Predictive State Representations (PSRs) are powerful techniques for modelling dynamical systems, which represent a state as a vector of predictions about future observable events (tests). In PSRs, one of the fundamental problems is the learning of the PSR model of the underlying system. Recently, spectral methods have been successfully used to address this issue by treating the learning problem as the task of computing an singular value decomposition (SVD) over a submatrix of a special type of matrix called the Hankel matrix. Under the assumptions that the rows and columns of the submatrix of the Hankel Matrix are sufficient (which usually means a very large number of rows and columns, and almost fails in practice) and the entries of the matrix can be estimated accurately, it has been proven that the spectral approach for learning PSRs is statistically consistent and the learned parameters can converge to the true parameters. However, in practice, due to the limit of the computation ability, only a finite set of rows or columns can be chosen to be used for the spectral learning. While different sets of columns usually lead to variant accuracy of the learned model, in this paper, we propose an approach for selecting the set of columns, namely basis selection, by adopting a concept of model entropy to measure the accuracy of the learned model. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

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.

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. "The train suddenly started making strange noises. These weren't the usual sounds that trains make, but something out of the ordinary. It was the same week that a train had overturned in Spain, injuring a lot of people. Everyone stopped talking and was getting very worried. I had the thought that maybe if there was a train mechanic or engineer sitting on the train, they could tell us if the noise was normal and, if not, where it was coming from and whether it could be safely ignored."

A recent National Business Research Institute (NBRI) survey of 235 business executives found that 38% of enterprises are already using AI technologies and 62% plan to use them by 2018. Big data has spawned the current interest and increased investment in artificial intelligence. The availability of large volumes of data--plus new algorithms and more computing power--have finally pulled AI out of its long "winter." At Straight Talk, we like to hear directly from tech executives, in this case those who have been applying machine learning, predictive analytics, text analysis, voice processing and other AI technologies to their organizations' data, processes, and interactions with customers. At Schneider Electric, Prith Banerjee, executive vice president and CTO, reports on the successful marriage of machine learning with another emerging set of technologies--the Internet of Things or the IoT.

Shanghai-based software company Zhizhen Network Technology, which sued Apple four years ago over a voice recognition patent for the iPhone Siri "personal assistant", plans to expand into the US in 2017 with ambitions of becoming a global leader in artificial intelligence. It will set up offices in Hong Kong and Silicon Valley to develop software to cater to local clients, a fresh sign that Chinese technology firms are speeding up their "go-global" drive to take on big-names such as Microsoft and Amazon for world markets. "Taking a global view, it's time to set sail for the largely untapped territory of artificial intelligence," said Yuan Hui, founder and chairman of Zhizhen. "Internationalisation is a necessary step we will take to develop our businesses." In a report on the top 10 technology trends for 2017, IT research firm Gartner listed Zhizhen's Xiaoi Robot, Siri, Cortana of Microsoft and Echo of Amazon as world leaders in intelligent conversational systems.