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We have been overhyping deep learning for too long. Its time to start embracing it

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. It is focused on improving the AI process of having machines learn things. The core of deep learning lies in fast enough computers and enough data to train large neural networks. Deep learning became the focus of a hype cycle. Many companies use deep learning and advanced artificial intelligence to solve problems and their product services.

But deep learning is overhyped for too long a period to revert back. Meanwhile, media outlets often carried stories about artificial intelligence and deep learning that were misinformed. They were written by people who did not have a proper understanding of how the technology works. Many experts believe that DL is overhyped. Other prominent experts admit that deep learning has hit a wall, and this includes some of the researchers who were among the pioneers of deep learning and were involved in some of the most important achievements of the field.

Some people say that deep learning is just another name for machine learning, but its not correct. Deep learning is a subset of machine learning. People should stop trying to make ML/DL the solution to problems that might be more easily resolved by simple math. ML techniques have been in use for a long time, but deep learning is far superior to its peers.

An ML project needs data and a robust pipeline to support the data flows. And most of all, it needs high-quality labels. This last point highlights the need to get to know data. To label, it needs to understand the data to some degree. All of this needs to happen before starting throwing random data into a deep learning algorithm and praying for results.

As such, it would help to stop overselling the future of deep learning, machine learning, and artificial intelligence and instead, focus on the present need to better integrate human ingenuity with brute-force and machine-driven pattern matching.

Deep learning is essentially a way to do pattern matching at scale. Most importantly, deep learning has had limited success in particular areas only. These areas include reinforcement learning, adversarial models, and anomaly detection.

Some experts believe reinforcement learning involves developing AI models without providing them with a huge amount of labeled data. While deep reinforcement learning is one of the more interesting areas of AI research, it has limited success in solving real-world problems.

There have been several efforts to harden deep learning models against adversarial attacks, but so far, there has been limited success. Part of the challenge stems from the fact that artificial neural networks are very complex and hard to interpret.

Conclusion: It is important to remain tempered in our expectations of deep learning. As the world seemingly scrambles for The Master Algorithm one must keep in mind that deep learning is not machine learning; its a subset. While deep neural networks have their place, they wont solve all of humanitys woes. While deep learning is making waves, and deservedly so, keep in mind that it is but another effective tool to be used in appropriate situations. Even so, people will have opinions running the gamut from it being overhyped, to being the solution to every problem they will ever experience, to somewhere more moderate in between.

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Is Deep Learning a Real Big Thing! Or is it Overhyped Among Users - Analytics Insight