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Man vs. Machine: Moving into the Future

Mark Gallagher, Vice President - Sales, Parascript
Wednesday, July 13, 2016
Mark Gallagher, Vice President - Sales, Parascript
Headquartered in Colorado, Parascript's solutions enable businesses to gather meaningful and contextual data from image or document-based information and support transactions, information governance, business processes and prevent frauds.

Today, you can have your cake and eat it too. Businesses are doing just that - cutting costs with advanced classification and data extraction technologies while simultaneously saving jobs. How are businesses accomplishing this?

Faster, Better, Smarter

The typical response to the need to improve speed, quality or accuracy is to automate. However, countless case studies show that humans need to be involved. Whether it is artificial intelligence, machine learning, deep learning or simply mechanical machines, the idea that automation can replace human-performed tasks has been popular in one form or another for years. Increasingly, businesses are putting advanced technology into the mainstream with the intent to supplement human-based work. What works is assistive, not replacement technology.

For example, at Parascript, the staff has been working with advanced machine vision and machine learning technologies for over 20 years, applying it to very practical needs in banking and government. Solving needs such as locating and interpreting handwritten or text-based information into transactional or actionable data should be the motto of every organization. These problems affect every organization and are core issues with customer service, operational effectiveness, and information governance. To put it simply, machine learning and artificial intelligence cannot provide both highly automated and highly accurate document classification results. AI does very well at defined tasks where there is a defined answer, but where transposition is required, humans do a superior job. The answer is to intelligently blend both computer-based heuristics with human-based workflows.


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