History of Deep Learning: Deep Learning has dramatically improved the state-of-the-art in different machine learning tasks such as machine translation, speech recognition, visual object detection and many other domains such as drug discovery and genomics (LeCun, et al., 2015).
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In addition to that, researchers are extending capabilities of deep learning beyond these traditional tasks such that Osaka et al. use recurrent neural networks to denoise speech signals, Gupta et al. use autoencoders to discover patterns in gene expressions, Gatys et al. use a generative adversarial network to generate images, Wang et al. use deep learning to allow sentiment analysis from multiple modalities simultaneously (Wang & Raj, 2017).
According to the Artificial Index 2021 report, peer-reviewed AI publications are growing exponentially. (Zhang, et al., 2021).
However, one must understand how deep learning has evolved over the years and formed the current models. The history of machine learning goes back to 300 BC, Aristotle and it is seen as starting point by Associationism (Wang & Raj, 2017).
As seen in Table 1, the progress in AI has stalled around the 60s and 70s. And there were some applications of machine learning such as machine translation which were not successful at all, especially US Navy-funded machine translation study from Russian to English. Minsky and Pappert also proved that Rosenblatt’s perceptron was only capable of solving linearly separable problems, even though they knew multiple layers could solve that, there was no algorithm at that time to train the network. Today that algorithm is known as back-propagation.
In 1973, the Lighthill report was published which gave a very pessimistic prognosis for many core aspects of the field such as “In no part of the field have the discoveries made so far produced the major impact that was then promised” (Lighthill, 1973). After this report, many funding resources were cut and a quiet period began which is known as the first AI winter. […]
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