In past decades, sifting through huge amounts of data, looking for patterns to draw conclusions about certain processes, was extremely time-consuming and in some cases difficult or even impossible. In addition to limited computing power, it was mainly people who had to sift through the data and draw conclusions.
What used to be a long and tedious process can now be done by artificial intelligence in a fraction of the time. Once trained, AI can find patterns, anomalies and deviations in huge amounts of data in no time at all. The applications of this technology are endless. Whether in climate research, medicine or the energy sector, AI can be used to optimise countless processes and gain completely new insights.
We have looked at three examples from INNO-VERSE where AI is shedding light on the data jungle, providing valuable forecasts for extreme weather, reducing the risk of heart attacks and maintaining energy networks to make them more stable. And at the end of the article, we also reveal how you too can use AI in data management.
Large-scale model with AI for global, accurate and timely prediction of extreme weather events
As global climate change intensifies, extreme weather events are becoming more frequent, posing enormous social and economic challenges. As a result, there is a growing need for more accurate and timely predictions of extreme weather events in order to minimise or prevent damage.
The Shanghai Academy of Artificial Intelligence for Science has formed an 'ecological alliance' to develop a large-scale meteorological model based on artificial intelligence for climate and weather forecasting. The flagship model, called 'Fuxi', was developed by the Academy, Fudan University and the Chinese National Climate Centre, and the first version was unveiled at the COP28 conference in Dubai. The enhanced version 2.0 now combines meteorology and climate science with various AI technologies. Using machine learning, the cascade forecast system should be able to accurately predict global weather, including extreme weather events (such as heavy rain or strong winds), for 15 consecutive days. The model is intended to improve early warning systems and risk management, as well as disaster prevention and mitigation measures in the event of extreme weather events. According to the development team, Fuxi surpasses the performance of existing short- and medium-range forecasting models from the European Centre for Medium-Range Weather Forecasts (ECMWF).
The alliance of 12 participating institutions, including meteorological companies, scientific organisations and leading industrial and logistics companies, aims to address the various challenges posed by climate change. [1]
Novel AI method dramatically shortens infarct analysis
In modern heart attack research, the precise determination of infarct size is of central importance for the further development of cardioprotective treatments. Researchers from the Medical Faculty of the University of Duisburg-Essen and the University Hospital Essen, supported by the Technical University of Dresden, have now developed a new method that uses artificial intelligence to drastically reduce the time needed to analyse heart attacks in pigs.
Traditional methods previously required up to 90 minutes of manual work to analyse the size of infarcts on heart slices. The new AI-based technique can complete this task in just 20 seconds. Using 3,869 digital images and a special deep learning model based on U-net architecture, infarct tissue is precisely identified and analysed. This innovation provides objective and reliable results that are 98% consistent with previous manual measurements.
The implementation of this technology provides the infarct research community with a powerful tool that can significantly improve both the speed and accuracy of data processing. Its application could significantly advance cardioprotection research by supporting the development of new therapeutic approaches. These advances could be applied not only in the laboratory but also in collaborative research organisations. [2]
Predicting energy grid maintenance with big data and machine learning
Power grids are the backbone of our modern society, and their key components are switching devices that interrupt the flow of electricity under load in milliseconds to prevent damage to the system itself or to consumers in the event of a short circuit. The maintenance of these devices is correspondingly complex and expensive for grid operators at all voltage levels.
Researchers at the University of Paderborn have developed a system called DigiGrid that uses predictive maintenance to analyse the actual condition of switchgear and proactively suggest maintenance work. The system uses a wide range of data from sensors that measure power flow, heat generation, air quality and much more. Additional camera monitoring is used to ensure the detection of animal intruders that may have damaged equipment.
This information is used to determine the current condition of the equipment, its expected lifespan and the best maintenance strategy. By incorporating AI into maintenance tasks, the aim is to further increase the efficiency of network maintenance in the face of increasingly complex renewable energy systems. [3]
AI not only makes data management easier, it also makes it much more efficient. In the INNO-VERSE you will not only find interesting articles and practical examples of AI-supported data processing – our platform also actively supports you in taking your own document management to the next level.
New: the AI Chat! Now you can upload hundreds of your own files and documents to the new RAG File System, search them specifically and extract valuable information at the touch of a button – no more tedious sifting and sorting. Simply ask your questions and AI Chat will provide the relevant answers from your data.
And that's just the beginning! There are many more intelligent AI tools waiting to be discovered.
🔗 Learn more & try it out: INNO-VERSE.com
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