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In 1943, a pair of scientists built a simple circuit to explain how neurons in the human brain work. The details warrant their own article, and their model gained acceptance within academic circles, leading to further development over the years. But as it would turn out much later, they did more than describe a complicated bodily function.

The model gave rise to the concept of ‘neural networks,’ artificial systems designed to mimic the functions of the human brain, down to decision-making. Decades of successes and failures led to the 21st century, where neural networks are no less a fundamental technology in multiple industries. There’s almost no app or piece of tech that doesn’t use one.

As these modern marvels continue to grow, it pays to know how they function. How do they make life less complicated for everyone, including the average person?

An Artificial Brain

Describing a neural network has a long and short way. In the latter form, a neural network is an artificial human brain, learning and making decisions as it goes. But putting it this simply won’t do neural networks much justice, let alone the technical minds behind them. For instance, a dev team like CNVRG explains one type of neural network in great detail.

Another reason is that, as many experts iterate, a neural network isn’t similar to an actual brain, at least structurally. As of this writing, there hasn’t been one whose hardware is arranged the same way as living neurons. Most neural networks are programmed simulations, a collection of equations and variables that determine the outcome based on the inputs.

Regardless, neurons as the basis for neural networks can’t be understated. Just as the brain has millions of neurons, a neural network can have hundreds to millions of units, divided into three layers: input, hidden, and output. Every unit in the network has a connection, allowing for near limitless outcomes based on the route the information takes.

Learn Like A Person

In the early decades, neural networks used to be simple: take some inputs, process them, and generate the output. The oldest of these, known as a perceptron, didn’t even have any hidden layer. Once developers added a hidden layer, they grew into feedforward types that became more reliable because they could now learn like a person.

Developers refer to this learning mechanism as backpropagation. They compare the results of the neural network and the results that it should’ve made and made adjustments to the unit’s weights. They backtrack as far as the input layer to ensure better accuracy when the neural network works with the same data in the future.

Today, they aren’t that simple anymore. The need for neural networks to be more reliable means creating more intricate patterns and new unit classes. The introduction of the deep feedforward model in the 1990s, which extended the number of hidden layers to more than one, opened new avenues in neural network development, most notably deep learning.

How Entrepreneurs Benefit

Recent market analyses predict that the neural network market will be worth over USD$150 billion by 2030, an increase of nearly 1,000% from 2020 (USD$14.35 billion). As the COVID crisis was a rude wakeup for businesses, their owners have started investing heavily in their IT. Similarly, research and development are barely showing signs of slowing down.

Due to these trends, neural networks have found extensive use in almost every industry. There isn’t even a need to look far for examples; personalized item recommendations, improving the business’s cybersecurity measures, and access to medical journals and records are some. Their ability to learn from an endless stream of incoming data is a blessing for these industries.

However, the financial sector—banks, consultancies, and trading firms—seems to bring out neural networks’ full potential. These companies benefit from the networks’ ability to detect potential risks through rigorous data analysis. They can predict, though without guarantees, where the market will move in the future, helping professionals make informed decisions.

As such, it isn’t unusual for businesses to employ more than one neural network in their strategy. That being the case, financial experts advise keeping the number between five and ten networks and combining them with traditional methods and principles. After all, neural networks are still relatively new to the scene, with plenty of room for improvement.

Conclusion

To say that neural networks are artificial brains is somewhat inaccurate, as they’re more like simulations than hardware. But they operate like human brains, taking in data to come up with possible outcomes and, more importantly, learn from the experience. They’re impressive enough to find use in businesses worldwide, no matter the industry.