The confusion over cognitive computing
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Ginni Rometty, chairman, president and chief executive officer of the $80 billion (around Rs5.3 trillion) International Business Machines Corp. (IBM), wants to dominate both the online and offline worlds with the company’s cognitive computing platform.
She insists that cognitive is “much more” than artificial intelligence (AI). To most of us, technologies that are built using the cognitive computing platform can hardly be distinguished from those built using AI.
Had the pronouncement been made by anyone less than the chief of the company that developed Watson, the supercomputing system that even beat Jeopardy players in 2011, we could have dismissed it as just another marketing buzzword.
For one, Rometty believes that machine learning is good for deciphering patterns. She says cognitive is more comprehensive because it can “reason” over all structured and unstructured data and deal with “grey areas” to help make judgements and decisions. She cited the example of AI technologies that can be used while trying to look for a blockage in a cardiogram. But if doctors were to inspect all the medical records, the previous tests that a patient has undergone, images and data from wearables, that would involve cognitive because doctors would be able to “reason” over that data.
IBM points out that cognitive computing is about the involvement of a human in the loop, which makes it “augmented intelligence” rather than “artificial intelligence”.
The questions still remain: Can’t all things done by cognitive computing be done by AI technologies? Is the involvement of the “human in the loop” a significant difference?
The definition of the word cognitive reads like this—of, or relating to, cognition; concerned with the act or process of knowing, perceiving, etc.
AI is a branch of computer science that takes inputs from linguists and roboticists, and relies on subjects like math, psychology, philosophy and neuroscience, to name a few. You may notice that in all such cases, the human element is “in the loop”, so to speak.
In some sense, AI is this desire to replicate intelligence in hardware, according to Shivaram Kalyanakrishnan, an assistant professor in the department of computer science and engineering at the Indian Institute of Technology, Bombay. He is the only author from India who is part of an 18-member study panel of the Stanford University-hosted report titled “Artificial Intelligence And Life In 2030”.
AI does not have a rigid definition, admits Kalyanakrishnan, pointing out that the Stanford report went with Nils J. Nilsson’s definition that “AI is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment”.
A data-driven machine-learning algorithm, for instance, can sift through a patient’s data and predict an illness. Deep learning uses Artificial Neural Networks (ANNs) to simulate the human brain. ANNs can be used to map inputs to predictions.
There is no definitive way to prove that cognitive computing scores over AI. What matters most to businesses is whether smart technologies can improve their productivity and business outcomes. That will always remain the case.
Cutting Edge is a monthly column that explores the melding of science and technology.