A User Guide to Neural Networks for the Business
A User Guide to Neural Networks for the Business
In numerous fields, from medicine to exploring space, neural networks will have an indispensable role. Overall, machine learning powered by ANNs is being put to good use.
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Today, the concept of neural networks doesn’t raise a lot of eyebrows. When someone mentions working with neural networks, people rarely imagine scientists conducting neurological or psychological research. Instead, we think of a clever system powered by artificial intelligence. It feels like we’re already in the future, where machines can learn and provide a fantastic output of new opportunities. With the current massive increase in available data and computational power, artificial intelligence, and artificial neural networks, in particular, can be applied to the vast areas of everyday life. What does that really mean, though, for business owners? Let’s have a closer look at neural networks in business.
What Are Neural Networks?
It’s hard to look at the application of neural networks as a stand-alone phenomenon. Artificial neural networks (ANN) relate to the machine learning (ML) domain which is a subfield of the bigger concept of artificial intelligence (AI). To be more precise, neural networks are a class of machine learning architectures and algorithms.
Neural networks are called artificial as opposed to the biological neural networks, the interconnected web of neurons in our brains that transmit information by forming connections with each other. An artificial neural network consists of multiple layers of simple units called nodes — artificial siblings of neurons, interconnected by valuable strings that transmit the output to each node from the units of the previous layer.
How Do Neural Networks Work?
An artificial neural network contains at least three, but often more, layers of nodes. The input layer is followed by one or several hidden layers and an output layer. ANNs work as predictors: they forecast a Y value or a set of Y values for a given set of X values. The input layer consists of the data given to generate the prediction. The layers are called hidden because they don’t interact directly with either the input or the output but work on the data transformed by the previous layers. The hidden layers then link to the output layer that displays the final prediction produced by the algorithm.
With colossal computing power and an impressive amount of data available nowadays, machine learning algorithms can work on raw and very abstract data. When a neural network is running, it takes a large dataset, splits it into tiny fragments, and disperses those information fragments among all the nodes within it. Artificial neurons accept the received data, operate on it using the stored value, and finally pass on the results to the output. All the outputs are aggregated to reach the desired conclusion.
Here’s another analogy to human intelligence: the neural network AI can learn by example. If the network is still in training, the received result will be evaluated for correctness. Then, the value of all nodes will reduce the values of the wrong ones and increase the ones that were correct. In this way, ANN improves its predictions by minimizing errors with more training examples.
It might seem that neural networks are a bit too complex to get a good grasp of them. However, provided that businesses can measure the effectiveness of ANN and have the ability to improve their performance, one can be totally okay just knowing the basics of machine learning.
Neural Networks and Their Business Applications
A good thing about applying neural network for businesses is that the challenges they solve can be analogous across industries. It’s highly possible that an algorithm that distinguishes a cat from other pets can also identify a pedestrian in front of a car.
Some of the ANN pioneers in business have already tapped into the best capabilities of neural network algorithms. Amazon uses ANNs to power their recommendation engines, Microsoft uses them for machine translation service, Facebook works with deep neural networks for facial recognition, and, of course, Google has been already using them across many of its products and services like Google Translate, the Google search engine, YouTube, Google Cloud Video Intelligence, Gmail, and others.
Putting neural network apps to work has enabled data scientists to crack a number of difficult cases, like pattern identification, speech recognition, and natural language generation. Now, let’s have a closer look at several ways to apply neural networks in business.
Pattern Recognition in Images
With the help of ANNs, businesses now witness a big leap in machines’ ability to recognize images. No wonder there are so many image recognition startups on the market now. Mainly, it’s because almost all industries require clever image-based apps. Design, architecture, mechanical engineering, the beauty industry, and many others would greatly benefit from using pattern recognition in different types of images.
There are book recommendation engines, film recommendation engines, music recommendation engines, and anything-whatsoever recommendation engines now. Netflix, Google, Amazon, Walmart, and dozens of other online businesses know that the use of recommendation engines means loyal, correctly targeted clients, and increased sales. What if these recommendation models were even more intelligent and powered by machine learning with the use of neural networks? That’s right: more loyal customers and a greater increase in sales.
Preventing Customer Churn
In online sales and marketing, there is an index called "churn rate," which is a percentage of clients who discontinue using a given service or product. For many online businesses, decreasing churn means a positive impact on revenue growth rate. However, when we speak about thousands or millions of customers, it becomes hard to predict why separate categories leave unsatisfied. Provided that a company has rich customer data, machine learning models with ANNs can help discover complex usage patterns and identify churning customers. With an expert data science team, business owners can now estimate the value of such ML models and their business impact on churn prevention.
Even though forecasting of financial data is a more than complex task for anyone, it’s an especially niche and challenging area for startups. The main issues with, for example, predicting stock prices is connected to the availability of training data and investigation of the correct historical range to train. We believe that with time, stock prices prediction, risk management, implementation of trading strategies, and other trading-related situations will be more easily tackled using machine learning algorithms.
The introduction of machine learning to translation services is comparatively new, which means that there are not that many players on the market except the above-mentioned giants like Google and Microsoft. Previously, machine translation was based on the historical language patterns and paradigms — but a system that can train itself is a real game-changer. ANNs are not limited like old machine translation models are since they use larger volumes of language patterns and can identify more connections between those patterns through constant training.
Artificial neural networks can handle complex patterns in data, which are useful in uncovering fraud. Machine learning systems are now capable of quickly learning and understanding anomalies in data patterns that differ from typical or historical transactions. It’s a mutually beneficial technology both for financial institutions and their clients.
In numerous fields, from medicine to exploring space, artificial neural networks will find their indispensable role. But will ANN help artificial intelligence conquer our world and replace human brains? Not yet. Here’s a simple example.
Teaching a machine to distinguish a dog from many angles could require thousands of images covering dozens of perspectives. Humans, even children, don’t need such an extensive training to learn how to recognize a pet. However, the brightest minds are doing their best today to fix the weaknesses in machine learning systems that limit their effectiveness. So, the outcome might be terrific.
Neural networks, being a part of the huge notion of AI, are an attractive but arduous business area. Even though they have been applied, mostly in pattern recognition, by different companies (including Google), now, more and more organizations are investing in artificial neural networks, looking at them as a solution to numerous business problems. From digital marketing to trading, translation services, and healthcare, machine learning powered by ANNs is being put to good use. The main success point here is to not be intimidated by its mathematical complexity and its status as a somewhat futuristic concept.
Published at DZone with permission of Mariana Diachuk . See the original article here.
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