Staying Grounded in the Hyped World of Deep Learning
Business + ManagementTechnologyBusiness|Data analysis|Deep Learningposted by Megan Elizabeth Shulby, ODSC March 6, 2018 Megan Elizabeth Shulby, ODSC
Deep Learning. It is a phrase that more and more executives and developers alike are hearing in stand-up meetings and the board room. Companies from Google to IBM to Microsoft, are investing millions of dollars and man-hours into the development of deep learning platforms and products and spending largely on advertising and marketing to show their efforts.
While such developments promise very exciting ventures in the months and years to come, organizations and individuals need to remember that deep learning is still a field that is expanding and going through its growing pains. True success with deep learning depends on the how, the what and the why of its application across business lines. Dr. Sid J. Reddy reflects these exact sentiments in his article, “Deep Learning is only as good as its data.”
This article provides a good reminder to both business and technology communities to stay grounded as technological terms such as “deep learning”, at times, can be hyped and misused. Deep learning offers unique abilities that traditional neural networks do not have but organizations and individuals need to truly understand how to apply the underlying technologies appropriately in order to capitalize on these abilities. “The bottom line is that much of what is marketed as “deep learning” is likely to be ineffective or difficult to manage properly,” Reddy states in his article.
Reddy cites overuse and mislabeling as two major issues that are currently associated with the hype surrounding deep learning. In order to overcome these common pitfalls, Reddy states that organizations need to ensure that efforts in deep learning use sufficient data and domain expertise. “Buying into deep learning hype without doing due diligence could lead to general disillusionment and another AI winter,” Reddy highlights.
With rapidly growing technological capabilities such as deep learning gaining more momentum by the day, it is necessary to balance actual, current capabilities with the promises of advancements in tomorrow’s world. Check out Reddy’s article for further details.
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