Dreaming with ARC
Chin, Sang; Banburski, Andrzej; Poggio, Tomaso; Ghandi, Anshula; Alford, Simon; Dandekar, Sylee
Current machine learning algorithms are highly specialized to whatever it is they
are meant to do –– e.g. playing chess, picking up objects, or object recognition.
How can we extend this to a system that could solve a wide range of problems?
We argue that this can be achieved by a modular system –– one that can adapt to
solving different problems by changing only the modules chosen and the order in
which those modules are applied to the problem. The recently introduced ARC
(Abstraction and Reasoning Corpus) dataset serves as an excellent test of abstract
reasoning. Suited to the modular approach, the tasks depend on a set of human
Core Knowledge inbuilt priors. In this paper we implement these priors as the
modules of our system. We combine these modules using a neural-guided program
synthesis.
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