WE NEED COMPUTATIONAL KINDNESS ………sudhanshu

WE NEED COMPUTATIONAL KINDNESS ………

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Student of New Batch PGDM ( Maharaja Agrasen Business School- New Delhi ) to me after my orientation lecture – ‘You seem to be a very kind teacher’ ……… I asked ‘how?’? She replied ….. ‘in your handling of ?students.’ ‘ This is rare in a world where everything is becoming mechanised,’ she quipped. She seemed to me an engineering student which she affirmed. I replied – ‘ As the world is becoming mechanised by tech guys like you ……. Human behaviour should become more ‘HUMANE’? …. Not only this, - tech guys - ?like you should make technology also kind. HUMANISM along with HUMAN DECISION SHOULD NOT ONLY BE KIND BUT MAKE TECHNOLOGY ALSO KIND. THERE SHOULD BE COMPUTATIONAL KINDNESS IN THIS MECHANISED SOCIETY.

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I had read an author MERRILL FLOOD long back about 10 years ago. And I quote …. “ I firmly believe that the important things about humans are social in character and that relief by machines from many of our present demanding intellectual functions will finally give the human race time and incentive to learn how to live well together.”

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WE NEED COMPUTATIONAL KINDNESS ………

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Any dynamic system subject to the constraints of space and time is up against a core set of fundamental and unavoidable problems. These problems are computational in nature, which makes computers not only our tools but also our comrades. From this come three simple pieces of wisdom.

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First, there are cases where computer scientists and mathematicians have identified good algorithmic approaches that can simply be transferred over to human problems. The 37% Rule, the Least Recently Used criterion for handling overflowing caches, and the Upper Confidence Bound as a guide to exploration are all examples of this.

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Second, knowing that you are using an optimal algorithm should be a relief even if you don’t get the results you were looking for. The 37% Rule fails 63% of the time. Maintaining your cache with LRU doesn’t guarantee that you will always find what you’re looking for; in fact, neither would clairvoyance. Using the Upper Confidence Bound approach to the explore/exploit tradeoff doesn’t mean that you will have no regrets, just that those regrets will accumulate ever more slowly as you go through life. Even the best strategy sometimes yields bad results—which is why computer scientists take care to distinguish between “process” and “outcome.” If you followed the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way.

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Outcomes make news headlines—indeed, they make the world we live in—so it’s easy to become fixated on them. But processes are what we have control over. As Bertrand Russell put it, “it would seem we must take account of probability in judging of objective rightness.… The objectively ?right act is the one which will probably be most fortunate. I shall define this as the wisest act.” We can hope to be fortunate—but we should strive to be wise. Call it a kind of computational Stoicism.

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Finally, we can draw a clear line between problems that admit straightforward solutions and problems that don’t. If you wind up stuck in an intractable scenario, remember that heuristics, approximations, and strategic use of randomness can help you find workable solutions. A theme that came up again and again in our interviews with computer scientists was: sometimes “good enough” really is good enough. What’s more, being aware of complexity can help us pick our problems: if we have control over which situations we confront, we should choose the ones that are tractable. But we don’t only pick the problems that we pose to ourselves. We also pick the problems we pose each other, whether it’s the way we design a city or the way we ask a question. This creates a surprising bridge from computer science to ethics—in the form of a principle that we call computational kindness.

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sudhanshu