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Wednesday, April 17, 2019

Peeking into the Black Box of Artificial Intelligence | Technology - BBN Times

While artificial intelligence (AI) applications are becoming increasingly capable of solving even the most complex of our problems requiring human-like cognition, the black box of artificial intelligence makes it difficult for us to understand how these systems actually go about solving these problems, as BBN Times reports.  

Photo: BBN Times
Although humans had always known about the existence of fire, it was only when we learned to control or “tame” fire that we really kickstarted the journey of rapid technological progress and evolution we currently find ourselves in. Now, over a million years later, we find ourselves at a similar juncture—albeit faced with an entity that we created instead of a natural phenomenon. The creation of artificial intelligence, undoubtedly, is a step into an era of unprecedented growth unlike any we’ve seen before. But, a true leap can only be achieved once we “tame” the technology, as it were, by first illuminating the black box of artificial intelligence that will enable us to better control the outcomes affected by the technology. 

Our brain, the apotheosis of human evolution and the most vital of our organs, also happens to be the most complex and enigmatic things known to us. Similarly, artificial intelligence, which represents the pinnacle of human technological development, is equally perplexing—despite the fact that it has been created by us. To be fair to ourselves, there is a lot we do know about our brain and can predict with some certainty how it reacts to different stimuli...

Deep Neural Networks: The Working of the Black Box of Artificial Intelligence 
Deep neural networks use multiple layers of algorithms that analyze data and classify or cluster items. Since these neural networks are designed to function in ways similar to our brains, they also gain the ability to classify objects like we do—through experience. A human baby learns to differentiate between objects as it grows by observing them and learning to label them based on what it is taught by its parents. Similarly, a deep learning algorithm learns through training data that gets fed to it so that the algorithm gains “experience”. But this isn’t where the similarity between the human brain and a deep learning algorithm ends. Have you ever noticed that we gain the ability to identify different breeds of cats and dogs as “cats” and “dogs” even when we’ve never seen some breeds before? We don’t need to see every breed of dogs there is to be able to identify one when we see it for the first time. This phenomenon is called ‘generalization’, where we identify and memorize certain distinctive features of dogs (or any other entity) and use those features to generally identify a dog as one, even when we see a totally new breed with radically different features. The similar ability to generalize is also seen in AI agents who can use what they learn from one dataset to generalize across new sets of similar input.

Source: BBN Times