Team Evaluation (AI)

Team Evaluation: AI

This is a team evaluation session where each team solves a quiz.
  • Let's use what we've learned so far to find the answer to the quiz.
  • It's time to find the answer to some of the quizzes below.
  • After answering the team quiz, there will be presentations by each team.
Topic:
  1. AI essential questions

    1. How AI systems handle complex decisions under uncertain circumstances, and how can techniques such as probabilistic modeling be applied to the autonomous driving field of robots?

    2. What is meta-learning, where AI systems learn how to learn, and how can this approach lead to the development of more adaptive and faster learning algorithms?

    3. How can AI use supervised learning to predict and prevent potential errors, and what role can it play in forecasting, maintenance, early disease diagnosis and risk mitigation?

    4. What is AI’s ability to discover hidden patterns and structures in data through unsupervised learning and discuss applications in the areas of market analysis, anomaly detection, etc.?

    5. What are the roles of layers, neurons, in the way neural networks simulate the interconnected structures of the brain, and what are the similarities between how humans and machines learn?

    6. What is the mechanism of a convolutional neural network (CNN) and how it processes visual information layer by layer to recognize objects, faces and scenes?

    7. How can natural conversations and emotionally intelligent interactions be created in the mechanism of AI-based chatbots?

    8. How can AI systems address ethical concerns, biases, and transparency issues, especially in critical applications such as criminal justice, finance, and healthcare, and what measures can be implemented to ensure responsible AI development and deployment?

    9. In the realm of reinforcement learning, how can AI systems be designed to balance exploration and exploitation effectively, and what are the implications for applications such as gaming, robotics, and personalized content recommendations?

    10. How can AI contribute to addressing global challenges, such as climate change, and what are the potential applications of AI in sustainable practices, resource management, and environmental monitoring?

  2. AI choice questions

    1. Is there a way to apply DetectNet to situations beyond human detection, such as counting people in crowded areas or tracking animals for wildlife research?

    2. The importance of SegNet in semantic segmentation, where AI assigns a class label to each pixel in an image, and examples of how it can be applied to things like medical imaging, urban planning, and scene recognition?

    3. Is there a way to extend PoseNet’s technology to estimate not only pose, but also depth information, contributing to 3D scene reconstruction and mixed reality experiences?

    4. In the problem of accurately segmenting a foreground subject in a complex background, how exactly does BackgroundNet’s architecture solve this problem?

    5. What is DepthNet’s role in estimating depth in 2D images so that artificial intelligence can detect the distance of an object from a camera, and how it can be applied to places like robotics, autonomous vehicles and 3D modeling?

    6. Can GPT (Generative Pre-trained Transformer) models be fine-tuned effectively for specialized tasks beyond natural language processing, such as code generation, image synthesis, or music composition?

    7. What role does Explainable AI (XAI) play in enhancing trust and understanding of AI systems, especially in critical applications like healthcare diagnosis, and what methods can be employed to make AI decision-making processes more transparent and interpretable?

    8. How does the integration of AI and Generative Adversarial Networks (GANs) contribute to the field of creative arts, and what are some examples of AI-generated art, music, or literature that have gained recognition?

    9. Can AI-powered recommendation systems, like those used by streaming services, effectively balance user personalization and serendipity to provide a more engaging and satisfying user experience?

    10. How does Quantum Machine Learning differ from classical machine learning, and what are the potential advancements and challenges in harnessing quantum computing power for solving complex AI tasks?

  3. Watch Videos

    1. Tesla Humanoid Robot Optimus - In what direction will Tesla robots develop in the future?

    2. NVIDIA GTC Conference - What are NVIDIA’s strengths in four areas and how will these develop in the future?

Evaluation Standard:

  1. Choose one AI essential question and one AI choice question to submit your report.

  2. Write a report about what you felt after watching the video.

  3. The report evaluation criteria are as follows.

    • A+ : Write at least 20 pages

    • A : Write at least 15 pages

    • B+ : Write at least 10 pages