Google builds AI agent that learns to generalize to new environments by ignoring distractions

In a study earlier this year accepted to the Genetic and Evolutionary Computation Conference (GECCO) 2020, Google researchers investigate the properties of AI software agents that employ self-attention bottlenecks. They claim that these agents not only demonstrate an aptitude for solving challenging vision-based tasks, but that they're better at tackling slight modifications of the tasks, due to their blindness to details that might confuse them. Inattentional blindness is the phenomenon that causes a person to miss things in plain sight; it's a consequence of selective attention, a mechanism that's believed to enable humans to condense information into a form compact enough for decision-making. Luminaries like Yann LeCun assert it can inspire the design of AI systems that better mimic the elegance and efficiency of biological organisms. The Google researchers' proposed agent -- AttentionAgent -- aims to devote most of its attention to task-relevant elements, ignoring distractions.