Artificial Intelligence is currently the most trending technology term in the industry. Though Artificial Intelligence is not a new concept, what comes as a baffling surprise is that analytics is being re-christened as artificial intelligence today. A lot of it to do with companies / consultants trying to on-board the frenzy around artificial intelligence. But, analytics and artificial intelligence are completely different technologies with some similarities in implementation and almost little overlap in terms of end results.
Analytics as an industry is already heavily jargonized and it important to clear the air building up around analytics being called artificial intelligence at lot of instances.
First, the overlaps
Analytics is an encompassing field that uses mathematics, statistics, computing and machine-learning techniques to discover insights hidden deep in the recorded data. Analytics helps enterprises understand their current scenario and discover the future steps to achieve growth as analysis of data fuels knowledge discovery.
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Artificial intelligence is about creating systems (call them machines) that can mimic human intelligence as closely as possible. How close? – Well, lets say a Turing Test can tell that. Artificial Intelligence is not a new phenomenon; it’s almost as old as computing itself. We already have seen similar frenzy around AI like today’s, atleast twice in the past followed by deep AI winters where funding dried up for all research and initiatives around AI.
But this time around, it’s looking different for AI. Partly because there are some concrete results that have surfaced. Though the real AI where computer systems can reach the level of human intelligence and behavior is still out of reach, the reason AI today has better results is because it is using the same concepts that analytics is using.
Earlier efforts in AI were centered around creating expert systems, which at the most basic levels were nothing more than rule-based algorithms. What’s happening today in AI is almost the same, but with much higher sophistication. Today, AI systems are using power of data, computing and statistics to create intelligence systems. Sounds like that’s exactly what analytics is. But, the similarities end here.
Now, the differences
When we hear a computer winning a chess match against the world’s best chess champion, we say the computer is intelligent. When our smartphone understands our voice commands, we call it intelligent. These are all forms of artificial intelligence and are build on top of a combination of data, computing and machine learning.
On the other hand, recommendation engines, forecasting loan defaults or predicting fraudulent transactions or spam emails are applications of analytics and are also build on top of data, computing and machine learning. But these applications cannot be termed artificial intelligence.
Organizations only need to know the right question and analytics will give them the outcome they need to focus on. Analytics helps to produce fast insights required to make fact-based decisions and allows us to find out answers to questions we might never have thought to ask.
Artificial Intelligence can be explained as the study of man-made computational devices and systems which can be made to act in an intelligent manner.
The very core of both AI and analytics can be same in terms of how they are being implemented. Yet, what they are used for, have completely different objectives. It would only be fair on our part to keep these terms to what they really mean. This would also bode well for the whole industry in long term.