The Real World Potential and Future Development

The talk will explore the evolution of complexity in data sets and the evolution of complexity in the algorithms that deal with them. References will be made to simple datasets demonstrating simple machine learning models up to more complex datasets such as images, text and now non-euclidean datasets - such as point clouds, graphs, manifolds, geospatial datasets and network like data structures that represent highly complex interactions in systems-of-systems. The talk will also make references to a new family of powerful deep learning methods such as Graph neural networks and their applications to non-euclidean data domains. 

Imagine someone enters the room. What is the first thing we do?
In milliseconds we have an assessment: Good or bad. Friend or foe.  
Most of the time we are not even aware of this mental process. It’s part of our survival instinct, deeply rooted in our DNA. This judgement is based on experience which results in patterns we try to match so we can make immediate decisions.  
This speech is about how to apply this survival strategy into a digital model - call it AI, Machine Learning, or something else -  that allows us to protect our digital life from evil.