ARTIFICIAL INTELLIGENCE: AN ANALYSIS OF ALAN TURING’S ROLE IN THE CONCEPTION AND DEVELOPMENT OF INTELLIGENT MACHINERY

The purpose of this thesis is to follow the thread of Alan Turing’s ideas throughout his decades of research and analyze how his predictions have come to fruition over the years. Turing’s Computing Machinery and Intelligence is the paper in which the Turing Test is described as an alternative way to answer the question “can machines think?” (Turing 433). Since the development of Turing’s original paper, there has been a tremendous amount of advancement in the field of artificial intelligence. The field has made its way into art classification as well as the medical industry. The main concept researched in this analysis focuses on whether or not a machine exists that has passed the Turing Test. Should it be determined that a machine has indeed passed this test, it is important to discuss what the ethical implications of this accomplishment entail. Turing’s paper, while raising great controversy regarding its ethical implications, proves to offer significant contribution to the field of artificial intelligence and technology

I Don't Want to Think About it Now:Decision Theory With Costly Computation

Computation plays a major role in decision making. Even if an agent is willing to ascribe a probability to all states and a utility to all outcomes, and maximize expected utility, doing so might present serious computational problems. Moreover, computing the outcome of a given act might be difficult. In a companion paper we develop a framework for game theory with costly computation, where the objects of choice are Turing machines. Here we apply that framework to decision theory. We show how well-known phenomena like first-impression-matters biases (i.e., people tend to put more weight on evidence they hear early on), belief polarization (two people with different prior beliefs, hearing the same evidence, can end up with diametrically opposed conclusions), and the status quo bias (people are much more likely to stick with what they already have) can be easily captured in that framework. Finally, we use the framework to define some new notions: value of computational information (a computational variant of value of information) and and computational value of conversation.Comment: In Conference on Knowledge Representation and Reasoning (KR '10

Neologisms in Modern English: study of word-formation processes

http://tartu.ester.ee/record=b2654513~S1*es

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Artificial Intelligence Through the Eyes of the Public

Artificial Intelligence is becoming a popular field in computer science. In this report we explored its history, major accomplishments and the visions of its creators. We looked at how Artificial Intelligence experts influence reporting and engineered a survey to gauge public opinion. We also examined expert predictions concerning the future of the field as well as media coverage of its recent accomplishments. These results were then used to explore the links between expert opinion, public opinion and media coverage

Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence\ud

The present paper will sketch the basic ideas of the complexity paradigm, and then apply them to social systems, and in particular to groups of communicating individuals who together need to agree about how to tackle some problem or how to coordinate their actions. I will elaborate these concepts to provide an integrated foundation for a theory of self-organization, to be understood as a non-linear process of spontaneous coordination between actions. Such coordination will be shown to consist of the following components: alignment, division of labor, workflow and aggregation. I will then review some paradigmatic simulations and experiments that illustrate the alignment of references and communicative conventions between communicating agents. Finally, the paper will summarize the preliminary results of a series of experiments that I devised in order to observe the emergence of collective intelligence within a communicating group, and interpret these observations in terms of alignment, division of labor and workflow

Design and refinement of NPC rules in digital board games

Was ist der grosse Unterschied zwischen menschlichen und computergenerierten Spielern?Wiesowaren “LAN Parties” so ein enormer Erfolg in den 90’er Jahren? Und warum glauben vieleMenschen daran, dass man beim Spielen mit einem Computergegner weniger Spass als mit einemmenschlichen Gegenspieler haben muss? Seit dem Beginn dieses neuen Jahrhunderts wandernimmer mehr Spieler von lokalen Spieleevents ab in das World Wide Web, in welchemes möglich ist jederzeit gegen oder mit einem anderen Menschen zu spielen. Alle diese Faktenscheinen mit dem menschlichen Spiel- und Spielerverhalten zusammenzuhängen. Dies bedeutedim Endeffekt, dass computergenerierte künstliche Spieler im Vergleich zu Menschen ein ungenügendes Verhalten oder spielerisches Talent aufweisen. In dieser Diplomarbeit versuche ich eine Herangehensweise zu entwickeln um computergenerierte Spielstrategien besser an die Bedürfnisse menschlicher Spieler anzupassen und ihnen einen menschlichen “Touch” zu verpassen. Um dies zu erreichen, werde ich ein Werkzeug vorstellen, mit welchem man das Spielverhalten untersuchen und analysieren kann. Im Anschluss wird ein Weg vorgestellt, mit welchem das resultierenden Wissen aus der Evaluation genutzt wird, um die künstlichen Spieler verbessern zu können. Diese Verbesserung soll mittels einer Evaluation überwacht und gerichtet werden um ein optimales Ergebnis zu garantieren. Die Verbesserung der Strategien wird durch eine evolutionäre Strategy durchgeführt, welche hand der menschlichen Wertung neue Strategien aus bereits exisitierenden generiert. Diese Generierung neuer Strategien findet unter dem Aspekt statt, dass neue Strategien menschliches Spielverhalten besser imitieren sollen.What is the big difference between playing a game against a human being or against a computer generated player? Why were “LAN parties” such a big success in the mid-1990s? And why do many people believe that it is more challenging to play against human beings than to play with an artificial player? At the beginning of this new century “LAN parties” are in the middle of a slight regression, but only because there is a constantly growing number of people which celebrates playing massive multi-player games. Consequently, players have moved from staging games as a local event, where it is possible to play against other human beings to the World Wide Web, where it is possible to play with and against other people on a daily basis. All these developments appear to be based upon human behavior. Based upon this evidence the current state of game AI is unsatisfactory if compared to the performance of human players. The following work presents the reader with a tool that was developed for analyzing basic computer games with incorporated AI modules. These incorporated AI modules contain the strategies to perform the behavior of artificial players.This sets the stage for a systematic evaluation and refinement of rule based game AIs.Ilmenau, Techn. Univ., Diplomarbeit, 200