Systems | Development | Analytics | API | Testing

Before building artificial intelligence, it’s worth asking whether we truly understand natural intelligence. Just as early pioneers of flight studied the principles of aerodynamics and observed how birds fly, this session argues that progress in AI requires a deeper understanding of human intelligence and the knowledge that already exists across related disciplines.

Katalon supports the full testing lifecycle by using Studio to create tests, TrueTest to generate additional user-based tests, and TestOps to manage everything: from organizing, planning, and executing tests to analyzing results and tracking defects. Together, they ensure tests scale across teams and projects. — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

Just like humans, AI systems are shaped by how they are brought up. In the case of Large Language Models, this upbringing happens through data collection, training, and productization. At each of these stages, bias can quietly enter the system through the data we select, the way models are trained, or the assumptions embedded into the final product. These biases, whether intentional or accidental, influence how models think, respond, and interact with users in the real world.

Generative AI can produce tests instantly, but speed alone does not guarantee quality or safety. Without proper validation, AI-written tests can become brittle, redundant, or misleading, creating a false sense of coverage. This session looks at the risks of relying on AI-generated tests without the right controls in place.