In the next five years, every important decision, whether it's business or personal, will be made with the assistance of IBM Watson. Watson, the company's artificial intelligence-fueled system, is working in fields like health care, finance, entertainment and retail, connecting businesses more easily with their customers, making sense of big data and helping doctors find treatments for cancer patients. The Watson system is set to transform how businesses function and how people live their lives. "Our goal is augmenting intelligence," Rometty said. "It is man and machine. This is all about extending your expertise. It doesn't matter what you do. IBM's conference this week, which the company said drew 17,000 attendees, explored how companies, including retailers, educators, human resources departments and financial institutions, amon others, can use Watson. "The challenge IBM has right now is to define the marketplace," said Jeff Kagan, an independent industry analyst, who attended the conference. "Ten years from now, will IBM be the leader?

A London-based startup has combined some of today's most disruptive technologies in a bid to change the way we'll build the future. By retrofitting industrial robots with 3D printing guns and artificial intelligence algorithms, Ai Build has constructed machines that can see, create, and even learn from their mistakes. When CEO and founder Daghan Cam was studying architecture, he noticed a disconnect between small-scale manufacturing and large-scale construction. "On one side we have a fully automated production pipeline," Cam explained at a recent conference in London. With the emergence of more efficient printing technologies, he thought there must be a better way.

Google parent Alphabet (Xetra: ABEA.DE - news) delivered higher profits for the third quarter, lifted by gains in mobile and video advertising as the tech giant narrowed losses on its "moon shots." Net (LSE: 0LN0.L - news) profit climbed 27 percent to $5.1 billion. Revenue rose to $22.5 billion from $18.7 billion in the same period a year earlier. Shares (Berlin: DI6.BE - news) rose nearly one percent in after-market trades that followed the release of the stronger-than-expected earnings figures. "We had a great third quarter," Alphabet chief financial officer Ruth Porat said in the earnings release.

Companies have been trying to drum up enthusiasm for them for years, with little success. Pepper, a humanoid machine carrying the hopes of SoftBank Group Corp.'s billionaire founder Masayoshi Son, was supposed to change that. Promoted as the first robot to be endowed with emotions, the company marketed Pepper aggressively after it was unveiled in 2014, promising the gadget was sophisticated enough for tasks usually handled by shop clerks, receptionists and translators. "It's not there to have a conversation," said Junichi Nishi, a municipal government official in Fujieda, a city of about 140,000 in central Japan. "We use it primarily as a tablet," he said, referring to the touch screen attached to the robot's chest.

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The goal is now in sight – if not yet in reach: a fully-automated operational production environment. The rise of DevOps shows the progress we've made in automating the provisioning and configuration of ops, as well as application deployment. IT Operations Management (ITOM), and in particular Application Performance Management (APM) are now well on their way to realizing this hands-off vision. In today's complex enterprise production environments, we still need people – but as the environments and applications become more difficult to manage, we must give our ops personnel smarter, more powerful tools. How to use an abacus – not the algorithms we're looking for First-generation monitoring tools simply took events and log entries and fed them to hapless ops personnel as alerts.

Jonathan Epstein, CMO of Sentient Technologies, joins us to talk about the advancements in Artificial Intelligence and how assistive marketing and merchandising is just the very beginning of AI for Commerce.

E-marketer explores how the world perceives artificial intelligence. This explosive growth of artificial intelligence brings a danger with it. Well maybe'danger' is a bit strong, but with so many companies exploring AI, how do you find the right match that aligns with your needs? We at MarianaIQ are proud to be one of the startups providing solutions in this field. Deep learning enables us to help you find the right leads by matching social, web and proprietary data regardless of source or vertical.

Your Twitter feed is about to get a lot smarter. CEO Jack Dorsey said Thursday that Twitter will expand machine learning algorithms and artificial intelligence in its core product, and there are changes coming to videos on the platform, too. "We're focused on adding more machine learning and artificial intelligence to everything that we do," Dorsey said during the company's third quarter 2016 earnings call. Twitter sees four areas where A.I. will be useful: As for video, Dorsey said there's plans for A.I. there, too: "We have some really cool and really awesome technology that enables more and more viewership because we can do just-in-time compression," he said. "So we can work on any device type through any network bandwidth and deliver a high-quality, high-definition experience. And we're just starting to apply that technology to our live experience and also to Periscope."

Recently we wrote about the development of AI and neural nets beyond the second generation Convolutional and Recurrent Neural Nets (CNNs / RNNs) which have come on so strong and dominate the current conversation about deep learning. Our research shows that the next generation of neural nets is most likely to be led by Spiking Neural Nets (SNNs) that are a return to the'strong' AI tradition and closely mimic actual brain function. Unlike CNNs that fire signals to every one of their deep layer connections every time, SNNs are modeled after the fact that in the brain neurons do not constantly communicate with one another. Rather they communicate in spikes of signals or more correctly short trains of spiking signals. As each spike in the train arrives at a neuron it raises the potential of that neuron until finally a spike arrives that tips it over its potential threshold and it in turn fires, propelling the signal onward.