[Tech-a-Break] Decoding AI Training through the Netflix Sci-Fi ‘The Great Flood’

Hello everyone! Today, let’s take a break from pure tech talk to review the latest Korean sci-fi sensation on Netflix, "The Great Flood." The audience's reaction has been polarized—mostly because the plot is a mind-bending loop that leaves many questioning who is who and why things keep resetting.

However, if you view it through a technical lens, it all clicks. This movie is a brilliant metaphor for AI Training processes. Today, I’ll break down the film’s logic and show you how it mirrors the creation of an AI Model.

⚠️ WARNING: SPOILER ALERT! If you haven’t watched it yet, bookmark this page for later, as we can’t discuss the logic without revealing the plot.

1. AI as a Digital Twin of Memory

The big reveal is that "Anna" (played by Kim Da-mi) is not exactly human. She is a Digital Consciousness (or AI) reconstructed from the memory database of the original Anna.

• In AI Terms: This is a Full-Scale Emulation. The goal is to preserve "identity" within a complex program so advanced that the entity itself doesn't realize it isn't human.

2. The Simulation Loop: A Sandbox for Training

The recurring flood in the apartment isn't "Time Travel"—it’s a Simulation running within a computer system.

• In AI Terms: This is a Synthetic Environment designed to train a model. The Objective Function (the goal) is for the AI to survive and protect the data (the child) under varying conditions.

3. Memory Leak and System "Deja Vu"

Why does Anna start seeing hallucinations from previous loops?

• In AI Terms: This represents a Memory Leak or an "unclean" state management. Data from previous Epochs (training cycles) leaks into the current loop, causing the AI to develop Self-Awareness that it is trapped in a testing cycle.

The Big Question: If memories are wiped, how does the AI improve?

This is what confuses most viewers: if she forgets how she died in the last loop, how does she learn? The answer lies in Data Science principles:

• Weights vs. Logs: In AI Training, we don't need the model to remember "Logs" (the specific history of every run). Instead, we focus on accumulating Weights & Biases.

• Model Optimization: Every time a loop fails, the system sends an error signal back to adjust internal variables—a process called Backpropagation. While "Episodic Memory" is wiped, the Model Optimization (skills and decision-making) becomes sharper.

• Intuition: What Anna displays in the final loops isn't "memory," it's "Intuition." Her neural network has been trained until the weights are so precise that her "gut feeling" naturally leans toward the path of survival.

Conclusion

The Great Flood might be a challenging watch due to its non-linear storytelling, but in terms of AI Architecture, it is a masterful display of how Intelligence is born. It’s not about perfect memory; it’s about the constant correction of errors until the result is stable.

If you are interested in Machine Learning or AI development, I highly recommend a re-watch. You’ll find a whole new layer of storytelling hidden in the code!