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What is a Neural Network?

Have you ever wondered how Google Assistant or Apple’s Siri responds to your commands? Do you encounter adverts on e-commerce websites for things you previously searched for? If you’re curious about how it all works, Artificial Intelligence (AI) is used on the rear to provide you with a rich user experience. And Neural Networks are the key to teaching robots to respond to commands in the same manner that people do.

What is a Neural Network?

Neural networks are computer systems made up of interconnected nodes that function similarly to neurons in the brain. They can discover hidden patterns and correlations in raw information using algorithms, organize and categorize it, and learn and improve over time.

Additionally, a neural network is a hardware or software system that mimics the behavior of neurons in the brain. Neural networks—often known as artificial neural networks—are a type of deep learning system.

“The neural network is this kind of technology that is not an algorithm, it is a network that has weights on it, and you can adjust the weights so that it learns. You teach it through trials.” ~ Howard Rheingold

Put simply, neural networks are a kind of AI that attempt to mimic the way human brains make connections and find patterns.

A Brief History of Neural Networks

Neural networks are a hot topic right now, but they have been around for almost 80 years.

1943:

Warren McCulloch and Walter Pitts invented the first neural network in 1943. (That’s before we coined the term “Artificial Intelligence” in the 1950s!) They published a seminal paper about how neurons could work. They represented their theories using electrical circuits to create a basic neural network.

In two areas, this groundbreaking framework laid the groundwork for neural network research:

1975:

Kunihiko Fukushima developed the first genuine multilayered neural network in 1975, accelerating AI development.

The goal of the neural network technique was to develop a computing system that could solve problems in the same way that a human brain could. Consequently, as time went on, researchers began to focus on employing neural networks to fit certain tasks, diverging from a solely biological approach. Neural networks have since been used to support a variety of tasks, spanning face recognition, voice recognition, machine translation, social network filtering, board, and video gameplay, and medical diagnosis.

Humans developed deep learning systems, that are fundamentally neural networks with multiple layers when the number of organized and unstructured data grew to big data levels. Deep learning makes it possible to acquire and mine greater and larger amounts of data, including unstructured data.

What is the significance of neural networks?

Neural networks are well suited to assisting people in real-world scenarios with difficult problems. They can learn and simulate complex and complicated interactions between inputs and outputs, generalize the results and inferences, uncover hidden correlations, patterns, and predictions, and model highly volatile data (such as economic and financial time series data) and variations to anticipate unusual events (such as fraud detection).

As a result, neural networks can help with decision-making in sectors like;

  • Controlling the process to ensure quality.
  • Identification of chemical compounds
  • Detection of credit card and Medicare fraud.
  • Transportation network logistics optimization.
  • Natural language processing, commonly known as word and speech recognition.
  • Diagnosis of medical conditions and diseases.
  • Marketing with a specific audience in mind.
  • Financial forecasts for stock prices, exchange, options, and futures.
  • Assessment of the ecosystem.
  • Control systems for robots.
  • Photographs and videos are interpreted using computer vision (for example, in medical imaging, robotics, and facial recognition).
  • Forecasting of electrical load and energy consumption.

Industries that Use Neural Networks

So who actually uses neural networks and how? Neural networks can be a useful tool is almost any industry. Here are just a few of the key industries that rely on neural networks and the technology that we have developed using them.

Healthcare:

Neural networks are used by health and life science research companies to offer predictive diagnosis, biomedical imaging, and monitoring systems.

Retail: 

Neural networks are used in the retail industry to operate interactive chatbots, improve and enhance customer insights, and conduct network research.

Manufacturing:

Neural networks are used by energy and manufacturing firms to streamline distribution networks, automate detection accuracy, and predict energy demands.

Banking:

To detect and prevent fraud, execute credit analysis, and automate financial consulting services, banks use neural networks.

Public Sector:

Smart cities, security intelligence, and facial recognition are all supported by neural networks in the public sector.

What is the future of Neural Networks?

We may see additional improvements in neural network applications in the near future, given the rapid pace at which AI and machine learning are being used by businesses today. AI and machine learning will provide people all over the world with a plethora of customized options. For instance, all mobile and web applications strive to provide you with a more personalized experience based on your search history, which neural networks may help with.

We can indeed foresee fascinating algorithmic discoveries to help learning systems. 

In the future, neural networks will be faster, and neural network tools will be included in every design surface. We already have a little neural network that can be plugged into a low-cost processing board or even your laptop. Focusing on the hardware rather than the software would make such gadgets even faster.

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