A subset of machine learning, deep learning studies the multiple levels of representation and abstraction to make sense of data such as sound, images, and text. This way it works towards bringing machine learning closer to artificial intelligence (AI). Regarded as one of the most significant technologies, it is a powerful tool for uncovering the hidden layers in the neural networks. A conventional neural network is likely to have a single hidden layer where weights are calculated with the aim of speech interpretation, facial recognition, or handwriting analysis. It typically has one input layer, one or few hidden layers, and one output layer. Unlike the conventional neural network, deep learning makes use of multiple layers to determine the relationship between variables. This is why the number of hidden layers in a deep learning network is more than that in a conventional neural network.
Deep learning systems help humans to extend their capabilities by providing expert assistance and improving the human & system interaction. One of the best examples of this is how deep learning has helped banks to concentrate more on customer interaction rather than carry on with the traditional transaction-based approach. It does this by providing tailored recommendations for the customers’ specific needs and tolerance for risks. Due to its immense usability in providing deeper insights, the analysts at Technavio predict the market for deep learning systems to grow at a steady pace in the forthcoming years, registering a CAGR of around 39% by the year 2020.
One of the one key benefits of deep learning software is that it enables better decision making, by bringing technology closer to humans. It also aids in recollecting previous interactions that assist in drawing conclusions independently while rapidly processing large quantities of data from highly diverse sources. Thus, it models human thought process to a great extent and can also interpret data that cannot even be detected by human senses, such as infrared radiation or sonar.
Five critical dimensions for evolution of deep learning systems
Source: Technavio
Artificial intelligence and deep learning
Now there is a certain section which believes that AI and deep learning are synonymous; whereas another group strongly believes that with the emergence of deep learning and its varied applications across industries, AI is “dead.” However, the fact lies somewhere in between these two assumptions.
To begin with, AI and deep learning are not synonymous. Deep learning is a subset of machine learning, which in turn is a subset of AI. Thus, deep learning is a part of AI and not AI itself. No doubt since its emergence, deep learning has taken AI to a whole new and much-improved level. This is because deep learning software can mimic voice and image recognition in a better manner. It can in fact accurately interpret the massive amount of data and thereby recognize objects and translate speech. Moreover, experts are of the opinion that with deep learning software, we have finally made AI rather ‘smart.’
Now coming to the presumption that AI is “dead.” This undoubtedly, is one of the most far-fetched conclusions, mainly because deep learning builds on the information gathered from other subsets of AI. Thus, for it to give useful and in-depth insights, it is imperative that a massive database be present. So, to believe in such an assumption is denying oneself from the complete reality.
With organizations like Google, Intel, IBM and Nvidia heavily investing in deep learning and improvising on their products, it just proves that AI is here to stay. Rather through deep learning and related subsets, it is poised to keep improving and blur the boundaries between human & machine interactions.
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