Robotic Wire Arc Additive Manufacturing (WAAM) is a metal additive manufacturing technology, offering flexible 3D printing while ensuring high quality near-net-shape final parts. However, WAAM also suffers from geometric imprecision, especially for low-melting-point metal such as aluminum alloys. In this paper, we present a multi-robot framework for WAAM process monitoring and control. We consider a three-robot setup: a 6-dof welding robot, a 2-dof trunnion platform, and a 6-dof sensing robot with a wrist-mounted laser line scanner measuring the printed part height profile. The welding parameters, including the wire feed rate, are held constant based on the materials used, so the control input is the robot path speed. The measured output is the part height profile. The planning phase decomposes the target shape into slices of uniform height. During runtime, the sensing robot scans each printed layer, and the robot path speed for the next layer is adjusted based on the deviation from the desired profile. The adjustment is based on an identified model correlating the path speed to change in height. The control architecture coordinates the synchronous motion and data acquisition between all robots and sensors. Using a three-robot WAAM testbed, we demonstrate significant improvements of the closed loop scan-n-print approach over the current open loop result on both a flat wall and a more complex turbine blade shape.

The Association for the Advancement of Artificial Intelligence's 2020 Fall Symposium Series was held virtually from November 11-14, 2020, and was collocated with three symposia postponed from March 2020 due to the COVID-19 Pandemic. There were five symposia in the fall program: AI for Social Good, Artificial Intelligence in Government and Public Sector, Conceptual Abstraction and Analogy in Natural and Artificial Intelligence, Physics-Guided AI to Accelerate Scientific Discovery, and Trust and Explainability in Artificial Intelligence for Human-Robot Interaction. Additionally, there were three symposia delayed from spring: AI Welcomes Systems Engineering: Towards the Science of Interdependence for Autonomous Human-Machine Teams, Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools, and Risks, and Towards Responsible AI in Surveillance, Media, and Security through Licensing. Recent developments in big data and computational power are revolutionizing several domains, opening up new opportunities and challenges. In this symposium, we highlighted two specific themes, namely humanitarian relief, and healthcare, where AI could be used for social good to achieve the United Nations (UN) sustainable development goals (SDGs) in those areas, which touch every aspect of human, social, and economic development. The talks at the symposium were focused on identifying the critical needs and pathways for responsible AI solutions to achieve SDGs, which demand holistic thinking on optimizing the trade-off between automation benefits and their potential side-effects, especially in a year that has upended societies globally due to the COVID-19 pandemic. Riding on the success of the AI for Social Good symposium that was held in Washington, DC, in November 2019, we organized the 2020 version of the symposium.

This transformation is being made possible by a surge in data and computing power that can help machine learning algorithms not only perform device-specific tasks, but also help them gain intelligence or knowledge over time. In the not-so-distant future, artificial intelligence and machine learning tasks will be carried out among connected devices through wireless networks, dramatically enhancing the capabilities of future smartphones, tablets, and sensors, and achieving what's known as distributed intelligence. As technology stands right now, however, machine learning algorithms are not efficient enough to be run over wireless networks and wireless networks are not yet ready to transmit this type of intelligence. With the support of a National Science Foundation Faculty Early Career Development Program (CAREER) grant, Tianyi Chen, an assistant professor of electrical, computer, and systems engineering at Rensselaer Polytechnic Institute and member of the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC), is exploring how to make such knowledge-sharing tools a reality. "I think in the future, the main terminal of intelligence will be our phones. Our phones will be able to control our computers, our cars, our meeting rooms, our apartments," Chen said.

This editorial introduces the special topic articles on reflections on successful research in artificial intelligence. Consisting of a combination of interviews and full-length articles, the special topic articles examine the meaning of success and metrics of success from a variety of perspectives. Our editorial team is especially excited about this topic, because we are in an era when several of the aspirations of early artificial intelligence researchers and futurists seem to be within reach of the general public. This has spurred us to reflect on, and re-examine, our social and scientific motivations for promoting the use of artificial intelligence in governments, enterprises, and in our lives. Ching-Hua Chen is a research staff member at the T.J. Watson Research Center in Yorktown Heights, New York.

The field of artificial intelligence and machine learning, particularly the subcategory of deep learning, has experienced massive growth in recent years, with applications ranging from speech recognition to material inspection, healthcare to gaming, to name but a few. One area that's being transformed by machine learning is tomographic imaging – in which a series of data projections (such as X-ray radiographs, for example) are reconstructed into a three-dimensional image. A newly published book, Machine Learning for Tomographic Imaging, presents a detailed overview of the emerging discipline of deep-learning-based tomographic imaging. The book arose from discussions among four colleagues with a long-standing interest in advanced medical image reconstruction: Ge Wang from Rensselaer Polytechnic Institute, Yi Zhang of Sichuan University, Xiaojing Ye from Georgia State University and Xuanqin Mou from Xi'an Jiaotong University. "Deep tomographic reconstruction is a new area, and the development of this area has been rapid over the past years," explains Wang.

"The addition of these faculty is expanding our interdisciplinary cohort of AI researchers across the entire campus. We expect these four outstanding faculty members are the first wave of hires who will increase our capabilities for AI and machine learning research across all five of Rensselaer's schools," said James Hendler, director of the AIRC, and a Rensselaer Tetherless World Professor of Computer, Web, and Cognitive Science. The Rensselaer-IBM AIRC is dedicated to advancing the science of artificial intelligence and enabling the use of AI and machine learning in research investigations, innovations, and applications of joint interest to both Rensselaer and IBM. The collaboration fosters the growth of AI and machine learning capabilities through faculty hires, by funding specific research initiatives, and through funding top graduate students as IBM AI Horizons fellows. For more information about the AIRC, watch this video.

Generating comprehensive molecular images of organs and tumors in living organisms can be performed at ultra-fast speed using a new deep learning approach to image reconstruction developed by researchers at Rensselaer Polytechnic Institute. The research team's new technique has the potential to vastly improve the quality and speed of imaging in live subjects and was the focus of an article recently published in Light: Science and Applications, a Nature journal. Compressed sensing-based imaging is a signal processing technique that can be used to create images based on a limited set of point measurements. Recently, a Rensselaer research team proposed a novel instrumental approach to leverage this methodology to acquire comprehensive molecular data sets, as reported in Nature Photonics. While that approach produced more complete images, processing the data and forming an image could take hours. This latest methodology developed at Rensselaer builds on the previous advancement and has the potential to produce real-time images, while also improving the quality and usefulness of the images produced.

Generating comprehensive molecular images of organs and tumors in living organisms can be performed at ultra-fast speed using a new deep learning approach to image reconstruction developed by researchers at Rensselaer Polytechnic Institute. The research team's new technique has the potential to vastly improve the quality and speed of imaging in live subjects and was the focus of an article recently published in Light: Science and Applications, a Nature journal. Compressed sensing-based imaging is a signal processing technique that can be used to create images based on a limited set of point measurements. Recently, a Rensselaer research team proposed a novel instrumental approach to leverage this methodology to acquire comprehensive molecular data sets, as reported in Nature Photonics. While that approach produced more complete images, processing the data and forming an image could take hours.

IBM Research and Rensselaer Polytechnic Institute (RPI) are collaborating on a new approach to help students learn Mandarin. The strategy pairs an AI-powered assistant with an immersive classroom environment that has not been used previously for language instruction. The classroom, called the Cognitive Immersive Room (CIR), makes students feel as though they are in restaurant in China, a garden, or a Tai Chi class, where they can practice speaking Mandarin with an AI chat agent. The CIR was developed by the Cognitive and Immersive Systems Lab (CISL), a research collaboration between IBM Research and RPI. CISL researchers demonstrate an AI-assisted Mandarin Chinese language learning aid.

The Center for Financial Studies in the Lally School of Management at Rensselaer Polytechnic Institute will host a one-day workshop titled Artificial Intelligence and Machine Learning in Financial Services. The workshop will take place on April 27 from 8 a.m. to 5 p.m. in the Center for Biotechnology and Interdisciplinary Studies Auditorium on campus. During the workshop, academic and industry experts will present a variety of perspectives and insights on using artificial intelligence and machine learning (AI/ML) tools to address several important challenges facing the financial services industry. Rensselaer President Shirley Ann Jackson will deliver the opening remarks at 8:45 a.m. Speakers include Kathryn Guarini, vice president for research strategy at IBM, who will deliver the Reinert Lecture, "Trends and Developments in AI for Financial Applications," and Akhtar Siddique, deputy director for enterprise risk and analysis in the office of the Comptroller of the Currency, who will speak on "Risk Measurement with Machine Learning Techniques."