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Satellite

Artificial Intelligence

This component introduces students to the foundational concepts of Artificial Intelligence (AI), focusing on the key aspects of neural networks. It explains how neural networks work, covering the basic principles behind their structure and function, and introduces the distinctions between AI, Machine Learning (ML), Deep Learning (DL), and Generative AI.

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  • Artificial Intelligence (AI): A broad field encompassing systems that simulate human intelligence.

  • Machine Learning (ML): A subset of AI focused on teaching systems to learn from data.

  • Deep Learning (DL): A more specialized subset of ML involving neural networks with many layers for advanced learning tasks.

  • Generative AI: AI that creates new content or data based on learned patterns, such as images, text, or sound.​

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Students will explore how each of these concepts applies differently to various applications and will learn to differentiate between them through practical exercises.

Applications in the Real World

Earth Observation (EO)

AI-powered satellites use neural networks to analyze vast amounts of Earth imagery, detect environmental changes, and improve climate monitoring.

Space Exploration

Deep Learning models are used to process massive datasets from planetary exploration missions and autonomous spacecraft.

Telecommunication

AI is vital in enhancing satellite communication networks, optimizing bandwidth allocation, and ensuring seamless global connectivity.

Generative AI for Space Data

Helps create simulations for space missions and predict satellite failure based on historical data patterns.

High-Level Curriculum

Server Room
1. Introduction to AI Concepts:

  • Overview of AI, ML, DL, and Generative AI.

  • Basic understanding of neural networks: input, hidden layers, and output.

2. Differences Between AI, ML, DL, and Generative AI:

  • Omparative study of different neural networks.

  • Hands-on project: Building a simple neural network model using open-source tools (e.g., TensorFlow).

3. AI in Space Applications:

  • Earth Observation using AI: analyzing satellite imagery for climate and environmental data.

  • Telecommunications and global connectivity: AI’s role in optimizing satellite networks.

  • Practical project: Simulating real-time Earth data using an AI-based tool to predict environmental changes.

4. Future of AI in Space:

  • How AI will shape future space missions and autonomous spacecraft.

  • Generative AI's potential in mission planning and satellite simulations.

5. Ethical Considerations:

  • Discussion on ethics, data privacy, and the responsible use of AI in space applications.

Ready to begin your voyage?

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