{"id":3729,"date":"2020-11-21T01:29:41","date_gmt":"2020-11-21T01:29:41","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/11\/21\/diu-awards-army-contract-to-advance-rpa-using-machine-learning\/"},"modified":"2020-11-21T01:29:41","modified_gmt":"2020-11-21T01:29:41","slug":"diu-awards-army-contract-to-advance-rpa-using-machine-learning","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/11\/21\/diu-awards-army-contract-to-advance-rpa-using-machine-learning\/","title":{"rendered":"DIU awards Army contract to advance RPA using machine learning"},"content":{"rendered":"

DIU awards Army contract to advance RPA using machine learning<\/a><\/p>\n

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\t\t\t\t Written by<\/p>\n

Jackson Barnett<\/a>
Nov 20, 2020 | FEDSCOOP<\/span><\/p>\n

The Defense Innovation Unit<\/a> selected Boston-based AI company DataRobot to help the Army use machine learning to boost the power of its robotic process automation (RPA)in financial accounting.<\/p>\n

The contract, which was initially solicited back in May<\/a>, advances the Army\u2019s use of RPA into more complex problem solving areas. Traditional RPA can only do simple, pre-programmed tasks, but adding machine learning from DataRobot allows the RPA to \u201cthink\u201d through more complex problems. The Army and other services have used financial management and other lower-risk problem sets as a starting point<\/a> for many of the more advanced data analytic technology the military hopes to one day use in warfighting.<\/p>\n

\u201cThe intersection of RPA and AI \u2014 what we call Intelligent Process Automation \u2014 can unlock increased efficiencies that have the power to transform organizations,\u201d Chad Cisco, general manager for DataRobot\u2019s federal market, said in a release.<\/p>\n

DIU\u2019s solicitation, which was issued in partnership with the Joint Artificial Intelligence Center, aimed to expand the use of RPA into territory that requires more artificial cognition. DIU was not looking for a cloud service provider or new RPA \u2014 just a platform that will simplify data flow and use open architecture to leverage machine learning, according to the solicitation.<\/p>\n

\u201cThe ML platform will identify and suggest corrections to business processes that are not limited to previously well-defined business logic methods,\u201d the solicitation stated.<\/p>\n

DataRobot said in its release that by integrating existing RPA into its AI platform, the company hopes to save DOD time and money by reducing the number of miss-matched financial transactions. The Army\u2019s hosts some of the largest financial management systems in the world that track billions of dollars a day<\/a>. The department recently migrated much of its data and services to the cloud, enabling greater use of RPA and machine learning systems.<\/p>\n<\/p>\n

Published at Fri, 20 Nov 2020 20:26:15 +0000<\/p>\n

UC Berkeley researchers create artificial intelligence to grasp, move objects<\/a><\/p>\n

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UC Berkeley researchers created an artificial intelligence, or AI, software that gives robots the ability to grasp and move objects smoothly, making it possible for them to eventually assist in warehouses.<\/p>\n

The COVID-19 pandemic has increased the demand of online retail while also reducing the ability for warehouse workers to fulfill orders, according to UC Berkeley postdoctoral researcher and primary author of the study Jeffrey Ichnowski.<\/p>\n

In response, Ichnowski and Ken Goldberg, the campus William S. Floyd Jr. Distinguished Chair in Engineering and senior author of the study, collaborated with graduate student Yahav Avigal and undergraduate student Vishal Satish to create an AI software with a deep learning neural network. This AI function allows robots to use data learned from examples to approximate how to execute an action in different situations, which gives robots the ability to assist with warehouse tasks, Ichnowski said.<\/p>\n

\u201cThere is a huge demand for this robotic operation in warehouses; however, automating robots to do warehouse tasks usually done by humans can be difficult,\u201d Ichnowski said. \u201cSo what we had to do was find a way to create a repetitive process for robots to pick up different objects and place them somewhere else rapidly.\u201d<\/p>\n

This AI improves the Grasp-Optimized Motion Planner, a prior creation of Ichnowski and Goldberg that gives robots the ability to compute how to pick up and transfer objects from one location to another, according to the study.<\/p>\n

The study said the original motion planner was flawed due to the \u201cjerk\u201d of its motions that could result in damage to the robots and its long computation time for planning out motions. These are problems that the AI attempts to fix by incorporating a deep learning neural network.<\/p>\n

\u201cTo speed up the process of planning motion, we trained a neural network to learn from examples and make approximations to execute motions over and over again,\u201d Ichnowski said. \u201cHowever, while the neural network made motion fast, it was still inaccurate and jerky. So it became necessary to combine it with the motion planner.\u201d<\/p>\n

Incorporating the neural network into the motion planner cuts down the computation time for planning out motion from 29 seconds to 80 milliseconds, according to the study. Additionally, the motion planner refines the neural network\u2019s approximations for picking up objects so that the \u201cjerky\u201d movements of robots are eliminated. <\/span><\/p>\n

Jason Dong, external officer of Machine Learning at Berkeley, said the student-led organization is inspired by the creation and what it could do for the future.<\/p>\n

\u201cThis creation is exciting because it allows robots to achieve what workers do just as efficiently, but with a certain level of confidence that they\u2019ll do it correctly every time,\u201d Dong said. \u201cBut there are also so many different cases that this can be used for beyond warehouse automation, which is another benefit to this creation.\u201d<\/p>\n

Annika Kim Constantino is a general assignment reporter. Contact her at [email protected]<\/span><\/a> and follow her on Twitter at @AnnikaKimC<\/a>.<\/em><\/p>\n<\/p>\n

Published at Fri, 20 Nov 2020 20:03:45 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

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