Leveraging Distributed Knowledge
As Digital Transformation becomes more pervasive the value of the cyberspace will increase, as part of the value rooted today in the world of atoms shifts to the cyberspace. Tools to manage data (platforms, cloud computing, artificial intelligence, big data, Digital twins, AR/VR) already make up a significant turnover. The recent acceleration caused by Covid-19 is already showing its impact on market forecast.
The whole area of knowledge management is likely to see a shift in paradigm, from accessing and acquiring knowledge to execute distributed knowledge. In other words, rather than trying to acquire the needed knowledge the focus shift to the ability of using knowledge owned by different players that will cluster towards a specific goal.
Hence the leverage of distributed knowledge, more and more mediated –by the end of this decade by Cognitive Digital Twins- is likely to become an important trend in this decade leading to different companies and value chains organizations with significant economic implications.
There are a few areas spearheading the transition, like the biomedical field and industry 4.0, but it will just be a matter of time and distributed knowledge will become the standard way of doing business.
An interesting aspect is leveraging Cognitive Digital Twins as Knowledge as a Service, once they reach stage 4. A Cognitive Digital Twin (of a person as well as a CDT of an enterprise) owns a specific set of knowledge and can be called upon to share this knowledge as needed. Being a software entity there can be many instances of a CDT engaged at the same time in sharing knowledge.
A person can leverage on her CDT to provide, at a price, her knowledge to a task entering the CDT into the mesh of distributed knowledge for that task.
Another instance of her CDT can be engaged, at the same time, in sharing knowledge in a different mesh of distributed knowledge with a different “customer”.
A similar situation goes for the CDT of a company. In this case that CDT may actually cluster a number of CDTs of people being employed by the company.
There are a number of issues arising from this use of a CDT:
- Being autonomous, each instance of the CDT is going to learn as it is engaged in a mesh of knowledge sharing. How are the different learning “reconciled” with one another (really not a big issue, the reconciliation will lead to an increased knowledge through experience for the CDT, hence propagate to all its instances) and how is the increased knowledge shared with the physical twin, i.e. with the person? This latter is much trickier and eventually it may not be possible to reconcile the CDT with the physical twin;
- An autonomous CDT, as just explained, can acquire a larger set of knowledge in a very short time, through participation in a mesh knowledge sharing as well as by acquiring new knowledge from the cyberspace and by performing data analytics on acquired data/knowledge. At this point the CDT may be used by its physical twin as a knowledge prosthetic, i.e. the sharing takes place between the physical person and the CDT (or the company and its CDT);
- Who is taking responsibility for the knowledge being shared? In current legislations the responsibility always ends up with a specific person (or with a company that is called to be accountable in different ways depending on the type of company). Would the responsibility for the sharing of knowledge (like the sharing of incorrect data) fall upon the physical twin (i.e. the person represented by that CDT)? Notice that the issue gets even more complex when the CDT embeds AI to self develop knowledge by analysing data and learning from experience. In this case part of the shared knowledge can be originated in the CDT, courtesy of AI. In this case would the accountability for this “acquired” knowledge rest on the developer of the AI algorithms? Furthermore, a mesh knowledge sharing involving CDT may actually generate through interaction new knowledge that is actually not originating in any specific CDT.
- The connection between factual knowledge (that derived from certified data used to create the CDT) and the actual knowledge acquired through the operation of the CDT becomes crucial to assess accountability and to certify the actual knowledge. This is a most important point that provides credibility and economic value to the CDT. So far there are no tools/methods to certify a CDT. Institutions that will be able to ensure this will be most valuable in the future (this is an evolution of the peer-review of scientific papers).
Although there are still many question marks and technical issues to be solved it is a sure thing that the evolution of knowledge access and execution will evolve along this –or very similar- path.
Consultancy companies will evolve along this line and most importantly any company that will leverage the Digital Transformation will have to capitalise on its data, that is on the knowledge that the very operation in the cyberspace generates. This is probably one of the major business opportunity to be derived from the Digital Transformation and at the same time one of the most difficult to leverage since it moves (part of) the company into a new, unchartered, business space.