Artificial Intelligence or Intelligence Augmentation. What’s in a name?
Even as we try to wrap our heads around the idea of Artificial Intelligence, or AI, and understand its impact on our lives, our businesses and jobs, some experts suggest we may be barking up the wrong tree. The answers to our questions, they believe, may lie in a concept called Intelligence Augmentation, or IA.
One of these experts, Murali Doraiswamy, a professor at Duke University, US, wrote in an opinion piece for the World Economic Forum in January that IA uses machine-learning technologies that are similar to AI, but instead of replacing humans, IA seeks to assist them.
This characteristic, insists Prof. Doraiswamy, may ensure that IA will make more “progress and headlines” than AI. He adds that combining machine learning with the existing power of the human brain can help us get the best of both worlds.
He has a point. On 27 June 2016, the science and technology policy office of the White House requested information on how to utilize AI for the public good. While AI technologies offer “great promise for creating new and innovative products, growing the economy, and advancing national priorities in areas such as education, mental and physical health, addressing climate change, and more…”, the White House said, they simultaneously carry “risks and present complex policy challenges”.
International Business Machines Corp. (IBM), in its response, argued that it was guided by the term “augmented intelligence” rather than “artificial intelligence”.
IBM calls this approach “cognitive computing”, defining it as a comprehensive set of capabilities based on technologies such as machine learning, reasoning and decision technologies; language, speech and vision technologies; human-interface technologies; distributed and high-performance computing; and new computing architectures and devices. When purposefully integrated, IBM believes that these capabilities are designed to solve a wide range of practical problems, boost productivity and foster new discoveries across many industries.
Ginni Rometty, IBM chairman, president and chief executive officer, insists that cognitive computing is “much more” than AI. This is not a distinction that most of us would notice or even care about. IBM understands AI very well, having developed Watson, the supercomputing system that beat champions of the TV quiz show Jeopardy! in 2011.
Rometty insists that while machine learning is good for deciphering patterns, cognitive computing is more comprehensive because it can “reason” over all structured and unstructured data and deal with “grey areas” to help make judgements and decisions.
Consider the example of a data-driven, machine-learning algorithm that can sift through a patient’s medical data and predict an illness. Deep learning that uses Artificial Neural Networks (ANNs) to simulate the human brain can be used to map inputs to predictions.
