I’m building Leo / PSCLS — an experimental system that learns relationships between sequences and updates its internal representations from experience.
Here’s how its actual output changed as it saw more stories.
1K stories
“Once upon a time to the store and said that there was a she bor and he lorander thing they were…”
Basically nonsense.
3K stories
“Once upon a time to the store and said that there was a she parted to see had a bided her tod and be bound aster…”
Still broken, but the output is becoming more structured.
40K stories
“Once upon a time, there was a big started to play with the should some too her mom and had a said, it was time. They happy and went to the park…”
Now we’re getting recognizable story-like patterns, characters, actions and dialogue — although the grammar is still heavily broken.
And the measured results improved too:
1K → 3K → 40K
BpB: 2.678 → 2.641 → 2.334
Accuracy: 52.37% → 53.62% → 58.11%
This is still an early experiment, not AGI.
But watching the same system change its outputs as it learns more experience is pretty interesting.
Next target: 250K → 500K → 1M stories.
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