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Artificial Intelligence: An Illusion or a Reality?

Everywhere I turn, I seem to encounter discussions on Machine Learning and Artificial Intelligence (AI). The Nasscom conference on Big Data and Analytics in June was heavily AI focused. The cover of a recent issue of The Economist reads “March of the Machines” with a special report on AI. Analytics websites are full of it.

So why is the analytics community so upbeat about this technology?

While the earlier phase of robotics was primarily driven by the technology community, the analytics community along with the technology one is equally evolving the AI landscape. The reason for this is fundamental:

Robotics was about embedding rules into technology to automate processes, while AI is about embedding analytics into the process through technology. In a hypothetical scenario where every process has AI in it, there would be no need for analytics outside of the process because all data would be analyzed at source and the actions based on that analysis already taken!

Take for example the recommendation engine in Amazon. The machine looks at what you have searched for, learns your preferences, stores it, compares it to others with similar preferences, analyzes it and converts all this data into action by showing you what could be of most interest to you next time you log in. Every time a user enters Amazon, the system learns. No human intervention, no analysis and no action outside of the system.

How Machines Learn

When I mention AI here, I am using it only in the context of machine learning and narrow AI. We went from automation to robotics based on rule-based learning. You tell the robot what to do and it does it. Now in the new world, you give the machine training data based on what humans have done earlier and it learns and decides what to do; or you give the machine an objective and set the parameters and through millions of iterations, it decides the best way to do it.

While the former is something that humans can comprehend, the latter is somewhat mind-boggling. The reason is that the machine often finds ways to do things that humans have never thought of or could not do earlier due to “computational constraints”. To take that one step further, the machine finds ways to reach the end goal that humans are not even able to comprehend after the fact, i.e., since it has done millions of iterations, humans cannot trace back to “how” the machine achieved the objective. Microsoft’s CEO, Satya Nadella, has come up with 10 commandments last week for how humans and machines should work together and one of them is that AI must be transparent and intelligible rather than just intelligent!

How Machine Learning, AI Will Impact Enterprises

So why is all this relevant to a normal enterprise? Is it all buzz like it has been for 30 years or is there something different this time?

The short answer is that after decades of lingering in the corridors of technology, AI has finally made its way into real life. I am not talking about the Google and Tesla cars or the chess and AlphaGo wonders, I am talking about basic functions such as online shopping, marketing, supply chain, and manufacturing. Every time we log into Google Search or Photos, use Facebook, or shop on Amazon, we are encountering machine learning. So this time is different.

While technology innovations in cognitive, visual, NLP, neural networks, deep learning etc. happened independently in diverse fields, it has all come together neatly to give AI the biggest boost in decades. Companies like IBM and Google DeepMind, Microsoft, and Amazon have made breakthroughs that were once unthinkable. An amazing phenomenon is sparking rapid incremental innovation; in the past, companies invented alone. In the current, not only are innovative companies investing heavily to change the game, but once they do, they are putting their algorithms on open source platforms to enable brains across the world to develop it further.

This democratization of algorithms and technology, along with crowdsourcing of brainpower and the downward spiral of computing costs has made this time different – it’s a geometric progression rather than an arithmetic one.

All the above is reality, not an illusion and we have entered a brave new world that is going to change the way we do business forever. In the next blog, we will explore what this means for the enterprise and what steps can be taken to embrace this change.

Divya Kumar is head of analytics and research at Capgemini. Read more Capgemini blogs here.

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