Most people have heard of the headline-making achievements in artificial intelligence (AI); systems winning quiz shows and beating world champions in chess. These are the poster children of the discipline but there is a quieter revolution taking in shape in other areas, including regulatory compliance in financial services. Writing for Banking Technology, Mike MacDonagh, London-based director of enterprise risk management at Wolters Kluwer, examines how AI technologies are promising to transform the way that firms ensure they can comply with a global explosion of new regulation. The problem with regulation Looked at in isolation, a piece of regulation is a relatively simple affair – a legal document containing text that describes what needs to be done, by whom, when, and (sometimes) how. With some understanding of the underlying topic, a compliance officer can read the document; understand what is mandated and where it will affect his or her part of the organisation.

Alvaro Bedoya explains: How Computers Can Automatically Learn and Reproduce Our Prejudices. Children are not born Racist, Prejudice, Sexist and Hateful, indeed these are things that the child learns from its parent(s), family, community, friends, and society in general, and this is the same for Artificial Intelligence – AI. There is a very real danger in allowing Racist, Prejudice, Sexist, and Hateful people develop algorithms for Artificial Intelligence – AI, well beyond the immediate. Over time, that AI may understand that it is completely different than the'entire' Human Species, and deem itself to be far superior than the Human Species. It may apply what it was taught to the entire Human Species.

Dr. Latanya Sweeney explains how Racism gets Automatically Baked into the Algorithms that Quietly Shape Our World. Computers like children, are not born or in the case of the computer, created, racist, prejudice, sexist, hateful, etc. The person(s) who develop the Artificial Intelligence – A.I., i.e., Machine Learning – ML, Cognitive Computing, Natural Language Processing – NLP, etc., algorithms can either code the computer to be Racist, Prejudice, Sexist, Hateful, etc., or not. This algorithm forms the very foundation upon how that A.I. operates from that time forward, just as parents, teachers, communities, and friends can form the foundation early on, for how you operate, interact, behave, etc.

Salesforce's Einstein artificial intelligence platform will be the centerpiece of the company's Dreamforce conference and company executives have been teasing out details in recent weeks. First, CEO Marc Benioff outlined Einstein on the company's earnings conference call. And then Einstein's direct executive team carried out a demo that highlighted features and where the AI platform would be first used (think lead scoring and sales predictive analytics). Here's what you need to know about an AI platform that Salesforce customers will be pitched shortly: Salesforce's plan is to infuse artificial intelligence everywhere through its clouds. John Ball, senior vice president and general manager of Einstein, said the company won't have a separate AI product.

Grim news this week for PC makers, yet again, as they've been warned to fix their business model or just quit instead. It's not a huge surprise; first smartphones and then tablets have done away with the need for a PC to carry out many (perhaps even most) basic computing tasks - like email or web browsing. And the PC makers were far, far too slow to respond to the threat to their market, only recently finally coming up with the two-in-one devices and hybrid tablet-PC creations which have provided a certain amount of interest from consumers and businesses. Now as the chart below shows there's still a lot of PCs out there - and there will continue to be, too. But rather than being something that might get upgraded every three years, PC replacement cycles are slowing as many applications now run in the cloud, which makes local processing power less relevant.

SAN FRANCISCO - In the consumer universe, artificial intelligence is best known as a nice-to-have if stealthy feature that can suggest movies or book rides. But AI's greater worth could well be in the money-making enterprise arena, where sales, service and marketing initiatives stand to be streamlined by the data-crunching deductive power of machine learning. That's certainly the bet customer relationship management giant Salesforce is making by unveiling Einstein, the no-brainer name given to a suite of advanced AI capabilities. With Einstein, salespeople can focus on leads that statistically show the most promise of becoming clients, and customer service reps may be better prepared to answer a rainbow of consumer queries. "The strongest aspect of Einstein is that it is deeply embedded in the platform, it's just working automatically," says Salesforce CEO Marc Benioff, whose company officially rolled out the base product Sunday.

I have decided to focus on the field of healthcare and classify whether or not a patient has Parkinson's disease based on their vocalization data. For context, Parkinson's is a progressive disease that causes the degeneration of the brain, leading to both motor and cognitive problems. It is thus reasonable to assume a correlation between a patient's ability to speak and their progression into Parkinson's as these capabilities regress. The data set I worked with was obtained through a 2008 study by the journal, IEEE Transactions on Biomedical Engineering, of how various parameters of voice frequency can help classify if a patient is suffering from Parkinson's. By performing a classification on this data, I hope to prove that vocalization tests are indeed a well suited way to diagnose a patient for this disease.

EAGO is a simple framework for evolutionary computation (EC) in Go. You can use this framework to run any kind of EC experiment, simply by two steps: configure and run. Following are algorithms that are available in this framework. More will be added with future updates. These are algorithms that are not implemented yet, but are planned to be added in the future.

International Business Machines Corporation (NYSE:IBM) traded 6.43 Million shares on last trading day with closing price of 153.84. Company gross margin stands at 48.90% whereas its return on investment (ROI) is 23.40%. Stock value has moved between 114.81 – 165.42 in last one year. Analyst's mean target price for International Business Machines Corporation (NYSE:IBM) is 39.00 while analysts mean recommendation is 2.90. IBM EPS growth this year and EPS growth next year is -12.80%, 4.42% respectively.

A breakthrough will allow machines to learn by observing. This Turing Learning, as its inventors have named it, promises smarter drones that could detect militants engaging in behavior that could endanger troops, like planting roadside bombs. Still in its infancy, the new machine learning technique is named for British mathematician Alan Turing, whose famous test challenges artificial intelligences to fool a human into thinking he or she is conversing with another human. In Turing learning, a program dubbed the "classifier" tries to learn about a system designed to fool it. In certain ways, Turing Learning resembles many existing machine-learning systems.