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