The arc prize problems are visual – naturally they encounter similar situations that visual editors do.
Q: Taken a shape, can you flood fill it?
Q: Can you take a shape and transform it by a consistent transform – scaling?
Q: Can you take a shape and transform it by a relative transform – relative size decides color?
Q: Can you take a shape and transform it by a consistent transform – painting more shapes?
Q: Can you duplicate a pattern by some offset? filling it to the boundaries in some direction?
Could we create a consistent framework?
- Given a starting grid
- Can you perform a series of paints into a result grid?
- Q: What data, patterns we can draw from:
- input,
- output
- transition of input -> output?
- The paints are based on “strategies” that we already use as humans.
- sense of empty space in a shape -> flood fillable spots
- sense of above, right, below, left -> locations, directions for additional paints
- sense of size -> for scaling
- sense of relativity -> for shape relative paints
- sense of uniqueness and counts -> for shape specific transformations
- Q: Is the secret to enumerate all of the human concepts of visual sense?
- or is the end goal to create a system, that could LEARN all of our visual senses
- well, there could be a different secret to the competition and one for visual sense AGI
- Q: What data, patterns we can draw from:
Python
def enrich_data(input, output):
return {
"input": input,
"output": output,
# insights
"input_data": analyze_input(...),
"output_data": analyze_output(...),
"transition_data": analyze_transition(...)
}
def strategy_finder(data):
data = enrich_data(data)
strategies = [...]
results = []
for strat in strategies:
paints = paint_solver(data, strat)
if draw(data.input) == data.output:
results.append(strat)
return results