{"id":26304,"date":"2021-05-28T06:00:00","date_gmt":"2021-05-28T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=26304"},"modified":"2021-05-25T11:15:35","modified_gmt":"2021-05-25T18:15:35","slug":"understanding-human-intent-and-behavior-with-computer-vision","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2021\/05\/28\/understanding-human-intent-and-behavior-with-computer-vision\/","title":{"rendered":"Understanding \u201cHuman Intent and Behavior\u201d with Computer Vision"},"content":{"rendered":"\n

Computer vision is one of the most eminent forms of statistical Artificial Intelligence in use today. Comprised of varying facets of object detection, facial recognition, image classification, and other techniques, it supports a range of pressing use cases from contact-less shopping to video surveillance.<\/p>\n\n\n\n

Its enterprise utility, however, is centered on one indisputable fact which, although perhaps not as exigent as the foregoing ones, redoubles its underlying merit\u2014and ROI. Quite simply, it enables machines to see and understand things the way humans do, providing the foundation for the former to act<\/em> like humans and effectively automate any critical business process, task, or function.<\/p>\n\n\n\n

This potential is frequently realized via Robotic Process Automation bots that, enriched by computer vision\u2019s machine intelligence, will \u201cact on your behalf, Mr. Human, and do the things you want me to do,\u201d remarked Automation Anywhere<\/a> CTO Prince Kohli. \u201cBut now to act like you, I must be able to interact with my environment, the applications on my desktop, or other things, like you. That means I need a certain amount of AI and cognitive computing. Computer vision, for example, must be able to understand invoice numbers, images, how to digitize documents, all those things.\u201d<\/p>\n\n\n\n

The tandem of computer vision and bots supports three foundational use cases yielding immediate enterprise value. It\u2019s integral for automating any series of tasks, performing Intelligent Document Processing (IDP), and discovering new processes to automate. Each of these applications considerably increases the throughput, scale, and celerity of enterprise automation for everything from back-end processes to external, revenue generating ones.<\/p>\n\n\n\n

Process Automation<\/strong><\/p>\n\n\n\n

The machine learning substratum upholding computer vision largely relies on deep neural networks<\/a>, many of which are highly domain specific. These machine learning models undergo a training period so that, for many enterprise users, they offer out-the-box functionality in robust solutions specializing in automation. Typically, computer vision is the means of empowering bots to view and comprehend human behavior with the propensity \u201cto look at a desktop, a screen in front of a person, and look at applications,\u201d Kohli revealed. \u201cFor each application and each window, by your interaction with each window, it understands what the window does.\u201d<\/p>\n\n\n\n

This perceptivity is the basis for gleaning how the information on a screen is used in a particular process\u2014such as accessing Salesforce information for support for sales teams dealing with prospects. With computer vision providing the intelligence about the significance of the onscreen items, applications, APIs, login information, and more, bots are able to implement action from this knowledge to automate processes. \u201cThey know the data on the window, the controls on the window, where the target is, where information gets extracted, and how do you, for example, submit something on Salesforce,\u201d Kohli mentioned. \u201cWhat do you have to fill in, what do you have to take out, what do you do with a task-based system or some local system, whether it be some IT system, etc.\u201d Bots can record such activities and, when desired, complete them perfectly.<\/p>\n\n\n\n

Intelligent Document Processing<\/strong><\/p>\n\n\n\n

IDP has practically become horizontally applicable and is regularly used in industries such as insurance, healthcare, financial services, and others. This computer vision application is so widely employed because of its rapidity for accurately processing documents in which intelligent bots \u201cget digitized documents, whether it be a PDF or whatever it may be, and understand what it says\u2014not just a text scan,\u201d Kohli noted. Such instantaneous comprehension is useful for classifying documents, extracting information from them, and automatically routing<\/a> it to downstream systems. Moreover, this use case is one of the best examples of the range of cognitive computing models\u2014many of which may be accessed via the cloud\u2014required to support computer vision. \u201cThe IDP space is so rich with AI models for understanding, for example, lung cancer or some other problem in the lung, or an AI model to understand disease, which is very different from an AI model to understand invoices or a factory layout,\u201d Kohli maintained.<\/p>\n\n\n\n

The diversity of machine learning models underpinning these various computer vision applications is readily accessible via platforms like Google Cloud, which not only specializes in these data science resources, but also partners with competitive RPA platforms for such purposes. \u201cWe have a few models that work very well for certain invoices, the mortgage space, and a few others as well,\u201d Kohli commented. \u201cAnd then, Google brings many, many others so the benefit that customers get is best of breed AI models for their use cases.\u201d The capacity to readily utilize models based on subject matter expertise in specific domains is another way computer vision equips machines to process information as well as, if not better, than humans can\u2014especially when aided by bots.<\/p>\n\n\n\n

Process Discovery<\/strong><\/p>\n\n\n\n

No matter which computer vision techniques a particular system or digital agent employs, the primary value they yield is the capability to intelligently observe what it\u2019s monitoring. For enterprise users attempting to pare costs, boost efficiency, and achieve more while using fewer resources, one of the most valuable deployments of this technology is discovering new processes to automate (which inherently meets these three objectives). Combining this optical characteristic with the action virtual agents create<\/a> delivers the best of both worlds: unwavering observation and automated action. According to Kohli, \u201cFinding processes is one of the hardest jobs of automation. If you have software that just passively sits there and understands all the processes in long, valuable processes, and helps you automate them very easily, that is very close to Nirvana.\u201d<\/p>\n\n\n\n

The parallels between a digital agent performing this function and a human (like a supervisor or a manager) watching the way workers process the various steps for insurance company adjudication, for instance, before suggesting ways to make them faster, easier, and more scalable\u2014then implementing those steps for them\u2014are perfectly clear. They\u2019re also consistently realized when deploying bots with computer vision that specialize in such functionality. In fact, Kohli observed that fundamentally, the capability to regularly discover processes and automate them requires the capacity to \u201cunderstand human intent and behavior.\u201d With bots, organizations can leverage this computer vision advantage to then duplicate that behavior.<\/p>\n\n\n\n

Machine Intelligence<\/strong><\/p>\n\n\n\n

There are several complexities in daily operations that humans take for granted. The notion of intelligence in this sense is relatively broad, and oftentimes easily overlooked. For example, people can visually assess things so quickly to determine, for example, a particular payment code for a medical procedure. \u201cI can look at an invoice, and in two seconds flat I can tell you that the invoice is about this asset, it was ordered on this date, here is the address to which it was going to be delivered, here is the address it came from, and this is the person to whom it\u2019s going,\u201d Kohli denoted. \u201cAs a human, I can do that very quickly for any invoice. But for a computer that\u2019s hard.\u201d<\/p>\n\n\n\n

Computer vision, however, makes such a task much less exacting. Grafting it to RPA creates repeatable, sustainable action from this knowledge, which is imperative for automating important business processes.<\/p>\n\n\n\n

About the Author<\/strong><\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance and analytics.<\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: @InsideBigData1 \u2013 https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, editorial consultant Jelani Harper discusses how computer vision is one of the most eminent forms of statistical Artificial Intelligence in use today. Comprised of varying facets of object detection, facial recognition, image classification, and other techniques, it supports a range of pressing use cases from contact-less shopping to video surveillance.<\/p>\n","protected":false},"author":10513,"featured_media":23145,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,180,67,268,56,97,1],"tags":[581,782,1010,771,96],"acf":[],"yoast_head":"\nUnderstanding \u201cHuman Intent and Behavior\u201d with Computer Vision - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2021\/05\/28\/understanding-human-intent-and-behavior-with-computer-vision\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Understanding \u201cHuman Intent and Behavior\u201d with Computer Vision - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, editorial consultant Jelani Harper discusses how computer vision is one of the most eminent forms of statistical Artificial Intelligence in use today. 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