What is AI?

Artificial intelligence allows machines to learn facts that enable them to make decisions. The system can make a data-based conclusion from the algorithms, which are not meant to address a specific problem. Artificial intelligence is mostly about using machine learning to train models with large amounts of data and then using the trained models to make predictions or generate the desired output. This description is oversimplified, and almost all of Artificial Intelligence fits into it.

Below we divide the machine learning approaches into the following categories/types depending on the learning types.

Supervised Learning

The machine learning task of learning a function that maps an input to an output based on example input-output pairs is supervised learning. The inferred process can map new examples when the algorithm examines the training data. Recommendations and time series prediction are some of the traditional problems built on top of classification and regression. Linear regression for regression problems is a well-known example of supervised machine learning.

Semi-supervised Learning

In semi-supervised learning, the dataset contains both labeled and unlabeled examples. The quantity of unlabeled examples is much higher than the number of labeled ones. The goal of a semi-supervised learning program is the same as that of a supervised learning program.

Also, for semi-supervised learning, uncertainty is added to the problem. However, when you add unlabeled examples, a large sample shows the probability distribution of the data we labeled. A learning program can use this additional information.

Unsupervised Learning

An unsupervised learning algorithm aims to create a model that takes a feature vector x as input and either transforms it into another vector or a value used to solve a practical problem. Unsupervised learning deals with data that does not have labels. Our models don’t have labels that show their desired behavior because there isn’t a trustworthy reference to judge their quality.

Reinforcement Learning

Reinforcement learning is a subfield of machine learning where the machine lives in an environment and can perceive the state of the environment as a set of features. The purpose of reinforcement learning is to learn a policy. A policy is a function that takes the feature of a state as input and outputs an optimal action to execute in that state like a supervised learning model.

Reinforcement learning solves a problem where decision-making is sequential and long-term goals, such as game playing, robotics, resource management, or logistics.

AI-Powered Test Automation Tools

Using basic automated testing, anyone (Developer, Tester, SDET) might use code that generates random inputs for each field. However, many of those tests will end up being wasted as they are repetitive or don’t match the business use of the application. In those cases, manually written tests are more valuable because the developer understands the correct business flow usage better.

Adding AI provides an excellent opportunity to enhance automated testing to work with business logic. I.e., users will place an item in their online shopping cart before they are taken to the page requiring an address, so they test an API with an address. Still, nothing is a waste of time. AI/ML automated testing could generate a dynamic set of input values that make sense and are a broader test of the applications with more confident results.

This testing method is an advanced software testing technique in which AI, ML, and DL technologies are integrated with automation testing. The process to improve the software’s quality and speed up software delivery. Suppose you want to unleash the power of AI and ML to enable robust automation. In that case, I genuinely recommend give a try to ACCELQ.