Challenges for the further development of AI include 1. expanding its use, 2. developing it in various fields, and 3. improving its reliability. Please explain in detail the steps involved in the development of generative AI and the essential technologies required.

 Certainly! Here is the English translation of the explanation regarding the development steps and essential technologies of Generative AI.

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1. The Four Key Steps of Generative AI Development

Developing Generative AI, such as Large Language Models (LLMs), follows a rigorous process of training a machine to recognize patterns and generate new content.

Step 1: Data Collection and Pre-processing

This is the phase of gathering the "textbooks" for the AI.

• Collection: Trillions of tokens (text, images, code) are gathered from the web, books, and scientific papers.

• Cleaning: Removing duplicates, harmful content, and personal information to ensure the data is high quality and safe for learning.

Step 2: Pre-training

The most resource-intensive phase where the "Foundation Model" is created.

• Learning: Using massive computing power (thousands of GPUs), the model spends months learning the statistical probabilities of how words or pixels relate to one another.

• Outcome: A model that understands general context and can generate basic responses.

Step 3: Fine-tuning

This "polishes" the foundation model for specific tasks, like conversation or medical analysis.

• Instruction Tuning: Training the model on high-quality Q&A datasets.

• RLHF (Reinforcement Learning from Human Feedback): Humans rank the AI's answers, teaching it to be more helpful, natural, and safe.

Step 4: Inference and Evaluation

The final check before the AI is released to users.

• Benchmarking: Testing the AI’s reasoning, knowledge, and safety against standardized exams to ensure it meets performance requirements.

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2. Three Essential Technologies for Development

To build and run Generative AI, the following three technological pillars are indispensable:

1. Transformer Architecture

The "brain" of modern Generative AI.

• Attention Mechanism: A technology that allows the AI to focus on the most relevant words in a sentence. This is why AI can now understand the context of very long documents.

2. GPUs and Distributed Computing

AI training requires an astronomical number of calculations.

• GPUs: Specialized processors (like those from NVIDIA) that excel at parallel processing.

• Distributed Computing: High-level infrastructure technology that splits massive computing tasks across thousands of interconnected servers.

3. Parameters and Quantization

Technologies that manage the "density" of the AI's intelligence.

• Parameters: Numerical weights representing the strength of connections (GPT-4 is rumored to have trillions).

• Quantization: A technique to compress these numbers so that high-performance models can run faster on smaller devices like smartphones or laptops.

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3. Latest Trend: RAG (Retrieval-Augmented Generation)

Rather than retraining a model from scratch, many developers now use RAG. This allows the AI to look up external, up-to-date databases (like your company's private files or today's news) to provide accurate answers without "hallucinating."

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Would you like me to translate any specific part into more technical terms, or shall we move on to the next topic regarding "Improving Reliability"?