Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new system involves large, usable structures built from an array of tiny identical subunits called voxels (the volumetric equivalent of a 2-D pixel). Researchers at MIT have made significant steps toward creating robots that could practically and economically assemble nearly anything, including things much larger than themselves, from vehicles to buildings to larger robots. The new work, from MIT's Center for Bits and Atoms (CBA), builds on years of research, including recent studies demonstrating that objects such as a deformable airplane wing and a functional racing car could be assembled from tiny identical lightweight pieces -- and that robotic devices could be built to carry out some of this assembly work. Now, the team has shown that both the assembler bots and the components of the structure being built can all be made of the same subunits, and the robots can move independently in large numbers to accomplish large-scale assemblies quickly.

But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture. "Current methods create a situation where each worker has expensive computational cost; GD-SEC is relatively cheap where only one GD step is needed at each round," says Blum. Professor Blum's collaborators on this project include his former student Yicheng Chen '19G '21PhD, now a software engineer with LinkedIn; Martin Takác, an associate professor at the Mohamed bin Zayed University of Artificial Intelligence; and Brian M. Sadler, a Life Fellow of the IEEE, U.S. Army Senior Scientist for Intelligent Systems, and Fellow of the Army Research Laboratory. But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture.

A new framework for neural networks' processing enables artificial intelligence to better judge objects and potential threats in hostile environments. Researchers from the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory and university partners from the Internet of Battlefield Things Collaborative Research Alliance, or IoBT CRA, developed a method for neural networks to be more confident in their understanding of battlefield environments. To achieve this, researchers reviewed frameworks to represent uncertainty, categorized sources of uncertainty in military information-networks' common operating environment, and most importantly created solutions to manage uncertainty within systems. The researchers developed insights from the uncertainty management approaches into a workflow that maximizes effectiveness in accomplishing mission goals despite the presence of uncertainty in data inputs. Through this process, they teach neural networks when to say, "I am sure," and be right about it.

The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-34) was to be held May 17-19, 2021, at the Double Tree Ocean Point Resort and Spa in North Miami Beach, Florida, USA. Due to COVID-19 pandemic and travel restriction, the conference held both virtual and in-person. The planned conference events included tutorials, invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Keith Brawner from the Army Research Laboratory. The program co-chairs were Roman Barták from Charles University, Prague, and Eric Bell, USA.

Spoken dialogue is the most natural way for people to interact with complex autonomous agents such as robots. Future Army operational environments will require technology that allows artificial intelligent agents to understand and carry out commands and interact with them as teammates. Researchers from the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory and the University of Southern California's Institute for Creative Technologies, a Department of Defense-sponsored University Affiliated Research Center, created an approach to flexibly interpret and respond to Soldier intent derived from spoken dialogue with autonomous systems. This technology is currently the primary component for dialogue processing for the lab's Joint Understanding and Dialogue Interface, or JUDI, system, a prototype that enables bi-directional conversational interactions between Soldiers and autonomous systems. "We employed a statistical classification technique for enabling conversational AI using state-of-the-art natural language understanding and dialogue management technologies," said Army researcher Dr. Felix Gervits.

When photons are used as the carriers of quantum information to transmit data, that information is often distorted due to environment fluctuations destroying the fragile quantum states necessary to preserve it. Researchers from Louisiana State University exploited a type of machine learning to correct for information distortion in quantum systems composed of photons. Published in Advanced Quantum Technologies, the team demonstrated that machine learning techniques using the self-learning and self-evolving features of artificial neural networks can help correct distorted information. This results outperformed traditional protocols that rely on conventional adaptive optics. "We are still in the fairly early stages of understanding the potential for machine learning techniques to play a role in quantum information science," said Dr. Sara Gamble, program manager at the Army Research Office, an element of U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory.

The Defense Department, Army and Howard University linked up to collectively push forward artificial intelligence and machine learning-rooted research, technologies and applications through a recently unveiled center of excellence. Work it will underpin will "shape the future," according to an announcement Monday from the Army Research Laboratory--and the $7.5 million center also marks a move by the Pentagon to help expand its pipeline for future personnel. "Diversity of science and diversity of the future [science and technology] talent base go hand-in-hand in this new and exciting partnership," Dr. Brian Sadler, Army senior research scientist for intelligent systems said. Tapped to manage the partnership, Sadler added that Howard University is "an intellectual center for the nation." Encompassing 13 schools and colleges, the institution is a private, historically Black research university that was founded in 1867.

Researchers at MIT's Center for Bits and Atoms have created tiny building blocks that exhibit a variety of unique mechanical properties, such as the ability to produce a twisting motion when squeezed. These subunits could potentially be assembled by tiny robots into a nearly limitless variety of objects with built-in functionality, including vehicles, large industrial parts, or specialized robots that can be repeatedly reassembled in different forms. The researchers created four different types of these subunits, called voxels (a 3D variation on the pixels of a 2D image). Each voxel type exhibits special properties not found in typical natural materials, and in combination they can be used to make devices that respond to environmental stimuli in predictable ways. Examples might include airplane wings or turbine blades that respond to changes in air pressure or wind speed by changing their overall shape. The findings, which detail the creation of a family of discrete "mechanical metamaterials," are described in a paper published in the journal Science Advances, authored by recent MIT doctoral graduate Benjamin Jenett PhD '20, Professor Neil Gershenfeld, and four others.

Sikhanda Satapathy, from DEVCOM ARL, and Prof. K.T. Ramesh, director of the Hopkins Extreme Materials Institute, will lead the research activities. To launch these projects, the partners held a joint virtual kickoff meeting Nov. 4."This collaborative agreement will enable and accelerate intelligent design of materials for extreme dynamic environments to support our Soldiers and address Army's future material needs," Satapathy said.Over the next two years, researchers will explore the use of artificial intelligence and machine learning to accelerate materials development. One of the projects is focused using artificial intelligence techniques to accelerate the processing and characterization of new materials."This The experimentally and computationally generated data will be used to train neural networks which will be used to accelerate the materials design process.Another project will incorporate machine learning of acoustic emission measurements to characterize materials deformation mechanisms. These measurements are non-trivial and require expertise in both instrumentation and data analysis."This

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Army researchers are working with the University of Illinois Chicago on unmanned technology for recharging drone swarms. The university has been awarded a four-year, $8 million cooperative agreement "to develop foundational science in two critical propulsion and power technology areas for powering future families of unmanned aircraft systems," according to a statement released by the Army Research Laboratory. "This collaborative program will help small battery-powered drones autonomously return from military missions to unmanned ground vehicles for recharging," the Army added.