What Are Neural Networks?

A branch of machine learning, neural networks (NN), also known as artificial neural networks(ANN), are computational models — essentially algorithms. Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon.

The most groundbreaking aspect of neural networks is that once trained, they learn on their own. In this way, they emulate human brains, which are made up of neurons, the fundamental building block of both human and neural network information transmission.

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“Human brains and artificial neural networks do learn similarly,” explains Alex Cardinell, Founder and CEO of Cortx, an artificial intelligence company that uses neural networks in the design of its natural language processing solutions, including an automated grammar correction application, Perfect Tense. “In both cases, neurons continually adjust how they react based on stimuli. If something is done correctly, you’ll get positive feedback from neurons, which will then become even more likely to trigger in a similar, future instance. Conversely, if neurons receive negative feedback, each of them will learn to be less likely to trigger in a future instance,” he notes.

 

How the Biological Model of Neural Networks Functions

What are neural networks emulating in human brain structure, and how does training work?

All mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system. The human brain has a massive number of processing units (86 billion neurons) that enable the performance of highly complex functions.

 

How Artificial Neural Networks Function

ANNs are statistical models designed to adapt and self-program by using learning algorithms in order to understand and sort out concepts, images, and photographs. For processors to do their work, developers arrange them in layers that operate in parallel. The input layer is analogous to the dendrites in the human brain’s neural network. The hidden layer is comparable to the cell body and sits between the input layer and output layer (which is akin to the synaptic outputs in the brain). The hidden layer is where artificial neurons take in a set of inputs based on synaptic weight, which is the amplitude or strength of a connection between nodes. These weighted inputs generate an output through a transfer function to the output layer.

 

How Do You Train a Neural Network?

Once you’ve structured a network for a particular application, training (i.e., learning), begins. There are two approaches to training. Supervised learning provides the network with desired outputs through manual grading of network performance or by delivering desired outputs and inputs. Unsupervised learning occurs when the network makes sense of inputs without outside assistance or instruction.

There’s still a long way to go in the area of unsupervised learning. “Getting information from unlabeled data, [a process] we call unsupervised learning, is a very hot topic right now, but clearly not something we have cracked yet. It’s something that still falls in the challenge column,” observes Université de Montréal’s Yoshua Bengio in the article “The Rise of Neural Networks and Deep Learning in Our Everyday Lives.”

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Bengio is referring to the fact that the number of neural networks can’t match the number of connections in the human brain, but the former’s ability to catch up may be just over the horizon. Moore’s Law, which states that overall processing power for computers will double every two years, gives us a hint about the direction in which neural networks and AI are headed. Intel CEO Brian Krzanich affirmed at the 2017 Computer Electronics Show that “Moore’s Law is alive and well and flourishing.” Since its inception in the mid-20th century, neural networks’ ability to “think” has been changing our world at an incredible pace.

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A Brief History of Neural Networks

Neural networks date back to the early 1940s when mathematicians Warren McCulloch and Walter Pitts built a simple algorithm-based system designed to emulate human brain function. Work in the field accelerated in 1957 when Cornell University’s Frank Rosenblatt conceived of the perceptron, the groundbreaking algorithm developed to perform complex recognition tasks. During the four decades that followed, the lack of computing power necessary to process large amounts of data put the brakes on advances. In the 2000s, thanks to the advent of greater computing power and more sophisticated hardware, as well as to the existence of vast data sets to draw from, computer scientists finally had what they needed, and neural networks and AI took off, with no end in sight. To understand how much the field has expanded in the new millennium, consider that ninety percent of internet data has been created since 2016. That pace will continue to accelerate, thanks to the growth of the Internet of Things (IoT).

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For more background and an expansive timeline, read “The Definitive Guide to Machine Learning: Business Applications, Techniques, and Examples.”

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Why Do We Use Neural Networks?

Neural networks’ human-like attributes and ability to complete tasks in infinite permutations and combinations make them uniquely suited to today’s big data-based applications. Because neural networks also have the unique capacity (known as fuzzy logic) to make sense of ambiguous, contradictory, or incomplete data, they are able to use controlled processes when no exact models are available.

According to a report published by Statista, in 2017, global data volumes reached close to 100,000 petabytes (i.e., one million gigabytes) per month; they are forecasted to reach 232,655 petabytes by 2021. With businesses, individuals, and devices generating vast amounts of information, all of that big data is valuable, and neural networks can make sense of it.

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Attributes of Neural Networks

With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:

• Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.

• Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.

• Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.

• Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.

• Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.

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Tasks Neural Networks Perform

Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:

• Classification: NNs organize patterns or datasets into predefined classes.

• Prediction: They produce the expected output from given input.

• Clustering: They identify a unique feature of the data and classify it without any  knowledge of prior data.

• Associating: You can train neural networks to "remember" patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

Neural networks are fundamental to deep learning, a robust set of NN techniques that lends itself to solving abstract problems, such as bioinformatics, drug design, social network filtering, and natural language translation. Deep learning is where we will solve the most complicated issues in science and engineering, including advanced robotics. As neural networks become smarter and faster, we make advances on a daily basis.

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Real-World and Industry Applications of Neural Networks

As an August 2018 New York Times article notes, “The companies and government agencies that have begun enlisting the automation software run the gamut. They include General Motors, BMW, General Electric, Unilever, MasterCard, Manpower, FedEx, Cisco, Google, the Defense Department, and NASA.” We’re just seeing the beginning of neural network/AI applications changing the way our world works.

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Source: Smart Sheet