"Owls have incredible eyesight that allows them to see at night and accurately judge distance, this combined with their super precise hearing enables them to pinpoint minute details making them formidable predators."
Developing an AI-based system or incorporating it into a website or mobile app presents some unique challenges. The testing of Artificial Intelligence or Machine Learning systems requires a completely different approach due to their non-deterministic nature.
Let's ensure your AI displays the right result!
Good AI solutions are built on good-quality data. Incorrectly labelled data, biased data, or inappropriate data variety can significantly impact the training of the AI model and cause incorrect outputs. Our QA team can methodically review the data sets and ensure clean and valid data is created.
Valuable insights and feedback to enable effective tuning of the AI algorithm via data modification and hyperparameter tuning. Our AI Testers will analyze the AI model’s prediction result based on a number of established AI metrics such as; Accuracy, Precision, Recall, Sensitivity, F1-Score etc.
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A range of AI testing activities are available to ensure the end user's experience is positive. From testing the AI’s prediction performance and API testing, right through to the implemented product's all important functionality, compatibility, accessibility and usability.
Ensuring all test data is correctly labelled to ensure effective training data sets.
Comparing two versions of the AI system to identify the better performer.
Testing to ensure the functionality matches that specified in any requirements.
Ensuring voice commands are recognized and the expected action is performed.
Making sure the intended overall end-user experience will be a positive one.
Ensuring your AI product is accessible to everyone regardless of their abilities.
Testing an NLP system against functional and non-functional requirements.
Creating a detailed set of functional test cases that be reused at any stage of the development.
Cleaning, transforming and augmenting to provide the best possible training data.
Making sure the AI system performs as intended in prediction accuracy and speed.
Creation of AI input tests based on research as to what the targeted end-user may enter.
Ensuring the AI implementation operates as intended on a variety of browsers & devices.
