They should designate "digital transformation" as the buzzphrase of the year. It all sounds so good, so modern and forward-thinking. Vendors love it, of course, since it means buying lots of shiny new systems. But enacting a "digital transformation" against an organization that is mired in calcified processes, non-customer-friendly behavior and restrictive, top-down thinking will only means lots adding a layer of shiny new systems on top of calcified processes, non-customer-friendly behavior, and restrictive, top-down thinking. It means doing digital transformation for the sake of digital transformation.
Manufacturers have historically viewed warranty management as a by-product of selling a piece of equipment or product, not as a strategic part of the business. This mindset is changing as manufacturers begin to understand that each interaction with a customer is an opportunity to deliver value and enhanced experiences. But in this new world, real-time insights are critical to delivering on this heightened level of support. Technologies like artificial intelligence will be the cornerstone of this transformation as real-time data insights drive better service and more personalized experiences.
The existing model of healthcare delivery & procurement is unsustainable. It is marred with bottlenecks across the operational model, making it inefficient on various fronts. In its current form, the situation will only worsen in the future. The solution to healthcare provisioning problems lies in adopting digital to the core and undergoing digital transformation. There is no denying the fact that transformation in healthcare has been slow due to the regulatory & digitalizing barriers.
As deep neural networks (DNN) have the ability to model the distribution of datasets as a low-dimensional manifold, we propose a method to extract the coordinate transformation that makes a dataset distribution invariant by sampling DNNs using the replica exchange Monte-Carlo method. In addition, we derive the relation between the canonical transformation that makes the Hamiltonian invariant (a necessary condition for Noether's theorem) and the symmetry of the manifold structure of the time series data of the dynamical system. By integrating this knowledge with the method described above, we propose a method to estimate the interpretable conservation laws from the time-series data. Furthermore, we verified the efficiency of the proposed methods in primitive cases and large scale collective motion in metastable state.