This course will present advanced topics in Artificial Intelligence (AI), including inquiries into logic, artificial neural network and machine learning, and the Turing machine. Upon successful completion of this course, students will be able to: define the term 'intelligent agent,' list major problems in AI, and identify the major approaches to AI; translate problems into graphs and encode the procedures that search the solutions with the graph data structures; explain the differences between various types of logic and basic statistical tools used in AI; list the different types of learning algorithms and explain why they are different; list the most common methods of statistical learning and classification and explain the basic differences between them; describe the components of Turing machine; name the most important propositions in the philosophy of AI; list the major issues pertaining to the creation of machine consciousness; design a reasonable software agent with java code. (Computer Science 408)

" This course explores a range of contemporary scholarship oriented to the study of 'cybercultures,' with a focus on research inspired by ethnographic and more broadly anthropological perspectives. Taking anthropology as a resource for cultural critique, the course will be organized through a set of readings chosen to illustrate central topics concerning the cultural and material practices that comprise digital technologies. We'll examine social histories of automata and automation; the trope of the 'cyber' and its origins in the emergence of cybernetics during the last century; cybergeographies and politics; robots, agents and humanlike machines; bioinformatics and artificial life; online sociality and the cyborg imaginary; ubiquitous and mobile computing; ethnographies of research and development; and geeks, gamers and hacktivists. We'll close by considering the implications for all of these topics of emerging reconceptualizations of sociomaterial relations, informed by feminist science and technology studies."

An quick overview of AI from both the technical and the philosophical points of view. Topics discussed include search, A*, Knowledge Representation, Neural Nets. Video of each class is available, as are problem sets.

Introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, constraint propagation, constrained search, inheritance, and other problem-solving paradigms. Applications of identification trees, neural nets, genetic algorithms, and other learning paradigms. Speculations on the contributions of human vision and language systems to human intelligence.

This is an upper level course for students in computer science and computer and information technologies programs. This course covers the basics of Artificial Intelligence in computer software. The course introduces the broad field of AI, then specializes in AI as it applies to computer gaming strategies. Students will be required to complete several programming assignments.

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems, understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, constraint propagation, constrained search, inheritance, and other problem-solving paradigms. Applications of identification trees, neural nets, genetic algorithms, and other learning paradigms. Speculations on the contributions of human vision and language systems to human intelligence.

Supplementary work on individual or group basis. Registration subject to prior arrangement for subject matter and supervision by staff. Common Sense Reasoning for Interactive Applications This course will explore the state of the art in common sense knowledge, and class projects will design and build interfaces that can exploit this knowledge to make more usable and helpful interfaces. Course requirements will consist of critiques of class readings [about 2 papers/week], and a final project [paper or computer implementation project]. Grades will be based primarily on the projects, as well as a small component for class and online participation.

This course will explore the state of the art in common sense knowledge, and class projects will design and build interfaces that can exploit this knowledge to make more usable and helpful interfaces. This year's theme will be about how common sense knowledge differs in different languages and cultures, and how machine understanding of this knowledge can help increase communication between people, and between people and machines.

This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?

Artificial Intelligence : Course lectures, hours 42. There are 11 pdf files, total 562 pages. Topics : Introduction to AI; Problem Solving - Search and Control Strategies; Knowledge Representation Issues - Predicate Logic, Rules; Reasoning System - Symbolic, Statistical; Game Playing; Learning; Expert System; Fundamentals of Neural Networks; Fundamentals of Genetic Algorithms; Natural Language Processing; Common Sense.

In Origin of Species, Darwin provided a model for understanding the existence of objects and systems manifesting evidence of design without positing a designer, and of purpose and mechanism without intelligent agency. Texts deal with pre-Darwinian and later treatment of this topic within literature and speculative thought since the eighteenth century, with some attention to the modern study of "feedback mechanism" in artificial intelligence. Readings in Hume, Voltaire, Malthus, Darwin, Butler, Hardy, H. G. Wells, and Freud. This subject offers a broad survey of texts (both literary and philosophical) drawn from the Western tradition and selected to trace the immediate intellectual antecedents and some of the implications of the ideas animating Darwin's revolutionary On the Origin of Species. Darwin's text, of course, is about the mechanism that drives the evolution of life on this planet, but the fundamental ideas of the text have implications that range well beyond the scope of natural history, and the assumptions behind Darwin's arguments challenge ideas that go much further back than the set of ideas that Darwin set himself explicitly to question - ideas of decisive importance when we think about ourselves, the nature of the material universe, the planet that we live upon, and our place in its scheme of life. In establishing his theory of natural selection, Darwin set himself, rather self-consciously, to challenge a whole way of thinking about these things. The main focus of attention will be Darwin's contribution to the so-called "argument from design" - the notion that innumerable aspects of the world (and most particularly the organisms within it) display features directly analogous to objects of human design and, since design implies a designer, that an intelligent, conscious agency must have been responsible for their organization and creation. Previously, it had been argued that such features must have only one of two ultimate sources - chance or conscious agency. Darwin proposed and elaborated a third source, which he called Natural Selection, an unconscious agency capable of outdoing the most complex feats of human intelligence. The course of study will not only examine the immediate inspiration for this idea in the work of Adam Smith and Thomas Malthus and place Darwin's Origin and the theory of Natural Selection in the history of ensuing debate, but it will also touch upon related issues.

" In the Origin of Species (1859), Charles Darwin gave us a model for understanding how natural objects and systems can evidence design without positing a designer: how purpose and mechanism can exist without intelligent agency. Texts in this course deal with pre- and post-Darwinian treatment of this topic within literature and speculative thought since the eighteenth century. We will give some attention to the modern study of feedback mechanisms in artificial intelligence. Our reading will be in Hume, Voltaire, Malthus, Darwin, Butler, H. G. Wells, and Turing."

In this video segment from NASA, robotics researcher Ayanna Howard uses engineering to improve the intelligence of robots in space exploration.

Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project is required for graduate credit.

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers.This course is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers. Meets with graduate subject 6.833 but assignments differ. 6.803/6.833 is a course in the department's "Artificial Intelligence and Applications" concentration.

Addresses trends in the Information Technology (IT) industry, with an emphasis on modern communications and Internet technologies and database and Web technologies, and their role in supporting the integration of information systems. Presents framework for understanding integrating concepts and the strategic and organizational factors impacting success of IT in business. The strategic importance of information technology is now widely accepted. It has also become increasingly clear that the identification of strategic applications alone does not result in success for an organization. A careful coordination of strategic applications, information technologies, and organizational structures must be made to attain success. This course addresses strategic, technological, and organizational connectivity issues to support effective and meaningful integration of information and systems. This course is especially relevant to those who wish to effectively exploit information technology and create new business processes and opportunities.

Introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, constraint propagation, constrained search, inheritance, and other problem-solving paradigms. Applications of identification trees, neural nets, genetic algorithms, and other learning paradigms. Speculations on the contributions of human vision and language systems to human intelligence.