AI is here now, available to anyone with access to digital technology and the Internet. But its consequences for our social order aren't well understood. How can we guide the way technology impacts society?

These and other insights are based on The Forrester Wave: Configure-Price-Quote Solutions, Q1 2017 by John Bruno published February 7, 2017 (17 pp., PDF; client access). A variety of CPQ vendors including Apttus, CallidusCloud, FPX, Oracle, PROS and others have licensed the report and provide a free downloadable copy in exchange for contact information. Please see page 14 of the study for a description of the methodology. The Forrester Wave is the latest in a series of studies that confirm Configure-Price-Quote (CPQ) is one of the hottest enterprise apps today. The rapid development and launch of intelligent agents by Apttus, Infor, Oracle, Salesforce, SAP, and others are creating a new era of intelligent selling.

Each of these areas already features a significant level of complexity, so the following description of data mining and artificial intelligence applications has necessarily been restricted to an overview. Vehicle development has become a largely virtual process that is now the accepted state of the art for all manufacturers. CAD models and simulations (typically of physical processes, such as mechanics, flow, acoustics, vibration, etc., on the basis of finite element models) are used extensively in all stages of the development process. The subject of optimization (often with the use of evolution strategies[31] or genetic algorithms and related methods) is usually less well covered, even though it is precisely here in the development process that it can frequently yield impressive results. Multi-disciplinary optimization, in which multiple development disciplines (such as occupant safety and noise, vibration, and harshness (NVH)) are combined and optimized simultaneously, is still rarely used in many cases due to supposedly excessive computation time requirements.

Sparse iterative methods, in particular first-order methods, are known to be among the most effective in solving large-scale two-player zero-sum extensive-form games. The convergence rates of these methods depend heavily on the properties of the distance-generating function that they are based on. We investigate the acceleration of first-order methods for solving extensive-form games through better design of the dilated entropy function---a class of distance-generating functions related to the domains associated with the extensive-form games. By introducing a new weighting scheme for the dilated entropy function, we develop the first distance-generating function for the strategy spaces of sequential games that has no dependence on the branching factor of the player. This result improves the convergence rate of several first-order methods by a factor of $\Omega(b^dd)$, where $b$ is the branching factor of the player, and $d$ is the depth of the game tree. Thus far, counterfactual regret minimization methods have been faster in practice, and more popular, than first-order methods despite their theoretically inferior convergence rates. Using our new weighting scheme and practical tuning we show that, for the first time, the excessive gap technique can be made faster than the fastest counterfactual regret minimization algorithm, CFR+, in practice.

In every field, there's a pioneer, a prototype, an individual or group that blazed the path forward to uncover previously hidden value. Observing the giants in artificial intelligence allows us to revisit the early instrumental concepts in the development and maturation of the field. Biological principles are the roots of swarm intelligence, and self-organizing collective behavior is its organizing principle. Better understanding these foundational principles results in the ability to accelerate the development of your business applications. Four pioneers shaped artificial intelligence as we know it today.

The cloud computing infrastructure has the power to increase the dependability, interoperability, and scalability of emerging cyber-physical systems (CPSs). Integrating intelligent agents and semantic ontologies can help manage the complexity of such systems and enable the development of large-scale CPSs.

An artificial intelligence accurately predicted Donald Trump's less-than-stellar first 100-day approval rating down to the percentage point. Unanimous AI was challenged by reporters at Modern Trader magazine to use its Swarm AI to predict the president's rating at the end of his first milestone in office. The machine correctly came up with the historically low figure of 42 percent--the same result presented by the latest ABC News/Washington Post polls. Not every outlet came up with the same approval rating for Trump's first 100 days. The CNN/ORC poll gave the president a 44 percent approval rating, while Gallup puts it at an even lower 41 percent.

A combination of real-time, biological systems blends knowledge, wisdom, opinions and intuition to unify intelligence. These simple agents interact locally, within their environment, and new behaviors emerge. Swarm intelligence is the self-organization of systems for collective decentralized behavior. Swarm intelligence enables groups to converge and create an independent organism that can do things that individuals can't do on their own. Fish detect ripples in the water.

Artificial Intelligence (AI) is an over-used phrase reflecting a genuine convergence of a number of technologies and methodologies. The banking and Financial industry as a whole has invested heavily in technology for a long time, and as the industry shifts from being led by large financial institutions, to one that is increasingly driven by disruptive FinTech companies, innovative technology is high on the agenda across the board. In banking specifically, the majority of use cases are in mid-level, rather than customer facing or operational roles. However, this will change over time as AI becomes more "customer facing", and ultimately more consumer orientated as a whole. Autonomous agents, algorithms which essentially act on behalf of a human, are the most well publicised example of Artificial intelligence in use in banks today.

In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computations and communications with their neighbors. We focus on the important distinction between single-task problems, where the underlying model is common to all agents, and multitask problems, where each agent might converge to a different model due to, e.g., spatial dependencies or other factors. Currently, most of the literature on distributed learning in the nonlinear case has focused on the single-task case, which may be a strong limitation in real-world scenarios. After introducing the problem and reviewing the existing approaches, we describe a simple kernel-based algorithm tailored for the multitask case. We evaluate the proposal on a simulated benchmark task, and we conclude by detailing currently open problems and lines of research.