Are we there yet, are we there yet?

At his talk, part of the Philosophy of AI: Optimistic and Pessimistic Views, Professor Kim Guldstrand Larsen reflected on how far (or near) are we from developing fully autonomous cars. This is a priority challenge for explainable and verifiable machine learning. The question is not easy to answer directly. One certainty, though, is that the answer lies in the cooperation, or lack thereof, between academia and political agents (municipalities). The mediating agent, which, none of these two seem to favour, stems from industry: what can commercial companies deliver to improve traffic? Companies seem to speak both the language of research and of politics. How much will we smartify traffic in the next, say, 10 years? The question translates, as Professor Ana Bazzan asked simply, “What will companies do”?

What companies do, in this regard, will impact not only policy but also academia. Success in delivering smart solutions for traffic is expected to guide curriculum development in computer science programs. For example, the commerical solutions will focus teaching on either neural networks, Bayesian networks or automata based models.