But those labor savings aren't the only gains to be had from the adoption of driverless trucks. Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day. That means the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost. And the savings become even more significant when you account for fuel efficiency gains. The optimal cruising speed from a fuel efficiency standpoint is around 45 miles per hour, whereas truckers who are paid by the mile drive much faster.

A recurring claim at security conferences is that'security is a big data / machine learning (ML) / artificial intelligence (AI) problem'. This is unfortunately wildly optimistic, and wrong in general. While certain security problems can be addressed by ML/AI algorithms, in general the problem of detecting a malicious actor amidst the vast trove of information collected by most organisations, is not one of them. Our faith in AI is based on personal experience ('everything cloud is big data and good') and the memes of the consumerisation era. It is tempting to project this optimism into an enterprise context: the idea that it ought to be possible to sift through large amounts of data to find signs of an attack of breach is intuitively reasonable.

The idea of conversational commerce is fairly new. At least if you think about it from a technological level. In reality, conversational commerce has existed since people started buying and selling goods and services thousands of years ago. You walk up to a person, talk to them, exchange currency, information or services, walk away. A conversation took place commerce happened conversational commerce. Somewhere along the way, the Web and apps economies lost the human element of commerce. Customer experience and customer service became a tangled series of browse-and-click, overloaded call centers, chat sessions and frustration.

How big should data be to be called'big'? Big data talk usually centers around those four V's: We say that data is big if it has volume, variety, velocity and veracity. It means a lot and lot of data; much, much more data than what a single server's disk can hold. That means not just numbers (Tendulkar's test and ODI scores), but also text (all that prose and poetry in praise of Tendulkar), video (Tendulkar's straight drive) and audio (Tendulkar exclaiming "Ailaa!"). Data on the trajectory of a launch vehicle, data on the growing spread of the Ebola virus, data about how rapidly some song is going viral on the Internet. Data that is valid, accurate, verifiable, not manipulated and trustworthy. What do you do with all this big data? You use this data to find associations, insights or connections.

It's easy to imagine robots as job-stealing nemeses that will ultimately unseat human workers. In an interview with Computerworld Thursday, Tom Davenport, co-author of the forthcoming book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, made the case for seeing artificial intelligence and robotics not as a threat in the workplace but as an advantage. As more organizations begin to adopt AI and robotic systems, workers who regard these new technologies not as murderous Cylons to be feared but as tools designed to make them more productive and efficient at their own job will fare best. "I think that in many cases, we'll be working with these machines as colleagues," Davenport told Computerworld. "I think the people who prosper will be the ones who like working with machines." This is where Computerworld trotted out the obligatory eyebrow-raising statistics: The Bank of England warned about robots assuming half of all U.S. and British jobs during the coming 20 years, leaving 95 million people unemployed.

When faced with the EUCJ-ruling on the "right to be forgotten", Google's Larry Page said it could help repressive governments and damage innovation. For sure those dictators use digital technology for oppression just as much as democracy activists can use them for change. And it is difficult to understand why start-ups should play by different rules than others. However, there is a deeper questions here: is everything online "speech"? Search, self-driving cars, auctions, mail order, spam filters… if everything online is speech, real speech is diluted. Freedom of speech should be reserved for the right to express one's own views without censorship or prosecution.

A panel comprised of government tech leaders and healthcare experts is set to scrutinise the work of Google DeepMind's healthcare team at an upcoming meeting, which could be held within the next month. DeepMind, a British AI startup acquired by Google in 2014 for 400 million, is processing NHS data through a new division called DeepMind Health. The London-based AI lab says its technology has the potential to improve the way patients are diagnosed and treated but it acknowledges that working in healthcare requires regular and independent oversight. As a result, it set up a reviewer board for DeepMind Health when it launched the unit in February. DeepMind states on its website that the board will meet four times a year but Business Insider understands that an official meeting between the nine individuals on the board is yet to take place.

ClickSoftware, a leading provider of field service management solutions for the enterprise, today introduced Click Field Service Edge, a new cloud-based, mobile workforce management platform designed to meet the needs of the largest and most demanding field service providers. Adoption of the cloud has more than doubled over the past two years and, with that, cloud-based software solutions have become entrenched in the business world. Recognising the technological benefits for field service companies, ClickSoftware's first, cloud only, SaaS-based solution delivers field service management in an entirely new way. With patented artificial intelligence, Click Field Service Edge connects enterprise customers with automated real-time insight and intelligent decision-making tools that provide a new level of productivity and efficiency to field workforce performance. "As a company that has successfully managed billions of service engagements, and is exclusively focused on providing field service technology – since creating the market – we know what it takes to help our customers always be a step ahead of the demand, not just move in lockstep with it," explained Tom Heiser, ClickSoftware's Chief Executive Officer.

Gary Baum, VP of Marketing at MyScript, recently wrote in InsideBigData, "Not long ago, many would scoff at the notion that a machine is'learning,' 'doing' or'knowing.' But neural networks and artificial intelligence (AI) technologies are layering those skillsets together to perform increasingly complicated, human-like functions. Google DeepMind, for example, is one of few very advanced neural networks that are driving the future of machine learning. While machines have previously been able to read and answer our questions about news articles, for example, their knowledge was often limited by the length of a piece or driven to brute force computation. Newly-developed algorithms enable those systems to learn from experience and online data – leading to a more sophisticated understanding of topics and language. Researchers put this theory to the test by inputting hundreds of thousands of Daily Mail and CNN articles into a system with the goal of accurately detecting missing words or predicting a headline. The neural network correctly answered more than half of all queries, struggling only with those that featured more complex grammatical structures."

Adjusted earnings per share exclude one-time items but include stock-based compensation expense. Management sounded upbeat and mentioned several initiatives including the collection of data from sellers, application of artificial intelligence for a better user experience, increased use of social media to drive traffic, and focus on discovery-based user experience to name a few. Therefore, the best way to understand the business is through its revenue/volume growth, customer growth, revenue per customer and cash flows. Revenues Gross revenue of 2.137 billion was up 3.7% on a year-over-year basis (up 6% on an Fx-neutral basis) and beat the Zacks Consensus Estimate of 2.081 billion. The Gross Merchandise Volume grew 5% year over year on an Fx-neutral basis.