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One of the most important challenge in machine language is Black Box. We are unable to understand how machine or AI made it's choices or predictions. We don't know how to audit it. Example of Clever Hans the German horse which we thought had learnt to add numbers, demonstrated by it's tapping of hoofs. But actually it was only evaluating how humans were reacting to it's tapping. The happier the human face, the more endorsement it would get that it was closer to the
Most articles and papers you read about Artificial Intelligence, start off with doomsday predictions on how it will replace humans. All research and numbers point towards it. But truly, are we even close to such a doomsday scenario?
AI: The Learning Curve
Machine winning games, providing structured answers does not mean they are intelligent. Yes they have specialized intelligence or are mechanized. But 'fake intelligence'. General intelligence is multi-dimensional (common sense, perception, planning, analysis etc.). The algorithm we use to compute in our brain, is still a far cry for AI.
Machine-to-machine interactions are where most implementations are working on. And not on replacing humans. AI is complementary to human judgement, so the value of human judgement increases. Mobile phones allowed us, not to shout and be heard from New Delhi to New York. It is an augmentation device. AI therefore provides us with expertise and assistance and augments us as professionals. RFM (Recency, Frequency, Monetary) analysis in Business Intelligence machine, helps CRM machine create effective Target Groups for prospecting - an example of machine-to-machine interaction. Look at automotive industry- how many are using AI for production activities. Currently very few. Self driving cars, self replenishing refrigerators are good to have, but can wait. Companies who are leading AI initiatives are focused on machine-to-machine interaction and not replacing jobs. It is a myth.
Image recognition, is an important area in machine learning. Putting thousands of images thru artificial network of billion connections and thousands of CPUs has lead to machines being able to process images much faster than humans. However machines are still learning to understand uncommon images (e.g. a puss in boots, dangling upside down from a tree and waving a sword).
Machines are still learning to learn. They have to first learn the facts. Have huge amount of data to gain experience. And finally be able to perceive patterns that are formed. Algorithms- in medical diagnosis - can, currently, solve a case, but cannot build a case. They still lack explanatory power. Learning includes learning from interactions (cognitive), learning from data (machine) and learning to compute like your brain (deep learning).
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