The human brain, a compendium of false and true memory, formed by past interactions and events, feels comfortable creating heuristics from history to deal with the future. For millennia, this was a dominant strategy as the ability to predict the presence and behavior of predators from historical data helped them survive. But now, this has become a huge liability. Even basic ideas in finance, such as sunk costs, have been difficult for many to internalize. Even those in the know, seem to make bad decisions because it has been difficult not to look back. The software giants found out recently that using historical data to model the future has some drawbacks and this has implications for decision-making and policy design at many levels.
Looking back has been costly for humans in the modern context. They may be better off rolling the dice to pick from available future states than using faulty heuristics shaped by the past. If machines can only learn from the past, then, they will be simply perpetuating the status-quo with no insights. This is equally true in education, where history and experience have been given undue credit and research, where conformation bias has led many astray. What is most problematic is a recent experiment (1) that shows that children have a tendency to conform to robots. In the current technology regime that appears to be accelerating toward fake humanoids, we may be dumbing ourselves down by using history and the prompts provided by robots.
Looking back is costly in many ways for humans. Looking back is value enhancing only if the cost of doing so provides future benefits. It is tough to find use cases where such an activity adds value. There is little practical value in history or how one lived last year. If the future generations can mend the ills caused by the "greatest," that went before them, they could inherit a world that is peaceful and forward-looking. In such a world, there will be no looking back and every day will start with fresh ideas. In such a world, there will not be any recordings, only future possibilities. In such a world, they will reject past theories in favor of uncertain future hypotheses. In such a world, thought experiments will dominate over attempts at proving what was observed. In such a world, experiments will triumph over institutions and legacy.
Only look forward, for anything else will be costly.
(1) http://robotics.sciencemag.org/content/3/21/eaat7111
Looking back has been costly for humans in the modern context. They may be better off rolling the dice to pick from available future states than using faulty heuristics shaped by the past. If machines can only learn from the past, then, they will be simply perpetuating the status-quo with no insights. This is equally true in education, where history and experience have been given undue credit and research, where conformation bias has led many astray. What is most problematic is a recent experiment (1) that shows that children have a tendency to conform to robots. In the current technology regime that appears to be accelerating toward fake humanoids, we may be dumbing ourselves down by using history and the prompts provided by robots.
Looking back is costly in many ways for humans. Looking back is value enhancing only if the cost of doing so provides future benefits. It is tough to find use cases where such an activity adds value. There is little practical value in history or how one lived last year. If the future generations can mend the ills caused by the "greatest," that went before them, they could inherit a world that is peaceful and forward-looking. In such a world, there will be no looking back and every day will start with fresh ideas. In such a world, there will not be any recordings, only future possibilities. In such a world, they will reject past theories in favor of uncertain future hypotheses. In such a world, thought experiments will dominate over attempts at proving what was observed. In such a world, experiments will triumph over institutions and legacy.
Only look forward, for anything else will be costly.
(1) http://robotics.sciencemag.org/content/3/21/eaat7111
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