Data Matters: Our thoughts and views on the state of digital data
Natural language processing (NLP) may very broadly be defined as the automatic processing and analysis of large amounts of language data by software. With the evolution and democratization of artificial intelligence and machine learning (AI/ML),as well as data-driven methodologies and theample availability of large amounts of online digitaldata, new sets of NLP tools have emerged and become largely mainstream. However, the key pre-requisite to build applications leveraging these tools and technologies is the availability of highly relevant representative data. Due to this dependence on the context, relevance, quality and availability of a good number of examples to work from,there is often a big challenge increating meaningfuldatasets.
On the heels of a historic and heated election, The UK announced it will be formally leaving the European Union as ofJanuary 31, 2020. Despite the landslide victory led by British Prime Minster Boris Johnson’s Conservative Party, there are still many obstacles to overcome before a withdrawal from the EU is finalized. Nevertheless, investors and financial analysts have many concerns about the impact Brexit willhave on the derivatives industry, including fragmented markets and liquidity shortfalls.
Intelligent automation is the latest buzzword thrown around by business leaders who want to reduce operational complexity and accelerate growth. Fair enough. In today’s hyper-competitive, global business environment, organizations are demanding to do more with less. Intelligent automation promises to reshape organizational structure and streamline complex routines. But are businesses really employing the type of intelligent automation technologies that can transform the business?
Artificial intelligence seems to be everywhere you look these days, this article notwithstanding. But seriously, while AI is commonly seen as a complex but innovative solution for curing diseases and fostering social good, today it is being applied to accomplish everything from drafting a better fantasy football team to help craft pick-up lines (how you
Data annotation (also referred to as data labeling) is quite critical to ensuring your AI and machine learning projects can scale. It provides that initial setup for training a machine learning model with what it needs to understand and how to discriminate against various inputs to come up with accurate outputs. There are many different