Practical Business Applications for Artificial Intelligence

practical-business-applications-for-artificial-intelligence

Almost from the first research grant 60 years ago, AI has been oversold more than almost any other software technology.  Advocates and boosters have consistently underestimated the complexity of human intelligence and the difficultly of mimicking it in code. Based on a number of false starts, it is easy to dismiss the concerns of Bill Gates, Elon Musk, and Stephen Hawking as being an abstract dilemma we can deal with in the future. This has changed. Recently, advances in statistical science, GPU chips, deep learning algorithms, and big data have made business applications of AI practical.

AI technologies are already being used in a number of different industries.  In Tractica’s recent report, Artificial Intelligence for Enterprise Applications, we looked at multiple sectors including online advertising services, automotive, agriculture, consumer finance, data storage, education, investment, healthcare, law, manufacturing, media, medical diagnostics, oil and gas, philanthropies, and retail and found that systems modeled on the human brain, such as deep learning, are being applied to tasks as varied as question-answering medical diagnostic systems, credit scoring, program trading, fraud detection, product recommendations, image classification, speech recognition, language translation, and self-driving vehicles.  Thanks to a few notable successes, the only limit to the problems AI is being asked to solve seems to be the human imagination.

Although AI systems are relatively small in size compared with other parts of computing, AI technologies are the sharp point of the information technology spear.  Both compute and storage intensive, AI systems require a sizable allocation of hardware resources and will drive significant sales of servers, networks, and storage.  Like any software, AI systems will have bugs, downtime, compatibility issues, and will need to draw data from other systems.  While in theory, AI systems could rewrite their own code to avoid some of these problems, in practice, any software-based AI systems will be very finicky and require a narrow and stable environment to function efficiently.

Once AI applications become integrated into critical business processes, the increasingly important decisions they are being called upon to make will require they be given more, rather than less, human support, at least in comparison to other enterprise systems.

The IEEE has invited report author Bruce Daley to join an online panel discussion to discuss his findings in Artificial Intelligence for Enterprise Applications  on Wednesday, May 27, 2015 at 1:00 P.M. EDT. Along with Seth Earley, Bruce Daley will be joined by Dr. Olly Downs, Chief Scientist of Globys, Mitchell Shuster, Data Scientist at Knowledgent Group, and Patrick Heffernan, Principal Analyst at Technology Business Research to discuss some of the implications of practical AI.

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