It is very common to observe crowds of individuals solving similar problems with similar information in a largely independent manner. We argue here that crowds can become "smarter," i.e., more efficient and robust, by partially following the average opinion. This observation runs counter to the widely accepted claim that the wisdom of crowds deteriorates with social influence. The key difference is that individuals are self-interested and hence will reject feedbacks that do not improve their performance. We propose a control-theoretic methodology to compute the degree of social influence, i.e., the level to which one accepts the population feedback, that optimizes performance. We conducted an experiment with human subjects ($N = 194$), where the participants were first asked to solve an optimization problem independently, i.e., under no social influence. Our theoretical methodology estimates a $30\%$ degree of social influence to be optimal, resulting in a $29\%$ improvement in the crowd's performance. We then let the same cohort solve a new problem and have access to the average opinion. Surprisingly, we find the average degree of social influence in the cohort to be $32\%$ with a $29\%$ improvement in performance: In other words, the crowd self-organized into a near-optimal setting. We believe this new paradigm for making crowds "smarter" has the potential for making a significant impact on a diverse set of fields including population health to government planning. We include a case study to show how a crowd of states can collectively learn the level of taxation and expenditure that optimizes economic growth.