Thanks to artificial intelligence, we have autonomous cars, chat bots, and speech recognition. Microsoft's CNTK (Cognitive Toolkit) is one among many platforms that trains computers to learn, and it's getting an upgrade. CNTK drives the Microsoft services Cortana and Skype language translation, and it boasts more than 90 percent accuracy in speech recognition tasks. Microsoft will soon release an upgraded CNTK toolkit, and one hardware maker wants to ensure the toolkit works best on its hardware. Nvidia is partnering with Microsoft to optimize its GPU development tools for CNTK.

Predictive maintenance is one of many appealing use cases for the Internet of Things (IoT). Using sensors to predict the health of a fleet of machines in the field can prevent down time without conducting unnecessary maintenance. Further, predictive maintenance allows for maintenance to be conducted at the most cost effective time. Allowing you to shift your operations from being reactive to proactive. Predictive maintenance has applications for the automotive, aerospace, health, and smart city industries, just to name a few.

Advances in machine learning are making security systems easier to train and more flexible in dealing with changing conditions, but not all use cases are benefitting at the same rate. Machine learning, and artificial intelligence, has been getting a lot of attention lately and there's a lot of justified excitement about the technology. One of the side effects is that pretty much everything is now being relabeled as "machine learning," making the term extremely difficult to pin down. Just as the word "cloud" has come to mean pretty much anything that happens online, so "artificial intelligence" is rapidly moving to the point where almost anything involving a computer is getting that label slapped on it. "There is also a lot of hype," said Anand Rao, innovation lead for US analytics at PricewaterhouseCoopers LLC.

Artificial intelligence will'inevitably' destroy millions of jobs and could bring down governments Automation so far dominates automotive, electrical and electronics fields Report warns shift could take two-thirds of jobs in developing countries And, some may put more focus on low-wage jobs that robots can't yet do And, some may put more focus on low-wage jobs that robots can't yet do The poll among 224 venture capitalists attending the Web summit in Lisbon found 53 percent believed AI would destroy millions of jobs and 93 percent saw governments as unprepared for this. Scientists to unleash killer bacteria to try and... Could a folding phone save Samsung? Firm patents radical... Do YOU count on your fingers? Experts say it could actually... Eyes on the prize: Hundreds queue for Snapchat's Spectacles... Scientists to unleash killer bacteria to try and... Could a folding phone save Samsung? Firm patents radical... Do YOU count on your fingers?

On Friday during the BlizzCon 2016 opening keynote, Blizzard revealed that it teamed up with Google to provide an application programming interface (API) for DeepMind to be used in StarCraft II. This will enable artificial intelligence (AI) and Machine Learning researchers from around the world to create intelligent "bots" to play the game. In return, the knowledge gained while playing will be used in real-world applications. "An agent that can play StarCraft will need to demonstrate effective use of memory, an ability to plan over a long time, and the capacity to adapt plans based on new information," said research scientist Oriol Vinyals of the DeepMind team. "Computers are capable of extremely fast control, but that doesn't necessarily demonstrate intelligence, so agents must interact with the game within limits of human dexterity in terms of'Actions Per Minute.'"

A team from Columbia University and Princeton University have developed an algorithm to accurately analyse genetic ancestry across'tera' sized datasets – a potentially significant development in the development of personalised healthcare. Since the completion of the Human Genome Project, and the savings in both time and money that next generation sequencing enables, genetic datasets have grown exponentially whilst analysis has fought to keep pace. Now, a team of researchers have developed a machine learning algorithm they call TeraStructure, capable of analysing very large data sets. Machine learning is a computer analysis method which allows an artificial intelligence to literally teach itself, using statistical principles and the growing capability of computers to process data. Tech giants such as Google, Microsoft and Apple all have their own programs, but promising applications in medical science are still relatively few.

Machine learning couldn't be hotter, with several heavy hitters offering platforms aimed at seasoned data scientists and newcomers interested in working with neural networks. Among the more popular options is TensorFlow, a machine learning library that Google open-sourced a year ago. In my recent review of TensorFlow, I described the library and discussed its advantages, but only had about 300 words to devote to how to begin using Google's "secret sauce" for machine learning. That isn't enough to get you started. In this article, I'll give you a very quick gloss on machine learning, introduce you to the basics of TensorFlow, and walk you through a few TensorFlow models in the area of image classification.

When I go to Google Photos and search my photos for'skyline', it finds me this picture of the New York skyline I took in August, without me having labelled it! When I search for'cathedral', Google's neural networks find me pictures of cathedrals & churches I've seen. But of course, neural networks aren't magic–nothing is! I recently read a paper, "Explaining and Harnessing Adversarial Examples", that helped demystify neural networks a little for me. The paper explains how to force a neural network to make really egregious mistakes. It does this by exploiting the fact that the network is simpler (more linear!) than you might expect. It's important to understand that this doesn't explain all (or even most) kinds of mistakes neural networks make. There are a lot of possible mistakes!

The next frontier for artificial intelligence may involve teaching computers to distinguish albacore tuna from its yellowfin cousin. The Nature Conservancy, an environmental non-profit, is working with several Pacific Island nations and a big tuna fishing company to more easily count and identify fish caught at sea using cutting edge technology. The goal is to use trendy artificial intelligence techniques like deep learning to help fishermen reduce the number of protected animals like sharks and turtles that are accidentally caught along with the tuna. The Nature Conservancy hopes that the program could prevent overfishing and help threatened and endangered sea life recover without putting fishermen out of work. "We have real optimism that data science community can help us differentiate a turtle from a tuna, and flag when a shark comes on board," said Mark Zimring, a project director for The Nature Conservancy.

However, many businesses may ask; are Chat Bots an innovation and investment too far? What should be the strategy for building a successful Bot? How to make the Bot human-like? A human-like Bot will require the use of text analytics, artificial intelligence and sentimental analysis in varying degrees. The study answers these questions and highlights a way to classify and explain the range of available Bot solutions.