When you perform a Google search for every day queries, you don't typically expect systemic racism to rear its ugly head. Yet, if you're a woman searching for a hairstyle, that's exactly what you might find. A simple Google image search for'women's professional hairstyles' returns the following: This is your chance to join them. Here, you'll find hairstyles, generally done in a professional setting by stylists. It returns what it thinks you're looking for based on contextual clues, citations and link data.

"An Introduction to Agent-based Modeling" is a well-written and honest look at the benefits and limitations of agent-based modeling. Agent-based modeling is a computer simulation that assigns properties to agents, and the environment they interact with. Agent-based modeling demonstrates that agents acting of their own accord will collectively self-organize into predictable macro-behavior (a concept that's similar to Adam Smith's invisible hand theory). Some of this macro-behavior will alter the environment and eco-system (a concept that's termed "emergent"). The authors are honest enough to admit that agent-based modeling is not predictive (it's too determinant on the algorithms and parameters that humans assign the model), but it can be a powerful tool for education and communication.

In a major boost to artificial intelligence (AI) research, five top-notch tech companies -- Facebook, Amazon, Google, IBM and Microsoft -- have joined hands to announce a historic partnership on AI and machine learning. It means that these companies will discuss advancements and conduct research in AI and how to develop best products and services powered by machine learning, Tech Crunch reported on Thursday. Initial financial help will come from these companies and as other stakeholders join the group, the finances are expected to increase. "We want to involve people impacted by AI as well," Mustafa Suleyman, co-founder and head of applied AI at DeepMind, a subsidiary of Alphabet (parent company of Google), was quoted as saying. According to the report, the organisational structure has been designed to allow non-corporate groups to have equal leadership side-by-side with large tech companies.

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.