If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Over the last few years, the quest to build fully autonomous vehicles has shifted into high gear. Yet, despite huge advances in both the sensors and artificial intelligence (AI) required to operate these cars, one thing has so far proved elusive: developing algorithms that can accurately and consistently identify objects, movements, and road conditions. As Mathew Monfort, a postdoctoral associate and researcher at the Massachusetts Institute of Technology (MIT) puts it: "An autonomous vehicle must actually function in the real world. However, it's extremely difficult and expensive to drive actual cars around to collect all the data necessary to make the technology completely reliable and safe." All of this is leading researchers down a different path: the use of game simulations and machine learning to build better algorithms and smarter vehicles.
When the analysis of individuals' personal information has value to an institution, but it compromises privacy, should individuals be compensated? We describe the foundations of a market in which those seeking access to data must pay for it and individuals are compensated for the loss of privacy they may suffer. The interests of individuals and institutions with respect to personal data are often at odds. Personal data has great value to institutions: they eagerly collect it and monetize it by using it to model customer behavior, personalize services, target advertisements, or by selling the data directly. Yet the inappropriate disclosure of personal data poses a risk to individuals.
We live in a global society where technology, especially information and communication technology, is changing the way businesses create and capture value, how and where we work, and how we interact and communicate. In her seminal 1988 book, In the Age of the Smart Machine: The Future of Work and Power,45 Shoshana Zuboff was among the first scholars to weave together the technological, sociological, and psychological processes that have converged to shape the modern workplace. Her insights concerned the nature of information and its significance in restructuring and redefining the patterns and meanings of work, even though at the time of her study the worldwide diffusion of the Internet had not yet occurred. Academic literature, not only in business9,32,42 but also in medicine,15,38 engineering,23,40 physical sciences,30 and social sciences,21,37 echo these observations in more recent times. To illustrate the effects of the changes on organizations, we consider their implications for the management of human talent.
Perhaps you remember the iconic theme of the globally popular Kung Fu Panda movies, "You are the secret ingredient!" This meant that self-belief is important and with it great things can be achieved--Po, for example, became the Dragon Warrior. My meaning here is that computer science is both a powerful enabler of rapid advances in all intellectual fields and a disruptor driving furious revolutions in commerce and society worldwide. Computer science is more important and potent than ever! Computing is driving unprecedented rapid change.
The heat method allows distance to be rapidly updated for new source points or curves. We introduce the heat method for solving the single- or multiple-source shortest path problem on both flat and curved domains. A key insight is that distance computation can be split into two stages: first find the direction along which distance is increasing, then compute the distance itself. The heat method is robust, efficient, and simple to implement since it is based on solving a pair of standard sparse linear systems. These systems can be factored once and subsequently solved in near-linear time, substantially reducing amortized cost.
The study of games is as old as computer science itself. Babbage, Turing, and Shannon devised algorithms and hardware to play the game of chess. Game theory began with questions regarding optimal strategies in card games and chess, later developed into a formal system by von Neumann. Chess subsequently became the drosophila--or common fruitfly, the most studied organism in genetics--of artificial intelligence research. Early successes in chess and other games shaped the emerging field of AI: many planning algorithms first used in games became pillars of subsequent research; reinforcement learning was first developed for a checkers playing program; and the performance of game-playing programs has frequently been used to measure progress in AI.
Poker is a family of games that exhibit imperfect information, where players do not have full knowledge of past events. While many perfect information games have been solved (e.g., Connect-Four and checkers), no nontrivial imperfect information game played competitively by humans has previously been solved. In this paper, we announce that the smallest variant of poker in-play, heads-up limit Texas hold'em, is now essentially weakly solved. Furthermore, this computation formally proves the common wisdom that the dealer in the game holds a significant advantage. This result was enabled by a new algorithm, CFR, which is capable of solving extensive-form games three orders of magnitude larger than previously possible.
It is fall in Heidelberg and the leaves on the trees are already turning. This is the fifth year of the Heidelberg Laureate Forum (http://www.heidelberg-laureate-forum.org/) and it continues to be a highlight of the year for me and for about 250 others who participate. This year, computer science was heavily represented. There were fewer mathematicians, but they made up for smaller numbers by their extraordinary qualifications. A new cohort of laureates was added this year: recipients of the ACM Prize for Computing.a
The use of the agent paradigm to understand and design complex systems occupies an important and growing role in different areas of social and natural sciences and technology. Application areas where the agent paradigm delivers appropriate solutions include online trading,16 disaster management,10 and policy making.11 However, the two main agent approaches, Multi-Agent Systems (MAS) and Agent-Based Modeling (ABM) differ considerably in methodology, applications, and aims. MAS focus on solving specific complex problems using autonomous heterogeneous agents, while ABM is used to capture the dynamics of a (social or technical) system for analytical purposes. ABM is a form of computational modeling whereby a population of individual agents is given simple rules to govern their behavior such that global properties of the whole can be analyzed.9
Michel Fornasier, one of the presenters of the Cybathlon, uses his bionic hand prosthesis to demonstrate one of the Cybathlon disciplines. In the movie Star Wars: The Empire Strikes Back, Luke Skywalker is given a mechanical hand that moves and perform functions as well as his real hand. Konrad Kording, an avid Star Wars fan, has no doubt that advances in brain-machine interfaces (BMIs) will make this bit of science fiction a reality; he just doesn't know when. "We have applications for one channel and a few channels," says Kording, a neuroscientist and professor of Physical Medicine and Rehabilitation, Physiology, and Biomedical Engineering at Northwestern University in Evanston, IL. "The question is, what are the BMI applications with hundreds of thousands of channels, and no one knows that at the moment." The channels he's referring to are electrical wires or optical connectors that can be attached to the brain and can be controlled and measured.