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Data Matters: Our thoughts and views on the state of digital data
What Is Computer Vision
Computer Vision (CV) is a field of research and industry focused on enabling computers to see. By combining machine learning and computation, machines are able to build visual systems that allow them to identify, contextualize, and understand the visual world the way humans do. Learn More >>
Technology And Its Impact On The Insurance Sector
Innodata: What is the impact of technology on Insurance? Meg: In the age of Amazon, taking more than two days to do anything is unacceptable. This includes acquiring a new policy, placing a claim, or receiving a settlement check. Traditional Insurance institutions must leverage technology to operate at high speeds with personalized service. The
Innodata Teams Up With Re•Work AI in Finance Virtual Summit
Innodata’s Chief Product & Marketing Officer Rahul Singhal will present at the AI in Finance Virtual Summit. Stay tuned for updates and be sure to join us on September 17th and 18th. Additional details can be found here: The event is RE•WORK’S LARGEST VIRTUAL SUMMIT!• Presentations from world-leading experts on the latest researchadvancements and real-world
Monitoring Public Health Regulations During Covid-19
Monitoring Government & State Regulations for Businesses Managing Ever-Evolving Mandates Local businesses and organizations are finding the ever-evolving changes to government and state regulations overwhelming and hard to stay ahead of. While governmental agencies are issuing rules and regulations almost daily to keep up with the changing Covid-19 situation, and rightly so, the pace of
The Data Challenge: How Lenders Can Expedite Loan Approvals of The CARES Act for Small Businesses
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
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
Understanding the Role of Taxonomies, Ontologies, Schemas and Knowledge Graphs
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
Semantic Versus Instance Segmentation
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
How Insurance Companies are Using Artificial Intelligence to Transform an Industry
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
The Whole Ground Truth, and Nothing But the Ground Truth
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.