LingPipe is a Natural Language Processing (NLP) library that is released under a dual commercial and an open-source AGPL license, and the basis for a NLP consulting company (Alias-I) that one of the authors (Breck Baldwin) founded. In fact, the preface of the book states that some of the recipes in this book come from Breck's private repository. This book is the first one devoted exclusively to LingPipe. While LingPipe provides comprehensive Javadocs and tutorials on its website, but it is fairly dense material (NLP is hard!) - the book is an easier, gentler way to understand it. One other reason LingPipe's API is so dense (even compared to other Java NLP libraries) is because it is written for performance, making heavy use of encapsulation to wrap common tasks and the visitor pattern to consume data in streaming mode. The book does a good job explaining the latter pattern in some depth, and deconstructing the code examples so the former becomes more obvious.

Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn. Very basically, a machine learning algorithm is given a "teaching set" of data, then asked to use that data to answer a question. For example, you might provide a computer a teaching set of photographs, some of which say, "this is a cat" and some of which say, "this is not a cat." Then you could show the computer a series of new photos and it would begin to identify which photos were of cats. Machine learning then continues to add to its teaching set.

The present invention relates generally to data mining and more specifically to a classifier and inducer used for data mining. Many data mining tasks require classification of data into classes. Typically, a classifier classifies the data into the classes. For example, loan applications can be classified into either "approve" or "disapprove" classes. The classifier provides a function that maps (classifies) a data item (instance or record; records and instances are used interchangeably hereinafter) into one of several predefined classes. More specifically, the classifier predicts one attribute of a set of data given one or more attributes. For example, in a database of iris flowers, a classifier can be built to predict the type of iris (iris-setosa, iris-versicolor or iris-virginica) given the petal length, sepal length and sepal width. The attribute being predicted (in this case, the type of iris) is called the label, and the attributes used for prediction are called the descriptive attributes. A classifier is generally constructed by an inducer.

SIEM is evolving; It is a Cat and a Mouse game with the bad guys. It's no just cyber criminals who are in the mix, we have state actors and hactivists too. Their means may be different but their methodology is similar. The environment has reacted – 5 yrs ago the attitude was "log everything". This caused the SIEM vendors to scale up and scale out to handle it, and then write the security rules to find issues.

September 29, 2016 – For those who have concerns about artificial intelligence (AI) and its potential to destroy humanity, the following may turn out to be good news. An organization to foster guidelines for AI development has formed with plans to seek public input on the technology as it develops. Called the Partnership on Artificial Intelligence to Benefit People and Society, it will study and develop best practices and serve as an open platform. The companies involved include Amazon. You are probably familiar with the first five. The last one is a British AI company recently acquired by Google.

An industry-wide organisation including five Silicon Valley giants has been formed to promote the fair and ethical development of artificial intelligence technologies. The organisation aims to "conduct research, recommend best practices, and publish research" rather that directly lobby legislators, and will focus on "ethics, fairness and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology". The group - known as the Partnership on Artificial Intelligence to Benefit People and Society - includes Facebook, Google, Amazon, Microsoft and IBM, with academic and non-profit organisations expected to join in the near future. "Over the past five years, we've seen tremendous advances in the deployment of AI and cognitive computing technologies," said IBM AI ethics researcher, Francesca Rossi, "ranging from useful consumer apps to transforming some of the world's most complex industries, including healthcare, financial services, commerce and the Internet of Things." "This partnership will provide consumer and industrial users of cognitive systems a vital voice in the advancement of the defining technology of this century - one that will foster collaboration between people and machines to solve some of the world's most enduring problems - in a way that is both trustworthy and beneficial."

Jim Andrews is in a medical office wearing just a hospital gown, staring at his doctor of 11 years, who is staring back at him through the sleek, metallic lens of Google Glass. As the doctor examines Andrews, a new kind of medical scribe is watching the examination, transcribing everything he sees. The scribe, named Rahul, is thousands of miles away in India, and he is viewing the office visit live through the pint-size, WiFi-connected camera attached to the doctor's glasses. "When was his last physical?" Rahul's nearly immediate answer pops up in a text bubble display in the right corner of the doctor's field of vision.

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.

This post was contributed by Zilliant Pricing Scientist Amir Meimand, who will be presenting at INFORMS in Nashville, Tennessee. In this post, Amir gives a primer on pricing science and explains how machine learning empowers organizations to respond swiftly with price after a change in market dynamics. Let's Start at the Beginning: A Primer on Pricing Science Pricing science started in the early 1980s with the airline industry. These models were mostly based on descriptive and predictive analytics to set price according to forecasted demand and available number of seats. Since about 2000, the application of pricing science has taken off in B2B.

The current situation with malware naming conventions is in disarray. Different antivirus vendors use different naming conventions and sometimes they don't follow their own standards. Let's look at a few results for a random virus. These are the results from VirusTotal, a meta-antivirus scanning service. We can see that it is a Trojan malware with some vendors (Dr.Web and TrendMicro) setting the family as StartPage, some saying it's in the Agent family, some saying it is in the FakeAV family and some saying it is Generic "KR" malware.