Future Trends in Computer Science

1. Relationships of IoT, Edge, Fog and Cloud Computing
2. Superclouds – The Rise of Unified Multi-Cloud Abstraction Layers
3. Cloud and Quantum Integration - How AWS Braket, Azure Quantum & Google Quantum AI Are Reshaping Computing
4. Sovereign Cloud – Country-specific cloud solutions for compliance and data residency
5. Cloud-native Generative AI – Deploying LLMs and multimodal AI directly within cloud-native platforms
6. AI for Cloud Operations (AIOps) – Self-healing, Auto-optimizing Infrastructure Powered by AI
7. Federated AI on Cloud – Privacy-first training of AI models across distributed data silos
8. Serverless 2.0 – Event-driven, low-latency functions with better cold start handling
9. Data Lakehouse on Cloud – Next-gen data management beyond lakes and warehouses
10. Introduction to Artificial Intelligence and Machine Learning
11. A Brief History of Generative Models: From early statistical models to the advent of neural networks
12. Core Concepts: Neural Networks and Deep Learning: The fundamental building blocks of modern Generative AI
13. Introduction to Large Language Models (LLMs): Understanding the technology that powers models like GPT
14. Ethical Considerations and Responsible AI: An early introduction to the societal impacts, biases, and safety measures in Generative AI
15. Generative Adversarial Networks (GANs): Exploring the duo of a generator and a discriminator for creating realistic data
16. Variational Autoencoders (VAEs): Understanding how these models learn latent representations of data to generate new samples
17. Transformer Models and the Attention Mechanism: The architecture that revolutionized natural language processing and is a cornerstone of modern LLMs
18. Diffusion Models: A deep dive into the process of adding and removing noise to generate high-fidelity images
19. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM): Foundational models for sequence data generation
20. The Art of Prompt Engineering: Techniques for crafting effective prompts to elicit desired outputs from generative models
21. Fine-Tuning Pre-trained Models: Adapting large models to specific tasks and datasets
22. Retrieval-Augmented Generation (RAG): Enhancing model responses by incorporating external knowledge sources
23. Introduction to Embeddings and Vector Databases: Understanding how data is represented and retrieved in Generative AI systems
24. API Integration and Development: Utilizing APIs from providers like OpenAI, Google, and others to build applications