Protests in the ethnic Russian enclave in Riga, Latvia have NATO on edge. Russian units in the Western Military District are on alert conducting snap exercises involving autonomous ground and air attack systems. The Russian president makes a speech promising to protect ethnic Russians wherever they are with military forces if necessary. In response, a U.S. Army brigade combat team bolstered by intelligence, air defense, and aviation support elements from U.S. Army Europe deploys. Their mission is to reassure Latvian forces, deter Russian aggression, and if necessary conduct a mobile defense.

Artificial Intelligence Is It Real? Artificial intelligence could improve diagnostic power of lung function tests European Lung ...

The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.

The purpose of this Initiative is to ensure every technologist is educated, trained, and empowered to prioritize ethical considerations in the design and development of autonomous and intelligent systems. We invite you to join experts that span the fields of engineering, law, science, economics, ethics, philosophy, politics, and health in this work. Vice Chair: Kay Firth-Butterfield, Chief Officer, Chair and member of the Lucid.ai

One of the great concerns about artificial intelligence (AI) is that it will replace people altogether. Ironically, however, it is increasingly apparent that interacting with people is one of the key tasks of AI. "Cognitive assistants"--systems that employ cognitive technology to interact with people and make our lives easier--are among the fastest-growing areas of this genre. They are likely to transform many aspects of business in the near future. Thus far, of course, the primary examples of this technology have served consumers rather than businesses. Cognitive assistants already set your sleep alarm, turn down your thermostat at night, and tell you what movies are playing at the mall.

Have you noticed that the best fitness functions that most creatures adopt for survival is to work in collectives? School of fishes, Hive of bees, the nest of ants, horde of wildebeests or flock of birds all have something in common. What is even more perplexing about nature is the ecological inter-dependence of different species, collectively surviving to see a better day. This fitness function is a sum of averages of sorts which enables a different form of collective strength. Its called Unity in Diversity.

Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand. It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start?

Another common AI technique for analyzing different alternative process flows, bottlenecks, resource constraints and customer/agent/employee behavior is discrete-event and agent-based simulation modeling.

The amount of digitally available but heterogeneous information about the world is remarkable, and new technologies such as self-driving cars, smart homes, or the internet of things may further increase it. In this paper we examine certain aspects of the problem of how such heterogeneous information can be harnessed by autonomous agents. After discussing potentials and limitations of some existing approaches, we investigate how \emph{experiments} can help to obtain a better understanding of the problem. Specifically, we present a simple agent that integrates video data from a different agent, and implement and evaluate a version of it on the novel experimentation platform \emph{Malmo}. The focus of a second investigation is on how information about the hardware of different agents, the agents' sensory data, and \emph{causal} information can be utilized for knowledge transfer between agents and subsequently more data-efficient decision making. Finally, we discuss potential future steps w.r.t.\ theory and experimentation, and formulate open questions.

The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. .The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behavior.