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Scientific Sense Podcast

Wednesday, August 21, 2019

Decision-making is different from finding cats and dogs

As AI moves toward the peak of the hype cycle, it is important to recognize that decision-making in an enterprise is distinctly different from training machines to differentiate between cats and dogs. Most of the field is focused on deep neural networks, convoluted and otherwise, to recognize text, pictures, audio clips and patterns. This is certainly interesting but an extrapolation from these techniques to improving decision-making in the enterprise is fraught with danger. As companies find that enterprise productivity is inversely proportional to the number of data scientists they have on staff, reality is beginning to sink in.

As technology and consulting companies try to mop up the last remaining "data scientist," on Earth, it may be interesting to take a measurement of how enterprise productivity is related to them. Data science, an ill-defined field, has been the latest hype that led many companies down rabbit holes with very little to show. Although there are interesting developments in Artificial Intelligence - in robotics and autonomous equipment, much of these are better called expert systems as they do not learn from data but work on coded heuristics. The stars in the field do not prefer the old terms such as "expert systems," and "neural networks," as they believe they have reinvented mathematics. This is symptomatic of a field beginning to go off the rails as the investors have unrealistic expectations of the "second coming," of AI.

Let's not throw out the baby with the bath water. It is just that the baby has a lot of growing up to do. Decision-making takes a lot more than supervised or unsupervised machine learning. Educational institutions do a disservice to the next generation by blindly following the latest trends and spawning "analytics courses," for everybody. The question educators should ask is whether such programs are leading to people who can take advantage of the technology to enhance enterprise value. To do this, they have to first understand how value is created and that is a lot harder than cranking the supercomputer in the cloud.

Artificial Intelligence has a lot of potential, but not in the hands of those who believe it is about games, computers, deep mind and deeper mathematical techniques. The beauty of mathematics is that it is fully democratized. However, to add value to an organization, it has to be combined with many other attributes.