Neural Networks: understanding how machines think
Have you ever heard of neural networks? Do you think that it is merely a biological term used to denote some connections in the human brain? Today, neural networks are related not only to biology but also to computer science. They are defined as a sequence of neurons that are interconnected by synapses. These connections help machines process, analyze, and memorize different types of data.
Being a machine interpretation of the human brain, a neural network is used for performing many activities and completing different tasks that earlier were done by humans only. Today, computers are able to see and hear as well as people can. The implementation of artificial technology in different spheres of our life facilitated the fast solution of complex issues that require analytical thinking skills similar to those that a human brain has.
Neural networks can perform the following tasks
- Classify data by a certain set of parameters. For example, if you should decide which candidates fit better your job, you can use an AI-based resume scanning technology. Your work will be done by a neural network. It will analyze such information as experience, expertise, age, and other parameters to select the best-suited candidates.
- Predict the next steps for different spheres of life. For example, based on the information processed by predictive analytics tools, people can make data-driven decisions which can significantly influence their business.
- Recognize images, identify scenes, objects in images. It is currently the key function of neural networks. For example, Google can recognize exactly which things are present on images and Siri can understand what you are saying. It is presumed that in the next few years, these applications will become even more intelligent as this technology is being developed rapidly.
- Recognize speech and turn what you say into written text. This is widely used in chatbots. Nowadays, people are able to use apps on their phones that can give a quick response to our questions or execute our commands.
- Process natural language and work with the text the way humans do. For example, with the help of neural networks, people are able to extract meaning from texts, generate readable texts that are free of grammatical mistakes, well-structured, etc.
The appearance of artificial intelligence, its accuracy, and availability allows companies to use it for their business purposes. For example, insurance product development companies use AI-based tools to read claims from customers, understand how difficult the claim is, and give a piece of advice on how to deal with the claim. The employees, in their turn, only need to do a quick check before approving the advice. AI implementation can save a lot of time and improve the quality of the work.
While neural networks are good for things like image recognition, other types of machine learning may be more useful for different tasks like defining your favorite music. Google claims that its music app will find you the music you want to listen to. Google analyzes your previous playlists. If you are not satisfied with the results, the machine will regard it as a failure. But if you choose one of the suggested lists, it will mark it as a success. The more you use the app, the better the offers will be. Machine learning is a more vague concept than neural networks, but it also implies that the software you use will rely on your feedback to improve your performance.
If you consider using neural networks in your business or software product development, the list of neural network benefits below will come in handy.
- A neural network can perform a task that a linear program cannot.
- When some of the elements in the sequence of networks fail, they can continue doing their task seamlessly.
- Neural networks are parallel structures which function the same way as the human brain.
- Every neuron influences every other neuron so contextual data is dealt with naturally.
- Neural networks are able to learn and generalize their knowledge.
- They are fast, flexible, and easy to maintain.
- Neural networks can provide highly accurate results.
- Neural network models can be easily updated. It means they are suitable for a dynamic environment.
Neural nets are good alternative tools and a complement to statistical techniques when data are multivariate with a high degree of interdependence between factors, noisy or incomplete, or when many hypotheses are to be pursued and high computational rates are required. The can become a powerful decision-making tool for your business.