Video games where people matter? If you're a video game fan of a certain age, you may remember Edge magazine's controversial review of the bloody sci-fi shooting game, Doom. Perhaps you enjoyed a good laugh, as many first-person shooter fans have, at the writer's much-mocked assertion: "if only you could talk to these creatures, then perhaps you could try and make friends with them, form alliances … Now that would be interesting." Of course, we all know what happened. There would be no room in the Doom series, nor any subsequent first-person blast-'em-up, for such socio-psychological niceties. Instead, we enjoyed 20 years of shooting, bludgeoning and stabbing, the ludicrous idea of diplomacy cast roughly aside. But during this era, something else was happening in game design, and in academic thinking around video games and artificial intelligence.

A major challenge in crowdsourcing evaluation tasks like labeling objects, grading assignments in online courses, etc., is that of eliciting truthful responses from agents in the absence of verifiability. In this paper, we propose new reward mechanisms for such settings that, unlike many previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. These mechanisms have the simple and intuitive structure of an output agreement mechanism: an agent gets a reward if her evaluation matches that of her peer, but unlike the classic output agreement mechanism, this reward is not the same across evaluations, but is inversely proportional to an appropriately defined popularity index of each evaluation. The popularity indices are computed by leveraging the existence of a large number of similar tasks, which is a typical characteristic of these settings. Experiments performed on MTurk workers demonstrate higher efficacy (with a $p$-value of $0.02$) of these mechanisms in inducing truthful behavior compared to the state of the art.

So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. My lecturer is a full-time Applied Math and CS professor at the Technical University of Denmark, in which his research areas are logic and artificial, focusing primarily on the use of logic to model human-like planning, reasoning and problem solving. The class was a mix of discussion of theory/core concepts and hands-on problem solving. The textbook that we used is one of the AI classics: Peter Norvig's Artificial Intelligence -- A Modern Approach, in which we covered major topics including intelligent agents, problem-solving by searching, adversarial search, probability theory, multi-agent systems, social AI, philosophy/ethics/future of AI.

IBM Watson Virtual Agent is a set of preconfigured cognitive components based on the IBM Watson Conversation service. By configuring the virtual agent with your company's information, you can quickly implement an automated chat bot that enables your customers to achieve their goals. The established model of creating a digital or virtual agent requires experienced developers with a highly specific skill set to create complex systems that rely on custom – and often cumbersome – rules. Watson Virtual Agent allows businesses to simply build and deploy conversational agents. Watson Virtual Agent helps accelerate users' ability to deploy bots, including pre-trained cross-industry content, with minimal configuration, simplifying the process for both seasoned developers or users without formal technical training.

Artificial Intelligence is poised to disrupt the entire eCommerce industry. An interesting convergence is taking place; one that will have enormous implications in the way retailers sell their products and services and the way consumers buy them. Artificial Intelligence capabilities and applications are attempting to solve real-world issues that eCommerce industry are facing. How Artificial Intelligence in e-commerce can play an important and game changing role, moving beyond customer segmentation to help them achieve the best possible results? The visual Search engine is one of the most exciting trends of Artificial Intelligence in eCommerce.

We consider the problem of maximizing a monotone submodular function under noise. There has been a great deal of work on optimization of submodular functions under various constraints, resulting in algorithms that provide desirable approximation guarantees. In many applications, however, we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function. We show that: - For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$; - For $k=1$ there is an algorithm whose approximation ratio is arbitrarily close to $1/2$. No randomized algorithm can obtain an approximation ratio better than $1/2+o(1)$; -If the noise is adversarial, no non-trivial approximation guarantee can be obtained.

Planned experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy. One critical shortcoming of classical experimental methods, however, is that they typically do not take into account the dynamic nature of response to policy changes. For instance, in an experiment where we seek to understand the effects of a new ad pricing policy on auction revenue, agents may adapt their bidding in response to the experimental pricing changes. Thus, causal effects of the new pricing policy after such adaptation period, the {\em long-term causal effects}, are not captured by the classical methodology even though they clearly are more indicative of the value of the new policy. Here, we formalize a framework to define and estimate long-term causal effects of policy changes in multiagent economies. Central to our approach is behavioral game theory, which we leverage to formulate the ignorability assumptions that are necessary for causal inference. Under such assumptions we estimate long-term causal effects through a latent space approach, where a behavioral model of how agents act conditional on their latent behaviors is combined with a temporal model of how behaviors evolve over time.

Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.

IBM Watson Virtual Agent is a set of preconfigured cognitive components based on the IBM Watson Conversation service. By configuring the virtual agent with your company's information, you can quickly implement an automated chat bot that enables your customers to achieve their goals. The established model of creating a digital or virtual agent requires experienced developers with a highly specific skill set to create complex systems that rely on custom – and often cumbersome – rules. Watson Virtual Agent allows businesses to simply build and deploy conversational agents. Watson Virtual Agent helps accelerate users' ability to deploy bots, including pre-trained cross-industry content, with minimal configuration, simplifying the process for both seasoned developers or users without formal technical training.

Watson Virtual Agent is a new way to provide automated services to your customers. It offers a cognitive, conversational self-service experience that can provide answers and take action. You can easily customize your Watson Virtual Agent to fit your specific business needs, provide custom content and match your business brand. Additionally, deep analytics provide insights on your customer's engagement with the Watson Virtual Agent and help with the understanding of your constantly changing customer's needs.