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Digital Transformation vs Continuous Education II

The IEEE is one of the largest Knowledge organisation, accruing knowledge worldwide and making it available. The Knowledge repository is becoming so large that normal ways of access are becoming insufficient and new ones have to be found. Image credit: FDC SAS

Over the centuries humankind has managed to increase the global knowledge and found ways to pass this growing knowledge from a generation to the next one. Knowledge, for most part of human history, was generated locally by very few people and used locally. It took centuries for knowledge to move from where it was created to other places. The slow propagation of knowledge and the lack of a “method” to create knowledge were the main reasons why progress was so slow. In the last hundred years propagation of knowledge has become easier and easier, today it is almost instantaneous and millions of people participate in the creation of new knowledge with education reaching billions of people.

It is obviously difficult to “quantify” knowledge. This is why this graphic is interesting. It measures the frequency in which years are mentioned in Wikipedia in relation to the invention and of occurrence of some notable event. The y axes represent the number of citations. For sure, this graphics shows that we have many more records of the last years of our history than of the past. It is also a measure of connectivity, how much in the last few years knowledge flew.

In the last few decades computers and sophisticated communications networks have kicked off an avalanche of knowledge creation that is becoming overwhelming, exceeding our individual capacity of grasping it all. As a matter of fact our intelligence as single individual has not increased through the millennia, we know more today than our ancestors not because we are smarter but because we are building our knowledge with bricks that are “bigger” than the ones our ancestors had available (we are also dedicating, on average, much more time to education as we grow up).

As a species we have been able to grow the collective intelligence significantly, the peak of intelligence of a few is now leveraged by everybody, Only very few humans would have been able to imagine and create the theory of relativity, but once one did millions could learn it and understand it and a few among these millions are, will be, able to move forward and develop something more advanced.

The distribution of knowledge has become a characteristic of our time. Yes, we always had knowledge distributed but today we rely on it and we use it every single day. Communications and processes are transforming this distributed knowledge in a collective knowledge.

We are now on the brink of a new era, knowledge is ( has started to) being created through the use of artificial intelligence. We derive knowledge from the analyses of a quantity of data that is beyond our human capability to process. We are flying planes that we won’t be able to pilot without the assistance of artificial “brains” controlling the thousands of parts that need to be synchronised in a fraction of a second. Our life and our knowledge is more and more relying on machines. The relation with machines will become more and more seamless and symbiotic. Relying on knowledge that is partly distributed and shared with machines will become the norm.

However, a seamless sharing of knowledge with machines is not reality today. Research on developing brain machine interfaces is going on but it seems unlikely it will bring usable results in the mass market within the next two decades. Mediation through our senses, such as is the case with Augmented Reality, is much more likely and this may become widespread in the next two decades.

Yet, augmented reality is not sufficient to connect our knowledge with the distributed knowledge hosted by machines. There is the need to match our personal knowledge with our immediate needs and fill in the gaps accessing the distributed knowledge.

An approach that is being considered is the one of using digital twins as our proxy in terms of knowledge. Our digital twin may interconnect with the distributed knowledge available in the cyberspace, and customised packages of knowledge may be created (and might even be co-located with our body, wearable knowledge, to be ready for use as need arises.

Look at the chart at the beginning of this post. A person interacts with the knowledge base of the IEEE that is also hosting the digital twin of that person (for the aspects related to the person knowledge). Through interactions the digital twin remains in synch with the person. Software based services connect the person knowledge and needs to the knowledge stored in the IEEE knowledge base and customise the release of knowledge “pills” as need arises. Companies might also be interested in accessing the knowledge base, as well as reaching out to the digital twins of people as intermediaries to assess the existence of needed competencies and engage them.

For a company digital twins may mirror the ensemble of the company knowledge and some of them may actually mirror the knowledge of machines operating in the company. The next step is obvious, although fraught with concern: a digital twin of a person may be replicated into a digital twin of a machine, once technology will be able to support and manage (operationalise) that knowledge,

Companies like Unanimous AI are creating a de facto distributed operational knowledge shared among people and machines that, as they claim, amplify systemwide intelligence.

At this point continuous education has to address both the single person education through his lifetime as well as system-wide education, for a company, for a city, for a Country and questions like “should we invest in educating machines or humans?” will be on the table. Notice that this does not mean that humans will end up “not being educated”, quite the contrary. Humans will need to be educated in the sharing of knowledge with machine, as we have learnt to be educated in using the reed, the quill, the fountain pen, the computer…  Next step is learning to use AI. This is bound to create bigger “bricks” of knowledge humankind will use to reach greater peaks.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. He's currently the Chair of the Symbiotic Autonomous Systems Initiative of IEEE-FDC. Until April 2017 he led the EIT Digital Italian Node and up to September 2018 he was the Head of the EIT Digital Industrial Doctoral School. 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. 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. He writes a daily blog,  http://sites.ieee.org/futuredirections/category/blog/, with commentary on innovation in various technology and market areas.

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