"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
There's a new frontier in fraud detection. Join Julie Conroy, Aite Group, and Swastik Bihani, Simility, to learn how companies are using machine-learning models and behavioral analytics across a wide variety of structured and unstructured data to accurately detect fraud and suspicious activity. You'll see how you can easily clean, transform, enrich, and deep dive into all the related suspicious activity that makes a potential transaction suspect. We'll cover how these techniques yield insights from professionals in threat detection, fraud, security, and compliance use cases.
Bayesian analysis is one of about a dozen machine learning methods typically used; other methods are logistical regression, simple linear regression, K-means clustering, decision trees and random forests. On a continuum between deep understanding of machine learning models and viewing it as a black box, Larry Lunetta, vice president of security solutions marketing for Hewlett Packard Enterprise's Aruba Networks, finds a middle ground. Here's how Larry Lunetta, vice president of security solutions marketing for Aruba Networks, describes trained and supervised machine learning: A ransomware attack, for example, typically does one of two things after it gains access to a network. While Chow can roughly explain the two machine learning algorithms they use most often in their work -- K-means clustering and simple linear regression -- he recommends leaving the front-end research to a value-added reseller (VAR).
Go programmers love Go's simplicity, ease of deployment, and tooling. But what if you want to infuse a little more intelligence in your Go applications? In this course, Pachyderm data scientist Daniel Whitenack, Ph.D., shows Go pros how to build and train a predictive machine learning app that combines native Go with a cool ML algorithm called linear regression.
Put another way, there's a lot of discussion around the ways people might interact with intelligent machines. Bigger shifts include machine learning algorithms that improve other machine learning algorithms. But as MI stacks become more complicated – and as open source libraries grow and individual components become more interoperable and accessible through consistent APIs – algorithms will take over aspects of the process, inserting a layer of hidden intelligence beneath the ones that interact more directly with people. Today, thanks to as-a-service infrastructure, machine learning API products, open source models and libraries, and other new resources, the barrier to MI entry is lower than ever.
Our story starts at IBM Research in the early 1970s, where the relational database was born. Two newly minted PhDs, Donald Chamberlin and Raymond Boyce, were impressed by the relational data model but saw that the query language would be a major bottleneck to adoption. They set out to design a new query language that would be (in their own words): "more accessible to users without formal training in mathematics or computer programming." Way before the Internet, before the Personal Computer, when the programming language C was first being introduced to the world, two young computer scientists realized that, "much of the success of the computer industry depends on developing a class of users other than trained computer specialists."
There are multiple quantum algorithms exhibiting quantum speedup that could act as subroutines, or building blocks, for quantum machine learning programs. Fueled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.
Artificial intelligence (AI), blockchain, and cloud technologies are increasingly appearing on the horizon. These three key technologies can fuel the financial services industry's evolution into the digital age. The advances in cloud technologies permit software applications to move seamlessly between legacy, private cloud, and public cloud solutions. Potential impacts of financial services' digital evolution include: Security is now a bottom-line concern.
One of the most widely applied types of machine learning is pattern recognition, based on clustering and categorization of data. Amazon customers have already experienced how machine learning-based analytics can be used in sales: Amazon's recommendation engine uses "clustering" based on customer purchases and other data to determine products someone might be interested in. Another startup, Respond Software, has expert systems that corporate Security Operations Centers (SOCs) can use to automatically diagnose and escalate security incidents. IBM partnered with Genesys to build Watson into Genesys' "Customer Experience Platform," providing a way to respond to customer questions directly and connect people with complaints to employees armed with the best information to resolve them.
Giphy's collection is a good way to find the right GIF to express your feelings... up until you're tracking down that one elusive GIF that's improperly tagged. The company has a clever solution, though: make AI technology look through the GIFs itself. If you're trying to find the "where are the turtles" quote from The Office, you'll actually see GIFs related to that quote -- not every vaguely turtle-related picture under the Sun. Many more people were clicking through to GIFs when they searched for phrases (32 percent more for "never give up never surrender," for example).