Even though deep learning has made significant progress in areas like speech recognition and game-playing, it is still far from a worldwide solvent and is dubious about yielding general intelligence all by itself.
FREMONT, CA: Deep learning in the present world dominates AI(Artificial intelligence) research and its applications and has successfully generated ample excitement among people. To comprehend its scope as well as limits can help in understanding the actual functions or working of deep learning.
Primarily, it approximates complicated relationships by learning to classify input examples into the output ones through a form of succeeding approximation that uses large quantities of training data. Later it attempts to extend the classifications it has gained to other sets of input test data relevant to the similar problem domains.
Deep learning needs to learn a mechanism for gaining abstractions through explicit verbal definition, unlike human reasoning. The current systems powered purely by deep learning face several restrictions that a company needs to know:
• As deep learning needs large sets of training data, it lacks in performance when it comes to problem areas having limited data.
• Deep learning methods are likely to fail if the test data vary significantly from the training ones, which often happen outside a controlled environment.
• The techniques of deep learning might not function well while dealing with data with twisted hierarchical structures. It recognizes correlations between sets of features that are themselves plain or non-hierarchical, meaning which it gives an unstructured list, less structured than human knowledge.
• The present-day deep learning procedures do not accurately draw open-ended inferences based on real-world knowledge. In cases where it is applied to reading, deep learning works exceptionally well when the answer to a given question lies within a text. But it lacks to provide better results when it comes to the tasks that require inference beyond what is explicit in a document.
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• The lack of visibility of deep learning makes the tech a potential liability when applied to back the decisions in areas like medical diagnosis. Billions of parameters utilized by deep learning to resolve an issue do not merely allow its results to be reverse-engineered.
• Deep learning needs more attention, and general intelligence requires complementary tools of a different nature closer to classical symbolic AI to complement the current techniques.
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