Small data is
the new big data.

Today’s approach to data science problems is focused on big data & deep learning. This combination of abundant data and very high-dimensional models has brought about highly effective solutions in applications such as image classification or text- and image-generation. However, in many real-world use cases – in business, industry, or research – data is often not as abundant as one initially assumes, because a large portion of the data may not be applicable to the problem at hand, because there has been a sudden regime-switch recently, or because the statistical properties of the data have slowly shifted over time. In some cases, only a tiny fraction of the data points that are initially deemed to be outliers or artifacts contain most of the signal.

The study of complex systems calls for novel methods that can “peak behind” the curtain of emergence, or at least not be mislead by it. At Artifact Research, we believe that Bayesian modeling, probabilistic programming, and model selection techniques make up a great set of tools to analyze complex systems, avoid overfitting, and solve problems involving scarce data, weak signals, and strong noise.

We consult in the development of novel tools, provide interactive educational resources, and offer innovative software products for the analysis of complex systems and small-data problems – from fundamental scientific research to applications like quantitative investment strategies.