Organizations create style guides to capture the rationale of their design decisions and help other teams build great experiences. You might have read gov.UK;s service manual or the U.S. Digital Services Playbook.

The electrical grid has become a network of billions of linked devices with highly complex energy and information flows. Add to this the elevated role of the consumer as a producer and you are looking at a massive volume, velocity and variety of data from smart meters, transformers, and substations that remains largely untapped. One extremely interesting solution to the impending challenges is cognitive computing, which is gaining traction among industry experts as a way to better manage data and improve both operating efficiencies and customer service. According to GTM Research, applying cognitive computing is expected to deliver an estimated $121 billion global return on investment (ROI) on grid analytics by 2020. There are three key areas where energy companies are already engaging with cognitive systems to experience real benefits.

This week, we heard the car service was using secret software to evade government regulators and a video showed its chief executive in a verbal altercation with one of the company's drivers. Previously, the company's self-driving cars raised safety concerns in San Francisco when, because of faulty and incomplete technology, they reportedly barreled through red lights and crossed over bike lanes. Uber has recently been accused of sexual harassment, intellectual property theft, and other questionable behavior. Silicon Valley is gaining a reputation for being obsessed with making money at any cost, i.e. Theranos, which made false claims and risked lives.

Machine Learning (ML), a branch of Computer Science that focuses on drawing insights and conclusions by examining data sets, is an increasingly popular discipline today in resolving enterprise business issues. However the field is vast and consists of numerous algorithms and approaches. Data sets are also often complex and require to be pre-processed before an ML algorithm can be'trained' to learn from such data. For a particular problem domain and data set, defining the pre-processing technique and selecting the ML algorithm (or set of algorithms) is still largely'an art rather than a science' depending on the knowledge and skills of the expert/data scientist in question. With time this will change and scientific guiding principles/best practices will emerge to pre-process data and to select appropriate algorithms for a particular problem domain - as the discipline matures.

IDC sees widespread adoption of cognitive systems and artificial intelligence (AI) across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. According to a new Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide from International Data Corporation (IDC), the market for cognitive/AI solutions will experience a compound annual growth rate (CAGR) of 55.1% over the 2016-2020 forecast period. "Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing," said Jessica Goepfert, program director, Customer Insights and Analysis at IDC. "In these segments, we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies. For instance, cognitive technologies are being used in the banking industry to detect and combat fraud – consistently a top industry pain point. Meanwhile, in manufacturing, executives cite improving product quality as a top initiative. In this case, cognitive systems recognize and know how to respond to dynamic fluctuations in product specs by adapting the production to stay within quality targets."

It's amazing how much progress the field of AI has achieved over the last 10 years, ranging from self-driving cars to speech recognition and synthesis. Against this backdrop, AI has become a topic of conversation in more and more companies and households who have come to see AI as a technology that isn't another 20 years away, but as something that is impacting their lives today. Indeed, the popular press reports on AI almost everyday and technology giants, one by one, articulate their significant long-term AI strategies. While several investors and incumbents are eager to understand how to capture value in this new world, the majority are still scratching their heads to figure out what this all means. Meanwhile, governments are grappling with the implications of automation in society (see Obama's farewell address).

Managing business data is a big trend right now. Through a couple of clicks and a couple drags, anybody can explore it. Businesses have been collecting and analyzing data since the 1950s. Data is the prime factor for businesses in order to gather the information and related analysis to increase operational efficiency, reduce costs and serve their customers. You might be wondering, why one must care about Data Analysis, in particular?

Artificial intelligence boosters predict a brave new world of flying cars and cancer cures. Detractors worry about a future where humans are enslaved to an evil race of robot overlords. Veteran AI scientist Eric Horvitz and Doomsday Clock guru Lawrence Krauss, seeking a middle ground, gathered a group of experts in the Arizona desert to discuss the worst that could possibly happen - and how to stop it. Their workshop took place last weekend at Arizona State University (ASU) with funding from Tesla co-founder Elon Musk and Skype co-founder Jaan Tallinn. Officially dubbed "Envisioning and Addressing Adverse AI Outcomes", it was a kind of AI doomsday games that organised some 40 scientists, cyber-security experts and policy wonks into groups of attackers - the red team - and defenders - blue team - playing out AI-gone-very-wrong scenarios, ranging from stock-market manipulation to global warfare.

The call for proposals for the O'Reilly Artificial Intelligence Conference, September 17-20, 2017, in San Francisco, is now open. "Bots are the new apps"--Satya Nadella, CEO of Microsoft Bots are a new, AI-driven way to interact with users in a variety of environments. As AI improves and users turn away from single-purpose apps and toward messaging interfaces, they could revolutionize customer service, productivity, and communication. Getting started with bots is as simple as using any of a handful of new bot platforms that aim to make bot creation easy; sophisticated bots require an understanding of natural language processing (NLP) and other areas of artificial intelligence. Since late 2015, bots have been the subject of immense excitement in the belief that they might replace mobile apps for many tasks and provide a flexible and natural interface for sophisticated AI technology.

LONDON--(BUSINESS WIRE)--Artificial intelligence and additive manufacturing or 3D printing are emerging technologies that have a huge potential in a variety of industries and applications. Despite both technologies being fairly new, particularly 3D printing, procurement market intelligence experts at SpendEdge state that these technologies have several applications in the supply chain for addressing procurement challenges, and will very soon become an integral part due to its improved and sophisticated functionalities. Rapid prototyping is one of the most common uses of additive manufacturing. Since designing a successful product can be very expensive and time-consuming, 3D printed prototyping makes it quite simple at the same time shortens the manufacturing cycle. The impact of AM on supply chain is massive, some of which include increased manufacturing flexibility, reduced material waste, and the ability to employ decentralized manufacturing strategies.