Thomas Kuhn proposed his paradigmatic view of scientific discovery five decades ago. The concept of paradigm has not only explained the progress of science, but has also become the central epistemic concept among STM scientists. Here, we adopt the principles of Kuhnian philosophy to construct a novel ontology aims at classifying and evaluating the impact of STM scholarly articles. First, we explain how the Kuhnian cycle of science describes research at different epistemic stages. Second, we show how the Kuhnian cycle could be reconstructed into modular ontologies which classify scholarly articles according to their contribution to paradigm-centred knowledge. The proposed ontology and its scenarios are discussed. To the best of the authors knowledge, this is the first attempt for creating an ontology for describing scholarly articles based on the Kuhnian paradigmatic view of science.

In parallel with the progressing digitalization of almost every area of life, artificial intelligence (AI) and analytics capabilities grew tremendously, enabling companies to transform random data trails into meaningful insights that helped them greatly improve business processes. Targeted marketing, location-based searches and personalized promotions became the name of the game. This eventually led to the ability to combine data from various sources into large datasets, and to mine them for granular user profiles of unprecedented detail in order to establish correlations between disparate aspects of consumer behaviour, making individual health risks and electoral choices ever more predictable – for those who held the data.

Over the past few years at Databricks, we've seen a new data management paradigm that emerged independently across many customers and use cases: the lakehouse. In this post we describe this new paradigm and its advantages over previous approaches. Data warehouses have a long history in decision support and business intelligence applications. Since its inception in the late 1980s, data warehouse technology continued to evolve and MPP architectures led to systems that were able to handle larger data sizes. But while warehouses were great for structured data, a lot of modern enterprises have to deal with unstructured data, semi-structured data, and data with high variety, velocity, and volume.

British Prime Minister Boris Johnson introduced a new fast-track visa to attract more of the world's best scientists to the U.K. in hopes of creating a global science "superpower." Johnson paired the announcement of the Global Talent route program with a pledge of 300 million pounds ($392 million) for research into advanced mathematics. The money will help fund researchers and doctoral students whose work in math underpins myriad developments such as safer air travel, smart phone technology and artificial intelligence. The new visa route will have no cap on the number of people able to come to the U.K. under the program. "The UK has a proud history of scientific discovery, but to lead the field and face the challenges of the future we need to continue to invest in talent and cutting edge research,'' Johnson said in a statement.

The recipes for those proteins--called genes--are encoded in our DNA. An error in the genetic recipe may result in a malformed protein, which could result in disease or death for an organism. Many diseases, therefore, are fundamentally linked to proteins. But just because you know the genetic recipe for a protein doesn't mean you automatically know its shape. Proteins are comprised of chains of amino acids (also referred to as amino acid residues). But DNA only contains information about the sequence of amino acids–not how they fold into shape.

Technology is reshaping the operating-model of financial institutions fundamentally, and the attributes necessary to build a successful business. AI is weakening various components of incumbent financial institutions, thereby creating an opportunity for an entirely new operating-models and category-dynamics focused on the scale and sophistication of product, tech & data much more than the scale or complexity of capital. Unlike past'AI Springs', the science and practice of AI is poised to continue an unprecedented multi-decade run of progress. A clear vision of the future financial landscape is critical for good governance and strategic decisions. AI systems will eventually underwrite credit and insurance across the world.

Technology is reshaping the operating-model of financial institutions fundamentally, and the attributes necessary to build a successful business. AI is weakening various components of incumbent financial institutions, thereby creating an opportunity for an entirely new operating-models and category-dynamics focused on the scale and sophistication of product, tech & data much more than the scale or complexity of capital. Unlike past'AI Springs', the science and practice of AI is poised to continue an unprecedented multi-decade run of progress. A clear vision of the future financial landscape is critical for good governance and strategic decisions. AI systems will eventually underwrite credit and insurance across the world.

To understand the natural world, scientists must measure it--but how do we define our units? Over the decades, scientists have gradually redefined classic units in terms of universal constants, such as using the speed of light to help define the length of a meter. But the scientific unit of mass, the kilogram, remained pegged to "Le Grand K," a metallic cylinder stored at a facility in France. If that ingot's mass varied for whatever reason, scientists would have to recalibrate their instruments. No more: In 2019, scientists agreed to adopt a new kilogram definition based on a fundamental factor in physics called Planck's constant and the improved definitions for the units of electrical current, temperature, and the number of particles in a given substance.

Bitlattice has or can have implemented instrumentation needed to act as a neural network. That idea is wild, but ultimately possible and potentially beneficial. While the globe wide network in this mode won't be fast (due to physical limitations of signals speed and delays of network) the fact that the middle layer contains far less nodes than actual number of participating devices makes that idea at least possible to implement. The practical aspect here could be, for instance, making a "feeling planet" like project.

Findings show that data practitioners spend a majority (up to 80%1) of their time on data wrangling instead of mining data for analytics and machine learning projects. Organizations want to find trusted datasets so they gain visibility into workloads across data sources as well as their upstream and downstream impact. Take the first step towards successful cloud modernization with Databricks and Informatica. The partnership provides an end-to-end data discovery and lineage enabled by Informatica's AI-powered Enterprise Data Catalog that helps enterprises be highly strategic about data engineering with complete visibility into their data stack. Register now to see an in-depth demo of the Databricks and Informatica joint solution for data lineage.