We believe man’s competence to think and define antecedents is an ingenuity that is difficult for machines to takeover. Science or analytics cannot yet contrive your planning for you. Nor can artificial intelligence (AI) and cognitive computing engage innovatively and emotionally with employees and shareholders. The human-computer partnership should be collaborative, with machines helping people work better and quicker – but talented individuals are still required at the heart of planning, procedure and implementation. If the harmony between investing in humans and machines goes wrong, the outcome will be an expensive miscalculation.
But while we leave no stone unturned for a world in which a computer will keep our garden perfectly manicured or keep us posted when to book our vacation we fail to note one compelling factor. When it comes to the most vulnerable portion of our existence—our communications, understanding and choices, —machines cannot meet our anticipations. We don’t actually want the computer to choose which flowers to plant in our gardens; we only want it to execute our whims and fancies quickly and efficiently. Neither do we want a computer to tell us which part of the world we will be taking our vacation.
A Machine’s learning techniques today could require a magnitude of more training data than a human might need for performing a equivalent task, e.g. a toddler can identify an elephant after seeing a picture of one a couple of times, but a machine requires a much wider (and possibly primed) data set. Humans are good at learning to learn. They can learn a new skill completely dissimilar to their existing skill set, can make decisions about what to learn and discover and collect data intuitively, can learn implicitly/subconsciously, can learn from a variety of schooling formats, and can ask relevant doubts to enhance their learning. Machines today are only beginning to learn to learn. Tragically in this case, that maxim is chillingly accurate. When the designer wrappings are stripped away we find no substance behind the appearance.Well , we can rationalize that they are supposed to be helping! Lets not be paralyzed by statistics. Machines are not responsible for what they cannot do but for what they can do.And these are hundreds of do-able things.
Against this backdrop pf pervasive machine skills we find that humans are good at exercising common sense, they can be realistic and practical, without thinking expansively or requiring large data sets. Machines are in their relative infancy in this field, in spite of speedy strides in Natural Language Processing using learning in depth.. They act structurally and spontaneousy. Scientists working on common sense reasoning estimate that additional new upgrades are required for machines to exhibit common sense. Summarizing the above ;while machines have made rapid strides at learning or prediction skills, they are still in the shrunken existence of attempting to copy truly human skills.They don’t have the same flesh we do. We are safe to believe that the impact of human-enhancing automation using Deep Learning and other machine learning techniques, would not undermine than what most think, while Full Automation is further away in the horizon than some recent reporting might indicate.
In the approaching decades, the idea of work and whether it is wielded by a human or a virtual being is a foregone conclusion. As they are striving to do currently, machines will manage the routine tasks, while humans take on the uncertain and unpredictable tasks that require creativity, ingenuity, adaptability and problem solving skills.
Though it goes without saying that the role of computers will increase in rapid strides and they will handle more sophisticated challenges. Today, machines ‘learn’ to carry out tasks which was beyond our wildest dreams 20 years ago. Did an algorithm just pick your favorite song? For example,sooner or later, computers can improve the efficiency of many different work processes such as customer care and toll collection on the highways. Now Google Glass can give you directions and tell you how long the Brooklyn Bridge is. Even without your asking, Google Glass can tell you if your flight is on time. True, today we have more reasons to be awestruck
Its even been argued that the most epic of all miracles is that, computers even may handle routine medical diagnosis.
But as we deploy computers to make our world more efficient, human work will take on more of a diplomatic problem solving role, administering processes and coming to the rescue when things go haywire..
On the contrary, instead of dividing up work between computers and humans, we expect to see a world where humans and machines work in collaboration to confront problems at lightning speed. To problem solve something that was not planned ahead, small teams will come together to merge what machines can do with the capacity of human imagination. Yes, this age is ripe with opportunity. This is the crux of Human Augmented Intelligence.