Learning in a Hyperconnected World

Learning as a sub-set of intelligence (the other elements of intelligence being capacity for logic, self-awareness, planning, creativity, problem solving, etc) has been re-defined in the world of futurism, context and change. The intelligence of learning therefore spans across several variables – and we do not have enough epistemological validation to determine how many variables there are, to learning. But that is simpler of the issues

Flynn effect related to measurement of rise in human IQ has been questioned by James Flynn himself, on the ground that very large increase in IQ does not measure intelligence but only a minor sort of abstract problem-solving ability with little practical significance. Additionally, simple reaction-time measures that correlate substantially with measures of general intelligence (g) and are considered elementary measures of cognition, have been found to have declined by around 1.16 points per decade , through a meta-analysis of 14 age-matched studies from Western countries between 1889 and 2004, according to a study by Woodley, Nijenhuis and Murphy.

Are we focusing too much on Intelligence that is Artificial, rather than one that is required on the ground and in organizational, team and personal learning and behaviour?

Alan Turing’s statement in London Mathematical Society in February 1947 on Artificial Intelligence (“… It would be like a pupil who had learnt much from his master, but had added much more by his own work…”) has a large element of assumption. Developing an equivalent of a Caudate Nucleus or Basal Ganglia is not easy, because human brains learn through too many variables.

Emphasis on machine-aided learning somewhere also risks assumptions of aesthetics being lesser in importance to structure (in paintings), emotions being lesser in importance to process (in social interactions), distortions being lesser in importance to notes (in rock music), volume being more important than essence (in literature) and artificial intelligence willing, people-line being less important than top or bottom lines (in business).

Learning and Development gains from AI and machine-aids may be achieved if the interface helps focus on the following and more, in organizational and employee life-cycle management:

1) Reducing information-overload from a hyper-connected world of IoT, to provide targeted inputs on situational learning (and applications) through a convergence of multi-domain intelligence for possible and reasonable solutions to a problem, 2) Help in non-linear and aperiodic sequence studies applied to talent turnover and predictability of successful engagement, 3) Provide pattern-recognition techniques over longitudinal periods for sustainable decision-making, 4) Irrational decision-process predictors and image-generators to synchronize heterogeneous team dynamics, 5) Exceptionally simple technology applications that change behavior, 6) Delivers custom-learning content that is congruent with actual abilities, skills and knowledge; and pivoting on reflectiveness, prior and emerging experience, creativity, originality and imagination.

There are absolute possibilities of magical and deliberate outcomes that can result in increased productivities and efficiencies, faster learning, wider exposures, etc. We need to know the solution however, to the following school-level problem:

There is a train that is moving towards a station. At any point in time its speed is same as its distance away from the station. When will it reach the station?

If one wishes to go to the extreme of assuming that artificial intelligence and machine learning is the way to go, one would be liable to find that it is gained at a price of intolerable loss of intelligence itself.

For a true learner, therefore, learning happens at a museum, over a run and work-out, story-telling with children, 360 degree social and official interactions, playing/listening to music, setting up a camp, controlling self-behaviour, etc - and over a machine-interface, possibly. There can hardly be anything artificial in this.

What's your thought on this, honestly?

(The sketch above is inspired by a recollection from years back while working as a developer of software programs and on a visit to my village: grain-hoppers in their repeated synchronized loops; no gadgets, no medium, no machine-aids and programmed ontology. Sheer natural !ntelligence.

Thanks to People Matters team for taking up the original article in their September 2017 issue - Learning Enablers in a Hyperconnected World)