Our apples and pears example also shows the limitations of ANNs: if networks are suddenly confronted with images of fire engines, they are unable to process them – or they incorrectly identify them as apples. ANNs must be retrained before they are able to process different data types. The data set needs to provide answers to a very narrow range of questions for neural networks to function properly.
A collection of neural networks
Neural networks can look and work very differently, and the one described above is just one of many. Recently, deep neural networks have come to prominence. These consist of several consecutive levels of neurons and are capable of greater accuracy, similar to our example. Other types include convolutional neural networks, which are used to analyse visual imagery and are already capable of identifying images more accurately than humans. Genetic neural networks take nature as their template. In these models, a number of ANNs compete with each other, instead of one constantly being adjusted using additional data. The best ones are retained, the worst are discarded.
Understanding neural networks better
It is not easy to understand exactly how artificial intelligence works, but there are many websites and projects that have taken on this task. We have made a list of them to help everyone who wants to find out more about the subject.
1. CGP Grey: YouTuber CGP Grey has created a short video that describes how genetic neural networks work in a simple and accessible way: How Machines Learn
2. 3Blue1Brown: This YouTuber explains in three parts how a deep neural network works. The explanation is more complex and involves some maths, but that is helpful to understanding how an image can be translated into a mathematical function, i.e. how AI ‘thinks’: What is a neural network?
3. Play around with neural networks: On http://playground.tensorflow.org/, anyone can have a go at neural networks in their browser. We recommend you read the instructions first as otherwise it is difficult to understand the purpose of each parameter.
4. Play around with neural networks II: Using your own photos, you can train an AI using the Teachable Machine Google app. Simply take three photos and the AI will learn to tell them apart and assign them to different outputs.
Want to find out more about AI? The Bremen.AI network is here to answer all your AI-related questions and provide an initial glimpse into the technology.