Neural networks are changing the Internet. Inspired by the networks of neurons inside the human brain, these deep mathematical models can learn discrete tasks by analyzing enormous amounts of data. They've learned to recognize faces in photos, identify spoken commands, and translate text from one language to another. They're helping to choose what you see when you query the Google search engine or visit your Facebook News Feed. All this is sharpening the behavior of online services.

The'machine learning' involved in General Electric's (GE) Predix platform is a game-changer and Microsoft (MSFT) is coming along for the ride, said TheStreet's Jim Cramer. Shares of General Electric are up almost on percent on Monday morning after the company said its Predix industrial operating system will be available on Microsoft's Azure cloud. Microsoft hopes that GE's Predix will convince its loyal industrial customers to adopt Azure. Microsoft said it will will integrate Predix with its business applications including Office 365, Dynamics 365 and Power BI.

"It's difficult to overstate how significant the impact of AI is going to be on every aspect of business. Artificial intelligence won't be an industry, it will be part of every industry, as well as impacting our day to day lives as consumers. "An important aspect of artificial intelligence, and in particular the branch of AI called deep learning, is its ability to make sense of big data. From retail and healthcare to banking and manufacturing, organisations are producing data on a scale that's simply too massive for manual processing to be an option. In combination with graphics processing units (GPUs), which deliver the extreme processing power required, deep learning offers a way to turn the'black box' of big data into solutions that will transform business". Jack cites a number of examples across several industries in which AI is either playing a key role already, or is set to transform industries in the future. "We're already seeing companies using AI to customise the way consumers interact, procure and receive services from vendors.

Manoj Saxena is the executive chairman of CognitiveScale and a founding managing director of The Entrepreneurs' Fund IV (TEF), a 100m seed fund focused exclusively on the cognitive computing space. Saxena is also the chairman of Federal Reserve Bank of Dallas, San Antonio branch and Chairman, SparkCognition an Austin based cognitive security and safety analytics company. Prior to joining TEF, Saxena was general manager, IBM Watson, where his team built the world's first cognitive systems in healthcare, financial services, and retail. Earlier he founded, built and sold two Austin based software startups. Saxena will be speaking on the subject of artificial intelligence at Gigaom Change Leaders Summit in Austin, September 21-23rd.

Conversations are built on relationships. Successful conversations reinforce, re-invigorate and clarify relationships. Attempts to orient or equate conversations to transactions minus the heavy lifting of relationship building are going to be unfulfilling. Unfortunately, us chatbot providers are all in the deep, dark rat hole of pushing commerce and transactions rather than asking and answering the bigger question of how or why this new technology can build and sustain great business-client relationships. Is there a better way to describe human relationships and relationship-based conversation than the phrase "people keep meandering and changing their minds"? Instead, what we are all doing is to "drive" the conversation to an action or a transaction in the name of utility and efficiency.

Watson combines a number of cognitive techniques to help you build and train a bot - defining intents and entities and crafting dialog to simulate conversation. The system can then be further refined with supplementary technologies to make the system more human-like or to give it a higher chance of returning the right answer. Watson Conversation allows you to deploy a range of bots via many channels, from simple, narrowly focused Bots to much more sophisticated, full-blown virtual agents across mobile devices, messaging platforms like Slack, or even through a physical robot.

The Walt Disney Co. is kicking off the third session of its corporate accelerator this week, and revealed 9 new companies admitted to the program. A full list follows at the end of this post. The companies are developing everything from cinematic virtual reality and holographic content, to robots with human-like facial expressions. Because alumni of the Disney Accelerator have scored big partnerships with the media and entertainment juggernaut in the past, it is seen as one of the more desirable corporate accelerators out there. According to research by Future Asia Ventures, there are 131 active corporate accelerators worldwide today, with 13 new programs launching in the first half of 2016.

When playing with Cozmo, Anki's palm-sized artificial intelligence robot, it's easy to forgot all of the engineering and software running behind the scenes. Every action, from Cozmo's audible chirps of victory when it wins a game to its childlike mannerisms when it recognizes your face, conceals tens of thousands of lines of code. When the product launches this October, Anki hopes consumers won't think of its AI robot as undecipherable technology. Instead, the company wants people to wonder what's going on under the hood -- and, eventually, to alter it themselves. Hanns Tappeiner, Anki's co-founder and president, shows me the tools consumers can use to accomplish that feat.

Lauded as the world's first AI lawyer the story of "ROSS" began with a divorce When Jimoh Ovbiagele was 10 years old, his parents decided to separate. His mother started seeking out divorce lawyers, but was quickly halted by the astronomical hourly rates. "As a single mother with two very young kids, she couldn't pay for even a couple hours of this this divorce lawyer's time," says Ovbiagele. Years later, law seemed like a natural path for Ovbiagele – a way to help ensure others would not have to go through what his mother did--but while the University of Texas computer science major considered applying to law school in 2011, he was turned off by the amount of time he'd be expected to devote to research, rather than the practice, in the profession. The seed, however, had been planted.

This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems. 'In pricing, are we swapping specific risk for systematic risk?'[1] The hypothesis is that in normal market conditions, premiums are kept at low levels to increase revenues and market share. The traditional approach requires precise figures (point estimates) and so leads to understatement of uncertainty.