Last time we investigated the k-nearest-neighbors algorithm and the underlying idea that one can learn a classification rule by copying the known classification of nearby data points. This required that we view our data as sitting inside a metric space; that is, we imposed a kind of geometric structure on our data. One glaring problem is that there may be no reasonable way to do this. While we mentioned scaling issues and provided a number of possible metrics in our primer, a more common problem is that the data simply isn't numeric. For instance, a poll of US citizens might ask the respondent to select which of a number of issues he cares most about. There could be 50 choices, and there is no reasonable way to assign these numerical values so that all are equidistant in the resulting metric space. Another issue is that the quality of the data could be bad. For instance, there may be missing values for some attributes (e.g., a respondent may neglect to answer one or more questions).

Likewise, I am convinced that the revolution in artificial intelligence will enable computers and robots to do many of the tasks that white-collar workers now do. It's not surprising, therefore, that many people are worried about the fate of those whose jobs are vulnerable -- or have already been lost -- to the latest disruptive technology. What will happen to the millions of men and women who now drive trucks and taxis when the trucks and taxis can drive themselves? What will happen to the accountants and health workers when computers can do their jobs? Some analysts have estimated that, with many fewer employees needed to produce the current volume of goods and services, a large share of current employment could be made redundant.

The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure. Most often, the suitability of the annotations as topological descriptors itself is not assessed, and without this it is not possible to ultimately distinguish between actual shortcomings of the community detection algorithms on one hand, and the incompleteness, inaccuracy or structured nature of the data annotations themselves on the other. In this work we present a principled method to access both aspects simultaneously. We construct a joint generative model for the data and metadata, and a nonparametric Bayesian framework to infer its parameters from annotated datasets. We assess the quality of the metadata not according to its direct alignment with the network communities, but rather in its capacity to predict the placement of edges in the network. We also show how this feature can be used to predict the connections to missing nodes when only the metadata is available, as well as missing metadata. By investigating a wide range of datasets, we show that while there are seldom exact agreements between metadata tokens and the inferred data groups, the metadata is often informative of the network structure nevertheless, and can improve the prediction of missing nodes. This shows that the method uncovers meaningful patterns in both the data and metadata, without requiring or expecting a perfect agreement between the two.

These days, a lot of workers are worried about robots taking their jobs, but now robots are taking jobs that literally no one else was doing--like poring through every inch of people's dense expense reports. AppZen, a startup that provides automated auditing services, has just expanded its software, which can now not only read basic items on attached receipts, but also scan entire documents to look for clues about invalid charges. Anant Kale, the company's CEO, tells Fast Company that the new offering, called ReceiptIQ, can audit 100% of the expense reports that employees submit, spotting them for "accidental fraud . . . The upgrade required moving beyond optical character recognition to computer vision that can understand the entire receipt, such as recognizing company logos. Many hotels put their logos on receipts rather than spelling out their name, says Kale. ReceiptIQ analysis goes deeper by looking for context, such as whether car rental bills include a fuel service charge--a penalty for not returning the car with a full gas tank.

Dream, imagine, don't be afraid to take a risk." That life advice came from Shimon Peres as I sat with him in his Tel Aviv apartment for a Fox News interview just two months ago. The former Israeli Prime Minister and President who was a founding father of the Jewish state died at age 93 Tuesday night, but as we met this summer he was full of life, optimistic, insightful, and wise. I felt as if I was sitting with a loving and kind grandfather who just happened to be one of the founding fathers of his nation and an iconic world statesman, as he shared his views in a very personal way about what he had learned over decades. I was in Israel to shoot interviews focusing on the anniversary of the raid on Entebbe and Israel's experience fighting terrorism. I had interviewed Israeli Prime Minister Benjamin Netanyahu the day before, and I was scheduled to sit down with Former Prime Minister Ehud Barak later on the day I met Peres. In a nearly one-hour interview, Peres not only expounded on the legacy of his nation, but philosophically spoke about the human spirit. "Look for new answers," he said. "Look for new questions, we have to have new questions.

Although cyber attacks against businesses like the recent one against Yahoo are increasing, hackers are using the same techniques they always have. Stuart McClure, the CEO of security startup Cylance, told Fortune's Robert Hackett that "there is nothing new" in how hackers are breaching computer defenses. He compared cyber attacks to thieves breaking into homes. There are only so many ways thieves can sneak inside, and no one has created new methods like a "teleportation device" for criminals to more easily get through the front door, he explained. What's different now is that organizations can now use an artificial intelligence technique called machine learning to better defend themselves against these attacks, McClure said.

Ashok Goel is a professor in the School of Interactive Computing. Soon scientists and the public will have the chance to easily test hypotheses about America's ecological challenges with the help of an ensemble of technologies, including artificial intelligence. Researchers at Georgia Institute of Technology will link their technology for systems thinking with IBM Watson and the Encyclopedia of Life at the Smithsonian. Scientists will then be able to use the information to create their own models about the environment and efficiently test them. The project is one of 10 "Big Data Spokes" announced by the National Science Foundation (NSF).

The deep-learning software driving the modern artificial intelligence revolution has mostly run on fairly standard computer hardware. Some tech giants such as Google and Intel have focused some of their considerable resources on creating more specialized computer chips designed for deep learning. But IBM has taken a more unusual approach: It is testing its brain-inspired TrueNorth computer chip as a hardware platform for deep learning. Deep learning's powerful capabilities rely on algorithms called convolutional neural networks that consist of layers of nodes (also known as neurons). Such neural networks can filter huge amounts of data through their "deep" layers to become better at, say, automatically recognizing individual human faces or understanding different languages. These are the types of capabilities that already empower online services offered by the likes of Google, Facebook, Amazon, and Microsoft.

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

One of the many questions in the design and use of Clinical Decision Support software is whether or not the user can recreate the logic used by the system in reaching its conclusions and recommendations–or alerts, or suggestions. If the CDS is based on sound medical logic, perhaps supported by specific reference material, then the user could in principle reach the same conclusions by reading the same literature, or perhaps reach a different conclusion. This transparency was part of the proposed criteria for some CDS systems not falling under FDA regulation in 2015 federal draft legislation--which didn't pass. The FDA has otherwise not been forthcoming on the general subject of CDS despite many pleas for guidance, and a draft guidance in this domain is an as yet unfulfilled part of the 2015 strategic plan. However underlying logic and science is not the only way to build "artificial intelligence" (AI), which might in some instances turn out to be artificial mediocrity if not artificial stupidity.