By ZIGFRIED HAMPEL-ARIAS and JOHN SPEED MEYERS
A seasoned intelligence professional can be forgiven for raising her eyebrows about artificial intelligence, a nascent and booming field in which it can be hard to sort real potential from hype. Addressing that raised eyebrow — and helping senior leaders understand how to invest precious time and money — will take more than vague generalities and myopic case studies. We therefore offer a hypothesis for debate: AI, specifically machine learning, can help with tasks related to collection, processing, and analysis — half of the Steps in the Intelligence Cycle — but will struggle with tasks related to intelligence planning, dissemination, and evaluation.When we talk about AI’s prospective value in intelligence work, we are generally talking about the specific field of deep learning, a term that refers to multi-layer neural network machine learning techniques. Deep learning tools have made tremendous progress in fields such as image recognition, speech recognition, and language translation. But there are limits to its abilities.
Deep learning excels at “tasks that consist of mapping an input vector to an output vector and that are easy for a person to do rapidly,” wrote three of the field’s leading lights — Apple’s Ian Goodfellow and University of Montreal professors Yoshua Bengio and Aaron Courville — in their 2016 textbook Deep Learning. “Other tasks, that cannot be described as associating one vector to another, or that are difficult enough that a person would require time to think and reflect in order to accomplish the task, remain beyond the scope of deep learning for now.”

















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