The University of Maine just unveiled the world's largest polymer 3D printer. The new printer, named Factory of the Future 1.0 (FoF 1.0), can print objects as large as 96 feet long by 32 feet wide by 18 feet high. It's also quite speedy, relatively speaking, as it can print up to 500 pounds per hour. It can dynamically switch between printing techniques to suit different aspects of complex jobs. The printer can flip between large-scale additive manufacturing, subtractive manufacturing, continuous tape layup and robot arm operations.

Power demand forecasting is a crucial and challenging task for new power system and integrated energy system. However, as public feature databases and the theoretical mechanism of power demand changes are unavailable, the known features of power demand fluctuation are much limited. Recently, multimodal learning approaches have shown great vitality in machine learning and AIGC. In this paper, we interact two modal data and propose a textual-knowledge-guided numerical feature discovery (TKNFD) method for short-term power demand forecasting. TKNFD extensively accumulates qualitative textual knowledge, expands it into a candidate feature-type set, collects numerical data of these features, and eventually builds four-dimensional multivariate source-tracking databases (4DM-STDs). Next, TKNFD presents a two-level quantitative feature identification strategy independent of forecasting models, finds 43-48 features, and systematically analyses feature contribution and dependency correlation. Benchmark experiments in two different regions around the world demonstrate that the forecasting accuracy of TKNFD-discovered features reliably outperforms that of SoTA feature schemes by 16.84% to 36.36% MAPE. In particular, TKNFD reveals many unknown features, especially several dominant features in the unknown energy and astronomical dimensions, which extend the knowledge on the origin of strong randomness and non-linearity in power demand fluctuation. Besides, 4DM-STDs can serve as public baseline databases.

CyberGuy explains a new factory in Oregon that can produce 10,000 robots a year. Maine, a state often admired for its serenity and scenic beauty, recently witnessed an unimaginable nightmare. Robert Card, an assault rifle-carrying gun instructor with documented mental troubles, gunned down 18 innocent people. The 40-year-old suspect was found dead two days later after an intense search by law enforcement. As families and communities mourn the loss, an important question is raised: Could alerting the police mere minutes earlier than the first 911 call have changed the outcome?

Facial recognition technology is mostly associated with uses such as surveillance and the authentication of human faces, but scientists believe they've found a new use for it -- saving seals. A research team at Colgate University has developed SealNet, a database of seal faces created by taking pictures of dozens of harbor seals in Maine's Casco Bay. The team found the tool's accuracy in identifying the marine mammals is close to 100%, which is no small accomplishment in an ecosystem home to thousands of seals. The researchers are working on expanding their database to make it available to other scientists, said Krista Ingram, a biology professor at Colgate and a team member. Broadening the database to include rare species such as the Mediterranean monk seal and Hawaiian monk seal could help inform conservation efforts to save those species, she said.

Automation of the labor force was feared for a long time. In 2017, a website sprung up to answer a question long on the minds of many: Will robots take my job? The creators based it on Bureau of Labor Statistics data and a 2013 research paper from Oxford University about "the susceptibility of jobs to computerization." Things have moved quickly since; even the term "computerization" now sounds desperately out of date. If you plug "journalist" into the site's search bar, for example, the site reveals an "automation risk score" of 9 percent.

A new center at Bigelow Laboratory is using cutting-edge artificial intelligence algorithms to forecast ocean activity, from toxic algal blooms to right whale migration, with the hopes of benefitting both coastal industries and the environment. People are expecting forecasts of all different kinds now, from COVID forecasts to political forecasts," said Nick Record, a senior research scientist at Bigelow Laboratory for Ocean Sciences in East Boothbay. "We're trying to tap into this societal need and demand for forecasts and apply it to ocean systems that we live in and rely on." The ability to accurately forecast complex ocean dynamics alone, such as temperature and salinity, is useful for the industries that use the coastline and the scientists that study it. With artificial intelligence, though, these forecasts will be constantly improving in accuracy even as the climate changes -- and, with it, Maine's ability to adapt to the changing coastline will improve as well.

Artificial intelligence was originally conceived as an engineering task. It has now become an important element of all kinds of business and government work, and an integral part of our everyday lives and a variety of jobs. AI education is valuable not only in the fields of computer science and engineering, but also in other sciences, both natural and social, even in the humanities such as the study of literature, history, politics, and in the creative and performing arts. Many educational institutions around the world have decided to integrate AI education into all their offerings, from engineering to business, and the sciences to the humanities, and even the arts. AI has already found applications in many non-traditional areas.

Maine has just passed the nation's toughest law restricting the use of facial recognition technology. LD 1585 was unanimously approved by the Maine House and Senate on June 16 and 17, respectively, and became law without the signature of Gov. Janet Mills. The bill's sponsor, Rep. Grayson Lookner, D-Portland, hopes that Maine's new law--which goes into effect Oct. 1--will "provide an example to other states that want to rein in the government's ability to use facial recognition and other invasive biometric technologies." The country's only other statewide law regulating facial recognition was passed in Washington in 2020, and it authorized state police to use facial recognition technology for "mass surveillance of people's public movements, habits, and associations." The Washington law--written by state Sen. and Microsoft employee Joe Nguyen-- was opposed by the ACLU.

Climate-driven change in the Gulf of Maine is raising new threats that "red tides" will become more frequent and prolonged. But at the same time, powerful new data collection techniques and artificial intelligence are providing more precise ways to predict where and when toxic algae will bloom. One of those new machine learning prediction models has been developed by a former intern at Bigelow Labs in East Boothbay. In a busy shed on a Portland wharf, workers for Bangs Island Mussels sort and clean shellfish hauled from Casco Bay that morning. Wholesaler George Parr has come to pay a visit.

According to The Wall Street Journal, virtual simulations incorporating artificial intelligence (AI) are being used to improve health care workers' bedside manner. Doctors, nurses and other caregivers in southern Maine are being trained in a virtual environment that simulates the conditions that patients with Alzheimer's disease, cancer and substance addictions experience. At the Hospice of Southern Maine in Scarborough, health care workers wear virtual reality headsets and are able to move their hands, pick up objects and get a panoramic view of the hospice setting as if they are the patient. The patient's family--in a simulated form--is present at the virtual bedside. Such examples demonstrate the remarkable advances in skills development and training, thanks to technology such as artificial intelligence--when machines learn based on experience--and virtual and augmented reality.