Deep learning models DON’T need to be black boxes — here’s how

Deep neural networks can perform wonderful feats thanks to their extremely large and complicated web of parameters. But their complexity is also their curse: The inner workings of neural networks are often a mystery — even to their creators. This is a challenge that has been troubling the artificial intelligence community since deep learning started to become popular in the early 2010s. In tandem with the expansion of deep learning in various domains and applications, there has been a growing interest in developing techniques that try to explain neural networks by examining their results and learned parameters. But these explanations are often erroneous and misleading, and…

This story continues at The Next Web

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Is there a more environmentally friendly way to train AI?

Machine learning is changing the world, and it’s changing it fast. In just the last few years, it’s brought us virtual assistants that understand language, autonomous vehicles, new drug discoveries, AI-based triage for medical scans, handwriting recognition and more.  One thing that machine learning shouldn’t be changing is the climate.  The issue relates to how machine learning is developed. In order for machine learning (and deep learning) to be able to accurately make decisions and predictions, it needs to be ‘trained’.  Imagine an online marketplace for selling shoes, that’s been having a problem with people trying to sell other things on the site – bikes… This story continues at The Next Web

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