EXECUTIVE VICE PRESIDENT AND CHIEF TECHNOLOGY OFFICER
Gulu Gambhir, D.Sc., is executive vice president and chief technology officer of Leidos. He provides strategic direction for the corporation’s technology offerings, guides its research and development program, and promotes business growth through technology innovation. He leads the corporate Office of Technology, including oversight of program execution, engineering, and internal information technology.
ExecutiveBiz: What technological advancement do you feel will most significantly impact your business in 2016?
Gulu Gambhir: The technologies with the most substantive impact on our business in 2016 are those that have been gaining market traction for some time now – mobility, adoption of public and hybrid cloud architectures, advancement in data analytics, and continued growth in the sophistication of cyber threats. Looking forward, there are a number of emerging technical advancements that I expect to have significant business impact: cognitive and quantum computing, big/fast/scalable data storage, advanced biology, artificial intelligence, and autonomy. Deep Learning, a subset of cognitive computing, is a particularly innovative area and one that we’ve been investing in. Its origins are in neural network technology, which has been around for at least two decades. What’s changed are advances in computing speed, communications, and data. Deep learning models take advantage of large sets of training data to “teach” the model. In recent years, there’s been a tremendous growth in metadata tagged data sets, like a photo with the tag “child eating ice cream cone.” Using a rule-based algorithm to detect a child and an ice cream cone consistently in an image is hard, while doing so with deep learning is now straightforward. Mobile network 5G technologies should support data rates greater than 1 gigabit per second and advanced processors are being made tailored for deep learning use, just as graphics processing units are made to tackle the challenges of graphics intensive applications. Taken together, these advances support a lot of very interesting deep learning applications like augmented reality, autonomy, multi-lingual analytics, and intelligent assistants.
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