Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculate reliability by performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-Scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments the AI-assisted tool leads to a 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators and therefore support the optimization of wind turbine operation and maintenance.

It's tempting to see AI as a solution to building a super-success investment engine. After all, if AI can solve text-to-speech or self-driving cars or landing rockets vertically, couldn't an artificially intelligent investing engine with access to all stock market, economy, weather, and trends data vastly outpace human investors and guarantee massive returns? And won't we be able to simply ask Alexa to buy a stock that's going to triple in value in six months? Well, never say never, but it's unlikely. One is that investment AI engines are returning benefits right now, but not Everest-sized performance that will blow your financial socks off and make you fire your investment advisor.

SoftBank is investing $146 million in the South Korean artificial intelligence (AI) company Qraft Technologies Inc. to help it expand into the U.S. As The Wall Street Journal (WSJ) reported Monday (Jan. The companies declined to disclose Qraft's valuation, per the WSJ. SoftBank, based in Tokyo, is one of the largest tech investors in the world, managing a portfolio in excess of $100 billion. Qraft has 50 employees, most of whom work on the company's AI project and who own about a third of the business, with outside investors controlling the rest. "SoftBank [now] makes up a large portion of that," Robert Nestor, the U.S. CEO of Qraft., told the WSJ.

Founded in 2016 by its chief executive, Marcus Hyung-Sik Kim, the Seoul-based firm plans to use the investment to further its expansion into the U.S. and other key markets, said Robert Nestor, Qraft's U.S. CEO. The companies declined to disclose Qraft's valuation. Tokyo's SoftBank is one of the world's largest investors in technology companies, with its Vision Fund and a successor managing a portfolio of more than $100 billion. Asset managers, once skeptical of the value of AI and mindful of their staffs' concerns that the programs would replace human stock- and bond-pickers, are now looking to add data-analysis tools that can help them combat chronic underperformance and justify the fees they charge investors. The industry's awakening has triggered an arms race to hire the programmers who can develop those tools and spot the market signals hidden in the data.

When you dive into the ocean, the physiology of your body changes. As you go deeper into the water, your heart rate slows. In an environment that is seemingly hostile to its survival, the body becomes remarkably efficient at keeping you alive. The mammalian dive reflex, more romantically termed the "Master Switch of Life" by its discoverer, the physiologist Per Scholander, helped shape how we view our relationship to the water. If our bodies were so at home in the ocean, scientists wondered, what did that say about our evolutionary history?

Fox News Flash top headlines for Dec. 30 are here. Check out what's clicking on Foxnews.com A squadron of drones flying over the Midwest every night for nearly two weeks have left both residents and officials wondering who's flying them and what purpose they are serving, a report said Sunday. In the past week, three more rural counties have experienced nightly flyovers from the northeast corner of Colorado to at least one county in neighboring Nebraska, the Denver Post reported. Sheriffs in Lincoln, Washington and Sedgwick counties say their offices have been inundated with calls this week about the devices, the newspaper reported.

Subjects of the test, the full findings of which were published in the eNeuro journal, were hooked up to electroencephalography (EEG) equipment by neuroscientists at the University of Toronto Scarborough. Adrian Nestor, one of the co-authors of the study, has before successfully reconstructed facial images from functional magnetic resonance imaging (fMRI) data. Speaking of the results, Professor Nestor said: "What's really exciting is that we're not reconstructing squares and triangles but actual images of a person's face, and that involves a lot of fine-grained visual detail. "The fact we can reconstruct what someone experiences visually based on their brain activity opens up a lot of possibilities.

A new technique developed by neuroscientists at U of T Scarborough can, for the first time, reconstruct images of what people perceive based on their brain activity gathered by EEG. The technique developed by Dan Nemrodov, a postdoctoral fellow in Assistant Professor Adrian Nestor's lab at U of T Scarborough, is able to digitally reconstruct images seen by test subjects based on electroencephalography (EEG) data. "When we see something, our brain creates a mental percept, which is essentially a mental impression of that thing. We were able to capture this percept using EEG to get a direct illustration of what's happening in the brain during this process," says Nemrodov. For the study, test subjects hooked up to EEG equipment were shown images of faces.

A business school in Paris will soon begin using artificial intelligence and facial analysis to determine whether students are paying attention in class. The software, called Nestor, will be used two online classes at the ESG business school beginning in September. LCA Learning, the company that created Nestor, presented the technology at an event at the United Nations in New York last week. Source: This French school is using facial recognition to find out when students aren't paying attention – The Verge This system will be used during videos to create quizzes based on when a student isn't paying attention. I don't understand the purpose since if they aren't paying attention a quiz isn't going to help them learn the material.

If you find yourself daydreaming during class, Nestor could help you regain your focus and help professors improve the least-engaging parts of their lecture. You may have gotten away with staring off into space during your college years, but alas, the students of tomorrow may not be so lucky. In fact, some business students of today will soon find that their attention spans (or lack thereof) are being closely monitored. It's all thanks to a combination of artificial intelligence and facial analysis, which researchers are using to detect whether or not students are actually paying attention in lectures. This combination forms a new kind of software called Nestor, and at its September launch, will be used in two online courses at the ESG business school. It's the brainchild of LCA Learning, and it may just change the way we take classes.