As seen in Part 1 and Part 2 of this series, it is hard not to feel excited about machine learning. First, it empowers machines to teach themselves the tasks that humans can perform but find difficult to "teach" a computer via conventional coding (e.g. Secondly, it enables computers to perform tasks that far exceed human abilities, like analysing terabytes of data at lightning speed to unearth hidden patterns and make sense of them. But it is also hard not to feel some unease about the prospect of self-improving computer systems with increasingly human-like and super-human aptitudes, whether it is the threat of mass unemployment, the erosion of privacy, or simply the inability to understand, validate and trust the technologies that will increasingly impact our lives. These problems that artificial intelligence (AI) is throwing back at us are complex and multifaceted, and to tackle them requires concerted endeavours by our technologists, entrepreneurs, lawmakers and thinkers from all fields and walks of life. It will be a test of humankind's collective wisdom to ensure that our social institutions keep up with our technological progress. The advent of autonomous vehicles (AVs) illustrates the wide-ranging economic, legal and ethical questions that new technologies raise. AVs are already roaming the streets and conveying passengers in parts of the world, and many more are expected to hit the roads over the next five years as tech companies like Google, Baidu and Lyft race against incumbent automakers to make reliable and affordable self-driving cars. This is likely to dramatically alter the economics of transportation, from ownership rate to utilisation rate. It is estimated that in the US and the UK our cars on average are being driven just 5% of the time and they spend the remaining 95% in a garage or a car park.[1] That ratio may well be reversed if the availability of door-to-door transport is no longer linked to the availability of human drivers.

An innovative AI startup from Israel is using deep-learning AI technology to listen to machinery and predict whether it's about to go wrong. Wondering if your car engine has a problem just got a lot easier. A lot of the most high-profile applications of deep learning technology involve aspects of computer vision, such as cutting-edge facial-recognition technology. However, an innovative artificial intelligence startup from Israel is looking to apply those same neural networks and smart algorithms to another area -- acoustics. Better yet, they are doing so to help users spot early warning signs that machines, such as cars, may be about to fail. "I was on a train about three years ago, going back to my hotel after a business meeting," 3DSignals CEO Amnon Shenfeld told Digital Trends.

Leading tech companies ship AI free within their products (Siri, Alexa, Google Assistant), powering our phones and the rapidly growing home personal assistant market. Indeed, they are becoming increasingly good at answering our questions, making us smarter. Teaching not rote facts and figures, but instead teaching students the paths to find this knowledge on their own. Teaching students -- as we do with computers through AI -- how to learn. We are stuck with centuries old methodologies, where schools and teachers act like the gateway to knowledge, but at a time when students can access all they want by simply asking Alexa.

Inference in complex and high-dimensional statistical models is a very challenging problem that is ubiquitous in applications such as climate informatics [Monteleoni et al., 2013], bioinformatics [Cohen, 2004] and machine learning [Wainwright and Jordan, 2008], to mention a few. We are interested in sequential Bayesian inference in settings where we have a sequence of posterior distributions that we need to compute. To be specific, we are focusing on settings where the model (or state variable) is high-dimensional, but where there are local dependencies. One example of the type of models we consider are the so-called spatiotemporal models [Wikle, 2015, Cressie and Wikle, 2011, Rue and Held, 2005]. Sequential Monte Carlo (SMC) methods comprise one of the most successful methodologies for sequential Bayesian inference. However, SMC struggles in high dimensions and these methods are rarely used for dimensions, say, higher than ten [Rebeschini and van Handel, 2015].

Aircraft fuel is without a doubt the biggest cost for any airline and often receives widespread attention, especially when airlines hedge their bets the wrong way. Cathay Pacific reported a HK$4.49 billion fuel-hedging loss in the first half of 2016, which has hurt the airline's profitability. The second biggest expense for an airline is human capital, and researchers from Hong Kong Polytechnic University and University of Nottingham Ningbo China Business School may have found a solution to ease some of Cathays financial woes through an unlikely source – Machine Learning and Data Science. The researchers say that a "poorly designed airline crew schedule can result in unreliable flight schedules, significantly jeopardizing airline operations and profitability if insufficient crew members are available or other glitches occur. For that reason, managing airline crew scheduling and costs are one of the most crucial topics for airlines because it yields enormous economic benefits and ranks as the second highest expenditure after fuel costs."

Almost exactly a year ago, Ehang surprised the world with its supersized drone, the 184, made for carrying a human passenger or artificial organs. Nevada even gave Ehang permission to test its quadcopter in the state as of June, but that was the last we heard from the drone maker, until now. As it turns out, the Chinese company has yet to perform a single test flight in the US, and earlier this month, it had to lay off about 70 people from its team of around 300, plus there were signs of financial problems -- including canteen food issues, missed payments to suppliers and diminishing consumer drone shipment numbers -- as far back as August, according to Chinese news site Xtecher. Perhaps to reassure worried folks, Ehang decided to shed some light on the 184's latest development. According to Ehang co-founder and CMO Derrick Xiong, his company has been mobilizing "a few" 184s that have so far accumulated over 200 test flights -- some were apparently fully autonomous -- in China.

"INDUSTRIE 4.0 connects embedded system production technologies and smart production processes to pave the way to a new technological age which will radically transform industry and production value chains and business models." Advanced manufacturing--in the form of additive manufacturing, advanced materials, smart, automated machines, and other technologies--is ushering in a new age of physical production.2 At the same time, increased connectivity and ever more sophisticated data-gathering and analytics capabilities enabled by the Internet of Things (IoT) have led to a shift toward an information-based economy. With the IoT, data, in addition to physical objects, are a source of value--and connectivity makes it possible to build smarter supply chains, manufacturing processes, and even end-to-end ecosystems.3 As these waves of change continue to shape the competitive landscape, manufacturers must decide how and where to invest in new technologies, and identify which ones will drive the most benefit for their organizations. In addition to accurately assessing their current strategic positions, successful manufacturers need a clear articulation of their business objectives, identifying where to play in newly emerging technology ecosystems and (as important) what are the technologies, both physical and digital, that they will deploy in pursuit of decisions they make about how to win.4 The charge is perhaps easier to execute in theory than in practice.

In the retail insurance space, consumers are more connected than ever, via a multitude of devices and through multiple platforms. This has two consequences: first, consumers increasingly expect a much better, smarter service. "People are frustrated with a clunky process for buying insurance, and want an easier and quicker process through simple digital channels," says John Salmon, a technology partner at Hogan Lovells. Second, a larger proportion of consumers fall into Generation Y or the millennial generation: these individuals are less likely to own property or cars, are less attracted by life assurance and are looking for more tailored cover they can buy easily and quickly. They are attracted by the sharing economy.

"Together with the Russian EMERCOM and other relevant departments we have planned a number of projects on creating the so-called'artificial intelligence operator' that will be able to completely substitute men in fulfilling various tasks linked to processing text, visual, acoustic and other types of information," Garbuk told RIA Novosti. He added that prototype testing of the technologies will be continued by the All-Russian Research Institute for Civil Defense and Emergency Situations that is the main project's contractor.

DEEPMIND'S office is tucked away in a nondescript building next to London's Kings Cross train station. From the outside, it doesn't look like something that two of the world's most powerful technology companies, Facebook and Google, would have fought to acquire. Google won, buying DeepMind for £400m ($660m) in January 2014. But why did it want to own a British artificial-intelligence (AI) company in the first place? Google was already on the cutting edge of machine learning and AI, its newly trendy cousin.