A rough estimate would show that thousands of Chinese robotics companies may have recorded, on average, annual sales of less than 100,000 yuan each. Mainland media reported that low-end robots have been installed on the assembly line of known labour-intensive factories as a showcase for local government subsidies and left unused. "There remains a big shortage of specialists who can assess the true situation in the country's robotics industry and determine arbitrary decision–making by local governments in granting subsidies," Wang said. His urgent call for a sweeping audit of subsidies to the domestic robotics industry followed a recent nationwide investigation launched by the Ministry of Finance on so-called "new energy vehicle" makers. The agency is targeting electric car manufacturers suspected of fraudulently obtaining subsidies from the government in 2013 and 2014, as well as those firms that applied last year.

As banks, financial services providers and brands predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Gideon Hyde, co-founder of Market Gravity, explains how and why artificial intelligence (AI) could hold the key to standing out in banking and financial services. AI is already around us and used everyday within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. It can process "big data" far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour, for example to detect fraud as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the human interaction for consumers.

Israeli startup Twiggle, which is developing next generation e-commerce search technologies, announced today that it secured additional funding from the Alibaba Group as the second tranche of its Series A financing. This follows the announcement in April of a 12.5 million round led by Naspers with participation from YJ Capital, State of Mind Ventures and Sir Ronald Cohen. The funding will be utilized to grow the company's R&D team in Israel and drive the company's global expansion plans. No details were disclosed about the amount Alibaba is investing but "Bloomberg" reported that it is 5-10 million. Twiggle uses advanced techniques in data science, artificial intelligence, machine learning and natural language processing to power the next generation of digital commerce.

This is the first article in what will become a set of tutorials on how to carry out natural language document classification, for the purposes of sentiment analysis and, ultimately, automated trade filter or signal generation. This particular article will make use of Support Vector Machines (SVM) to classify text documents into mutually exclusive groups. Since this is the first article written in 2015, I feel it is now time to move on from Python 2.7.x and make use of the latest 3.4.x Hence all code in this article will be written with 3.4.x in mind. There are a significant number of steps to carry out between viewing a text document on a web site, say, and using its content as an input to an automated trading strategy to generate trade filters or signals. In this particular article we will avoid discussion of how to download multiple articles from external sources and make use of a given dataset that already comes with its own provided labels. This will allow us to concentrate on the implementation of the "classification pipeline", rather than spend a substantial amount of time obtaining and tagging documents. In subsequent articles in this series we will make use of Python libraries, such as ScraPy and BeautifulSoup to automatically obtain many web-based articles and effectively extract their text-based data from the HTML.

AI is one of those technologies whose promise is resurrected periodically, but only slowly advances into the real world. I remember the dog-and-pony AI shows at IBM, MIT, Carnie-Melon, Thinking Machines, and the like in the mid-1980s, as well as the technohippie proponents like Jaron Lanier who often graced the covers of the era's gee-whiz magazine like "Omni." AI is an area where much of the science is well established, but the implementation is still quite immature. It's not that the emperor has no clothes -- rather, the emperor is only now wearing underwear. There's a lot more dressing to be done.

Nvidia has staked a big chunk of its future on supplying powerful graphics chips used for artificial intelligence, so it wasn't a great day for the company when Google announced two weeks ago that it had built its own AI chip for use in its data centers. Google's Tensor Processing Unit, or TPU, was built specifically for deep learning, a branch of AI through which software trains itself to get better at deciphering the world around it, so it can recognize objects or understand spoken language, for example. TPUs have been in use at Google for more than a year, including for search and to improve navigation in Google Maps. They provide "an order of magnitude better-optimized performance per watt for machine learning" compared to other options, according to Google. That could be bad news for Nvidia, which designed its new Pascal microarchitecture with machine learning in mind.

Po, the wisdom-seeking hero of the "Kung Fu Panda" films, might recognize this temple in China where the world's first robot monk dwells. For Po's Jade Palace, there is Longquan (Dragon Spring) Temple, a place of Buddhist worship in the mountains northwest of Beijing, where gnarled gingko and cypress trees tower over red-walled buildings underneath rocky Phoenix Ridge. For his Hall of Warriors, there is the Comic Center deep inside the temple, at the end of winding stone paths and steps, past a flower-shaped audio device that crackles sutras. As for Po himself, there is Xian'er, the two-foot-tall, advice-dispensing robot whose full title is Worthy Stupid Robot Monk. A childlike creature in an orange Buddhist robe, Xian'er is an object of fascination in China amid an increasingly urgent pursuit of spirituality and, more recently, artificial intelligence.

According to MarketandMarkets, the artificial intelligence (AI) market is estimated to grow from 419.7 million in 2014 to 5.05 billion by 2020, growing at a CAGR of 53.65% from 2015 to 2020. The Media and Advertising sector is expected to drive the growth of AI during this period. IBM, Microsoft, and Google are key players in the market, and now Salesforce is trying to make inroads into it. For the first quarter of fiscal 2017, Salesforce's revenue grew 27% over the year to 1.92 billion, above analyst estimate of 1.89 billion. Net income was 38.8 billion or 0.06 per share. Non GAAP EPS was 0.24, beating analyst forecast of 0.25.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.

IBM Watson is going to Japan via IBM's new alliance with Japanese telecommunication giant SoftBank, on Tuesday, February 10, 2015. SAN FRANCISCO -- Japanese telecommunications giant SoftBank is selling 8 billion in Alibaba stock in order to pay down debt, the company said in a statement Tuesday. SoftBank, which was among the earliest investors in Chinese e-commerce juggernaut Alibaba (BABA), will sell 2 billion in stock back to Alibaba. It will offer another 5 billion in securities that in three years will convert into Alibaba shares. Another 500,000 in stock will be sold to an unnamed wealth fund and 400,000 to the Alibaba Partnership, which controls nomination of the company's directors.