The Secret to AI Could be Little-Known Transfer Learning


Consumers have spoken, artificial intelligence is a profitable industry. From Amazon to Google to Apple, major tech companies have made inroads, crafting intelligent software — housed in sleek, accessible hardware — that has drawn massive customer attention.

This trend is set to soon move out of home devices, like Echo and Google Home, and onto the streets, where self-driving cars leverage major breakthroughs in computer vision so passengers can ride easy, knowing their vehicles will “see” and react to objects and road signs in real time without their input. In fact, cars with these features are already popular with consumers, and by 2020 10 million cars with self-driving attributes will be on roadways.

But while there are plenty of ways for consumers to leverage AI, enterprises are asking themselves how they can get in on this wave of innovation. And a big part of the answer lies at the crossroads of computer vision and an emerging field known as transfer learning.

Transfer learning has the potential to unlock dozens of new AI use cases in the enterprise by reusing existing, state-of-the-art deep learning models.

In short, transfer learning is an approach to taking existing AI models and applying them to new data. In this case, we’ll talk about computer vision models that make deductions based on images and visual data, applying that learning on numerical sets of data. It means that businesses could use the very advanced deep learning models that perform computer vision functions, like for self-driving cars, and apply that level of sophistication to a whole new set of, non-image datasets on a spreadsheet.

The way transfer learning works is that the algorithm functions much like the human brain does when looking at a small dataset in Excel. We use our eyes to scan through information and mentally detect patterns. Computer vision applied to numerical data does the same thing, through convolutional neural networks, which look for both high- and low-detail features of an image to help classify what is pictured.

  • By Mo Patel, Practice Director for AI and Deep Learning at Teradata

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