Building machines that learn and think for themselves.

We agree with Lake and colleagues on their list of "key ingredients" for building human-like intelligence, including the idea that model-based reasoning is essential. However, we favor an approach that centers on one additional ingredient: autonomy. In particular, we aim toward agents that can both build and exploit their own internal models, with minimal human hand engineering. We believe an approach centered on autonomous learning has the greatest chance of success as we scale toward real-world complexity, tackling domains for which ready-made formal models are not available. Here, we survey several important examples of the progress that has been made toward building autonomous agents with human-like abilities, and highlight some outstanding challenges.

Sparse coding of birdsong and receptive field structure in songbirds.

Auditory neurons can be characterized by a spectro-temporal receptive field, the kernel of a linear filter model describing the neuronal response to a stimulus. With a view to better understanding the tuning properties of these cells, the receptive fields of neurons in the zebra finch auditory fore-brain are compared to a set of artificial kernels generated under the assumption of sparseness; that is, the assumption that in the sensory pathway only a small number of neurons need be highly active at any time. The sparse kernels are calculated by finding a sparse basis for a corpus of zebra-finch songs. This calculation is complicated by the highly-structured nature of the songs and requires regularization. The sparse kernels and the receptive fields, though differing in some respects, display several significant similarities, which are described by computing quantative properties such as the seperability index and Q-factor. By comparison, an identical calculation performed on human speech recordings yields a set of kernels which exhibit widely different tuning. These findings imply that Field L neurons are specifically adapted to sparsely encode birdsong and supports the idea that sparsification may be an important element of early sensory processing.