Learning of layered or “deep” representations has recently enabled low-cost sensors for autonomous vehicles and efficient automated analysis of visual semantics in online media. But these models have typically required prohibitive amounts of training data, and thus may only work well in the environment they have been trained in. I’ll describe recent methods in adversarial adaptive learning that excel when learning across modalities and domains. Further, these models have been unsatisfying in their complexity–with millions of parameters–and their resulting opacity. I’ll report approaches which achieve explainable deep learning models, including both introspective approaches that visualize compositional structure in a deep network, and third-person approaches that can provide a natural language justification for the classification decision of a deep model.

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Humans are much better than current AI systems at generalizing out-of-distribution. What ingredients can bring us closer to that level of competence? We propose 4 ingredients combined: (a) meta-learning (to learn end-to-end to generalize to modified distributions, sampled from a distribution over distributions), (b) designing modular architectures with the property that modules are fairly independent of each other and interacting sparsely while made to be composed in new ways easily, (c) capturing causal structure decomposed into independent mechanisms so as to correctly infer the effect of interventions by agents which modify the data distribution, and (d) building better and more stable models of the invariant properties of possibly changing environments by taking advantage of the interactions between the learner and its environment to learn semantic high-level variables and their interactions, i.e., adopting an agent perspective on learning to benefit deep learning of abstract representations. The last ingredient implies that learning purely from text is not sufficient and we need to strive for learning agents which build a model of the world, to which linguistic labels can be associated, i.e., performing grounded language learning. Whereas this agent perspective is reminiscent of deep reinforcement learning, the focus is not on how deep learning can help reinforcement learning (as a function approximation black box) but rather how the agent perspective common in reinforcement learning can help deep learning discover better representations of knowledge.

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