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Some techniques for black box testing of AI include input/output testing, where various inputs are assessed for expected outputs; boundary value analysis, focusing on edge cases; and equivalence partitioning, which groups inputs to reduce test cases. Additionally, scenario-based testing evaluates AI behavior in real-world situations.

One disadvantage of black box testing is that it doesn't provide insight into the internal workings of the application, making it difficult to identify specific issues or bugs. Additionally, it may overlook edge cases or system interactions that could lead to failures, resulting in incomplete test coverage.

Static black box testing involves evaluating the software's documentation and requirements without executing the code, focusing on design and specifications. Dynamic black box testing, on the other hand, tests the software by executing it, assessing its behavior and performance in real-time. Both methods provide valuable insights during testing.

According to QA specialists, neither white box testing nor black box testing is universally better; both serve different purposes. White box testing is effective for finding logical errors and code coverage, while black box testing excels in validating user requirements and functionality. A combination of both is often recommended for thorough testing.

Functional testing is a black box testing technique. It focuses on evaluating the software's functionality against specified requirements without any knowledge of the internal code or logic. Testers assess whether the application behaves as expected when given specific inputs, ensuring that all features work correctly from the user's perspective.

Automation testing can be both black box and white box testing, depending on how it's implemented. 

- Black Box Testing: When automated tests focus on verifying the functionality of the application without knowledge of the internal code, it's considered black box testing. 

- White Box Testing: If the automation involves testing the internal logic and structure of the code, such as unit tests, it falls under white box testing.

So, it really depends on the context and the specific tests being automated!

Black box testing has several important uses, including:

1. Functional Verification: Ensures that the software meets its functional requirements and behaves as expected from a user's perspective.

2. User Acceptance Testing (UAT): Validates the application with real users to confirm it meets their needs and expectations.

3. Regression Testing: Checks that new code changes do not adversely affect existing functionality.

4. Integration Testing: Evaluates the interaction between different components or systems to ensure they work together correctly.

5. Performance Testing: Assesses the application's performance under various conditions, such as load and stress.

6. Security Testing: Identifies vulnerabilities and ensures that the software is secure from threats.

Overall, black box testing helps ensure software quality and usability without needing to understand the underlying code.

White box testing differs from black box testing primarily in its approach. White box testing examines the internal code and logic, focusing on code coverage and structure, while black box testing evaluates functionality based on specifications, without knowledge of the internal workings. Each serves distinct purposes in the testing process.

Demerits of black box testing include limited insight into internal code structure, potentially missing bugs related to system interactions. For white box testing, drawbacks involve its complexity, requiring in-depth knowledge of the code, and the possibility of overlooking untested paths, leading to incomplete coverage and missed defects.

Black-box testing focuses on evaluating a system's functionality without knowledge of its internal code or structure, emphasizing input-output behavior. In contrast, white-box testing involves examining the internal workings of the application, assessing code logic, and ensuring all paths are tested. Both methods are essential for comprehensive software quality assurance.