10. Asking the Right Questions

This article is Talk 10 of Honest Data Science Talks series. Each talk of around 500 words length, is personal thoughts and deliberations on the domain.




We must realise that when a machine starts demonstrating the problem-solving capabilities as humans do, it is only a first step towards constructing and simulating a human thinking mind and behaviour. It is only a baby step towards building a better information processing system. It’s definitely past high time that we start asking the meaningful questions and not just stop after asking, but in point of fact, work towards obtaining a holistic solution. Not easy, I must say!


One should understand that presently social, economic, ecological, cultural, political, technological and various other components are tightly coupled. We are living in socioecological systems, and the systems that we build must adhere to these norms. Human evolution cannot be evaluated in isolation, and all these have a significant effect for today and tomorrow.


When one thinks of building a solution or systemic solution, there are several aspects that need to be considered.  Our systems are non-linear. They cannot be understood in isolation. Each of its components needs to be understood and also their interaction with every other component.  Our systems are chaotic. Our predictions are always under limitation. The question, however, is how limited we are? Does our prediction make sense at all? The systems are dynamic. It’s hard to realise an equilibrium point. They are evolving. Maybe they don’t have one.


The systems have an innate complex hierarchy. It a mesh and complex to visualize as a simple ladder system.


If I am to build a model for a next-generation vehicle, I don’t want to talk about a driver-less car. I want to know what will be the number of vehicles allowed on the road ten years down the line. I want to know the climate change for the future. I want to know the economy before I begin my design. I want to know the definition of smart. I want to know the living conditions and family structure so as to know the form factor. I want to know the average technology portfolio investment per family before deciding the features that the vehicles must exhibit. The list goes on. We have reached that stage.


Smartness is not having a high-end technology. Smartness is features that exhibit the true enriching experience to humanity with the environment in its significant consideration.


We know that changes are episodic. However, each change has a due contribution to the systems. There possibly and probably is a formally verifiable abstraction to design and encompass change and its special effects on the system.  This can be discussed only when the system is designed to simulate and trigger a change and observe the combined effects of it on the entire system. In simple terms, does the system model react and behave to every kind of change? Have we got the principles right? Do we have the workflows?


There is also another important question. Are we knowledgeable enough to ask the right question? How do we ever know? Can a system help us decide? Now that would be a factual and evocative data science system!


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