How neural networks work
Neural networks in machine learning are a subset of algorithms that make the AI magic happen. The way they operate is very similar to the inner workings of the human brain, with artificial neurons as the core. The role of neural networks is to process large data sets and, like the brain, play an important role in learning by compiling information.
Neural networks consist of three layers:
- Input layer
- Hidden layer
- Output layer
The first layer collects the data and sends it forward (each neuron from the input layer sends data to each neuron in the hidden layer). The second layer is a learning process where the links between neurons are searched. In the third layer, we learn the results of the analysis and the conclusions.
This simple layout conveys the idea of a potentially very complex system. The network can include an infinite number of neural layers. The learning process is very complicated and quite mysterious at the same time.
Unusual neural networks
The applications of neural networks include working with fragmented or corrupted databases. This task is much like trying to complete a puzzle with missing pieces or solving equations with many unknown variables.
Moreover, systems of this class don’t need constant access to data since they can “learn”, just like a human. People have three types of memory:
- sensory, which receives information through senses and transfers it to short-term memory
- short-term, which can be compared to a scratch pad for temporary storage of processed information
- long-term, where knowledge is held indefinitely
In terms of reproduction of acquired knowledge, the human brain is falling far behind the perfect memory of artificial intelligence.
Today, neural networks are experiencing a renaissance, mainly because of their use in analyzing ever-increasing data sets and the continuous influx of new variables that other programs can’t handle. Neural networks have also found use in decision-making situations where people would be overwhelmed with data. Who knows – maybe we will soon be hiring artificial intelligence based on neural networks instead of people.
Machine learning: neural networks in action
Neural networks form the basis for machine learning – the process of acquiring and processing information by machines. For machine learning to be effective, it has to grow in stages and be enhanced with new sources of data. I’ll use an example to illustrate how machine learning systems collect data and come to conclusions.
Use case: automated cars
Stage 1. The system collects data through pressure sensors, checking the level and temperature of the oil and other parameters of the car.
Stage 2. The data on other vehicles is collected when testing such factors as:
- driving styles
- climatic conditions
- level of resource consumption of individual components in the car
- air pollution levels
Stage 3. After data analysis, the system picks key parameters to build a classification model.
Stage 4. It’s time to create tests for a car’s behavioral models in correct and incorrect operation. The tests are then carried out under different weather and lighting conditions.
Stage 5. Based on collected data from the environment around the car and its components, the algorithm will continue to learn and improve its performance. The algorithm undergoes real-time tests to confirm monitoring and alerts.
The choice of cars as an example was no coincidence. This field has recently received much attention due to its revolutionary advances. Names like Google, Tesla and Stanford University are mentioned in the context of driving automation. Such technological progress is of course highly involved with machine learning and other aspects of artificial intelligence through the use of robotics and mechanics.
What’s fascinating about these advances is that complex machines like cars can use programming that works on distinctions detected by sensors instead of fixed algorithms and rules. The programming learns how to detect danger and deal with threats on the road. The more data it collects, the more reliable it is.
How do we know that a machine learning algorithm works?
Now that you know the stages of creating a machine learning system for cars, let’s find out how to verify that the algorithms actually work. Do they effectively avoid threats and anticipate dangers on the road?
Staying with the example of automated cars, they would have to be monitored. Their data is constantly updated to not only to deal with different situations on the road, but also to track changes due to aging of the vehicle itself. As the car gets older, the less it resembles the car that the original programming was designed for. This underlines the importance of a system that is able to learn and adapt.
Machine learning is essential
Now let’s turn to another example that demonstrates how machine learning is essential to the development of intelligent systems. Imagine that a local government wants to increase public safety by modernizing the lighting throughout a complex road network.
The many variables of the road network make it impossible to create a system based on fixed conditions or rigid rules of action. In this case, machine learning has the flexibility to analyze various events on the road and suggest a solution – even ones not used before. Machine learning can collect data on any number of factors and variables that supply data that can be analyzed for solutions.
Making the world a better place?
Machine learning is not so much a system as a way of managing objects and sometimes whole buildings or cities. It’s a way to connect things with people and to use those things for support and to make our lives easier. Most importantly, systems of this type or software based on machine learning are tailor-made. We can finally feel special because someone — or something — learns our preferences, tastes and habits and adapts perfectly to them.