Banks are now offering an ever-growing number of digital functionalities1 targeted at delivering a superior client experience, exploring so-called "new" technologies to serve clients and providing more integrated and seamless services. However, the true yardstick for differentiation in an emerging banking ecosystem will be not the number of digitised services or banking apps but the value added or enhanced experience for clients. This leads to questions about how banking services should be digitised, what they should consist of in the short- and long-term, and more importantly, what capabilities and technologies banks should focus on to maintain their leading roles in emerging banking ecosystems in the future. With a new generation of'digital native' clients, core banking values and banking preferences will change entirely. According to Deloitte research, already today, 29% of millennials trust global technology firms to provide financial services.

As many folks continue to look skyward in expectation of drone delivery services, they're missing the development of an increasingly popular delivery technology back on terra firma. Wheel-based delivery robots may not be quite as sexy as their flying counterparts, but with fewer regulatory hoops to jump through than drone services, the technology is likely to become more widely used sooner rather than later by companies looking to make their delivery operations more efficient. The latest outfit to join the race is U.S. startup Refraction A.I. with its autonomous Rev-1 machine. The brainchild of University of Michigan professors Matthew Johnson-Roberson and Ram Vasudevan, the Rev-1 tootles along on three wheels instead of the four or six that we see with most of the competition. The wheels and storage compartment mean the look of the Rev-1 is similar to efforts from dominant player Starship Technologies, as well as delivery robots from FedEx and Amazon.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

At HPE Discover 2019, TechRepublic's James Sanders spoke with Aruba's Larry Lunetta about what is needed to deploy AI in security products. The following is an edited transcript of the interview. Larry Lunetta: So the AI washing is absolutely true--and you can reach back to the late '90s, it was web washing, right, as the internet became popular and then roll forward, it's cloud washing. There's a set of waves that the marketers ride, and AI is certainly one now. The interesting thing is as you go through those phases, the technologies get tougher and tougher to actually execute.

Years ago, Landing.ai founder and former Google Brain researcher Andrew Ng famously declared that artificial intelligence is the "new electricity." In short, AI will revolutionize the way all businesses will work in the future. But as more companies race to integrate AI into their operations, many are finding that it's not as easy as they thought it would be. At VentureBeat's Transform 2019 conference in San Francisco, Landing.ai VP of transformation Dongyan Wang explained why companies seem to fail so often and the steps they need to take to make meaningful progress.

"The Boy Who Cried Wolf" has long been a staple on nursery room shelves for a reason: It teaches kids that joking too much about a possible threat may turn people ignorant when the threat becomes an actual danger. President Donald Trump has been warning about "fake news" throughout his entire political career putting a dark cloud over the journalism professional. And now the real wolf might be just around the corner that industry experts should be alarmed about. The threat is called "deepfaking," a product of AI and machine learning advancements that allows high-tech computers to produce completely false yet remarkably realistic videos depicting events that never happened or people saying things they never said. A viral video starring Jordan Peele and "Barack Obama" warned against this technology in 2018, but the message was not enough to keep Jim Carrey from starring in "The Shining" earlier this week.

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Vegebot, a vegetable-picking robot, uses machine learning to identify and harvest a commonplace, but challenging, agricultural crop. A team at the University of Cambridge initially trained Vegebot to recognize and harvest iceberg lettuce in the laboratory. It has now been successfully tested in a variety of field conditions in cooperation with G's Growers, a local fruit and vegetable co-operative. Although the prototype is nowhere near as fast or efficient as a human worker, it demonstrates how the use of robotics in agriculture might be expanded, even for crops like iceberg lettuce which are particularly challenging to harvest mechanically. The researchers published their results in The Journal of Field Robotics.

There is an unprecedented growth in the percentage of aging population throughout the world, particularly in growing economies such as Europe, Japan and China. Form 2000 to 2050, the percentage of the world's population who is 60 years of age and older will approximately double from about 12% to 22% (from 605 million to 2 billion). During the same period, the number of people aged 80 years and older will quadruple. In the USA, 14.5% of the population is 65 years or older, but by 2030 these number is anticipated to grow to 20%. This rapid aging demographic will directly affect social, economic and health outcomes for these growing economies.

The promise of autonomous cars has no doubt been an exciting and intoxicating one for technophiles everywhere, not to mention those who see driving as a chore that must be endured, rather than enjoyed. There's no shortage of autonomy evangelists - Tesla CEO Elon Musk is a famous example, Ford reckons it will have its first truly self-driving car ready by 2021 and BMW and Mercedes have similar timelines - while ridesharing companies and Silicon Valley tech giants like Uber and Google are also bullish on fully-autonomous vehicles. And why wouldn't they be? Without a driver taking a cut of fares, profit margins for those businesses would explode, while also giving the public cheaper rides and potentially freeing them from the financial burden of owning a car. But according to one of the world's biggest manufacturers of the advanced sensors that make autonomous cars possible, the utopian vision of completely hands-off, point-to-point driving is highly unlikely for the foreseeable.