Artificial intelligence (AI) has become a part of our everyday lives, and major companies around the globe are using it, if they don’t depend on it entirely. AI software adaptability is quite impressive and seems endless, yet many concerns and doubts about it still exist. Some of the potentially frightening effects of AI have already emerged, such as the first fatal self-driving car accident, which happened back in 2018.
AI algorithms allow a piece of software to learn from patterns or features in data. More than that, AI-powered machines are capable of understanding and reacting though for now only limited to scenarios they are familiar with. That makes AI testing a crucial step for developers to determine a piece of software’s ability to adapt and perform properly.
What Is AI Testing?
AI testing refers to quality testing activities that aim to verify a piece of software’s accuracy by comparing test with desired results. Since AI behaviors in terms of decisions, actions, and criteria are not usually engraved in the code, experts cannot just develop the algorithm, add training data, and release the software. A human test is required, too, to make sure that the training data serves its purpose correctly without underfitting or overfitting. As such, the testing process is a core element of any AI development project that actually works.
How Do You Test AI?
AI testing is done using a part of the training data that is later kept aside to perform validation. If the algorithm, associated metadata, and hyperparameter configuration do not provide the results you expect when they work together, then the validation phase is not complete. You need to change the hyperparameter configuration or rebuild the model until it works.
After this part of the process is done, you use another portion of the testing data to ensure the model indeed serves its intended purpose. For example, you are a college student who decided to develop an AMA formatting generator that adapts and improves for a class. If, after writing the code, developing the algorithm, and feeding the training data, the tool does not serve the purpose accurately, then the validation phase failed, and you need to rebuild.
Why Is AI Testing Necessary?
IT companies and developers spend a lot of money and time developing various AI products, such as robots, academic tutors, interteam chat tools, automated financial investing robots, or smart assistants. If any of these products fails to do its task properly, it might cause serious problems that may lead to chaos. Imagine a healthcare application designed to provide cancer treatment advice. If the system recommends incorrect treatments, severe if not fatal consequences may occur.
To grasp the process of AI testing better, it’s important to understand how AI products learn. When they use a concept for the first time, AI systems build neural network layer “nodes” on it. Those node layers are connected to one another. Each node is a small calculator that processes data delivered by the top layers and sends back calculated results. Whenever the network gets an example in the training data, it systematically configures itself, so the different layers process aspects of the input. That means the final calculation generated from all layers is the result delivered to the network.
So, if the first concept was not inserted correctly or any of the nodes generated a different result from what you expect, then the final result will also differ. That may lead to creating “bias,” which we call “indecisiveness” in human intelligence. As such, the machine will not serve its purpose correctly, making AI testing an essential part of the development process.
The Bottom Line
Machine learning (ML) or AI adaptation does not differ from human learning. If you are trying to teach a child a lesson or explain a concept, and you fail to pass on the right information, the child’s intelligence may evolve with the wrong concept, which may cause indecisiveness. It is the same with AI. If you make even a single mistake in the algorithm, hyperparameter configuration, or associated metadata, the machine may not function as it should.
Therefore, whenever you decide to develop AI-based software, make sure to test and verify the algorithm and training data several times before sending the product to the customer or releasing it to the public.