This research report introduces the generation of textual entailment within the project CSIEC (Computer Simulation in Educational Communication), an interactive web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project. Up to now we have found few literatures related with GTE. Simulating the process that a human being learns English as a foreign language we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition (matching), and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.
In this paper, we report on the efforts at the University of Southern California to teach computer science and artificial intelligence with games because games motivate students, which we believe increases enrollment and retention and helps us to educate better computer scientists. The Department of Computer Science is now in its second year of operating its Bachelor's Program in Computer Science (Games), which provides students with all the necessary computer science knowledge and skills for working anywhere in industry or pursuing advanced degrees but also enables them to be immediately productive in the game development industry. It consists of regular computer science classes, game engineering classes, game design classes, game crossdisciplinary classes and a final game project. The Introduction to Artificial Intelligence class is a regular computer science class that is part of the curriculum. We are now converting the class to use games as a motivating topic in lectures and as the domain for projects. We describe both the new bachelor's program and some of our current efforts to teach the Introduction to Artificial Intelligence class with games.
Robotics projects coupled with agent-oriented trends in artificial intelligence education have the potential to make introductory AI courses at liberal arts schools the gateway for a large new generation of AI practitioners. However, this vision's achievement requires programming libraries and low-cost platforms that are readily accessible to undergraduates and easily maintainable by instructors at sites with few dedicated resources. This article presents and evaluates one contribution toward implementing this vision: the RCXLisp library. The library was designed to support programming of the Lego Mindstorms platform in AI courses with the goal of using introductory robotics to motivate undergraduates' understanding of AI concepts within the agent-design paradigm. The library's evaluation reflects four years of student feedback on its use in a liberal-arts AI course whose audience covers a wide variety of majors. To help establish a context for judging RCXLisp's effectiveness this article also provides a sketch of the Mindstormsbased laboratory in which the library is used.
On today's episode of the podcast, I got to chat with software engineer Jackson Bates who lives and works in Melbourne, Australia. Jackson used to be a high school English teacher, but gradually taught himself to code and landed a pretty sweet gig as a React dev, partly by chance. Today he works part time as a developer, part time as a stay at home dad, and volunteers his time with various open source projects. Jackson grew up in England, and studied English in school. Although going into education seemed a logical choice, he dabbled in other fields - like working at a prison cafeteria - for a while before landing a teaching job.
With the rapid evolution of technology, new tools for creativity and development are constantly emerging. Musicians today are beginning to use machine learning, where computers "learn" over time by being fed large amounts of data, to create music in new and innovative ways. The computers process this data and identify patterns, allowing them to act on future data. After identifying these patterns, computers can classify new information, make predictions, or even generate novel, creative content. In the world of music, the possible applications of this technology are endless.