A rule-based system may be viewed as consisting of three basic components: a set of rules [rule base], a data base [fact base], and an interpreter for the rules. In the simplest design, a rule … can be viewed as a simple conditional statement, and the invocation of rules as a sequence of actions chained by modus ponens.
– from The Origin of Rule-Based Systems in AI. Randall Davis and Jonathan J. King, reprinted as Ch. 2 of Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley Series in Artificial Intelligence). Bruce G. Buchanan and Edward H. Shortliffe (Eds.). Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1984.
Unlike rules-based systems, which are fairly easy for fraudsters to test and circumvent, machine learning adapts to changing behaviors in a population through automated model building. With every iteration, the algorithms get smarter and more accurately find activities that represent risk to the firm. It's easy to see the value of machine learning for keeping pace with evolving fraud tactics. Learn 10 proven ways machine learning can boost the efficiency and effectiveness of fraud and financial crimes teams – from data collection to detection to investigation and reporting.
I am by no means a particularly good example of study habits, but generally I tend to read what I need and go from there... Basically this in practice often means starting somewhere relevant to whatever work/assignment/project I'm trying to do, and then going backwards building a recursive stack of readings that seem important to understanding the previous thing until I reach a point where I am familiar with the material already. Then I work through the stack until I'm back to wherever I started. Essentially this is the backward chaining algorithm. I also, if I need to learn a lot from a book for some reason (i.e. a course) or have no particular goal in mind but find my self with a text that piques my interest, then I tend to skim from cover to cover everything that actually attracts my attention, occasionally flipping back to something that I realize is important for understanding later stuff. If it seems especially critical and I can't understand it, then I'll look through exercises and maybe do them if it seems worthwhile.
Last week's IBM Think 2018 conference brought together numerous IBM partners and other vendors interested in pitching their wares to the crowd of IBM employees, partners, and customers. Most of these exhibitors had an IBM angle to their story – either how they go to market with IBM, or perhaps how they address some limitation in one IBM product or another. Given IBM's focus on cloud computing, artificial intelligence (AI), and blockchain, furthermore, many of the vendors touted how they are using these capabilities to deliver value to their customers. Of the dozens of exhibitors at Think, I looked for the best examples of a particular mix of disruptive capabilities and offerings complementary to IBM's. Even though cloud and VMware virtual machine (VM) instances can respond to shifts in demand, they often remain underutilized.
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.
Apache Spark is a leading platform for large-scale data mining, batch processing and stream processing. Touted as a "lightning-fast unified analytics engine," Spark modernizes data analytics with machine learning to help businesses uncover patterns at new levels. Best of all, Spark is included within many other software solutions, so this powerful tool may already be part of your modern data analytics infrastructure. From its inception at the AMPLab at U.C. Berkeley in 2009, Spark has become one of the key big data distributed processing frameworks in the world. It's used by banks, telecommunications companies, games companies, governments, and nearly all major tech giants, including Apple, Facebook, and Microsoft.
Manual knowledge acquisition is usually a costly and timeconsuming process. Automatic knowledge acquisition methods can then significantly support the knowledge engineer. In this paper, we propose an approach for rapid knowledge capture. The methodology is based on textual subgroup mining in order to discover dependencies for rule prototyping.