Artificial intelligence (AI) and machine learning are the cornerstones of the digital transformation in computing. These technologies hinge on the ability to recognize patterns then, based on data observed in the past, predict future outcomes. This interesting topic came to us from Venture Beat in their article, “Deep learning is bridging the gap between the digital and the real world.”
Think of the suggestions coming from Amazon and Netflix that feel a little too perfect. Although machines utilizing AI principles are often referred to as smart, most of these systems don’t learn on their own. Human programming is still necessary. Data scientists prepare the inputs, selecting the variables to be used for predictive analytics. Deep learning, on the other hand, can do this job automatically.
Deep learning methods are a modern update to artificial neural networks that exploit abundant cheap computation. Algorithms have always been at home in the digital world, where they are trained and developed in perfectly simulated environments. The current wave of deep learning facilitates AI’s leap from the digital to the physical world. The applications are endless, from manufacturing to agriculture, but there are still hurdles to overcome.
Melody K. Smith
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