00:00 Introduction and background of Daniel Nilsson

01:55 The intersection of AI and testing

03:32 The toolbox of testers in AI

05:31 The need for scrutiny around data

08:09 The challenge of garbage in, garbage out in software delivery

13:15 Taking small steps and incremental gains with AI

17:20 Being solution agnostic and understanding the problem

20:47 The importance of embedded systems testing

26:37 The need for evidencing for critical systems

29:20 Fostering a culture of continuous learning

36:06 The role of leaders in cultivating a learning culture

• Questioning is a valuable tool for testers to improve the quality of AI testing.

• Proper data sets are crucial for training and validating AI models.

• The problem of garbage data in software delivery needs to be addressed.

• Trust issues and the need for education are barriers to the widespread adoption of AI in testing.

• Taking smaller steps and being solution agnostic can lead to more successful AI implementations in testing.

• There is a lack of focus on embedded testing in the testing community. Embedded software testing is often overlooked in the testing community, despite its importance in safety-critical systems.

• Rigor and evidence-based testing, common in embedded software testing, can benefit other application-style testing.

• Creating a culture of continuous learning and curiosity is crucial for testers to stay ahead and innovate.

• Test Scouts is an example of an organization that promotes continuous learning and provides mentorship to testers.