Should Your Business Enter the Field of Deep Learning?

Deep Learning

Nothing has made the tech headlines recently quite like the news of Google transforming itself into a new holding company, Alphabet. Under this scheme, Google will be one of the many companies in Alphabet’s diversified portfolio. Other companies are involved in the manufacturing of self-driving cars, low-cost and sustainable eco-housing, and health technologies, among others. Prior to this, Google made another bold move by acquiring DeepMind Technologies. Google’s acquisition of the company is one of the most tangible pieces of evidence of the interest in artificial intelligence and deep learning. Indeed, deep learning has been thrown around casually these days but what does deep learning mean exactly?

Deep Learning vs. Machine Learning

Deep learning is a top emerging topic in artificial intelligence. It is considered a subcategory of machine learning and deals with the use of neural networks to improve services like face and speech recognition, language processing, computer vision and predictive analysis.

Over the last few years, it has become one of the most sought-after fields in computer science because of the advances it can trigger in many areas. Such areas include object perception, machine translation, and voice recognition which have been traditionally hard to master even for AI practitioners.

Deep learning, while often associated with machine learning, must not be confused or interchanged with the latter. To understand what it is, an understanding of the evolution of discipline under artificial intelligence is also necessary. Early forms of artificial intelligence efforts dealt with explicit forms of knowledge, programs that essentially command computer programs how to interact with users based on finite and pre-programmed facts, rules and parameters.

Machine learning evolved as an outgrowth of AI. In machine learning, the computer extracts knowledge through supervised experience. An operator gives the computer thousands and thousands of examples to help the machine “learn” while manually spotting for and correcting the computer’s mistakes. While certainly advantageous, machine learning has its limits. Apart from being massively time-consuming, machine learning relies a lot on human ingenuity to come up with the abstractions to enable the machine to learn. Thus, the programmer ends up encoding information about the task at hand manually and waiting until the machine learns on top of that.

Deep learning, on the other hand, takes this a step further. Deep learning researchers aim to get the system to engineer its own features as much as is feasible. It is largely unsupervised and involves creating large-scale neural networks that allow the computer to learn and “think: by itself, making the associations and recognizing the patterns without the need for direct human intervention. This is done with the use of deep learning algorithms which are more abstract representations of concepts. Just like the human mind, the system tries to come up with multiple types of representations with simpler features at the lower levels and higher levels of abstractions as the representations move up. This enables the machine to generalize more easily.

Going Deep Into Deep Learning

Top companies have already taken the plunge into deep learning. Facebook revolutionized its photo tagging system by using deep learning techniques in order to automate photo recognition and quickly identify faces and objects. Over 350 million videos and photos are uploaded on Facebook daily, and with their Ai program, they have reduced the burden of tagging each manually.

Voice recognition programs such as Google’s Now and Apple’s Siri also rely on deep learning mechanisms. According to Google-sponsored research, its shift to deep learning from prior models of Gaussian Mixture Models (GMMs) have reduced the error rates by a dramatic 25%.

Another area that stands to benefit from deep learning is natural language recognition. Deep learning researchers have made efforts to understand the meaning of text that people type or say. This understanding will translate into better user interfaces, advertisements, and targeted posts. This would add value in content immensely.

Deep Learning, Still a New Field

Computer science is a very young field, and within that is the science behind deep learning. Its implications in the different areas of business, technology, and health are great, the impact and possibilities are just as exciting as the profit to be made. Let’s take the case of the current popularity of big data analytics.

A lot of companies have opted to use big data to gain competitive advantage and knowing the actual preferences of their clientele. Fairly recently, analytics platforms powered by deep learning have been developed to help industries grow and adapt with their customers.  The incorporation of deep learning comes as an implement to level the proverbial playing field, allowing small players to healthily compete with big companies.

In order to capitalize on this impending market standard (as opposed to being merely a trend), however, the business owner must assess the need for deep learning applications in the business model and choose a reliable tech consulting company with a history of experience in various areas of data science and high-performance computing. Knowing that deep learning is the way to go is one thing, effectively joining the fray is another.

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