Signaloid: Shaping the Future of Uncertainty Quantification in Computing

When it comes to data about the real world, it is typically a distribution of possible outcomes—some more likely than others—rather than a single point. Yet, computers make calculations with individual numbers that represent points rather than distributions; thus, estimating uncertainty becomes a lot of overhead. But what if a new way of implementing the maths within computers could inherently account for distributions and, thus, uncertainty?