  Recent breakthroughs in AI have shown the remarkable power of deep learning
and deep reinforcement learning. These developments, however, have been tied to
specific tasks, and progress in out-of-distribution generalization has been
limited. While it is assumed that these limitations can be overcome by
incorporating suitable inductive biases, the notion of inductive biases itself
is often left vague and does not provide meaningful guidance. In the paper, I
articulate a different learning approach where representations do not emerge
from biases in a neural architecture but are learned over a given target
language with a known semantics. The basic ideas are implicit in mainstream AI
where representations have been encoded in languages ranging from fragments of
first-order logic to probabilistic structural causal models. The challenge is
to learn from data the representations that have traditionally been crafted by
hand. Generalization is then a result of the semantics of the language. The
goals of this paper are to make these ideas explicit, to place them in a
broader context where the design of the target language is crucial, and to
illustrate them in the context of learning to act and plan. For this, after a
general discussion, I consider learning representations of actions, general
policies, and subgoals ("intrinsic rewards"). In these cases, learning is
formulated as a combinatorial problem but nothing prevents the use of deep
learning techniques instead. Indeed, learning representations over languages
with a known semantics provides an account of what is to be learned, while
learning representations with neural nets provides a complementary account of
how representations can be learned. The challenge and the opportunity is to
bring the two together.
