The Defense Innovation Unit 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.
The contract, which was initially solicited back in May, advances the Army’s 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 “think” through more complex problems. The Army and other services have used financial management and other lower-risk problem sets as a starting point for many of the more advanced data analytic technology the military hopes to one day use in warfighting.
“The intersection of RPA and AI — what we call Intelligent Process Automation — can unlock increased efficiencies that have the power to transform organizations,” Chad Cisco, general manager for DataRobot’s federal market, said in a release.
DIU’s 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 — just a platform that will simplify data flow and use open architecture to leverage machine learning, according to the solicitation.
“The ML platform will identify and suggest corrections to business processes that are not limited to previously well-defined business logic methods,” the solicitation stated.
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’s hosts some of the largest financial management systems in the world that track billions of dollars a day. The department recently migrated much of its data and services to the cloud, enabling greater use of RPA and machine learning systems.
Published at Fri, 20 Nov 2020 20:26:15 +0000
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.
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.
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.
“There is a huge demand for this robotic operation in warehouses; however, automating robots to do warehouse tasks usually done by humans can be difficult,” Ichnowski said. “So 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.”
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.
The study said the original motion planner was flawed due to the “jerk” 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.
“To 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,” Ichnowski said. “However, while the neural network made motion fast, it was still inaccurate and jerky. So it became necessary to combine it with the motion planner.”
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’s approximations for picking up objects so that the “jerky” movements of robots are eliminated.
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.
“This creation is exciting because it allows robots to achieve what workers do just as efficiently, but with a certain level of confidence that they’ll do it correctly every time,” Dong said. “But there are also so many different cases that this can be used for beyond warehouse automation, which is another benefit to this creation.”
Published at Fri, 20 Nov 2020 20:03:45 +0000