Generative AI: A Fresh Survey
The past, the present and the future of Language Models
In the dynamic landscape of Generative Artificial Intelligence (GenAI), maintaining pace with the latest developments can be a daunting task. But don’t worry, I’ve recently found a great paper on ArXiV that explores recent trends and future directions, and I’ll break it down in this story.
Outline:
1. The History of Generative AI: Let's Start from the Beginning
2. Current Challenges: Fine-Tuning, Hallucinations and Alignment
3. New Trends: Mixture of Experts (MoE) and Multimodal Models
4. Real-world Applications of Generative AI and Ethical Considerations
5. The Future of AI: The OpenAI's Q* Project
1. The History of Generative AI: Let’s Start from the Beginning
The rise of Generative AI has been marked by significant milestones, with each new model paving the way for the next evolutionary leap. Models, indeed, have undergone a transformative journey, evolving from rudimentary statistical methods to the complex neural network architectures that underpin today’s Large Language Models (LLMs).
