Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities still classified as AI include advanced Chess and Go systems and self-driving cars. AI research is divided into subfields[4] that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[5] General intelligence is among the field's long-term goals.[6] Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it."[7] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.[8] Attempts to create artificial intelligence has experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the collapse of the Lisp machine market in 1987. In the twenty-first century AI techniques became an essential part of the technology industry, helping to solve many challenging problems in computer science.[9]

Southern California recently experienced a 55-hour closure of the 91 Freeway, resulting in a 6-mile backup that intersected State Route 71 and Interstate 15. The closure was called the "Coronageddon" (it ran through the heart of Corona). Just a few years ago a big closure of Highway 405, dubbed "Carmageddon," resulted in a traffic jam that reached immense proportions and made national news. These are extreme instances of massive traffic congestion, but more commonly we all deal with daily traffic jams as people get to work and school, the lunch rush hour, and the all-too-familiar and stressful evening commute. Traffic patterns are studied by cities, but most use a low tech approach.

A swarm intelligence similar to the one that predicted Oscar winners and Kentucky Derby finishers has come to nearly unanimous conclusions about the presidential potential of Hilary Clinton and Donald Trump. From social issues to trustworthiness and ethics, the swarm spoke loud and clear, expressing practically the same sentiment for both candidates -- extreme pessimism. The swarm consisted of 85 Democratic, Republican, or independent American voters who were asked to answer identical questions on Clinton and Trump through the swarm intelligence platform UNU. The speed at which they came to a conclusion helps calculate the percentage of "brainpower" for a particular swarm. Anywhere between 70 and 85 people participated in each round.

Chinese Premier Li Keqiang called on Saturday for greater global cooperation against terrorism, state media said, as the Asian giant seeks greater international support for its anti-terror fight. Speaking at an Asia-Europe summit, Li said various security challenges - conventional and unconventional - remain prominent even though those regions had remained generally stable and peaceful. "Acts of terrorism are common challenges faced by every nation. Countries should work more closely to fight terrorism, and build societies that are truly open and tolerant so to root out the soil where it grows," said Li. China has sought Western support for its own "war on terror" since the attacks in Paris last November.

The basic goal of every eCommerce company is to bring the best of offline shopping experience to the online space, by offering the consumers a seamless way to discover the products they are looking for. The avenue is taking a big leap towards becoming the facilitator of a more efficient, personalized, even automated customer journey with the introduction of cognitive technologies and the employment of'smart data'. Today, the most important area of focus in eCommerce is hyper personalization which could be facilitated only by learning consumer behaviour and making predictive analyses with the help of the huge amount of data collected from user activities on smartphones, tablets and desktops, and intelligent algorithms to process them. Machine learning and artificial intelligence are no more restricted to personal assistance technology, smartphone companies are creating. They have flouted these conventions to disrupt a much wider space with limitless possibilities. One of the areas radically transformed by AI is eCommerce.

Judgment aggregation problems form a class of collective decision-making problems represented in an abstract way, subsuming some well known problems such as voting. A collective decision can be reached in many ways, but a direct one-step aggregation of individual decisions is arguably most studied. Another way to reach collective decisions is by iterative consensus building - allowing each decision-maker to change their individual decision in response to the choices of the other agents until a consensus is reached. Iterative consensus building has so far only been studied for voting problems. Here we propose an iterative judgment aggregation algorithm, based on movements in an undirected graph, and we study for which instances it terminates with a consensus. We also compare the computational complexity of our itterative procedure with that of related judgment aggregation operators.

We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies, and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network, and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks, and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive networkwide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results.

Agents have existed already for a long time. In software engineering, they are small distributed applications that demonstrate some form of autonomous behavior. Early examples are monitoring agents, web crawlers, and chat bots. Also, today's trends in distributed and intelligent architectures can be viewed from this existing Agent perspective. Software Agents could be viewed as an evolution of, or an addition to, the (Micro)Services that are underpinning these trends.

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted endeavor of building a “Creative Economy” and “Software Centric Society”. However, AI was not always flourishing as it is now. Similar to the history of AI worldwide, AI research and industry in Korea have faced both the ups and downs in its history.