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A proposed IEEE Computer Society student chapter model is designed to immerse students in activities that expose them to entrepreneurial thinking, with guidance from faculty and community members who are entrepreneurs.

The educational applications of AI are a combination of what Pasteur's Quadrant describes as use-inspired basic and pure applied research. This article gives an overview of the classical and emerging architectures for AI in education. Early researchers focused on creating personalized teaching systems based on solitary learners, whereas recent work takes account of other people and the learning context. Various Grand Challenges illustrate the issues still facing AI in education.

To view the relationship between computing and science as a one-way street is mostly untrue today, argues Ian Foster, and will be even less true by 2020.

A growing number of sciences, from atmospheric modelling to genomics, would not exist in their current form if it were not for computers. A simplistic analysis of this relationship focuses on hardware, and sees science as largely a passive beneficiary of the computing industry's relentless innovation, acquiring and applying to its own ends the fastest computers, largest disks and most capable sequencing machines. In this view, science and computing (as an intellectual discipline) have little to say to each other: it is the computer industry that drives the advances that have an impact on science.

The notion of a "tipping point" isn't new, al though the concept has relevance in differing ways. Academia seems to be at a tipping point, whereby the steady state of disciplinary specialization is about to give way to an interdisciplinary, collaborative approach to knowledge acquisition. To understand this particular tipping point, it must first appreciate the various emergent viewpoints associated with the concept.