Amazon today announced the general availability of Amazon Personalize, an AWS service that facilitates the development of websites, mobile apps, and content management and email marketing systems that suggest products, provide tailored search results, and customize funnels on the fly. It's available in select AWS regions to start, including US East (Ohio), US East (North Virginia), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Singapore), and EU (Ireland). Additional locations are on the way. "We are excited to share with AWS customers the expertise we've developed during two decades of using machine learning to deliver great experiences on Amazon.com," said AWS' VP of machine learning Swami Sivasubramanian. "Customers have been asking for Amazon Personalize, and we are eager to see how they implement these services to delight their own end users. And the best part is that these artificial intelligence services, like Amazon Personalize, do not require any machine learning experience to immediately train, tune, and deploy models to meet their business demands."

Newton sits underneath an apple tree. History is spiced with Eureka! moments when human understanding leaps forward. Thomas Edison chose a more deliberate approach: Rather than waiting for lightning to strike, he saw innovation as a replicable, continuous process. His facility in Menlo Park, New Jersey, was the first industrial lab specifically designed to produce a continuous stream of commercially viable technology innovations built on new and emerging technologies.1 Of course, the pace of technology innovation today--and its impact on business--is exponentially faster than in Edison's industrial age.

One of China's fastest growing robotic startups is ramping up its international expansion, even as Trump's tariffs force the company to dial back on its US ambitions. Geek Plus Robotics automates supply chains by replacing warehouse workers with bots. The Beijing-based company recently raised more than 100 million dollars and expects to more than double that at its next funding round. China correspondent Tom Mackenzie spoke exclusively to the company's founder and CEO in Beijing about the firm' plans.

Cyber attackers currently focus most of their efforts on manipulating existing artificial intelligence (AI) systems for malicious purposes, instead of creating new attacks that use machine learning. That is the key finding of a report by the Sherpa consortium, an EU-funded project founded in 2018 to study the impact of AI on ethics and human rights, supported by 11 organisations in six countries, including the UK. However, the report notes that attackers have access to machine learning techniques, and AI-enabled cyber attacks will be a reality soon, according to Mikko Hypponen, chief research officer at IT security company F-Secure, a member of the Sherpa consortium. The continuing game of "cat and mouse" between attackers and defenders will reach a whole new level when both sides are using AI, said Hypponen, and defenders will have to adapt quickly as soon as they see the first AI-enabled attacks emerging. But despite the claims of some security suppliers, Hypponen told Computer Weekly in a recent interview that no criminal groups appear to be using AI to conduct cyber attacks.

New technology sometimes makes the world around us look unrecognizable. But human nature--even over the centuries--remains remarkably familiar. Again and again, as we've researched the stories that go into these podcast episodes, we've seen that the past is full of hints about what's coming around the corner. We hope you'll join us as we reveal The Secret History of the Future.

Machine learning has become popular for solving machine vision and other embedded computing problems. While classical machine learning algorithms need human intervention to extract features from data, machine learning algorithms or network models learn how to extract important features in data and make intelligent predictions about that data. Below, figure 1 shows a few examples where machine learning technology is adding intelligence to a variety of devices. In smart home appliances like a smart oven, machine learning can be used to classify food inside the oven and set the cooking temperature and time of the oven accordingly. In factories, machine learning can be used for detecting defects in the products or it can be used for predictive maintenance to help predict the remaining useful life of the motor or detecting anomaly in motor operations.

The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds--and looks--surprisingly low-tech. This window to the future is none other than a piece of glass. University of Wisconsin-Madison engineers have devised a method to create pieces of "smart" glass that can recognize images without requiring any sensors or circuits or power sources. "We're using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass," says UW-Madison electrical and computer engineering professor Zongfu Yu. Yu and colleagues published details of their proof-of-concept research today in the journal Photonics Research.

Just about everyone agrees that the traditional, annual-review-based way of evaluating employees simply doesn't work anymore, if it ever did. In fact, it's hard to think of anything businesses routinely do that's more universally unpopular. A recent global Mercer survey of HR leaders worldwide found that just 2% believe their current performance-management systems are very effective. No wonder McKinsey reported, in another recent study, that two-thirds of employers say they're making big changes, or trying to. "Managers and employees alike see the old annual-review approach as too subjective, too bureaucratic, and too backward-looking," notes Bryan Hancock, a McKinsey partner in Atlanta who has worked closely with companies where artificial intelligence is being applied to evaluating humans' performance.

Do chatbots, robots and digital assistants intrigue you? What about automated vehicles and virtual assistants? AI is a field of computer science that focuses on the creation of a machine that can replicate human behaviour. The science fiction of yesterday quickly becomes reality. AI statistics surrounding the business and tech industries are changing.

Robots can drive on the plains and craters of Mars, but what if we could explore cliffs, polar caps and other hard-to-reach places on the Red Planet and beyond? Designed by engineers at NASA's Jet Propulsion Laboratory in Pasadena, California, a four-limbed robot named LEMUR (Limbed Excursion Mechanical Utility Robot) can scale rock walls, gripping with hundreds of tiny fishhooks in each of its 16 fingers and using artificial intelligence (AI) to find its way around obstacles. In its last field test in Death Valley, California, in early 2019, LEMUR chose a route up a cliff while scanning the rock for ancient fossils from the sea that once filled the area. For Climbing Robots, the Sky's the Limit: The climbing robot LEMUR rests after scaling a cliff in Death Valley, California. The robot uses special gripping technology that has helped lead to a series of new, off-roading robots that can explore other worlds.Credit: NASA/JPL-Caltech LEMUR was originally conceived as a repair robot for the International Space Station.