Over the past few years, technology has become very dynamic. It is fuelling itself at an ever-increasing rate. Computers are a prime component of this whole revolution. We often tend to think that computers can only decide on whether a statement is true or false. Such logical statements are then linked information required by businesses, traditional together to form a series of rules.
Given the high stakes and intense competition within all areas of industry, intelligent business decisions are more important than ever. A different approach was needed to give computers more 'human-like' abilities. This is where Neural Networks, AI and Machine Learning comes into the scene. Today, neural networks (NN) are revolutionizing the whole world and everyday life, bringing us to the next level in artificial intelligence (AI).
What are Neural Networks?
Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.
The original goal of the neural network approach was to create a computational system that could solve problems like a human brain. However, over time, researchers shifted their focus to using neural networks to match specific tasks, leading to deviations from a strictly biological approach. Since then, neural networks have supported diverse tasks.
Then came the era of the big data ecosystem and ss structured and unstructured data sizes increased to big data levels, people developed deep learning systems, which are essentially neural networks with many layers. Deep learning enables the capture and mining of more and bigger data, including unstructured data.
Why are neural networks important?
You may be wondering what can neural networks do for you? A better question is, what can’t it do? With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:
Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, the software uses adaptive learning to teach math and language arts.
Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.
They can learn and model the relationships between inputs and outputs that are nonlinear and complex; make generalizations and inferences; reveal hidden relationships, patterns and predictions; and model highly volatile data (such as financial time series data) and variances needed to predict rare events (such as fraud detection). As a result, neural networks can improve decision processes in various areas.
Real-World and Industry Applications of Neural Networks
Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the various fields where neural networks can be used:
- Credit card and Medicare fraud detection.
- Optimization of logistics for transportation networks.
- Character and voice recognition, also known as natural language processing.
- Medical and disease diagnosis.
- Targeted marketing.
- Financial predictions
- Robotic control systems.
- Electrical load and energy demand forecasting.
- Process and quality control.
- Chemical compound identification.
- Ecosystem evaluation.
- Computer vision
Final Thoughts
With all those strengths fueling the future of neural nets regardless of what your business goals are, there’s a good chance neural nets will be able to help you achieve them—if not now, then in the very near future. Despite the shortage of developers, companies and engineers are working constantly to refine their neural net efforts, which means we’re in store for a “golden age” of neural networks.
It’s hard to say whether neural net development will continue indefinitely or whether some new, more efficient technology will take its place, but either way, this breakthrough in the field of AI deserves your attention.