IBM's Response to - Request for Information: Multi-disciplinary research

Response to - Request for Information: Multi-disciplinary research

This is the last part in my blog series about IBM's answer to the White House Request for Information.

I come from multi disciplinary research approaches. It began with my involvement in Artificial Neural Network (ANN) research as a young student of psychology in the early 1990's. When our tiny three person research nucleus went through the book series parallel distributed processing it became apparent that we had to learn much more than in the usual curriculum for students of psychology. There were uncommon mathematical concepts like gradient descent, numerical approximations of differential equations and  topology. We had to understand the basics of learning vector quantization to enable us to implement self organizing maps contributed by Professor Teuvo Kohonen. Our programming skills had to improve too, to be able to implement meaningful ANN's. So we learned object oriented programming by teaching ourselves C++ with the second edition of the book 'The C++ Programming Language' by Bjarne Stroustrup. We also used Pascal and later Delphi to be able to discuss our coding with our Professor, who knew Turbo Pascal and was unwilling to switch to a fancy new programming language. But I have to honorably mention Professor Gert Haubensak who was nevertheless willing to get involved in the concepts of object oriented programming with Object Pascal besides his duties as a full professor of psychology.

My minor in my psychology studies was sociology where I learned to work with qualitative research methods. I specialized on 'The Civilizing Process' by Norbert Elias which gave me insights into a completely different approach to view and describe social systems.

Later, in my Neuroscience years, I worked in projects together with mathematicians, physicists, physicians, linguists and computer science folks. My field of research was 'dynamic causal modelling for fMRI research', a highly multi disciplinary research field. Still today researchers with various backgrounds, spanning from engineering to medical science, work in this field.

So I know a bit about multi disciplinary research.

'AI researchers will need to work with educators, researchers, and entrepreneurs on developing cognitive assistants to help learners to follow alternative career pathways, moving from academic disciplines to entry level jobs to additional occupational transitions.'

Above a point is a bit hidden I will expand on now. In my opinion one of the biggest issues of multi disciplinary research is translation of concepts. All international scientists collaborate using the English language, however the most confusing experiences in multi disciplinary research environments are that the same words don't mean the same to everyone. Furthermore researchers of different fields might use the same concepts but express them using different words.

For example when a physician mentions a 'vector' it is probably about transmission of viral diseases, whereas the mathematician is confused how his 'vector' is linked to the common flu and the by standing psychologist knows 'vector' from his statistics courses about factor analysis and  tries desperately to link the elaborations to intelligence theory.

When a physicist is new in the field of human neuroscience he might be confused by the disparate lingo for a certain brain area. Let's assume he begins his work in a team researching perception of visual movement. A neurosurgeon might describe this certain area of the brain using Brodmann's system. Where the neurophysiologist might want to show off with his knowledge of Talairach and MNI coordinates. The psychologist in the team is not interested in brain geography and just speaks of the 'V5 or MT' area to emphasize his interest in function over location. The poor physicist scratches his head and maybe wonders how it happened that talking about a chunk of grey matter at a specific location got so complicated!

This is all very confusing! 

So multi disciplinary researchers could really use AI systems to 'translate' between disciplines and enhance the learning speed for young researchers new in multi disciplinary research. In multi disciplinary research everyone is a 'young researcher' from time to time. Because new theories might require the involvement in formerly unknown areas of research.

'T-shaped professionals with depth and breadth better at teamwork and more adaptable than I-shaped professionals who only have depth.'

Well this is pretty much common knowledge in multi disciplinary research nowadays. For example when our tiny research nucleus about artificial neural networks meddled with mathematical concepts and new programming techniques we were far from being experts in these fields. Also later I didn't claim and still don't claim full understanding of the mathematical formulations of brain state dynamics.
But it is important to have an understanding of foreign terminology and concepts to a certain degree. This degree should enable one to apply multi disciplinary concepts and to collaborate with colleagues from different professional backgrounds.

This ends my mini series about IBM's response to the White House's RFI about 'preparing for the future of artificial intelligence'. Find part one here and part two here.