We go back Home to look again at the sapling under 'About Us'  It had a seed and we now consider 'AI seed'.

 

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1. In the context of AI, especially for tasks like image generation, a "seed" is a numerical value that acts as a starting point for the AI's random number generator.
2. Using the same seed will consistently produce the same output from the AI, making it a tool for reproducibility and controlled experimentation. Essentially, it's a way to ensure that the AI's random behavior is predictable and repeatable. source 
3. Two types of RNG exist: True Random Number Generator (TRNG) and Pseudo-random number generator (PRNG). The first use an unpredictable physical means to generate numbers, while the second use mathematical algorithms, completely computer-generated.

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1. An AI's random number generator can use either pseudo-random or true random methods. 
2. Pseudo-random number generators (PRNGs) use algorithms to generate sequences that approximate randomness, while true random number generators (TRNGs) rely on physical phenomena like electronic noise or radioactive decay for true randomness. 
3. AI models themselves don't inherently generate true randomness but can be integrated with PRNGs or TRNGs to generate random numbers for various applications.
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5. The Random Number Generator uses advanced AI algorithms to generate a random number within your defined range. It ensures that each number within the range has an equal chance of being selected, ensuring true randomness.
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