A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criteria. These criteria can be difficult to formalize, even when it is easy for an analyst to know a good clustering when they see one. We present a new approach to interactive clustering for data exploration called TINDER, based on a particularly simple feedback mechanism, in which an analyst can reject a given clustering and request a new one, which is chosen to be different from the previous clustering while fitting the data well. We formalize this interaction in a Bayesian framework as a method for prior elicitation, in which each different clustering is produced by a prior distribution that is modified to discourage previously rejected clusterings. We show that TINDER successfully produces a diverse set of clusterings, each of equivalent quality, that are much more diverse than would be obtained by randomized restarts.

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

We are on the cusp of a major breakthrough in how organizations collect, analyze, and act on knowledge. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This is the third in a special series of commissioned essays MIT Sloan Management Review will publishing in Frontiers over the Spring and Summer of 2016. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" Artificial Intelligence is about to transform management from an art into a combination of art and science.

It seems like almost every day a new headline warns us that artificial intelligence (AI) will soon take over the world, or at the very least steal jobs. Even when AI is not in the news, Hollywood offers up a steady stream of entertainment that depicts a very near future in which life as we know it is threatened by super-intelligent machines. These scenarios have something in common: they oversimplify and misrepresent an important and broader set of transformative technologies that hold great promise for business and society. They indulge in fantasy rather than take into account a rational and better-informed dialogue currently underway in the scientific, policy and business communities about what we consider the third age of computing – the cognitive era. What is Cognitive Computing Cognitive computing -- of which AI is but one part – refers to an entirely new class of technologies whose purpose is to deepen human engagement, scale and elevate expertise, enable new products and services, and enhance exploration and discovery.

Google announced today that it is expanding its largest non-U.S. The new Machine Learning Research Group will be based in Zurich, Switzerland, which is already home to Google's largest research center outside the U.S. The company did not say specifically how many positions will be added. But in a blog post, Google executives said machine learning has become critical to the company's development efforts across a wide range of services. "Google's ongoing research in Machine Intelligence is what powers many of the products being used by hundreds of millions of people a day -- from Translate to Photo Search to SmartReply for Inbox," wrote Emmanuel Mogenet, head of Google Research in Europe. Indeed, the Zurich research center has already had a sizable impact on Google.

The purpose of this web site is to help you understand, run, and interpret logistic regression analyses as quickly and easily as possible. Many visitors find this web site because they realize that their data does not fit the assumptions of regular linear regression (least-squares regression). Instead they realize they need to use a method specifically designed for data where the Y-variable is binary (all explained below). Other visitors are users of logistic regression and are seeking answers to a specific question. But in both cases, this web site is here to help you.

Intelligent, often autonomous, systems are making headway in the datacenter as developers seek to offload manual processes and move beyond traditional approaches like prescriptive IT automation as they struggle to keep up. That's the conclusion of a vendor-backed survey of IT executives about the adoption of intelligent machines and systems based on new AI approaches and other expert systems. Still, the survey sponsored by IT management software specialist Ipswitch notes that early adopters of automated systems are struggling to gauge security and access risks associated with handing the keys to bots and other electronic assistants. "IT decision makers recognize that, while a force for good, these technologies also expose the enterprise to new internal and external risk vectors," Tony Lock, an analyst with survey author Freeform Dynamics, noted in a statement. "As the pace of adoption increases, there will be no escaping the impact of intelligent systems on the enterprise, regardless of whether or not organizations directly invest in such technologies."

I come from the Cambridge machine learning group. More than once I heard people referring to us as "the most Bayesian machine learning group in the world". I mean, we do work with probabilistic models and uncertainty on a daily basis. Maybe that's why it felt so weird playing with those deep learning models (I know, joining the party very late). You see, I spent the last several years working mostly with Gaussian processes, modelling probability distributions over functions. I'm used to uncertainty bounds for decision making, in a similar way many biologists rely on model uncertainty to analyse their data. Working with point estimates alone felt weird to me. I couldn't tell whether the new model I was playing with was making sensible predictions or just guessing at random. I'm certain you've come across this problem yourself, either analysing data or solving some tasks, where you wished you could tell whether your model is certain about its output, asking yourself "maybe I need to use more diverse data? or perhaps change the model?". Most deep learning tools operate in a very different setting to the probabilistic models which possess this invaluable uncertainty information, as one would believe. I recently spent some time trying to understand why these deep learning models work so well – trying to relate them to new research from the last couple of years. I was quite surprised to see how close these were to my beloved Gaussian processes. I was even more surprised to see that we can get uncertainty information from these deep learning models for free – without changing a thing. Update (29/09/2015): I spotted a typo in the calculation of \tau; this has been fixed below.

Various land-use configurations are known to have wide-ranging effects on the dynamics of and within other city components including the transportation system. In this work, we particularly focus on the complex relationship between land-use and transport offering an innovative approach to the problem by using land-use features at two differing levels of granularity (the more general land-use sector types and the more granular amenity structures) to evaluate their impact on public transit ridership in both time and space. To quantify the interdependencies, we explored three machine learning models and demonstrate that the decision tree model performs best in terms of overall performance--good predictive accuracy, generality, computational efficiency, and "interpretability". Results also reveal that amenity-related features are better predictors than the more general ones, which suggests that high-resolution geo-information can provide more insights into the dependence of transit ridership on land-use. We then demonstrate how the developed framework can be applied to urban planning for transit-oriented development by exploring practicable scenarios based on Singapore's urban plan toward 2030, which includes the development of "regional centers" (RCs) across the city-state.

Respondents were asked what they saw as the biggest area of opportunity for Big Data in comparison to traditional systems, with 62% agreeing that they consider real time analysis as the biggest area of opportunity today. "It's not long ago we were visiting enterprises and having to explain why they should look at big data. Today in 2016, Big Data Analytics is already considered a necessity to remain competitive by 63% of organizations," explains Serge Haziyev, VP Technology Services, SoftServe. "It's very encouraging that machine learning has featured so prominently in this survey. I find that businesses that take the plunge and implement machine learning techniques realize the benefits early on – it's a big step forward because it delivers prescriptive insights enabling businesses to not only understand what customers are doing, but why."