Thai Buses Go Inter (TBGI)

Thai Buses Go Inter (TBGI)
ระบบสนับสนุนพหุภาษาสำหรับพนักงานเก็บค่าโดยสารรถเมล์ไทย

TBGI is a multilingual support system for Thai bus fare collectors

This is a multilingual support system for Thai bus conductors.

ระบบช่วยแปลภาษาสำหรับกระเป๋ารถเมล์ไทย พัฒนาขึ้นโดย ผศ. ดร. จันทร์พา ทัดภูธร

This support system has been developed by Assistant Professor Dr. Janpha Thadphoothon, a lecturer in the Business English program, at International College, Dhurakij Pundit University, in Bangkok, Thailand.

Test Your Knowledge of Generative AI

Question 1:

Which of the following best describes Generative AI?

1. Creating artificial intelligence from scratch
2. Generating new data based on existing data
3. Enhancing the performance of existing AI models
4. Designing algorithms for pattern recognition

Question 2:

What is the primary goal of Generative Adversarial Networks (GANs)?

1. Supervised learning
2. Unsupervised learning
3. Reinforcement learning
4. Transfer learning

Question 3:

Which of the following is a common application of Generative AI?

1. Machine translation
2. Speech recognition
3. Image generation
4. Sentiment analysis

Question 4:

Which technique is used in Variational Autoencoders (VAEs)?

1. Reinforcement learning
2. Generative modeling
3. Unsupervised learning
4. Convolutional neural networks

Question 5:

What is the main difference between generative and discriminative models?

1. Generative models generate new data, while discriminative models classify existing data.
2. Generative models classify existing data, while discriminative models generate new data.
3. Generative models use labeled data, while discriminative models use unlabeled data.
4. Generative models require less training data compared to discriminative models.

Question 6:

Which type of generative model is used in text generation tasks?

1. Autoencoders
2. Markov Chains
3. Recurrent Neural Networks (RNNs)
4. Decision Trees

Question 7:

What is the main challenge in training generative models?

1. Overfitting
2. Underfitting
3. Lack of labeled data
4. Model interpretability

Question 8:

Which technique allows generative models to learn from unlabeled data?

1. Reinforcement learning
2. Transfer learning
3. Semi-supervised learning
4. Active learning

Question 9:

Which of the following is an example of a popular generative model architecture?

1. LeNet
2. VGGNet
3. ResNet
4. GPT

Question 10:

What are the potential ethical implications of Generative AI?

1. Increased automation and job displacement
2. Privacy concerns related to generated data
3. Misinformation and fake content generation
4. All of the above

Grading Results

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