The wisdom of the crowd has long become the de facto approach for eliciting information from individuals or experts in order to predict the ground truth. However, classical democratic approaches for aggregating individual \emph{votes} only work when the opinion of the majority of the crowd is relatively accurate. A clever recent approach, \emph{surprisingly popular voting}, elicits additional information from the individuals, namely their \emph{prediction} of other individuals' votes, and provably recovers the ground truth even when experts are in minority. This approach works well when the goal is to pick the correct option from a small list, but when the goal is to recover a true ranking of the alternatives, a direct application of the approach requires eliciting too much information. We explore practical techniques for extending the surprisingly popular algorithm to ranked voting by partial votes and predictions and designing robust aggregation rules. We experimentally demonstrate that even a little prediction information helps surprisingly popular voting outperform classical approaches.

The AI processor market is set to be worth US$43.3 billion in 2023, whereas it was worth $8.6 billion last year. This growth will proceed at an estimated annual compound rate (or CAGR) of 33.9% over the next five years. It is thought to be driven by consumer-grade electronics and the services that are provided by them - or that exist to provide them. For example, AI is thought to be a necessary component in processing the mountains of data generated by social media in the future. In addition, e-retailers may also need AI to process their ever-mounting data burden.