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.
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.
Last year, in the height of the election season, the Obama administration quietly released a national strategic plan for artificial intelligence (AI) research and development. The plan was the beginning of a national effort to prepare Americans for a future with AI--a future some computer scientist believe our nation is ill-equipped to handle. AI has become a part of the American fabric for some time. Siri and Alexa are already taking orders, self-driving cars have hit some streets, and the concept of interconnectivity is now a reality through the Internet of Things. But experts assert that in order for the society to fully embrace AI, learning machines should not replace human workers, but complement them.
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.