Assistant Professor Wim van Rees and his team have developed simulations of self-propelled undulatory swimmers to better understand how fish-like deformable fins could improve propulsion in underwater devices, seen here in a top-down view. MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean's many challenges, and seize its opportunities. There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. "The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures," says Wim van Rees, the ABS Career Development Professor at MIT. "At the same time, the ocean holds countless opportunities -- from aquaculture to energy harvesting and exploring the many ocean creatures we haven't discovered yet."

In the face of daily pandemic-induced upheavals, the notion of "business as usual" can often seem a quaint and distant notion to today's workforce. But even before we all got stuck in never-ending Zoom meetings, the logistics and transportation sectors (like much of America's economy) were already subtly shifting in the face of continuing advances in robotics, machine learning and autonomous navigation technologies. In their new book, The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, an interdisciplinary team of MIT researchers (leveraging insights gleaned from MIT's multi-year Task Force on the Work of the Future) exam the disconnect between improvements in technology and the benefits derived by workers from those advancements. It's not that America is rife with "low-skill workers" as New York's new mayor seems to believe, but rather that the nation is saturated with low-wage, low-quality positions -- positions which are excluded from the ever-increasing perks and paychecks enjoyed by knowledge workers. The excerpt below examines the impact vehicular automation will have on rank and file employees, rather than the Musks of the world.

There are few environments as unforgiving as the ocean. Its unpredictable weather patterns and limitations in terms of communications have left large swaths of the ocean unexplored and shrouded in mystery. "The ocean is a fascinating environment with a number of current challenges like microplastics, algae blooms, coral bleaching, and rising temperatures," says Wim van Rees, the ABS Career Development Professor at MIT. "At the same time, the ocean holds countless opportunities -- from aquaculture to energy harvesting and exploring the many ocean creatures we haven't discovered yet." Ocean engineers and mechanical engineers, like van Rees, are using advances in scientific computing to address the ocean's many challenges, and seize its opportunities. These researchers are developing technologies to better understand our oceans, and how both organisms and human-made vehicles can move within them, from the micro scale to the macro scale.

We're still quite a ways away from wielding proper Power Loaders but advances in exosuit technology are rapidly changing how people perform physical tasks in their daily lives -- some designed to help rehabilitate spinal injury patients, others created to improve a Marine's warfighting capabilities, and many built simply to make physically repetitive vocations less stressful for the people performing them. But German Bionic claims only one of them is intelligent enough to learn from its users' mistaken movements: its 5th-generation Cray X. The Cray X fits on workers like a 7kg backpack with hip-mounted actuators that move carbon fiber linkages strapped to the upper legs, allowing a person to easily lift and walk with up to 30kg (66 lbs) with both their legs and backs fully supported. Though it doesn't actively assist the person's shoulders and arms with the task, the Cray X does offer a Smart Safety Companion system to help mitigate common lifting injuries. "It's a real time software application that runs in the background and can warn the worker when the ergonomic risk is getting too high," Norma Steller, German Bionic's Head of IoT, told Engadget.

Researchers at ETH Zurich and the Frankfurt School have developed an artificial neural network that can solve challenging control problems. The self-learning system can be used for the optimization of supply chains and production processes as well as for smart grids or traffic control systems. Power cuts, financial network failures and supply chain disruptions are just some of the many of problems typically encountered in complex systems that are very difficult or even impossible to control using existing methods. Control systems based on artificial intelligence (AI) can help to optimize complex processes--and can also be used to develop new business models. Together with Professor Lucas Böttcher from the Frankfurt School of Finance and Management, ETH researchers Nino Antulov-Fantulin and Thomas Asikis--both from the Chair of Computational Social Science--have developed a versatile AI-based control system called AI Pontryagin which is designed to steer complex systems and networks towards desired target states.

Online master's in electrical engineering programs prepare graduates for lucrative roles as electrical and electronic engineers. Many are designed for working professionals looking to advance their careers while earning full-time. Electrical engineering jobs focus on manufacturing and installing electrical power equipment, while electronics engineers design and develop electronic equipment. The U.S. Bureau of Labor Statistics reports a median annual salary for electrical engineers of $100,830 and $107,540 for electronics engineers. These professionals can specialize in aerospace, bioengineering, computer hardware, and more.

The artificial-intelligence industry is often compared to the oil industry: once mined and refined, data, like oil, can be a highly lucrative commodity. Now it seems the metaphor may extend even further. Like its fossil-fuel counterpart, the process of deep learning has an outsize environmental impact. In a new paper, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide equivalent--nearly five times the lifetime emissions of the average American car (and that includes manufacture of the car itself). It's a jarring quantification of something AI researchers have suspected for a long time.

The graph represents a network of 1,583 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 19 January 2022 at 13:47 UTC. The requested start date was Wednesday, 19 January 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 14-day, 1-hour, 46-minute period from Tuesday, 04 January 2022 at 19:34 UTC to Tuesday, 18 January 2022 at 21:20 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

With the continuous increase of installed capacity of wind power, the influence of large-scale wind power integration on the power grid is becoming increasingly apparent. Ultra-short-term wind power prediction is conducive to the dispatching management of the power grid, and improves the operating efficiency and economy of the power system. In order to overcome the intermittency and uncertainty of wind power generation, this paper proposes the DE-BP (Dfferential Evolution-Back Propagation) algorithm to predict wind power, and addresses such shortcomings of BP neural network as its falling into local optimality and slow training speed when predicting. In this paper, the differential evolution algorithm is used to find the optimal value of the initial weight and threshold of the BP neural network, and the DE-BP neural network prediction model is obtained. According to the data of a wind farm in Northwest China, the short-term wind power is predicted. Compared with the application of the BP model in wind power prediction, the results show that the accuracy of the DE-BP algorithm is improved by about 5%; Compared with the GA-BP(Genetic Algorithm-Back Propagation) model, the prediction time is shortened by 23.1%.