Artificial intelligence (AI) is one of the pervasive disruptive technologies, touching every part of our lives and fundamentally changing the way we interact with technology. AI is revolutionizing the current digital age using neural network technology and machine learning. AI’s ability to transform vast amounts of complex, ambiguous information into real insights has the potential to help solve some of the world’s most enduring problems and to undertake tasks with greater efficiency and scale than a human could. A study by Markets and Markets revealed that the AI market is expected to become a $190 billion industry by 2025.

From the age of Netscape, to Google, the computing intelligence has come a long way from basic to contextual to conversational and so on with tremendous accuracy. If you need to know the 7th root of 12 all you have to do is ask google the same thing. So, the people who will be able to survive are the people who will ask relevant WHY questions to bring out differentiators, find purpose rather than mundane HOW!

Usable AI that has been developed up to now is essentially for solving specific areas or addressing a particular problem, i.e. it’s mostly being used to recommend what you should buy next online, to understanding what you say to virtual assistants, such as Amazon’s Alexa and Apple’s Siri, to recognize who and what is in a photo, to spot spam, or detect credit card fraud and so on.

According to a Stanford University report, produced in partnership with McKinsey & Company, Google, PwC, OpenAI, Genpact and AI21Labs, found that AI computational power is accelerating faster than traditional processor development. “Prior to 2012, AI results closely tracked Moore’s Law, with computational power doubling every two years.,” the report said. “Post-2012, AI capability has been doubling every 3.4 months.”

Along with the AI hype what we can’t ignore is AI’s limitations. An AI system can be deceived or mystified by situations they haven’t seen before. An autonomous self-driving car gets puzzled by a scenario that a human driver could handle easily, as of today but this will be resolved too in the near future.

These shortcomings exist because AI systems don’t understand causation or interconnection. They see that some events are associated with other events, but they don’t ascertain which things directly make other things happen. It’s as if AI knew that the presence of clouds made rain likelier, but it didn’t know clouds cause rain.

Today’s AI has only a limited ability to conclude what will result from a given action. In reinforcement learning, a technique that has allowed machines to master games like chess and Go, a system uses extensive trial and error to distinguish which moves will essentially make them win. But this approach doesn’t work in real world where things are messier.

However, there is a growing consensus that advancement in AI will stall if computers don’t get better at causation. If machines could grasp that certain things lead to other things, they wouldn’t have to learn everything anew all the time—they could take what they had learned in one domain and apply it to another. And if machines could use common sense we would be able to put more trust in them to take actions on their own, knowing that they aren’t likely to make dumb errors.

Essentially understanding cause and effect is a big aspect of what we call common sense, is an area in which AI systems today need to evolve further several notches before it produces pragmatic real life solutions. This also means the need of the hour is WHY – “Y” factor to get better at the right, relevant questions to elicit the best collaboration between humans and AI; thus leading to confluence of domain expertise and technology advancements leading to pragmatic AGI – the final frontier!

Conclusion:

An even higher level of causal thinking would be the ability to reason about why things happened and ask “what if” questions. This kind of reasoning is far beyond the current capability of artificial intelligence. In the new world order for the AI technology to rule it needs to ask the fundamental question of Why?! This also means everyone need to pay attention to the “Y” factor!

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