AI (Artificial Intelligence) is becoming a topic of increasing social importance. Political reactions like the publication of the AI strategy of the German Government in late 2018 are one indicator for that. But more importantly, we are already interacting with AI systems as if it were the most natural thing in the world, for example, when using language assistants such as Siri or Alexa. Nevertheless, according to surveys, over 50% of Germans do not know what artificial intelligence is.

To address this issue, we have put together a collection of Unplugged Activities related to the topic of AI. Unplugged Activities provide approaches that help learners of all ages to experience the ideas and concepts of computer science actively and do
without the use of a computer.

This brochure contains five activities you can use to teach ideas and concepts of artificial intelligence to learners of all ages.

Nowadays, AI is primarily realized through machine learning, but artificial intelligence is far more than that: AI is not only about technical aspects, but also raises questions of social relevance. This brochure shows possibilities, how these topics can be discussed with children and adults

" 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."

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.

Students read a short passage and determine the author’s purpose. They compete against the game or a partner.

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?

This course examines computers anthropologically, as artifacts revealing the social orders and cultural practices that create them. Students read classic texts in computer science along with cultural analyses of computing history and contemporary configurations. It explores the history of automata, automation and capitalist manufacturing; cybernetics and WWII operations research; artificial intelligence and gendered subjectivity; robots, cyborgs, and artificial life; creation and commoditization of the personal computer; the growth of the Internet as a military, academic, and commercial project; hackers and gamers; technobodies and virtual sociality. Emphasis is placed on how ideas about gender and other social differences shape labor practices, models of cognition, hacking culture, and social media.

Humans are social animals; social demands, both cooperative and competitive, structure our development, our brain and our mind. This course covers social development, social behaviour, social cognition and social neuroscience, in both human and non-human social animals. Topics include altruism, empathy, communication, theory of mind, aggression, power, groups, mating, and morality. Methods include evolutionary biology, neuroscience, cognitive science, social psychology and anthropology.

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.

Welcome to the Hands-On AI Projects for the Classroom series, a set of guides for teachers who are seeking
instructional and curricular resources about artificial intelligence (AI) for various grade levels and across a range of
subject areas.

Students will learn about the basics of machine learning and create their own apps that implement these concepts through image classification. The students will take photos with their mobile devices and the apps will identify objects within those photos. Each classification comes with a confidence level, a value of how confident the app is with its classification. Students will use MIT App Inventor’s machine learning extension called the LookExtension when creating this app.

This Introduction to Machine Learning includes tutorial lessons as well as suggestions for student explorations and project work. The unit also includes supplementary teaching materials: lesson plans, slides, unit outlines, assessments and mappings against the Computer Science Teachers of America (CSTA) computing standards.

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.

Presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. Emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Technical focus on decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems. Students produce a final project using the methods learned in the subject, based on actual clinical data. (Required for students in the Master's Program in Medical Informatics, but open to other graduate students and advanced undergraduates.)

Relationship between computer representation of knowledge and the structure of natural language. Emphasizes development of the analytical skills necessary to judge the computational implications of grammatical formalisms, and uses concrete examples to illustrate particular computational issues. Efficient parsing algorithms for context-free grammars; augmented transition network grammars. Question answering systems. Extensive laboratory work on building natural language processing systems. 6.863 is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.

Neural structures and mechanisms mediating the detection, localization, and recognition of sounds. Discussion of how acoustic signals are coded by auditory neurons, the impact of these codes on behavorial performance, and the circuitry and cellular mechanisms underlying signal transformations. Topics include temporal coding, neural maps and feature detectors, learning and plasticity, and feedback control. General principles are conveyed by theme discussions of auditory masking, sound localization, musical pitch, speech coding, and cochlear implants, and auditory scene analysis.

Increasingly, we are realizing that to make computer systems more intelligent and responsive to users, we will have to make them more sensitive to context. Traditional hardware and software design overlooks context because it conceptualizes systems as input-output functions. Systems take input explicitly given to them by a human, act upon that input alone and produce explicit output. But this view is too restrictive. Smart computers, intelligent agent software, and digital devices of the future will also have to operate on data that they observe or gather for themselves. They may have to sense their environment, decide which aspects of a situation are really important, and infer the user's intention from concrete actions. The system's actions may be dependent on time, place, or the history of interaction. In other words, dependent upon context. But what exactly is context? We'll look at perspectives from machine learning, sensors and embedded devices, information visualization, philosophy and psychology. We'll see how each treats the problem of context, and discuss the implications for design of context-sensitive hardware and software. Course requirements will consist of critiques of class readings [about 3 papers/week], and a final project [paper or computer implementation project].

These days Artificial Intelligence and Machine Learning are all the craze, but have you ever wondered how in the world is it really possible to teach a machine to learn something, anything really, and become, well, artificially intelligent? In this project, using the context of one of the simplest children's games, Rock-Paper-Scissors, you are challenged to create a program that allows the machine to observe and learn from its user's game choices using a Markov Model to quickly become intelligent enough to repeatedly beat the user at the game.

In an extremely controversial case, Wisconsin v. Loomis, a machine learning algorithm was used to assist a sentencing decision. This case has triggered an impassioned debate in the legal community regarding the proper use, if any, of artificial intelligence (AI) and machine learning in the justice system. In response to this case and advances in technology, the US Supreme Court and its justices are beginning to contemplate AI and more generally, technology's role, in influencing law.You will review three articles exploring the controversial case in Wisconsin and strong arguments for and against using artificial intelligence in the justice system. After reading the articles, you will answer short response questions and prepare for your class debate.

This course is an introduction to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.

A graduate-level introduction to artificial intelligence. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning.