Bioinspiration is the name of a field of study that aims to understand the diversity of nature and use learning as inspiration to develop products. One of the technologies that came from bioinspiration is artificial neural networks (ANN).
In 1944, researchers at the Massachusetts Institute of Technology (MIT) published the first articles comparing the functioning of a neuron to the processing of data in a computer.
The development of this idea continued over the following decades and has now gained new prominence with the innovations of Artificial Intelligence (AI). However, talking about ANNs for laypeople is still a challenge due to the complexity of the subject. In this article, we'll help you understand what ANNs are and how they are used.
What are Artificial Neural Networks?
Artificial neural networks (ANN) are a data processing technique whose bioinspiration is the functioning of the human brain.
To better understand how they work, we first need to explain the structure of the human central nervous system. The brain is the basis of this system and within it there are billions of neurons that analyze and process the information received on a daily basis.
Neurons are distributed throughout the structure of the brain, so they can process different information simultaneously. All this at an extremely high speed, higher than any computer.
When a neuron transmits information to another, we call this movement a synapse. With each neuron activated in the path of the information, it becomes more refined and is converted into learning.
Artificial neural networks therefore seek to act in a similar way. The computer receives information, processes it through ANNs and transforms it into machine learning. This is what we call machine learning.
Learning Artificial Neural Networks
Simulating this learning model of the human brain is a way of training the computer. In the same way that a person needs to practice the piano to become a good musician, the machine also needs practice to deliver the desired results.
Machine learning can take place in three ways.
Supervised Learning
Think of the following analogy: a pastry chef has bought an industrial biscuit-cutting machine. He makes a dough, puts it in the machine and chooses which shape he wants the cookies to be cut into, be they circles, stars or hearts.
When the machine is switched on, the confectioner knows what his input product is and what his output product is.
This is how supervised learning works. The information is fed into the computer and the way it comes out is predetermined, so the machine only needs to adjust the processing.
Unsupervised Learning
Information goes into the machine, but there is no set pattern for how it comes out.
The idea behind unsupervised learning is to allow artificial neural networks to identify the properties of the data and do the processing. From this, learning takes place.
Hybrid Learning
As the name suggests, supervised and unsupervised learning take place at the same time. This simultaneous work is a way of processing a greater volume of information in a single period.
How Artificial Neural Networks are Used
Artificial neural networks are used to analyze complex information and provide the best possible result for users. Three ways stand out among the applications of ANNs.
Voice recognition
A person's voice is full of variables: volume, timbre, intonation and accent. Therefore, artificial neural networks are used to learn an individual's voice patterns and adjust electronic devices to execute commands more accurately.
Text Scanner
Another interesting application is the recognition of text on credit cards, bank slips or documents. Artificial neural networks are used to read the characters and transcribe the text into digital form.
Financial Market
One of the most complex applications of artificial neural networks is in the financial market. Companies use ANNs as a way of making predictions and assessing risks in an objective way, removing the subjectivity of opinions from the equation.
Another benefit of using artificial neural networks is the ability to analyze a large amount of data - from different sources - in a single piece of software. This makes it possible to obtain more accurate insights, which is what every financial market operator wants!
The Future of Artificial Neural Networks
ANNs are a technology that is constantly being developed because they are not yet capable of getting every situation right. Research in this field aims to improve processing and machine learning.
However, producing something that has the same capacity as a human brain remains aspirational. To get closer to the organ's power and speed, researchers still have a lot of work to do.
Another point worth highlighting about innovations in ANNs is the fact that they are being developed on different fronts. On the one hand, artificial neural networks help to build more creative, generative artificial intelligences, on the other, they are the future of financial analysis.
This technology is full of possibilities and that's what makes it such an interesting subject, even if it is technically complex.
Conclusion
Artificial neural networks are like the bricks that build a house. In this case, they are the foundation of many of today's artificial intelligences. But this house needs more time to be completed.
Now you know what ANNs are, how they work and how we are using this technology today to make AI even more powerful. Whether for professional purposes or to perform everyday tasks, artificial neural networks are an incredible innovation.
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