Question

1. Explain what artificial intelligence (Al) means.

2. Name three (3) applications of Al and explain each of them briefly.
3. What is an agent in the context of Al?
4. Explain the concept of "environment" in the context of AI.
5. A student asserts that "A rational agent in Al does not need to be perfect. Also, it is only designed to achieve the best possible expected performance rather than the best possible actual performance." Do you agree with this student? Answer this question and give your justification.

1. Explain the following three concepts: underfitting, overfitting and generalization in the context of machine learning.
2. A student asserts that "perceptrons cannot effectively approximate the XOR function." Do you agree with this student? Give your answer with justification.
3. Plot the model of a neuron developed by McCulloch \& Pitts in 1943 and explain each of its components.
4. Gradient ascent algorithm is important for neural network model training. Answer the following questions
a. Explain the purpose of gradient ascent in neural network model training process.
b. When the number of training samples increases significantly, how to make the gradient ascent algorithm maintain its computational efficiency?

Answer 1

1. Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

2. Three applications of AI are:

- Natural Language Processing (NLP): This involves the use of algorithms to analyze, understand, and generate human language. Examples of NLP applications include chatbots, virtual assistants, and sentiment analysis tools.

- Computer Vision: This involves the use of algorithms to interpret and understand visual data from the world, such as images and videos. Examples of computer vision applications include facial recognition, object detection, and autonomous vehicles.

- Robotics: This involves the development of intelligent machines that can perform tasks that are typically done by humans. Examples of robotics applications include industrial automation, medical robotics, and drones.

3. In the context of AI, an agent is a program or system that is capable of perceiving its environment and taking actions to achieve a goal. Examples of agents include chatbots, autonomous vehicles, and recommendation systems.

4. In the context of AI, an environment refers to the external context in which an agent operates. This can include physical environments, such as the real world or virtual environments, such as a game world. The environment provides the agent with information and feedback on its actions and decisions.

5. Yes, I agree with the student. A rational agent in AI does not need to be perfect, but it should be able to make decisions that maximize its expected utility or performance. This means that the agent is designed to make the best possible decision based on the information it has, but it may not always achieve the best possible outcome due to factors outside of its control.

1. Underfitting, overfitting, and generalization are concepts in machine learning:

- Underfitting occurs when a model is too simple and does not capture the complexity of the data. This results in poor performance on both the training and test data.

- Overfitting occurs when a model is too complex and fits the training data too closely. This results in good performance on the training data but poor performance on the test data.

- Generalization refers to a model's ability to perform well on new and unseen data. A well-generalized model is one that can perform well on both the training and test data.

2. No, I do not agree with the student. Perceptrons can effectively approximate the XOR function by using a multi-layer perceptron (MLP) with a hidden layer. The hidden layer allows the