If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Inside a lab at Stanford University's Precourt Institute for Energy, there are a half dozen refrigerator-sized cabinets designed to kill batteries as fast as they can. Each holds around 100 lithium-ion cells secured in trays that can charge and discharge the batteries dozens of times per day. Ordinarily, the batteries that go into these electrochemical torture chambers would be found inside gadgets or electric vehicles, but when they're put in these hulking machines, they aren't powering anything at all. Instead, energy is dumped in and out of these cells as fast as possible to generate reams of performance data that will teach artificial intelligence how to build a better battery. In 2019, a team of researchers from Stanford, MIT, and the Toyota Research Institute used AI trained on data generated from these machines to predict the performance of lithium-ion batteries over the lifetime of the cells before their performance had started to slip.
Rockwell Automation, Inc. and Microsoft Corp. announced a five-year partnership expansion to develop integrated, market-ready solutions that help industrial customers improve digital agility through cloud technology. By combining each company's expertise in the industrial and IT markets, respectively, teams can work together more seamlessly, enabling industrial organizations to save on infrastructure costs, speed time-to-value, and increase productivity. Microsoft and Rockwell are working to deliver innovative edge-to-cloud-based solutions that connect information between development, operations and maintenance teams through a singular, trusted data environment. This will allow development teams to digitally prototype, configure and collaborate without investing in costly physical equipment. This unified information environment also enables IT and OT teams to not only securely access and share data models across the organization, but with their ecosystem of partners as well.
General Electric is engaged in a broad range of research and development activities in artificial intelligence, with the dual objectives of improving the productivity of its internal operations and of enhancing future products and services in its aerospace, industrial, aircraft engine, commercial, and service sectors. Many of the applications projected for AI within GE will require significant advances in the state of the art in advanced inference, formal logic, and architectures for real-time systems. New software tools for creating expert systems are needed to expedite the construction of knowledge bases. Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments.
Vanadium flow batteries (VFBs) are promising for stationary large-scale energy storage due to their high safety, long cycle life, and high efficiency. The cost of a VFB system mainly depends on the VFB stack, electrolyte, and control system. Developing a VFB stack from lab to industrial scale can take years of experiments due to complex factors, from key materials to battery architecture. Novel methods to accurately predict the performance and cost of a VFB stack and further system are needed in order to accelerate the commercialization of VFBs. Recently, a research team led by Prof. Li Xianfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences proposed a machine learning-based strategy to predict and optimize the performance and cost of VFBs.
On Nov 28th 2019, the EU parliament declared a global climate and environmental emergency. They say that all politics is local and across the world climate change seems to be coming home to roost. In the hills around San Francisco the bankrupt PG&E power company pre-emptively shutoff power to homes for several days as it worried that its ageing electrical equipment would act as a match to the parched trees and vegetation. In Europe extreme flooding has been immersing ancient towns in apocalyptic scenes. In Australia it was hard to discern the iconic Sydney Opera House for all the smoke from the raging bush fires.
Peaxy CEO and President Manuel Terranova joins us to discuss some of the biggest challenges facing the battery industry, and how smart software like Peaxy Lifecycle Intelligence (PLI) for Batteries can solve them. Peaxy's Lifecycle Intelligence offers predictive battery analytics, powered by machine learning. What do you see as the top data challenges in the battery industry, and how can they be solved? Batteries are unique and fickle industrial assets, and yet many companies use fleet-level or system level models to manage them. While that can be helpful, I don't believe such models are good at predicting and optimising industrial equipment, including batteries. Simply put, if you're unable to resolve data down to the individual battery -- a unique serial number -- chances are you won't be able to monetise your analytics.
SAN RAMON, California – Tesla is working on new battery technology that CEO Elon Musk says will enable the company within the next three years to make sleeker, more affordable cars that can travel dramatically longer distances on a single charge. But the battery breakthroughs that Musk unveiled Tuesday at a highly anticipated event didn't impress investors. They were hoping Tesla's technology would mark an even bigger leap forward and propel the company's soaring stock to even greater heights. Tesla's shares shed more than 6 percent in extended trading after Musk's presentation. That deepened a downturn that began during Tuesday's regular trading session as investors began to brace for a potential letdown.
Elon Musk is hailed as an innovator and disrupter who went from knowing next to nothing about building cars to running the world's most valuable automaker in the space of 16 years. But his record shows he is more of a fast learner who forged alliances with firms that had technology Tesla lacked, hired some of their most talented people, and then powered through the boundaries that limited more risk-averse partners. Now, Musk and his team are preparing to outline new steps in Tesla's drive to become a more self-sufficient company less reliant on suppliers at its "Battery Day" event on Tuesday. Musk has been dropping hints for months that significant advances in technology will be announced as Tesla strives to produce the low-cost, long-lasting batteries that could put its electric cars on a more equal footing with cheaper gasoline vehicles. New battery cell designs, chemistries and manufacturing processes are just some of the developments that would allow Tesla to reduce its reliance on its long-time battery partner, Japan's Panasonic, people familiar with the situation said.
This paper presents a novel algorithm, called $\epsilon^*$+, for online coverage path planning of unknown environments using energy-constrained autonomous vehicles. Due to limited battery size, the energy-constrained vehicles have limited duration of operation time. Therefore, while executing a coverage trajectory, the vehicle has to return to the charging station for a recharge before the battery runs out. In this regard, the $\epsilon^*$+ algorithm enables the vehicle to retreat back to the charging station based on the remaining energy which is monitored throughout the coverage process. This is followed by an advance trajectory that takes the vehicle to a near by unexplored waypoint to restart the coverage process, instead of taking it back to the previous left over point of the retreat trajectory; thus reducing the overall coverage time. The proposed $\epsilon^*$+ algorithm is an extension of the $\epsilon^*$ algorithm, which utilizes an Exploratory Turing Machine (ETM) as a supervisor to navigate the vehicle with back and forth trajectory for complete coverage. The performance of the $\epsilon^*$+ algorithm is validated on complex scenarios using Player/Stage which is a high-fidelity robotic simulator.
Today's robot-mounted batteries provide electrical power but at the expense of added mass that in turn requires added power to move and use. But a team of researchers from the University of Michigan have devised a clever solution that will enable tomorrow's batteries to provide power while negating their own weight -- it just needs a bit of Kevlar. Led by Nicholas Kotov, a professor of chemical engineering at U of Michigan, the team has developed a battery system that is strong enough to also serve as a structural support for the rest of the robot. "Robot designs are restricted by the need for batteries that often occupy 20% or more of the available space inside a robot, or account for a similar proportion of the robot's weight," Kotov told the University of Michigan News "No other structural battery reported is comparable, in terms of energy density, to today's state-of-the-art advanced lithium batteries. We improved our prior version of structural zinc batteries on 10 different measures, some of which are 100 times better, to make it happen," he continued.