The US Department of Energy (DOE) has long stood out as one of the most science, technology, and innovation-focused US federal agencies. It should come as little surprise then that the DOE continues to invest in transformative technology such as artificial intelligence and machine learning. The DOE established the Artificial Intelligence and Technology (AITO) office to help transform the DOE into a world leading Artificial Intelligence (AI) enterprise by accelerating the research, development, delivery, and adoption of AI. Pamela Isom, the new Director of the AITO, will be presenting at the February 2022 AI in Government event to share how they are maximizing the impacts of AI through strategic coordination, planning, and customer service excellence. In this interview article Ms. Isom goes into greater detail about how the DOE is leveraging data, and transformative technologies to help advance the agency's core missions.

The Royal Surrey Foundation Trust treated Emma McCormick, 44, using adaptive radiotherapy after she was diagnosed with the cancer last April and was referred to St Luke's Cancer Centre. The treatment, called Ethos, involves a machine, created by healthcare company Varian, which uses artificial intelligence to deliver a prescription dose to tumours. The AI technology uses daily CT scans to target the specific areas that need radiotherapy, which helps avoid damage to healthy tissue and limit side-effects. Patients are required only to lay still on a flat surface inside the machine for the duration of the treatment. There is a screen above the machine which shows different images, and medical staff can play music to make the treatment more comfortable.

Something a little more unusual in this blog post as we are going to be exploring how Artificial Intelligence can be a tool that we can use to help and support people, of any age with additional needs or disabilities, in our churches. "Technology has changed the world, bringing knowledge within reach and expanding a range of opportunities. Persons with disabilities can benefit enormously from such advances, yet too many lack access to these essential tools…" So, has anything changed since then? And what does today's Artificial Intelligence, or'AI', offer as technological solutions for disabled people, particularly in our church settings? What can we learn about'AI' together, that can enable us to better serve and support disabled people in our church communities?

Death by suicide is the seventh of the leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI application in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of researches that applies AI to extract audiovisual non-verbal cues for mental illness assessment. However, the majority of the recent works focus on depression, despite the evident difference between depression signs and suicidal behavior non-verbal cues. In this paper, we review the recent works that study suicide ideation and suicide behavior detection through audiovisual feature analysis, mainly suicidal voice/speech acoustic features analysis and suicidal visual cues.

Ben Meisner is the Founder of the leading online photo editing platform Ribbet.com. Artificial intelligence (AI) may seem like a buzzword of the 21st century, but it entered the human psyche some time ago. A Harvard article on the history of AI points out that science fiction brought the concept into our minds in the first half of the 20th century through characters like the Tin Man in The Wizard of Oz and the humanoid robot impersonating Maria in Metropolis. Mankind is now taking the concept from idea to reality, and today AI has tremendous application in everything from medicine, construction and finance to home appliances, social media and copywriting. It has the unique capability to quickly learn from significant amounts of data, enabling it to tackle some of our most challenging technological issues.

Organizational intelligence (OI) is the capability of an organization to comprehend and create knowledge relevant to its purpose. In other words, it is the intellectual capacity of the entire organization. Lumenore is a powerful, intuitive, and cloud-based BI and analytics platform that delivers organizational intelligence by sifting data from any business application. Analytics Insight has engaged in an exclusive interview with Naren Vijay, EVP of Lumenore. Lumenore is a powerful, intuitive, and cloud-based BI and analytics platform that delivers organizational intelligence by sifting data from any business application.

The Association for Computing Machinery has named Anupam Gupta and Matthew T. Mason 2021 ACM fellows. The ACM recognized Gupta, a professor in the Computer Science Department, for his contributions to approximation algorithms, online algorithms, stochastic algorithms and metric embeddings. Mason, a professor emeritus in the Robotics Institute, was honored for his contributions to robotic manipulation and manipulation path planning. Gupta and Mason were among 70 fellows recognized in 2021. "Computing professionals have brought about leapfrog advances in how we live, work and play," said ACM President Gabriele Kotsis. "New technologies are the result of skillfully combining the individual contributions of numerous men and women, often building upon diverse contributions that have emerged over decades.

Artificial intelligence helps build physical infrastructure like modular housing, skyscrapers, and factory floors. "…many problems that we wrestle with in all forms of engineering and design are very, very complex problems…those problems are beginning to reach the limits of human capacity," says Mike Haley, the vice president of research at Autodesk. But there's hope with AI capabilities, Haley continues "This is a place where AI and humans come together very nicely because AI can actually take certain very complex problems in the world and recast them." And where "AI and humans come together" is at the start of the process with generative design, which incorporates AI into the design process to explore solutions and ideas that a human alone might not have even considered. "You really want to be able to look at the entire lifecycle of producing something and ask yourself, 'How can I produce this by using the least amount of energy throughout?'" This kind of thinking will reduce the impact of, not just construction, but any sort of product creation on the planet. The symbiotic human-computer relationship behind generative design is necessary to solve those "very complex problems"--including sustainability. "We are not going to have a sustainable society until we learn to build products--from mobile phones to buildings to large pieces of infrastructure--that survive the long-term," Haley notes. The key, he says, is to start in the earliest stages of the design process. "Decisions that affect sustainability happen in the conceptual phase, when you're imagining what you're going to create." He continues, "If you can begin to put features into software, into decision-making systems, early on, they can guide designers toward more sustainable solutions by affecting them at this early stage."

Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron Courville and Marc G. Bellemare won an outstanding paper award at NeurIPS2021 for their paper Deep Reinforcement Learning at the Edge of the Statistical Precipice. In this blog post, Rishabh Agarwal and Pablo Samuel Castro explain this work. Reinforcement learning (RL) is an area of machine learning that focuses on learning from experiences to solve decision making tasks. While the field of RL has made great progress, resulting in impressive empirical results on complex tasks, such as playing video games, flying stratospheric balloons and designing hardware chips, it is becoming increasingly apparent that the current standards for empirical evaluation might give a false sense of fast scientific progress while slowing it down. To that end, in "Deep RL at the Edge of the Statistical Precipice", given as an oral presentation at NeurIPS 2021, we discuss how statistical uncertainty of results needs to be considered, especially when using only a few training runs, in order for evaluation in deep RL to be reliable.