We live in an age where self-driving cars are no-longer just an interesting philosophical thought experiment, but a reality. When things go wrong, there is an immediate and heated debate around where the blame lies (Programmer, Manufacturer, Owner, Driver), but the more interesting discussion always starts further back than that, before the car is even built. How is the algorithm to be coded? What are the rules that govern the decision making processes of the car. Are those hard-coded or is a machine learning as it drives, taking into account the behaviour of other road users etc?

Object recognition and obstacle avoidance are easy things to integrate these days - I'll demo how easy it is to train a Model to recognise and avoid objects, and we'll see how to deploy that model to an Azure Percept device. The tech is the easy part though...

This is a session about Responsible AI Principals, about Sustainable Engineering Practices - about Thinking before we Build and being intentional and transparent in what we code (and playing with robot cars and LEGO men)