Artificial Intelligence (AI) leaders and experts in Enterprise Search and Knowledge Management solutions, RAVN Systems, is blending the world of tech and law with their latest AI endeavors: a self-service robot that promises big things for the legal industry. Through the use of a self-service AI portal, RAVN Systems' newest tech allows law firms to train an AI robot to perform any custom tasks necessary. The robot, known as RAVN Extract Direct, is a self-service version of the London company's older model RAVN Extract, which allowed users to use AI technology to automatically summarize, analyze, and extract key information from documents. The distinction of RAVN Extract Direct's self-service feature is integral, as it's what enables clients to be in complete control of the information obtained by the robot. RAVN's AI robot gives clients a more nuanced AI experience by allowing them to have complete control over the platform.

Artificial intelligence is everywhere, at times obscured and sometimes fully hidden. It lurks in the Facebook newsfeed algorithm that curates the news you see, it's being implemented in the programs of semi-autonomous vehicles that decide who lives in case of an accident, and it spectacularly beat the top Go champions in the world with its deep neural network technology. The applications of AI are evolving with increased sophistication, sparking considerable, complex questions related the social impact, governance, and ethics of its technology. These questions are particularly salient as accountability mechanisms for algorithms are yet in a nascent stage, where the balance of power is skewed towards industry giants who control these technologies. In this particular moment, the research, development, and deployment of AI is primarily taking place in the private sector, while governments around the world are increasingly contracting out their own use of these powerful technologies. In this context, the future role of universities emerges as one that is particularly meaningful when it comes to addressing these questions of social impact, ethics, and governance of AI.

When Facebook Messenger first proclaimed the future of commerce was messaging in April 2016, it provided companies with a viable distribution mechanism (to over 1 billion monthly active users and growing) but excluded an important element in the equation -- a discovery mechanism. Developers and brands who chose Kik as their distribution platform fared a bit better, as Kik's official Bot Shop launch allowed discoverability and curation within multiple categories, modeled after Apple's App Store. By August over 20,000 bots had been created on Kik's Bot Shop. At the time Facebook Messenger announced it was supporting payments in September, over 30,000 bots had been built on Messenger. Although that's a far cry from the 2 million-plus apps in both Apple and and Google's app stores, discoverability remained a hurdle for bot creators.

Traditional outbound marketing campaigns are far less effective at winning and retaining customers than they once were. To achieve sustainable growth in today's always-connected, real-time world, marketers must deliver continuous, customized, two-way, insight-driven interactions with customers on an individual level. Brands that understand this and put the right Systems in place to scale are creating competitive advantages that are very difficult for their competitors to replicate because it's not just about technology. Forrester Research refers to this as creating a "Contextual Marketing Engine". In their 2016 survey of 115 technology, marketing, and customer experience professionals, Forrester found that, across the board, organizations' investments revolve around implementing customer personalization initiatives, solving people's challenges, and assembling digital experience systems.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. Over the past few weeks, we published a series of posts outlining expert opinions in data science, machine learning, artificial intelligence, and related fields. In an encore post of this series, we bring you the collected responses to an amalgam question -- including experts from all of the previous posts' fields -- while adding a second dimension this time around. I'd like to thank one of my researchers, Alekh Agarwal, for great input here. The way to increase the number of women in AI, ML and data science is two-fold. First, we must expand the definitions of the fields to include their interaction with the other sciences, including the biological and social sciences.

For the latest information, please visit: http://www.wolfram.com Speaker: Anton Antonov Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and more.

Massimiliano "Max" Versace traces the birth date of his startup to when NASA came knocking in 2010. The U.S. space agency had caught wind of his military-funded Boston University research on making software for a brain-inspired microprocessor through an IEEE Spectrum article, and wanted to see if Versace and his colleagues could help develop a software controller for robotic rovers that could autonomously explore Mars. NASA's vision proved no easy challenge. Mars rovers have limited computing, communications, and power resources. NASA engineers wanted artificial intelligence that could rely solely on images from a low-end camera to navigate different environments.

The combination of machine learning, deep learning, natural language processing, and cognitive computing will change the ways that humans and machines interact with our environments. AI-driven smart services will sense one's surroundings, know one's preferences are from past behavior, and subtly guide people and machines through their daily lives in ways that will truly feel seamless. This quest to deliver AI driven smart services across all industries and business processes will usher the most significant shift in computing and business this decade and beyond. Organizations can expect AI driven smart services to impact future of work flows, IOT services, customer experience journeys, and block chain distributed ledgers. Success requires the establishment of AI outcomes (see Figure 1).

Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. AI – technically, a computing field that involves the analysis of large troves of data to predict outcomes and patterns – is as old as modern computers but its esoteric nature means it has long endured caricatures of its actual potential – think for example, the 1960s space age cartoon The Jetsons, which featured a sentient robot maid and automated flying cars (both of which we are still waiting for, even 50 years on). Now, a confluence of factors has given rise to hopes that computers with human-like cognitive ability may soon be a reality.

Computers can already hold a massive amount of instantly retrievable data in a manner that puts most humans to shame, but getting them to actually display intelligence is an entirely different challenge. A team of researchers from Northwestern University just made a huge stride toward that goal with a computational model that actually outperforms the average American adult in a standard intelligence test. Don't miss: Apple new 2017 iPad models reportedly have been delayed As PhysOrg reports, the witty computer system utilizes an AI platform called CogSketch that gives it the power to solve visual problems just by looking at them, which is something that has traditionally held back many examples of artificial intelligence. Being able to visually understand, interpret, and then use that data to come to a solution brings the computer system closer to the functioning of the human brain than many before it, and so the team pitted its creation against a popular standardized test called Raven's Progressive Matrices. The Raven's test (or RPM for short) is composed of 60 multiple-choice questions that measure the taker's ability to reason, using visual puzzles.