ARMONK, N.Y. - 18 Nov 2016: IBM (NYSE: IBM) today announced several cloud data services and features on Bluemix designed to help organizations accelerate the migration of their data to the cloud and more easily generate business insights. Now available on Bluemix, IBM Decision Optimization, Bluemix Lift and dashDB for Transactions can help organizations overcome this challenge and make more informed business decisions by enabling them to more easily aggregate, ingest and analyze expanding workloads. "Cloud is the platform that enables cognitive intelligence," said John Murphy, Vice President, IBM Watson Data Platform. "We're continuing to grow our catalog of cloud data services on Bluemix so that we can help developers and data scientists better manage and more quickly interpret data for business innovation." IBM Decision Optimization on Cloud (including the CPLEX engines) is now in beta on Bluemix. It can ingest large amounts of data including predictions, master and transactional data, business goals, and business rules to prioritize and rank business decisions such as plans and schedules.

The fields of artificial intelligence and machine learning are moving so quickly that any notion of ethics is lagging decades behind, or left to works of science fiction. This might explain a new study out of Shanghai Jiao Tong University, which says computers can tell whether you will be a criminal based on nothing more than your facial features. The bankrupt attempt to infer moral qualities from physiology was a popular pursuit for millennia, particularly among those who wanted to justify the supremacy of one racial group over another. But phrenology, which involved studying the cranium to determine someone's character and intelligence, was debunked around the time of the Industrial Revolution, and few outside of the pseudo-scientific fringe would still claim that the shape of your mouth or size of your eyelids might predict whether you'll become a rapist or thief. Not so in the modern age of Artificial Intelligence, apparently: In a paper titled "Automated Inference on Criminality using Face Images," two Shanghai Jiao Tong University researchers say they fed "facial images of 1,856 real persons" into computers and found "some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle."

Founded by two of the creators of Siri, Apple's signature digital assistant, Viv Labs said its technology represents a "new paradigm" for how people interact with computers. The Viv AI uses something called dynamic program generation that creates software on the fly based on the intent of the request given to it. In a briefing reported yesterday by Reuters, Samsung said the addition of Viv's technology to the Galaxy S8 will enable a wide variety of new services for its customers. The more services that developers integrate with Viv, the smarter the AI will become, according to Samsung Mobile Business CTO Injong Rhee. "Even if Samsung doesn't do anything on its own, the more services that get attached the smarter this agent will get, learn more new services and provide them to end-users with ease," Rhee told Reuters.

In the world of science fiction, robots given artificial intelligence often play a menacing role capable of killing human beings either on their own volition or at the behest of their programmers. Think movies like "2001: A Space Odyssey," "Blade Runner" or even "I, Robot," for instance. However, even with all of the recent advances leading to an increasing daily reliance on artificial intelligence, the prospect of technology running the show and outpacing humans may still be a long ways off, a Google executive said this week. "There is a lot that machine learning doesn't do that humans can do really, really well," Diane Greene, Google's senior vice president of cloud businesses operations, said Tuesday at the Code Enterprise conference in San Francisco. "Nobody expected some of the advances we are seeing as quickly as we're seeing them but, the singularity, I don't see it in my sentient lifetime."

A child can learn from a picture book to associate images with sounds, but building a computer vision system that can train itself isn't as simple. Using artificial intelligence techniques, however, researchers at Disney Research and ETH Zurich have designed a system that can automatically learn the association between images and the sounds they could plausibly make. Given a picture of a car, for instance, their system can automatically return the sound of a car engine. A system that knows the sound of a car, a splintering dish, or a slamming door might be used in a number of applications, such as adding sound effects to films, or giving audio feedback to people with visual disabilities, noted Jean-Charles Bazin, associate research scientist at Disney Research. To solve this challenging task, the research team leveraged data from collections of videos. "Videos with audio tracks provide us with a natural way to learn correlations between sounds and images," Bazin said.

This is how humans operate. We try something, we judge the result and modify our behavior. What some considered to be science fiction only a few years ago, AI is edging closer to reality as decades of research -- combined with advances in compute power, memory, storage, network connectivity, sensors and the software that unites them all -- is poised to enable new classes of intelligent predictive analytics. These innovations will bring benefits to multiple industries, and to society as a whole in the way we lead our everyday lives. Al is going to change our lives for the better as machines learn, reason, act and adapt -- transforming industries by amplifying human capabilities, automating tedious or dangerous tasks, and solving some of our most challenging societal problems.

The purpose of the report is to detail the process of training an Artificial Neural Network to control a robot. This report will be divided into several sections. The goal of this report is to demonstrate the ability of an ANN to control a robot. Specifically, it will stand and walk. In the previous reports, the GA was used to evolve an ideal Artificial Neural Network topology, which was then refined via backpropagation learning.

Bogdan Botezatu discusses how defenders are using machine learning algorithms to help beat the malware and give themselves the best possible chance of evading and protecting against APTs. While some predict that particular activities could be replaced almost entirely (78 percent) by machine learning and artificial intelligence algorithms, they mostly refer to physical and predictable activities, such as operating machinery or assembly line working. When it comes to machines completely taking over our jobs and lives, rest assured, we still have a long way to go. As for cyber-security, with more than 300,000 unique malware samples emerging each month, using flesh-and-blood security researchers to manually go through that much data is unrealistic and counterproductive. To that end, modern internet security companies have started developing and training machine learning algorithms to take over a great deal of the daily automation involving malware detection and analysis, with the same accuracy as a highly skilled and experienced security researcher.

"Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology." After using AWS Machine Learning for a few hours I can definitely agree with this definition, although I still feel that too many developers have no idea what they could use machine learning for, as they lack the mathematical background to really grasp its concepts. Here I would like to share my personal experience with this amazing technology, introduce some of the most important – and sometimes misleading – concepts of machine learning, and give this new AWS service a try with an open dataset in order to train and use a real-world AWS Machine Learning model. Luckily, AWS has done a great job in creating this documentation, so that everybody can understand what machine learning is, when it can be used, and what you need in order to build a useful model. You should check out the official AWS tutorial and its ready-to-use dataset.