Towards the end of deep learning and the beginning of AGI

Adversarial examples are a hot research topic in deep learning nowadays. Subtle, often invisible changes in the data can push our networks to make terrible mistakes. We, as human beings, seem to be way more resilient to these perturbations in our sensory inputs (though not totally immune). There is a certain pattern in our deep learning systems. They achieve remarkable things, but they are also at times delicate and brittle.