Data Matters: Our thoughts and views on the state of digital data
Our nation’s small businesses are facing an unprecedented economic disruption due to the Coronavirus (COVID-19) outbreak. On Friday, March 27, 2020, the President signed into law the CARES Act, which contains $376 billion in relief for American workers and small businesses. The Challenge to Lenders: Since the CARES Act was signed, banks, credit unions and
The Strange Case of Document Analysis and Text Processing An Ode to a Document When people ask me what I do for a living, I proudly answer: I drive technology innovation for a company that does Document Processing and Analysis. The reaction I receive is typically a blank gaze. Let me explain why Document Analysis
The Often-Forgotten but Critical Step in Scaling AI and Machine Learning When most people think of artificial intelligence (AI) they conjure up notions of advanced machine learning algorithms, deep neural networks or computational cybernetics. You know, the sexy, futuristic-sounding concepts that are having an impact on the world around us. What doesn’t come to mind
Why the Difference is Crucial for Image Annotation For any business trying to create a distinguished competitive edge in the market, computer vision has been a go-to enabler. From improving customer experience to reducing costs, computer vision is being applied across a diverse set of industries to accurately identify and classify objects. One of the
Why Data Annotation is the Key to Success Insurance companies are trying very hard to shake the stigma that they are behind the times, and with good reason. The popular consensus among consumers is that dealing with an insurance provider is like getting a root canal without anesthesia. Ouch. To be fair, the process is
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.