Home / Blog / Applying Cognitive Digital Twins to Professional Education – VIII

Applying Cognitive Digital Twins to Professional Education – VIII

Artificial Intelligence can play a major role in many professions by flanking the knowledge of humans with additional one as well as facilitating the cooperation among different “knowledge” owned by human and macine players. The cognitive digital twin may act as an intermediator providing data to AI applications and making use of some of the results from these applications. Image credit: Johnson K.W. et al, JACC

This lengthy discussion has brought me to the very starting point: the knowledge space is far too broad (and getting broader) to be manageable by our brain. We need tools, and this is nothing new since we have become accustomed to using tools, to the point that they have become part of ourselves: “look at the nice diagram I created! Well, as a matter of fact it was created by some software I would have no idea how to write and doing computations I would not be capable of doing”.

The cognitive digital twin can be such a tool, of course, leveraging itself on a variety of other tools. The goal of my cognitive digital twin is:

  • to know what I know here and now
  • to know what I should know to do what I am planning/supposed to do
  • to share some knowledge with me by “educating” me
  • to perform some cognitive activity on my behalf
  • to act as my proxy, doing some cognitive task using part of my knowledge
  • to share knowledge with other cognitive twins for cognitive team-working

Let’s analyse each of these goals one by one.

  1. Know what I know here and now

This is easier said than done. My cognitive digital twin has to learn what I know at this particular time and in this particular context. Knowing that I know how to take a photo with a correct digital exposure is irrelevant if the situation requires analysing the symptoms of a patient that is looking for a diagnose and a cure. My cognitive digital twin should be much more focussed on what I learnt at the medical school, what kind of experiences I gained through hospital internship, what medical papers I read. It should also, ideally, know what I am likely to have forgotten. There are means to infer this knowledge by looking at what I am doing every day, what patients I have, the prescriptions I wrote, my interactions with other doctors… Notice that I am taking the medical profession as an example but I could have chosen any other profession. Clearly, some professions involve a much higher level of digital interaction and it is simpler to intercept them and extract inferences. As a professional enrolled in an IEEE membership I am declaring every year what my areas of interest are, I download technical papers, attend conferences, interact with my peers in technical communities, I submit papers and these get reviewed, I may be giving lectures and these are announced and sometimes recorded in the cyberspace.

For every job there are plenty of clues on the type and level of knowledge a specific person has. If we are looking at industry 4.0 where more and more activities are performed in the cyberspace there is clearly plenty of behavioural and cognitive data that can be used to create a sort of knowledge map of a person.
We can also notice that many companies’ HR have this sort of (limited) knowledge map of their employees. Automated tools can make these map way more accurate and effective. Clearly, in the case of a knowledge map used by HR there are issues of privacies as there are if some automatic system is in place to track your knowledge (or lack thereof…).

  1. know what I should know to do what I am planning/supposed to do
    Here the first point is to have a grasp of what would be required to perform an activity, a job and this can entail some very basic “have-to-know” specs or can become very sophisticated, such as in the case of a medical doctor where the sky is the limit to the desirable knowledge. What is the latest knowledge provided by research papers, what has been tested in similar cases bringing good results, who are the practitioners that may help in this situation and so on and on. It gets also more complicated if you think that in most cases knowledge transfer takes time so it requires a forward looking of several months. I am planning to change job? How can I leverage my present knowledge and how should I update it, complement it to become appealing for the type of jobs I am going to apply? From a company point of view what is the mix of knowledge skills that will be required next year as business demands change, where should it make more sense to invest in training, …
  2. share some knowledge with me by “educating” me
    As my cognitive digital twin interacts with other CDT there may be some knowledge that it is acquiring and that with limited effort can be transferred to me. My CDT will need to evaluate the what, when and how to make this transfer efficient. This is about me and my learning capabilities, but it is also about the time available (the one I am willing to dedicate) and the cost involved considering alternatives…
    This goal is of high interest in a company where the set of employees CDT could come up with some proposed team mix that will both serve the task at hand by providing the right ensemble of skills and knowledge and lead at the same time to a growth of knowledge in (some part of) the team.
    A crucial part in achieving this goal is the how, both in terms of effectiveness in learning and in cost of learning. By customising the way knowledge gets exposed one can make the transfer more effective.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.