Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Originating in 2006, deep learning refers to the depth of a neural network, how many layers of artificial neurons data is passed through.
A recent report argues that the dominance of the deep learning approach may fade in coming years, as it runs out of answers for tough questions of building artificial intelligence (AI).
The report, known formally as “The One Hundred Year Study of AI,” is the second installment in what is planned to be a series of reports every five years on the state of the discipline.
Deep neural networks will move past their shortcomings without help from symbolic AI, says three pioneers of deep learning. In their paper, Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, recipients of the 2018 Turing Award, explore recent advances in the field that might provide blueprints for the future directions for research in deep learning.
The deep learning pioneers believe that better neural network architectures will eventually lead to all aspects of human and animal intelligence, including symbol manipulation, reasoning, causal inference, and common sense.
Why is the business of deep learning so successful? It’s because deep learning, regardless of whether it actually leads to anything resembling intelligence, has created a paradigm to use faster and faster computing to automate a lot of computer programming.
Scientists continue to provide various solutions to close the gap between AI and human intelligence. However, most users and their organizations have little visibility and knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied.
Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
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Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
