Intellifest 2012: Stephen Grossberg: Mind, Brain and Autonomous Artificial Intelligence
Professor Grossberg, currently at Boston University, delivered a wide ranging presentation on the scientific developments in the area of modeling how the brain works, and how that leads to intelligent mental functions. Professor Grossberg is well known in the industry: among many other accomplishments, he is the co-inventor of ART (Adaptive resonance theory) which has been used in application such as predicting business insolvency, etc.
One key aspect he focused on is how the concept of autonomy has brought a new understanding on how the brain learns how to decide, and how to define what the reaction to the input should be, what the optimal next action should be.
A key question Professor Grossberg addresses is this:
How does behavior arise as emergent properties of neural networks?
This is a key question to understand how brain and mind are intertwined and related.
One of the difficulties in answering the question is that you need to simultaneously describe and manage at least 3 different levels of perspectives: the behavior, the network and the neuron, and you need a modeling approach that links them.
Interestingly, it turns out the over 40 years of modeling have lead to a single modeling theme that combines these different perspectives. Professor Grossberg describes it as “autonomous adaption to a non-stationary environment”: how does an individual (entity) adapt on its own (autonomously) in real time to a complex changing environment.
His work has allowed him to introduce a modelling cycle built around this theme that can be used to model brain and behavior applied to a variety of problems with impressive results
Professor Grossberg spent some time describing – at a high level – what ART is, and provided an excellent summary on what makes ART successful in so many different application areas.
Professor Grossberg’s work can be found in his web page. You can also find some of Gail Carpenter‘s work (she is the other half of ART) here. Her work is very relevant to the capture of rules out of complex evolving data: check in particular her papers on self-supervised ARTMAP from a few years ago.