Exari, the leading provider of enterprise contract management software, announced today that together with IACCM it will host a live and complimentary webinar titled "An Artificial Intelligence Survival Guide for Contracts Professionals," on Tuesday February 28, from 11:00AM to 12:00PM Eastern Standard Time. The webinar will offer expert advice from Jamie Wodetzki, Co-Founder and Chief Product Officer at Exari and Tim Cummins, CEO of IACCM. Interested professionals can register for the webinar here. "We are in a golden age of machine learning," said Jamie Wodetzki. "Artificial Intelligence (AI) is transforming our daily lives and our professional careers. AI has entered into the contract management space and is already giving us new and different ways to explore key facets of contract documents."

When Worlds Collide, the 1951 Sci Fi film, painted a rather gloomy vision of the future of mankind on earth. However, the prospects for blended reality, in which the physical and digital worlds collide, couldn't be more promising. By fusing the virtual and real worlds, the possibilities are endless. By eliminating filters and limitations, blended reality enables expression at the speed of thought to improve experiences and inspire new technology. Using healthcare as an example, blended reality has significant implications for more personalized care, prevention and treatment by converging human biology, real-world context and technology advancements.

While the latter happened IRL in late 2016, a lot of the film's other future predictions were a little off. Though what's not too far fetched is the idea of robots, or artificial intelligence, working its way into our very real and ordinary lives in the not too distant future. Self-driving cars are already a thing, and that's only the beginning. "Artificial Intelligence (or AI) is likely to do to white collar jobs like how machines have been doing blue collar work. In other words, just like our brawns have been digitised, so will our brains be," Anders Sorman-Nilsson, global futurist and TEDx speaker told The Huffington Post Australia. Sorman-Nilsson is the author of Seamless: The Futurephile's Guide To Leading Digital Adaptation And Human Transformation.

Currently, IP Australia's online virtual assistant powered by Nuance's Nina technology delivers a dynamic and engaging customer experience that lets customers easily understand trademark, patent, designs and plant breeder's rights processes. The human elements of dialogue and personalised interaction connect customers to the right information and tools, which translates into immediate, easy and effective self-servicing and increased customer satisfaction. At present, Alex is capable of answering IP rights questions in layman's terms, minimising customer confusion and maximising successful first-time transactions.

SAN FRANCISCO--Cryptographers said at the RSA Conference Tuesday they're skeptical that advances in quantum computing and artificial intelligence will profoundly transform computer security. "I'm skeptical there will be much of an impact," Ron Rivest, a MIT professor and inventor of several symmetric key encryption algorithms, said early at the annual Cryptographers' Panel here. Susan Landau, a professor who specializes in cybersecurity policy and computer science at Worcester Polytechnic Institute, said that while artificial intelligence can be helpful when it comes to processing lots of data effectively, she doesn't think it will be useful in fingering out series attacks or anomalous situations. Adi Shamir, Borman Professor of Computer Science at the Weizmann Institute, said he was optimistic about AI's potential when it comes to defense – anything that involves finding deviations in behavior – but said he doubts it can ever be used in offensive sense, such as in identifying zero days, something he said requires more ingenuity and originality. The discussion was steered by a report recently released by the Global Risk Institute on the emergence of quantum computing technologies.

For years, marketing was considered more art than science. But more recently, as marketing automation software has proliferated, marketers have had to blend the art of storytelling with the science of data. Then along comes artificial intelligence (AI) and machine learning, which promise to help marketers make sense of all that data. Some experts believe AI's impact on marketing will be hugely significant, that it could even change the nature of marketing entirely -- enabling brands to break through the noise and deliver a more personalized experience to customers. Not surprisingly, though, there are challenges ahead for organizations seeking to add AI to their marketing technology stack.

Overwrought love letters began turning up on the notice board at the University of Manchester's computer lab in August, 1953. Dripping with lustful vocabulary, they were all variations on a basic syntactic template: "YOU ARE MY [adjective] [noun]. And the signatory was always the same: "M.U.C.," for the Manchester University computer, a Ferranti Mark 1, the world's first general-purpose and commercially available machine of its kind. But the real author of the letters (in the first instance, anyway) was Christopher Strachey, a pioneering programmer. As he confessed in an article the following year, "There are many obvious imperfections in this scheme (indeed very little thought went into its devising), and the fact that the vocabulary was largely based on Roget's Thesaurus lends a very peculiar flavor to the results." For Strachey, though, the interesting thing was how a simple setup, using only about seventy base words, could produce a combinatorial explosion of results--on the order of three hundred billion different letters. The lovelorn user could run the program over and over until his fingers seized up, and never see the same letter twice. Strachey was something of an outlier, according to Martin Campbell-Kelly, a historian of computing at the University of Warwick. While scientists and mathematicians of the day typically used computers strictly for numerical calculations, like analyzing weapons trajectories or seeking prime factors of huge numbers, his fascination was with non-numerical computations--what soon became known as artificial intelligence. "Strachey grabbed hold of that much more than anybody else," Campbell-Kelly told me. The results were not always lovey-dovey. Besides training the Mark 1 to churn out billets-doux, he also taught it to play checkers ("draughts," in British parlance). If M.U.C.'s opponent made too many mistakes, it would get crotchety and print out a reprimand: "I refuse to waste any more time.

We provide a brief overview on some hot topics in the area of evolutionary computation. Our main focus is on recent developments in the areas of combinatorial optimization and real-world applications. Furthermore, we highlight recent progress on the theoretical understanding of evolutionary computing methods.

The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.

Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.