questions about the eCommerce operator's plans and intentions have outnumbered answers. That's still true, and may remain so for the time being, but this week new details emerged that, at the least, is shedding light on Amazon's healthcare vision. Here's the scoop, courtesy of court testimony: Amazon wants to, in the words of The Wall Street Journal, "redesign health insurance." And that's only one possible aim -- others involve using artificial intelligence to improve diagnoses and the overall patient experience, but we'll get to that in a bit. Among the biggest ongoing mysteries for even the most sophisticated Amazon watchers is the specific purpose of the independent healthcare company that includes Amazon, Berkshire Hathaway and JPMorgan Chase, a venture that was announced last year. Thanks to a lawsuit and unsealed court testimony over a non-compete clause involving a United Healthcare executive who joined that Amazon-backed venture, at least one of the goals of the venture -- which still has no official public name -- has emerged.

Last month OpenAI rather dramatically withheld the release of its newest language model, GPT-2, because it feared it could be used to automate the mass production of misinformation. The decision also accelerated the AI community's ongoing discussion about how to detect this kind of fake news. In a new experiment, researchers at the MIT-IBM Watson AI Lab and HarvardNLP considered whether the same language models that can write such convincing prose can also spot other model-generated passages. The idea behind this hypothesis is simple: language models produce sentences by predicting the next word in a sequence of text. So if they can easily predict most of the words in a given passage, it's likely it was written by one of their own.

At the world's top computer-vision conference last June, Google and Apple sponsored an academic contest that challenged algorithms to make sense of images from twin cameras collected under varied conditions, such as sunny and poor weather. Artificial intelligence software proficient at that task could help the US tech giants with money-making projects such as autonomous cars or augmented reality. But the winner was an institution with very different interests and allegiances: China's National University of Defense Technology, a top military academy of the People's Liberation Army. That anecdote helps illustrate China's broad ambitions in AI and recent prominence on the field's frontiers. In 2017, the country's government announced a new artificial intelligence strategy that aimed to rival the US in the crucial technology by 2020.

Since the 1950s, researchers have documented the many types of predictions in which algorithms outperform humans. Algorithms beat doctors and pathologists in predicting the survival of cancer patients, occurrence of heart attacks, and severity of diseases. Algorithms predict recidivism of parolees better than parole boards. And they predict whether a business will go bankrupt better than loan officers. According to anecdotes in a classic book on the accuracy of algorithms, many of these earliest findings were met with skepticism.

The ethical landscape surrounding the use and widespread implementation of artificial intelligence (AI) is vast and complex. The forthcoming AI revolution will change the world, and our lives, in ways no other industrial revolution has done. Everyone should be forming opinions on the ethics of AI. Humans are capable of great acts of positive progress (e.g. AI will be no different.

It doesn't have to be perfect, but it needs to have enough quality and consistency for useful patterns to emerge. However, many companies are overwhelmed by the volume, velocity and variety of their data and find themselves unable to access data's fourth V: value. So how should we think about data preparation strategies to avoid potential data paralysis or over-ambition with your AI projects? The better the data, the better the AI. But for many companies, there's a problem: 85 percent of their data is either dark (whereby its value is unknown), redundant, obsolete or trivial.

Germany and France are two flourishing AI hubs, but the UK has been named the European AI "powerhouse" in a new report from London-based investment firm MMC Ventures. Published earlier this month, the analysis sheds light on the hype around the technology, but identifies healthcare as an area of increasing focus for AI entrepreneurs as systems start to embrace emerging technologies. Out of around 1,600 European early stage AI software companies, 21 percent are said to be focusing on health and wellbeing, with the UK dubbed the "heartland of European healthcare AI". This is due to a variety of factors, the research suggests, from the UK having universities that are world-renowned for medicine and teaching hospitals, to what is described as the "flywheel" effect of companies like DeepMind, Babylon or Benevolent AI, which are "simulating, attracting and recycling talent, capital and commercial engagement in the UK ecosystem". And although engaging with the NHS is still perceived to be "challenging", the analysis indicates that early stage companies can now benefit from a wider range of "accessible deployment opportunities". Health secretary Matt Hancock outlined today in a message to delegates at the Digital Health Technology Show in London his ambition to "make the UK the best place in the world to start and grow a health tech business".

Technology is being developed to track and monitor every tiny movement of a car's driver and its passengers. Radar, lasers and cameras are making their way inside cars with the intention to keep occupants safe and make controlling the vehicle even easier than normal. A plethora of firms are employing millimetre-wave radar which has such a small wavelength it can take highly detailed measurements. They are working on various applications which involve measuring biometrics, tracking respiration and heart rate, identifying males, female and pets. It also opens up a wealth of possibilities to control a car's features using only gestures and even by just looking at them. Radar, lasers and cameras are making their way inside cars with the intention to keep occupants safe and make controlling the vehicle even easier than normal.

WhatsApp appears to be working on a new feature to help users identify whether an image they receive is legitimate or not. While picking apart update 2.19.73, WABetaInfo discovered a "search by image" function that will let you upload a received image directly to Google to reveal "similar or equal" images on the web. With this info, you should be able to more accurately judge whether the picture is real, or fake news. The feature isn't available yet, and there's no official word on when it will be.