final essay questions for semester 2 grade 6 elementary school coding and artificial intelligence
example of end of semester summative essay questions (SAS) coding and artificial intelligence for grade 6 elementary school independent curriculum
The material tested in the end of semester summative essay questions (SAS) for the 6th grade elementary school coding and artificial intelligence (KA) subjects includes various basic concepts, the ethics of using technology, and the process of developing the model. The purpose of these questions is to train students’ understanding of how technology can be used responsibly in everyday life.
Here are some examples of questions and answers that can be a reference for students and teachers in preparing for the final semester exam.
1. Explain the main and highest objectives of the development of artificial intelligence, and why this goal should be held as the foundation of ethics by every user!
Answer:
The ultimate goal: The ultimate goal of the creation and development of artificial intelligence is to help and serve mankind and improve human well-being.
The foundation of ethics: This goal must be the foundation of ethics because artificial intelligence is a great power. If artificial intelligence is used to harm, hurt, or create injustice, then its main goal (human welfare) will fail miserably. Therefore, principles such as empathy and not hurting are very obligatory to be held to ensure artificial intelligence is always directed for good.
2. Imagine an online loan application using artificial intelligence to decide who is entitled to receive the loan. If this application always rejects registrants from certain regions because historical data shows that the people there are often late in paying, why does this include an algorithm bias? And mention the main solution to solve this problem!
Answer:
Causes of Algorithm Bias: Rejection cases in certain areas include algorithmic bias because artificial intelligence is trained with unfair or one-sided (bias) data. The decision of artificial intelligence is not based on the feasibility of the registrant’s individual, but based on the stereotypes or patterns of the past that are discriminatory against the entire group of the region.
The main solution: ensure data fairness. Developers must use more diverse and fair data when training artificial intelligence, and include other factors (other than location) that are more relevant and unbiased in the decision model.
3. Imagine you want to train artificial intelligence to recognize whether a video contains “sports” or “cooking activities”. Explain why you have to make sure the wide variation in the video training data!
Answer:
The importance of wide variation:
The system must ensure wide variations so that artificial intelligence not only “memorizes” the existing data, but actually recognizes the general pattern of the activity.
Examples of variations: Sports videos should include different types of sports (football, swimming, basketball), and cooking videos should include different types of cuisine (cutting, stirring, frying) and different lighting. Otherwise, artificial intelligence may only recognize “cooking” if there is only a stirring scene in the bright kitchen.
4. Explain the three main processes that a reliable artificial intelligence model must go through before it can be called an artificial intelligence model of production (ready to use)!
Answer:
Three main processes of developing artificial intelligence models:
1) Training: learning model of thousands or millions of labeled input data (high quality, large number, wide variation) to recognize patterns.
2) Testing: the model is tested for its ability with new data (which has never been seen) to see how accurate the prediction is.
3) Validation (Validation): The model is retested with different data to ensure it does not just “memorize\” training data. If the results are good, he is ready to become a model of artificial intelligence production.
5. Imagine you created artificial intelligence that uses a camera to help robots sort plastic bottle waste. Explain how cameras and artificial neural networks work together in this artificial intelligence system!
Answer:
Cooperation between cameras and artificial neural networks:
Camera: serves as a “eye” or sensor for artificial intelligence. The camera captures the visual data of a plastic bottle (color, shape, texture, condition) and converts it into digital data.
Artificial Neural Network (ANN): Visual data from the camera is sent to the ANN (artificial brain artificial intelligence). ANN process this data, compare the incoming features with the pattern of the plastic bottles that have been trained, and decide (classify) whether it is a “plastic bottle” or not. This ANN decision then activated the sorting robot arm.
