Knowledge and information are becoming the primary resources of the emerging information society. To exploit the potential of available expert knowledge, comprehension and application skills (i.e. The ability to acquire these skills is limited for any individual human. Consequently, the capacities to solve problems based on human knowledge in a manual (i.e. We envision a new systemic approach to enable scalable knowledge deployment without expert competences. Eventually, the system is meant to instantly deploy humanity's total knowledge in full depth for every individual challenge. To this end, we propose a sociotechnical framework that transforms expert knowledge into a solution creation system. Knowledge is represented by automated algorithms (knowledge engines). Executable compositions of knowledge engines (networks of knowledge engines) generate requested individual information at runtime. We outline how these knowledge representations could yield legal, ethical and social challenges and nurture new business and remuneration models on knowledge. We identify major technological and economic concepts that are already pushing the boundaries in knowledge utilisation: E.g. in artificial intelligence, knowledge bases, ontologies, advanced search tools, automation of knowledge work, the API economy. We indicate impacts on society, economy and labour. Existing developments are linked, including a specific use case in engineering design. 1 INSTANTLY DEPLOYABLE EXPERT KNOWLEDGE - NETWORKS OF KNOWLEDGE ENGINES For decades we experience an ongoing structural shift in value creation: from agricultural and industrial production to services and, more recently, to information-and knowledgebased services. Information and knowledge are becoming primary resources of the emerging knowledge society.

The legal industry is ripe for tech disruption. That's partly because the field is rife with paperwork, whether it's the records that first-year attorneys and other staffers need to search for discovery or the hard copies of legal documents that many firms are legally required, or think it's critical, to keep. All that paperwork represents costs--the cost of the work hours needed to search through, organize and file all that documentation, as well as the cost of physical storage space itself. If you're paying, say, $10 per square foot to rent space and need an extra 200 square feet for paper storage, that will set you back $24,000 per year. It's critical to start looking for the technological solutions that will help your office keep up with the times--whether that's getting a new Kyocera MFP or investing in a better eDiscovery provider.

Margaret Talbot's article on polychromy in classical Greek and Roman sculpture reveals that the figures we are used to seeing as white were, in fact, fully colored ("Color Blind," October 29th). It also shows that the techniques used to identify the applications of those pigments are clearly in their infancy. Nothing Talbot writes credibly explains how these ancient sculptors--driven by a naturalistic aesthetic so intense that they labored in marble in order to replicate muscles beneath the surface of human skin and to painstakingly re-create delicate drapery--would allow painters to effectively obliterate the subtlety of their hard effort with daubs of color, at least in the way that pigment is unconvincingly applied to modern replicas. Talbot's article brought to mind the video game Assassin's Creed Odyssey. The game is a fictional re-creation of the Peloponnesian War in all its colorful glory.

A legal fight over net neutrality has come to an end, with the US Supreme Court refusing to hear an argument over the future of the internet. The ongoing dispute over the 2016 court ruling, which upheld Obama-era regulations that protected a free and open internet, came to a close with the court refusing to hear it. That means the broadband industry's attempt to overturn those protections – which ensure that people can't be blocked from using their favourite apps and services – come to an end, and the net neutrality rules will stay in place. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Algorithms are a new tool in the fight against the trade of black market ivory tusks, pangolin scales and tiger skins. A group of researchers at the University of Southern California is working on technology to help rangers stay a step ahead of poachers. The Teamcore lab at USC's Center for Artificial Intelligence in Society is working on an AI-driven application called PAWS, short for Protection Assistant for Wildlife Security, which aims to equip wildlife defenders with optimized patrol routes. The illegal wildlife trade is considered the fourth most profitable criminal enterprise in the world, after drugs, weapons and human trafficking, U.K. Foreign Secretary Jeremy Hunt announced at the 2018 Wildlife Illegal Trade Conference in mid-October. Poaching continues to threaten the survival of species around the globe.

Blameworthiness of an agent or a coalition of agents is often defined in terms of the principle of alternative possibilities: for the coalition to be responsible for an outcome, the outcome must take place and the coalition should have had a strategy to prevent it. In this paper we argue that in the settings with imperfect information, not only should the coalition have had a strategy, but it also should have known that it had a strategy, and it should have known what the strategy was. The main technical result of the paper is a sound and complete bimodal logic that describes the interplay between knowledge and blameworthiness in strategic games with imperfect information.

In September of 2017, Governor Scott Walker, Republican of Wisconsin, signed a contract that would make his state the home of the first U.S. factory of Foxconn, the world's largest contract electronics manufacturer. The company, which is based in Taiwan and makes products for Apple, Sony, Microsoft, and Nintendo, among others, would build a 21.5-million-square-foot manufacturing campus, invest up to ten billion dollars in Wisconsin, and hire as many as thirteen thousand workers at an average wage of fifty-four thousand dollars a year. For Walker, whose approval had fallen to the mid-thirties after his aborted Presidential run, the deal was seen as a crucial boost to his reëlection prospects. "The Foxconn initiative looked like something that could be a hallmark of Walker's reëlection campaign," Charles Franklin, a professor and pollster at Marquette University Law School, told me. "He could claim a major new manufacturing presence, one that would also employ blue-collar workers in a region where blue-collar jobs are more scarce than they used to be." The idea of putting the plant in southeastern Wisconsin originated in April of 2017, during a helicopter ride President Donald Trump took with Reince Priebus, a Wisconsin native and Trump's chief of staff at the time. Flying over Kenosha, Priebus's home town, they passed the empty lot that once held the American Motors Corporation plant.

Just hearing the words "open enrollment" is enough to make anyone groan with boredom. But what if you could enroll in health insurance at work by talking to a computer? You're asked a few simple questions about your family, your lifestyle and your medical history, and within nine minutes, you're done. Enter Jellyvision and Alex, a virtual benefits counselor powered by artificial intelligence to help employees better understand their benefits packages. In the complicated and ever-evolving landscape of health care, Alex shows employees which medical plans can save them the most money based on their personal situation.

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it's important to remember Box's maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly.

This study introduces a stochastic predictive-coding RNN model that can learn to extract probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the uncertainty of latent variables. The learning process of the model involves maximizing the lower bound on the marginal likelihood of the sequential data, which consists of two terms. The first one is the expectation of prediction errors and the second one is the divergence of the prior and the approximated posterior. The main focus in the current study is to examine how weighting of the second term during learning affects the way of internally representing the uncertainty hidden in the sequence data. The simulation experiment on learning a simple probabilistic finite state machine demonstrates that the estimation of uncertainty in the latent variable approaches zero at each time step and that the network imitates the probabilistic structure of the target sequences by developing deterministic chaos in the case of the high weighting. On the contrary, in the case of the low weighting, the estimate of uncertainty increases significantly because of developing a random process in the network. The analysis shows that generalization in learning is most successful between these two extremes. Qualitatively, the same property has been observed in a trail of learning more complex sequence data consisting of probabilistic transitions between a set of hand-drawn primitive patterns using the model extended with hierarchy.