History as a guide to IoT growth trajectory
Internet of Things (IoT) has generated a ton of excitement and furious activity. However, I sense some discomfort and even dread in the IoT ecosystem about the future – typical when a field is not growing at a hockey-stick pace . . .
“History may not repeat itself but it rhymes”, Mark Twain may have said. What history does IoT rhyme with?
I have often used this diagram to crisply define IoT.
Even 10 years ago, the first two blocks in the diagram were major challenges; in 2017, sensors, connectivity, cloud and Big Data are entirely manageable. But extracting insights and more importantly, applying the insights in, say an industrial environment, is still a challenge. While there are examples of business value generated by IoT, larger value proposition beyond these islands of successes, is still speculative. How do you make it real in the fastest possible manner?
In a slogan form, the value proposition of IoT is ”Do more at higher quality with better user experience”. Let us consider a generic application scenario in industrial IoT.
IoT Data Science prescribes actions (“prescriptive analytics”) which are implemented, outcomes of which are monitored and improved over time. Today, humans are involved in this chain, either as observers or as actors (picking a tool from the shelf and attaching it to the machine).
BTW, when I mentioned “Better UX” in the slogan, I was referring to this human interaction elements improved by “Artificial Intelligence” via natural language or visual processing.
Today and for the foreseeable future, IoT Data Science is achieved through Machine Learning which I think of as “competence without comprehension” (Dennett, 2017). We cannot even agree on what human intelligence or comprehension is and I want to distance myself from such speculative (but entertaining) parlor games!
Given such a description of the state of IoT art in 2017, it appears to me that what is preventing us from hockey-stick growth is the state of IoT Data Science. The output of IoT Data Science has to serve two purposes: (1) insights for the humans in the loop and (2) lead to closed-loop automation, BOTH with the business objective of “Do More at Higher Quality” (or increased throughput and continuous improvement).
Machine Learning has to evolve and evolve quickly to meet these two purposes. One, IoT Data Science has to be more “democratized” so that it is easy to deploy for the humans in the loop – this work is underway by many startups and some larger incumbents. Two, Machine Learning has to become *continuous* learning for continuous improvement which is also at hand (NEXT Machine Learning Paradigm: “DYNAMICAL” ML).
With IoT defined as above, when it comes to “rhyming with history”, I make the point (in Neural Plasticity & Machine Learning blog) that the current Machine Learning revolution is NOT like the Industrial Revolution (of steam engine and electrical machines) which caused productivity to soar between 1920 and 1970; it is more like the Printing Press revolution of the 1400s!
Printing press and movable type played a key role in the development of Renaissance, Reformation and the Age of Enlightenment. Printing press created a disruptive change in “information spread” via augmentation of “memory”. Oral tradition depended on how much one can hold in one’s memory; on the printed page, memories last forever (well, almost) and travel anywhere.
Similarly, IoT Data Science is in the early stages of creating disruptive changes in “decision making” via Machine Learning or *competence without comprehension” based on Big Data analysis. Humans can process only a very limited portion of Big Data in their heads; Data Science can make sense of Big Data which improves competence in decision-making.
From a more abstract point of view, Memory involves more organization in the brain and hence a reduction of entropy. Printed page can hold a lot more “memories” and hence the Printing Press revolution gave us an external way to reduce entropy of “the human system”. Decision-making is also an exercise in entropy reduction; raw data and background information go into making decisions. IoT Data Science is very competent in handling a ton of Big Data and analyzing them instantly to support decision-making; thus, IoT Data Science revolution gives us an external way to reduce entropy.
If I were an Industrial IoT futurist, I would look for situations where entropy reduction may be possible in the industry. What does that mean in practical terms? Printing Press revolution resulted in Michelangelo paintings, fractured religions and a new Scientific method – all major entropy reduction events. What are similar entropy reduction opportunities for IoT?
The two improvements in IoT that I mentioned earlier – democratization and dynamical machine learning – will indeed engender entropy reduction but nothing compared to the *disruptive* entropy drop due to Renaissance, Reformation and the new Scientific method. Standing here in 2017, it is not apparent what new disruptions IoT Revolution will spawn that drop entropy precipitously. I for one am excited about the possibilities and surprises in store in the next few decades.
PG Madhavan, Ph.D. – “LEADER . . . of a life in pursuit of excellence . . . in IoT Data Science”
History as a guide to IoT growth trajectory