There is an interesting move underway to establish a pan-European AI research federation - a sort of decentralised CERN for AI. From their website: "CLAIRE is an initiative by the European AI community that seeks to strengthen European excellence in AI research and innovation. To achieve this, CLAIRE proposes the establishment of a pan-European Confederation of Laboratories for Artificial Intelligence Research in Europe that achieves "brand recognition" similar to CERN." "The CLAIRE initiative aims to establish a pan-European network of Centres of Excellence in AI, strategically located throughout Europe, and a new, central facility with state-of-the-art, "Google-scale", CERN-like infrastructure – the CLAIRE Hub – that will promote new and existing talent and provide a focal point for exchange and interaction of researchers at all stages of their careers, across all areas of AI. The CLAIRE Hub will not be an elitist AI institute with permanent scientific staff, but an environment where Europe's brightest minds in AI meet and work for limited periods of time. This will increase the flow of knowledge among European researchers and back to their home institutions."

JANICE S. AIKINS Dr. Aikins received her Ph.D. in computer science from Stanford University in 1980. She is currently a research computer scientist at IBM's Palo Alto Scientific Center. She specializes in designing systems with an emphasis on the explicit representation of control knowledge in expert systems. ROBERT L. BLUM Dr. Blum received his M.D. from the University of California Medical School at San Francisco in 1973. From 1973 to 1976 he did an internship and residency in the Department of Internal Medicine at the Kaiser Foundation Hospital in Oakland, California, where he was chief resident in 1976.

DETROIT - Every year at the Detroit auto show, good-looking women -- and men -- are deployed by the carmakers to present their new vehicles. But with the shock wave created by the #MeToo movement still reverberating across the U.S., there are fewer auto show models of the human variety -- and they are not just pretty faces. The "product specialists" still have picture-perfect smiles, but they also can tick off the features of each car and prices with such assurance that the iPads they carry for reference can seem merely decorative. Auto companies are also making sure their fleet of specialists are ethnically and physically diverse. Perched on stilettos, Priscilla Tejeda is working for Toyota.

Out of the 188 cognitive biases that exist, there is a much narrower group of biases that has a disproportionately large effect on the ways we do business. These are things that affect workplace culture, budget estimates, deal outcomes, and our perceived return on investments within the company. Mental mistakes such as these can add up quickly, and can hamper any organization in reaching its full bottom line potential. Today's infographic from Raconteur aptly highlights 18 different cognitive bias examples that can create particularly difficult challenges for company decision-making. Financial biases These are imprecise mental shortcuts we make with numbers, such as hyperbolic discounting – the mistake of preferring a smaller, sooner payoff instead of a larger, later reward.

Humans by their nature have many cognitive biases. This can become detrimental to real scientific progress. Research tends to be bias in favor of approaches that many experts have invested countless years of study. The consequence of this is that we ignore many intrinsic characteristics found in the very system under study. Thus researchers can unfortunately consume a lifetime pursuing a wrong and pointless path. History is littered with research that in hindsight were discovered to be incorrect and therefore worthless.

Companies from a wide range of industries use machine learning data to do everyday business. From consumer marketing and workforce management to healthcare treatment decision solutions and public safety and policing solutions, whether you realize it or not your life is increasingly more affected by the outcomes of machine learning algorithms. Machine learning algorithms make decisions like who gets a bonus, a job interview, whether or not your credit card limit (or interest) is raised, and who gets into a clinical trial. Machine learning algorithms even help make decisions about who gets parole and who languishes in prison. The result is that people's lives and livelihood are affected by the decisions made by machines.

Most people, at this point, believe that climate change is a real thing that will harm future generations of humans. And yet, a cognitive dissonance exists around that knowledge and our sense of responsibility: A much smaller percentage of people believe that climate change is impacting them personally, according to Yale's climate survey program. It is indeed impacting humans right now, with clear and compelling evidence that the global average temperature is much higher than anything modern society has experienced. And that has lead us to a whole host of issues, some of which WIRED writer Adam Rogers discusses with the Gadget Lab team on this week's podcast. So what can we humans do to fix things – and how much of it can actually be fixed by personal actions, versus widespread policy?

In this paper, we make a review on the concepts of rationality across several different fields, namely in economics, psychology and evolutionary biology and behavioural ecology. We review how processes like natural selection can help us understand the evolution of cognition and how cognitive biases might be a consequence of this natural selection. In the end we argue that humans are not irrational, but rather rationally bounded and we complement the discussion on how quantum cognitive models can contribute for the modelling and prediction of human paradoxical decisions.

My research aims to contribute to research in the narrative authoring domain by using cognitive models in narrative plan generation. These cognitive models determine how actions and events in narrative affect the audience. My research intends to leverage these models in narrative planning and use them to provide intelligent narrative plans that are structured to invoke specific responses from audiences when they experience the narrative. This sort of approach would greatly benefit the enrich growing set of variables of narrative planning.

In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of agent plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model.