"Increased reliability" with AI and ML in automated testing means:

1. _Predictive maintenance_: AI and ML predict potential failures, enabling proactive maintenance.

2. _Fault tolerance_: AI and ML enable systems to adapt and recover from faults, reducing downtime.

3. _Error detection and correction_: AI and ML detect and correct errors, improving system reliability.

4. _Automated testing and validation_: AI and ML automate testing and validation, ensuring system reliability.

5. _Root cause analysis and remediation_: AI and ML identify and address root causes of issues, improving reliability.

6. _Real-time monitoring and alerting_: AI and ML enable real-time monitoring and alerting, reducing downtime.

7. _Improved mean time between failures (MTBF)_: AI and ML increase MTBF, reducing the frequency of failures.

8. _Improved mean time to recovery (MTTR)_: AI and ML reduce MTTR, minimizing downtime and improving reliability.

By increasing reliability, AI and ML in automated testing enable systems to operate consistently and efficiently, reducing downtime and improving overall performance.