We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The advance of quantum computing has the promise of reshaping artificial intelligence (AI) as it's known and deployed today. This development is drastically expanding AI's enterprise and commercial outreach, perhaps even getting closer to artificial general intelligence. And there is another promise of convergence of quantum computing, AI, and programming languages into a single computational environment. The potential effects of this coalescence of capabilities are nothing short of formidable.
Cloud computing, a term that elicited significant hesitation and criticism at one time, is now the de facto standard for running always-on services and batch-computation jobs a like. In more recent years, the cloud has become a significant enabler for the IoT (Internet of Things). Network-connected IoT devices--in homes, offices, factories, public infrastructure, and just about everywhere else--are significant sources of data that must be handled and acted upon. The cloud has emerged as an obvious support platform because of its cheap data storage and processing capabilities, but can this trend of relying exclusively on the cloud infrastructure continue indefinitely? For the applications of tomorrow, computing is moving out of the silos of far-away datacenters and into everyday lives.
It was previously presented using a heuristic argument. This study extends CRS to handle dynamic or streaming data, which much better reflect the real-world situation than assuming static data. Compared with other known sketching algorithms for dimension reductions such as stable random projections, CRS exhibits a significant advantage in that it is one-sketch-for-all.'' Although a fully rigorous analysis of CRS is difficult, we prove that, with a simple modification, CRS is rigorous at least for an important application of computing Hamming norms. A generic estimator and an approximate variance formula are provided and tested on various applications, for computing Hamming norms, Hamming distances, and $\chi 2$ distances.