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Rapid development of AI applications has stimulated demand for, and has givenrise to, the rapidly growing number and diversity of AI MSc degrees. AI andRobotics research communities, industries and students are becomingincreasingly aware of the problems caused by unsafe or insecure AIapplications. Among them, perhaps the most famous example is vulnerability ofdeep neural networks to ``adversarial attacks''. Owing to wide-spread use ofneural networks in all areas of AI, this problem is seen as particularly acuteand pervasive.  Despite of the growing number of research papers about safety and securityvulnerabilities of AI applications, there is a noticeable shortage ofaccessible tools, methods and teaching materials for incorporating verificationinto AI programs. LAIV -- the Lab for AI and Verification -- is a newly openedresearch lab at Heriot-Watt university that engages AI and Robotics MScstudents in verification projects, as part of their MSc dissertation work. Inthis paper, we will report on successes and unexpected difficulties LAIV faces,many of which arise from limitations of existing programming languages used forverification. We will discuss future directions for incorporating verificationinto AI degrees.

Neural Network Verification for the Masses (of AI graduates)