Could not catch up with the challenge then so doing it now.
For this I considered @pricilla09 article on ai and ml for testers in the #TestSigma Blog
Link to the article: Artificial Intelligence and Machine learning for Software Testers %
Here is what i learnt
Introduction to AI and Machine Learning (ML)
Artificial Intelligence (AI): Machines’ ability to perform tasks requiring human intelligence.
Machine Learning (ML): A subset of AI where algorithms learn from data and improve over time without explicit programming.
Origin: AI coined by John McCarthy (1955), ML by Arthur Samuel (1959).
Types of AI
Narrow AI: Performs specific tasks (e.g., Siri, facial recognition).
General AI: Matches human intelligence, can perform varied tasks (not yet achieved).
Super AI: Exceeds human capabilities (theoretical, not yet realized).
Categories of AI by Functionality
Reactive Machines AI: Reacts to inputs, doesn’t learn from the past.
Limited Memory AI: Retains past data for decision-making.
Theory of Mind AI: Simulates human understanding (still in development).
Self-Awareness AI: Understands its existence and has self-reflection (theoretical).
Types of Machine Learning
Supervised Learning: Algorithms learn from labeled training data.
Unsupervised Learning: Algorithms find patterns in unlabeled data.
Semi-Supervised Learning: Mix of labeled and unlabeled data.
Reinforcement Learning: Agents learn through interaction with the environment and rewards.
AI and ML in Software Testing
Automation: AI and ML can automate repetitive testing tasks.
Defect Detection: AI/ML can identify potential coding defects.
Test Case Generation: AI/ML can create test cases based on software requirements.
Real-Time Feedback: AI/ML can provide instant feedback to testers.
Anomaly Detection: AI/ML identifies unusual patterns or behaviors in software.
Benefits of AI and ML in Software Testing
Improved Accuracy: AI/ML detects patterns that humans might miss.
Increased Efficiency: AI/ML speeds up testing by automating tasks.
Reduced Costs: Automation can lower overall testing costs.
Improved Risk Management: AI/ML helps identify potential risks.
Enhanced Data Analysis: AI/ML analyzes large datasets for insights.
Challenges of AI and ML in Software Testing
Autonomicity: AI-driven testing might miss things requiring human intuition.
Cost: Initial costs for AI/ML technology can be high.
Bias: AI/ML can inherit biases from training data.
Security: Protecting sensitive information is crucial.
Complexity: Understanding AI/ML algorithms can be challenging.
Conclusion
AI and ML offer significant potential in software testing but come with challenges.
They should be used to augment, not replace, human testers.
Testers should leverage AI/ML while applying their domain knowledge and creativity to ensure effective testing.
I liked this article very much as it gave me a foundation to AI and ML. It’s also written in a simple manner amongst all articles on the internet.
Thank you for this task. 
It fills my knowledge gap.