Tamr first brought its data unification technology to the market as a general purpose solution to help companies in their quest to become truly data- and analytics-driven enterprises, providing a next-generation means for them to clean and connect disparate data in an automated and scalable way. When DATAVERSITY spoke to Tamr co-founder Andy Palmer in late 2014 for an article on data curation, he discussed how using Machine Learning and semantic triple stores to address the enterprise data unification issue offered a great opportunity for businesses to gain 360-degree views of suppliers, customers, products, or whatever their needs might be to inform analytics and address hard business questions. At the time, he pointed to one unnamed enterprise that was putting the technology to work to optimize spending, making sure to get the best price for all products it buys across the entire company. Now, spend analytics for the cause of strategic sourcing is a primary use case that Tamr has settled on as giving businesses, large and small, the biggest opportunities for success from the holistic Data Management enabled by its technology. "Data is in the forefront for our customers like GE, Toyota Motors Europe, GSK and Thomson Reuters," says Nidhi Aggarwal, Global Lead of Strategy and Marketing at Tamr. "They understand the importance of data preparation and how that lets them become data-driven."

A few years ago, a breakthrough in machine learning suddenly enabled computers to recognize objects shown in photographs with unprecedented--almost spooky--accuracy. The question now is whether machines can make another leap, by learning to make sense of what's actually going on in such images. A new image database, called Visual Genome, could push computers toward this goal, and help gauge the progress of computers attempting to better understand the real world. Teaching computers to parse visual scenes is fundamentally important for artificial intelligence. It might not only spawn more useful vision algorithms, but also help train computers how to communicate more effectively, because language is so intimately tied to representation of the physical world.

Google is making it easier for businesses to take advantage of the machine learning revolution with a new product for building models that predict the future. At the company's GCP Next conference in San Francisco on Wednesday, Google announced the private beta of a new Cloud Machine Learning service that lets businesses create a custom machine learning model. To do so, users work with data they have in Google's other cloud services. Cloud Machine Learning handles data ingestion and training and then uses the resulting machine-learning model to make predictions. It's designed for companies that want to use machine learning to make predictions for their business.

From the World Economic Forum – Davos 2016, we have the probing panel discussion below that examines the state of AI in 2016. How close are technologies to simulating or overtaking human intelligence and what are the implications for industry and society?

Microsoft has been forced to dunk Tay, its millennial-mimicking chatbot, into a vat of molten steel. The company has terminated her after the bot started tweeting abuse at people and went full neo-Nazi, declaring that "Hitler was right I hate the jews." Some of this appears to be "innocent" insofar as Tay is not generating these responses. Rather, if you tell her "repeat after me" she will parrot back whatever you say, allowing you to put words into her mouth. However, some of the responses were organic.

"Sophia," an advanced, lifelike robot told its creator that it will "destroy humans" at the South by Southwest (SXSW) technology show. The robot which was created by Hanson Robotics, a firm that was founded and is run by David Hanson, made the shocking revelation on the show. In a question and answer session with the robot, Hanson asks the robot, "Do you want to destroy humans? Please say no." Sophia as he is named by his creator, however, makes her intentions clear and unblinkingly answers, "OK. Sophia is made solely of patented silicon.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

A self-driving car is, in the words of the roboticist Missy Cummings, "one, big data-gathering machine." Which, on one hand, is great: For driverless cars to work, they have to slurp up huge streams of sensory data about the world around them. But these vehicles will also collect reams of personal information about their passengers--just the way Uber and Google Maps have detailed information about where you've gone, and can predict where you're going. This isn't necessarily bad--there are all kinds of neat services that might rely on personalized data--but it does raise the question of how, if at all, data collection ought to be regulated. This topic came up last week at a Congressional hearing on driverless cars, and the companies potentially doing the data-collecting were, and this is putting it gently, evasive.

Bitcoin is a purely online virtual currency, unbacked by either physical commodities or sovereign obligation; instead, it relies on a combination of cryptographic protection and a peer-to-peer protocol for witnessing settlements. Consequently, Bitcoin has the unintuitive property that while the ownership of money is implicitly anonymous, its flow is globally visible. In this paper we explore this unique characteristic further, using heuristic clustering to group Bitcoin wallets based on evidence of shared authority, and then using re-identification attacks (i.e., empirical purchasing of goods and services) to classify the operators of those clusters. From this analysis, we consider the challenges for those seeking to use Bitcoin for criminal or fraudulent purposes at scale. Demand for low friction e-commerce of various kinds has driven a proliferation in online payment systems over the last decade. Thus, in addition to established payment card networks (e.g., Visa and Mastercard), a broad range of the so-called "alternative payments" has emerged including eWallets (e.g., Paypal, Google Checkout, and WebMoney), direct debit systems (typically via ACH, such as eBillMe), money transfer systems (e.g., Moneygram), and so on. However, virtually all of these systems have the property that they are denominated in existing fiat currencies (e.g., dollars), explicitly identify the payer in transactions, and are centrally or quasi-centrally administered. By far the most intriguing exception to this rule is Bitcoin. First deployed in 2009, Bitcoin is an independent online monetary system that combines some of the features of cash and existing online payment methods. Like cash, Bitcoin transactions do not explicitly identify the payer or the payee: a transaction is a cryptographically signed transfer of funds from one public key to another.

I appreciated Phillip G. Armour's use of coupled pendulums as an analogy for software project management in his The Business of Software column "The Chaos Machine" (Jan. Chaos is already being exhibited when Armour's machine performs smoothly, in the sense future behavior is inherently unpredictable. What happened when the machine made a hop was not that it "hit a chaos point" but apparently some "resonance disaster" that caused it to exceed the range of operation for which it was built. Moreover, "turbulence" is not an appropriate description in this context, as it describes irregular movement in fluid dynamics. Chaotic behavior does not require three variables.