  We consider learning and compositionality as the key mechanisms towards
simulating human-like intelligence. While each mechanism is successfully
achieved by neural networks and symbolic AIs, respectively, it is the
combination of the two mechanisms that makes human-like intelligence possible.
Despite the numerous attempts on building hybrid neuralsymbolic systems, we
argue that our true goal should be unifying learning and compositionality, the
core mechanisms, instead of neural and symbolic methods, the surface approaches
to achieve them. In this work, we review and analyze the strengths and
weaknesses of neural and symbolic methods by separating their forms and
meanings (structures and semantics), and propose Connectionist Probabilistic
Program (CPPs), a framework that connects connectionist structures (for
learning) and probabilistic program semantics (for compositionality). Under the
framework, we design a CPP extension for small scale sequence modeling and
provide a learning algorithm based on Bayesian inference. Although challenges
exist in learning complex patterns without supervision, our early results
demonstrate CPP's successful extraction of concepts and relations from raw
sequential data, an initial step towards compositional learning.
