The ill-posed problem of learning is one of the main gateways to making intelligent machines and to understanding how the brain works. In this talk I will give an up-to-date outline of some of our recent efforts in developing machines that learn, especially in the context of visual interfaces. Our work on statistical learning theory is being applied to classification (and regression) in various domains -- and in particular to applications in computer vision and computer graphics. In this talk, I will summarize our work on trainable, hierarchical classifiers for problems in object recognition and especially for face and person detection. I will also describe how we used the same learning techniques to synthesize a photorealistic animation of a talking human face. Finally, I will speculate briefly on the implication of our research on how visual cortex learns to recognize and perceive objects and on related work on brain-machines interfaces.