The opportunity is for a Data Engineer to play a critical role in architecting and developing components forming the Analytics platform, whilst implementing new ideas to solve novel challenges related to geospatial analytics at scale. The Data Engineer will collaborate with Data Scientists to bring geospatial algorithms into production at scale, identify business requirements and opportunities, such as utilising new data sources or ways to process and store data. Working primarily in Python & Scala, the data engineer will gain exposure to a range of technologies including Spark, Kafka, AWS, Airflow, Rust and much more. Our mission is to transform the way humans and machines understand the world. We are doing this by creating a real-time index of reality, enabling billions of machines and trillions of sensors to land, index, share and consume each other's data about the world as they move through it.

After decades in the wilderness, AI has swaggered back onto center stage. Cheap computer power and massive datasets have given researchers alchemical powers to turn algorithms into gold, and the deep pockets (and marketing prowess) of Silicon Valley's tech giants haven't hurt either. But despite warnings from some that the creation of super-intelligent AI is just around the corner, those working in computational coal mines are more realistic. They point out that contemporary AI programs are extremely narrow in their abilities; that they're easily tricked, and simply don't possess those hard-to-define but easy-to-spot skills we usually sum up as "common sense." They are, in short, not that intelligent.

In this paper we extend our previous results on sets of graded attribute implications with witnessed non-redundancy. We assume finite residuated lattices as structures of truth degrees and use arbitrary idempotent truth-stressing linguistic hedges as parameters which influence the semantics of graded attribute implications. In this setting, we introduce algorithm which transforms any set of graded attribute implications into an equivalent non-redundant set of graded attribute implications with saturated consequents whose non-redundancy is witnessed by antecedents of the formulas. As a consequence, we solve the open problem regarding the existence of general systems of pseudo-intents which appear in formal concept analysis of object-attribute data with graded attributes and linguistic hedges. Furthermore, we show a polynomial-time procedure for determining bases given by general systems of pseudo-intents from sets of graded attribute implications which are complete in data.