Imputation can make interpolation appear to be extrapolation.

So, to fake AGI's capacity for accurate extrapolation (data efficiency), one may take a big pile of money and throw it at expanding the training set to infinity and expanding the matrix multiplication hardware to infinity. This permits more datapoints within which one may interpolate over a larger knowledge space.

But it is fake.

If, on the other hand, you actually understand the content of Wikipedia (the Hutter Prize's very limited, high quality corpus), you may deduce (extrapolate) the larger knowledge space through the best current mathematical definition of AGI: AIXI's where the utility function of the sequential decision theoretic engine is to minimize the algorithmic description of the training data (Solomonoff Induction) used as the prediction oracle in the AGI.