If you've walked around any security conferences recently, you'll have heard dozens of vendors talking about artificial intelligence (AI) and machine learning. But what do they actually do? Are they really going to usher in a grand age of cybersecurity? And are security analysts about to be collectively out of a job? Recently, Recorded Future co-hosted a webinar with SANS Institute with the goal of helping security conscious organizations understand how machine learning can help them process an almost infinite number of inputs into a small number of actionable outputs.

Supervised learning algorithms are nowadays successfully scaling up to datasets that are very large in volume, leveraging the potential of in-memory cluster-computing Big Data frameworks. Still, massive datasets with a number of large-domain categorical features are a difficult challenge for any classifier. Most off-the-shelf solutions cannot cope with this problem. In this work we introduce DAC, a Distributed Associative Classifier. DAC exploits ensemble learning to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. Furthermore, it adopts several novel techniques to reach high scalability without sacrificing quality, among which a preventive pruning of classification rules in the extraction phase based on Gini impurity. We ran experiments on Apache Spark, on a real large-scale dataset with more than 4 billion records and 800 million distinct categories. The results showed that DAC improves on a state-of-the-art solution in both prediction quality and execution time. Since the generated model is human-readable, it can not only classify new records, but also allow understanding both the logic behind the prediction and the properties of the model, becoming a useful aid for decision makers.

With online criminals getting highly skilled at attacking enterprises every day, it's getting difficult for businesses to tell the difference between legitimate and fraudulent activity. Fraud detection has always been a cat and mouse game. With improvements in techniques to detect and prevent frauds, fraudsters keep changing their attack patterns to unearth new holes and vulnerabilities – which the detection solutions then shore up. However, the nature of this game is changing. The days where enterprises were able to keep fraudsters at bay by using static detection rules are long gone.

You've probably been to a supermarket that printed coupons for you at checkout. Or listened to a playlist that your streaming service generated for you. Or gone shopping online and seen a list of products labeled "you might be interested in…." that did indeed contain some stuff that you were interested in. Recommendation engines take data about you, similar consumers, and available products, and use that to figure out what you might be interested in and therefore deliver those coupons, playlists, and suggestions. Recommendation engines can be extremely complex.

The concept of machine learning, which is a subset of artificial intelligence, has been around for some time. Ali Ghodsi, an adjunct professor at UC Berkeley, describes it as "an advanced statistical technique to make predictions on a massive amount of data." Ghodsi has been influential in areas of Big Data, distributed systems, and in machine learning projects including Apache Spark, Apache Hadoop, and Apache Mesos. Here, he shares insight on these projects, various use-cases, and the future of machine learning. There are some commonalities among these three projects that have been influenced by Ghodsi's research.

The effective use and adoption of machine learning requires algorithms that are not only accurate but also understandable. To address this need, BigML now includes functionality that allows for prediction explanation, model-independent explanations of classification, and regression predictions. In this post, we will summarize what it means for a prediction to be explainable, why this is important, and share a use case in which prediction explanation plays a key role. Rather than being hard-programmed with an exhaustive set of "if-then" rules, machine learning algorithms "learn" rules based on large datasets of examples. Understanding what these rules are and how they are applied to new data is generally referred to as the interpretability or explanation of the model.

Technology's tentacles have encroached every aspect of our lives. Sitting in the comfort of your home you can tune in to live discussions and gain new understanding about technologies that are reshaping our world view. NDTV Tech Conclave 2018 saw a congregation of leading minds in the technology, mobile, and digital industries. The conclave aimed to showcase and create opportunities by bringing together many of the top entrepreneurs, investors, enterprise leaders, academics, and policymakers from around the world. The moderator of this session outlined two diametrically opposite views of AI and threw it open to the panelists.

When it comes to digital business, Andrew McAfee knows a thing or two. A principal research scientist at MIT, prolific writer and management expert, McAfee is a leader in understanding and explaining how digital technologies are changing business, the economy, and society. At the recent SAP Leonardo Live event in Chicago that focused on digital transformation, McAfee urged his audience to throw out the business playbook they've been using for the past 30 years. "The right way to run a factory in the steam era became a really, really bad way to run it in the era of electrical power," he said. "Similarly, during a technology transition -- and afterwards -- the advice you used to follow becomes bad advice."

Declarative modeling uses symbolic expressions to represent models. With such expressions one can formalize high-level mathematical computations on models that would be difficult or impossible to perform directly on a lower-level simulation program, in a general-purpose programming language. Examples of such computations on models include model analysis, relatively general-purpose model-reduction maps, and the initial phases of model implementation, all of which should preserve or approximate the mathematical semantics of a complex biological model. Multiscale modeling benefits from both the expressive power of declarative modeling languages and the application of model reduction methods to link models across scale. Based on previous work, here we define declarative modeling of complex biological systems by defining the semantics of an increasingly powerful series of declarative modeling languages including reaction-like dynamics of parameterized and extended objects, we define semantics-preserving implementation and semantics-approximating model reduction transformations, and we outline a "meta-hierarchy" for organizing declarative models and the mathematical methods that can fruitfully manipulate them.

The terms "artificial intelligence" and "machine learning" immediately bring up thoughts from movies like "The Matrix" where machines become self-aware and want to end the world. While this may make for an exciting plot in Hollywood, it is not reality outside of the theater. In real life, however, machine learning--which gives computers the ability to see hidden patterns in existing data and progressively improve performance ("learn") without being explicitly programmed--serves as a practical tool to data analysts. The job is not to turn robots into people, but instead efficiently find recurring themes that would otherwise remain obscured inside of large amounts of data to provide end-users with actionable information. These technologies have played a pivotal role in reducing fraud, waste and abuse in organizations of all types and sizes, including departments of revenue that collect taxes.