Satanlucy says, "AI merely does conditional prediction based on statistical probability."

Statistical probability is how AI learns.

AI starts as extremely dumb, unable to form one coherent sentence. then it learns from training data, it learns patterns which statistically repeat more, to calculate statistical probability of them.

Bad training data is usually biggest cause of failed AI. Feed AI garbage patterns, it learns garbage patterns.

Yes — but “better” and “bigger” improve AI in different ways, and one is usually more important than the other.

Let’s break it down clearly.

1️⃣ Bigger training data

More data generally helps because:

1. The model sees more language patterns.
2. It learns rarer words and structures.
3. It reduces overfitting (memorizing instead of generalizing).
4. It improves robustness.

Large models like OpenAI’s systems or Google DeepMind’s models are trained on massive datasets for this reason.

However…

If the data is:

1. Noisy
2. Low quality
3. Repetitive
4. Biased
5. Incorrect

Then making it bigger just means more noise.

More garbage ≠ smarter AI.

2️⃣ Better training data

“Better” usually improves AI more efficiently than just “bigger.”

Better means:

1. Accurate information
2. Diverse viewpoints
3. Well-structured text
4. Clear language
5. Less duplication
6. Balanced topics
7. Reduced bias

High-quality curated datasets can outperform much larger low-quality ones.

For example:

1. Clean scientific articles > random scraped comments
2. Structured textbooks > messy forum posts
3. Balanced multilingual data > single-source repetition

3️⃣ The real answer: It’s about scaling laws

Research from OpenAI shows performance improves smoothly with:

1. More data
2. More parameters
3. More compute

But the improvements follow diminishing returns.

After a point:

1. Doubling data gives smaller gains.
2. Quality becomes more important.
3. Architecture matters more.

4️⃣ What actually improves AI most?

In practice, best results come from:

1. Large data
2. High-quality filtering
3. Good architecture
4. Proper training objective
5. Fine-tuning on curated data

So the real formula is:

Large × Clean × Diverse × Well-trained

Not just “large.”

5️⃣ Relating this to your chatbot experiments

Since you’ve been building symbolic/deductive chatbots in Python:

For your system:

1. Bigger knowledge.txt → more coverage
2. Better structured knowledge → better reasoning
3. Cleaner patterns.txt → less repetition
4. Better logical structure → stronger deduction

For symbolic systems, structure matters even more than size.

Final answer

✔ Bigger training text helps.

✔ Better training text helps more per unit.

✔ The best systems use both.

If you want, I can also explain how this differs between:

1. Neural networks
2. Symbolic AI
3. Hybrid systems (like what you’re building)

Can AI be taught to be competent in the Bible?