If I ask you what is better, a computer or a human brain? Most of you will jump on a chance to say you want a brain like a computer. But our scientists have been trying really hard over the past decades to make their computers more like human brains! You know how? It’s with the help of Neural Networks!
These are computer programs assembled from hundreds, thousands and even millions of artificial brain cells that can behave and learn exactly in a similar way to our human brains. Scientist have been working on this technology that is so powerful that it can learn, adapt and make decisions almost like a human brain.
Let’s take a closer look as we understand what exactly neural networks are. How do they really work?
What is Neural Network?
As I said, Neural Networks are the usual representations we make of the brain- neuron interconnected to other neurons that form a network. A simple piece of information that travels through this before resulting in a actual action, like “move the hand to pick this pencil.”
Computers and human brains have much in common, but also they are quite different. So, what will happen if you combine the best out of both of them? That is nothing but the power of computer and the densely interconnected cells of a brain- a perfectly useful neural network. The operation of a complete neural network is straightforward: One has to enter variables as inputs and after some calculations, an output is returned. A simple example here is feeding an image of a Dog should return the word “Dog”.
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Real and artificial neural networks
As we continue, it is worth clarifying some terminologies. Strictly speaking, neural networks produced this way are called Artificial Neural Networks or ANNs. These are of course different from the real neural networks we find inside brains.
Neural Networks normally contain three layers-
- Input Layers: Where raw data enters the system
- Hidden Layers: Where complex processing happens
- Output Layers: Here the final result or prediction emerges
The Learning Process
Imagine teaching a young child to recognize different animals. Initially, they might struggle to distinguish between a cat and a dog. But with repeated exposure and guidance, they learn to identify subtle differences. Neural networks work similarly; they learn by being exposed to numerous examples, gradually refining their understanding. Neural networks do not follow strict, pre-programmed rules. Instead, they adapt and learn through a process called training. This is how it works:
- Input Data: The network receives a massive amount of information.
- Processing: Each neuron processes the information, adjusting connection strengths.
- Prediction: The network makes an initial guess or prediction.
- Error Calculation: The difference between the prediction and actual result is measured.
- Adjustment: The network fine-tunes its internal connections to reduce errors.
This process is known as Backpropagation and it is like a continuous feedback loop. We as students always took practice tests, learnt from each mistake, and gradually improved our performance. Another example that you can easily relate to is ‘Bowling’. Think back to when you first learned to play a game like Bowling. You learn how to do skillful things like this with the help of the neural network inside your brain. Every time you throw the ball wrong, you learn what corrections you need to make next time.
Real-World Importance: Neural Networks in Action
Nasa
For the last two decades, NASA has been experimenting with a self-learning neural network called Intelligent Flight Control System (IFCS) that can help pilots land planes after suffering major failures or damage in battle. IFCS is designed to incorporate self-learning neural network concepts into flight control software to enable a pilot to maintain control and safely land an aircraft that has suffered a major systems failure or combat damage.
Handwriting Recognition
Handwriting recognition on a touchscreen computer or a tablet is one of many applications perfectly suited to a neural network. Each character (letter, number, or symbol) that you write is recognized on the basis of key features it contains (vertical lines, horizontal lines, angled lines, curves, and so on) and the order in which you draw them on the screen. Neural networks get better and better at recognizing over time.
Many of the things we all do everyday involve recognizing patterns and using them to make decisions, so neural networks can help us out in zillions of different ways. They can help us forecast the stockmarket or the weather, operate radar scanning systems that automatically identify enemy aircraft or ships, and even help doctors to diagnose complex diseases on the basis of their symptoms. There might be neural networks ticking away inside your computer or your cellphone right this minute.
Conclusion
All in all, neural networks have made computer systems more useful by making them more human. So next time you think you might like your brain to be as reliable as a computer, think again—and be grateful you have such a superb neural network already installed in your head! If you want to learn more about Data Analytics, subscribe to our CBDA Course.
