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.

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include, applicability to a wide range of diseases, and ability to capture disease dynamics - including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China and India. We noted that temporal topic trends extracted from disease-related news reports successfully captured the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.

We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational cost of re-training the classifier would be prohibitively large. In this paper, we propose a novel method for efficiently incorporating such a data modification effect into the classifier without actually re-training it. The proposed method provides bounds on the unknown optimal classifier with the cost only proportional to the size of the data modification. We demonstrate through numerical experiments that the proposed method provides sufficiently tight bounds with negligible computational costs, especially when a small part of the dataset is modified in a large-scale classification problem.