Uber said its head of finance is leaving, and the privately held ride-hailing company also said that its first-quarter loss narrowed substantially from the prior quarter, putting it on a path toward profitability. Head of finance Gautam Gupta is leaving in July to join another startup in San Francisco, the company said, making Gupta the latest high-profile executive to leave Uber. Uber, which has been rocked by several high-level executive departures in the past few months as it grapples with a series of controversies, has been looking for a chief operating officer to help change its now-notorious "bro" culture. Gupta's exit sets the stage for a second major executive search, now for a chief financial officer who has public company experience. About a dozen top executives have left Uber since February.

This list was KDnuggets most popular tweet recently, but the tweet only had the image and no company names or details. Here we list companies by category and in the order of high to low equity funding in each group. Company overview (e.g., headquarters location and total equity funding raised) is based on information as it have appeared on crunchbase.com The top countries for startups are: US 39, China 3, Israel 3, UK 3, France 1, Taiwan 1. For US, the top states are CA 23, MA 6, and NY 4.

You have gathered gigabytes or terabytes of unstructured text, for instance scraping the Internet, or pieces of email from your employees or users, or tweets, or millions of products that you want to categorize (only product description and product name is available - sometimes with typos). Now you want to make sense of it, and extract value, possibly design a nice search engine so that your customers can easily find your products. The core algorithm that you need is an automated cataloguer, also called indexer. I am going to explain in layman's terms how it works. First, let's assume that the data consists of Typically, these "pages" are stored as large repositories containing millions or billions of (sometimes compressed) text files spread across a number of folders and sub-folders, or multiple servers.

Classical machine learning algorithms are currently used for performing complex computational tasks, such as pattern recognition or classification in large amounts of data, and constitute a crucial part of many modern technologies. The aim of quantum learning algorithms is to bring these features into scenarios where information is in a fully quantum form. The scientists, Alex Monràs at the Autonomous University of Barcelona, Spain; Gael Sentís at the University of the Basque Country, Spain, and the University of Siegen, Germany; and Peter Wittek at ICFO-The Institute of Photonic Science, Spain, and the University of Borås, Sweden, have published a paper on their results in a recent issue of Physical Review Letters. "Our work unveils the structure of a general class of quantum learning algorithms at a very fundamental level," Sentís told Phys.org. "It shows that the potentially very complex operations involved in an optimal quantum setup can be dropped in favor of a much simpler operational scheme, which is analogous to the one used in classical algorithms, and no performance is lost in the process. This finding helps in establishing the ultimate capabilities of quantum learning algorithms, and opens the door to applying key results in statistical learning to quantum scenarios."

The EU must adopt policies for the development, deployment and use of artificial intelligence (AI) in Europe in such a way as to ensure it works for, rather than against, society and social well-being, the European Economic and Social Committee (EESC) said in an own-initiative opinion on the societal impact of AI, identifying 11 areas that need to be addressed. "We need a human-in-command approach to AI, where machines remain machines and people retain control over these machines at all times," said rapporteur Catelijne Muller (NL – Workers' Group). She did not refer to technical control alone. "Humans can and should also be in command of if, when and how AI is used in our daily lives – what tasks we transfer to AI, how transparent it is, if it is to be an ethical player. After all, it is up to us to decide if we want certain jobs to be performed, care to be given or medical decisions to be made by AI, and if we want to accept AI that may jeopardise our safety, privacy or autonomy," Muller argued.

More than 80 years ago, a robot could be found in the unlikely surroundings of Kettering, directing traffic and smoking a cigarette. Today, the story of Robert the Robot is little known, even in the Northamptonshire town where he was once a celebrity. Yet in the 1930s, his fame reached as far as Czechoslovakia and the United States, where he even featured in Time magazine. And the reason he came to be? "Someone bet me £5 I could not make a robot in three weeks," inventor Charles Lawson, who had a radio shop, told a newspaper at the time. The electrical engineer's creation first hit headlines in the Northamptonshire Evening Telegraph, which proclaimed: "Kettering robot'enjoys' smoke."

The duopoly has gobbled up artificial intelligence startups, and it's hungry for more. Research released earlier this month by CB Insights found it acquired 34 AI startups in the first quarter of 2017, which more than doubled the total acquired during the same period last year and easily surpassed the previous high of 28, which came in the third quarter of last year. Last week, Axios reported that Google has launched an AI-specific venture fund, one that's overseen by engineers rather than venture capitalists, to hand out investments up to $10 million. The big picture • Google's parent, Alphabet, has acquired 11 AI startups over the past five years, the most of any public company. Apple is next, with seven, followed by Facebook and Intel, with five apiece.

If you think your job is safe, think again. The looming prevalence of automation has the potential to seize anyone's role, no matter how cushy. This is how the foreseeable future in 21st century looks from the vantage point of 2017, and governments and businesses need to be aware of the changes headed their way. Twenty years ago, I described my day to day tasks as an experienced investment banker in London to an optician. For fun, I decided not to disclose the nature of my role, nor the name of my employer.

What are you going to do with your life when the machines are better at your job than you are? World Go champion Ke Jie of China has already found out. He just played a best-of-three tournament against an artificial intelligence program called AlphaGo and he lost 3-0. To watch the video of him playing and losing is to be reminded that current debates over automation and the future of work go a lot deeper than the single issue of whether or not robots will take our jobs. The real issue is more likely to be whether or not robots will take our souls. Reflecting on his loss, Ke Jie noted what he considered to be his human failings.

After acquiring Dropcam back in 2014, Nest waited another year to launch its own home monitoring camera. It wasn't much of an improvement over the old Dropcam Pro, and aside from releasing an outdoor version with weatherproof body, Nest has focused on improving its software ever since. But now the smart home company is back with a second-gen connected camera -- the Nest Cam IQ -- which features serious upgrades in the hardware department and an inflated price tag to match. The $299/£299 Nest Cam IQ looks a lot like last year's Nest Cam Outdoor. In contrast to the very first camera, it has cute and curvy, all-white polycarbonate shell and a fancy, patented hinge that allows you to orientate the lens any way you want it.