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Category: robotics/AI – Page 1,513

H-Tec Systems receives research funding to advance research project PEP.IN with PEM electrolysers expertise as part of the H2Giga hydrogen project.

Augsburg-based hydrogen technology company H-Tec Systems is now officially part of the H2Giga hydrogen project for research & development into the series production of PEM electrolysis stacks and electrolysers. The German Federal Ministry of Education and Research (BMBF) is supporting H2Giga with a total funding volume of up to 500 million euros. The aim is to identify and test novel processes for the competitive mass production of electrolysers in Germany to make green hydrogen affordable and competitive.

As an expert and visionary in green hydrogen technology, H-Tec Systems is receiving research funding for the H2Giga project “PEP.IN” to further advance series production. By spring 2025, H-Tec Systems will identify new production processes for PEM electrolysis stacks and electrolysers in the gigawatt range and develop corresponding test facilities to quickly set up and commence series production.

The H2Giga project PEP.IN stands for Industrialisation of PEM Electrolysis Production and aims to optimise processes and equipment to produce electrolysers and electrolysis stacks in mass production. Part of the research work within the project is the investigation of a production plant for electrolysis stacks and electrolysers. In addition, a central component of the project is the further development of the electrolysis stacks to make them as simple and cost-effective as possible to produce. For smooth, highly available and cost-effective production, it is necessary to align the corresponding supply chains to the needs of the production. In addition, the individual components of an electrolyser will be considered in order to implement automated production of electrolysers in the gigawatt range. By collecting smart stack and plant operating data, H-Tec Systems will also contribute to the further development of the plants.

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From the Terminator to Spiderman’s suit, self-repairing robots and devices abound in sci-fi movies. In reality, though, wear and tear reduce the effectiveness of electronic devices until they need to be replaced. What is the cracked screen of your mobile phone healing itself overnight, or the solar panels providing energy to satellites continually repairing the damage caused by micro-meteorites?

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HB1 is an automated wall-climbing robot that was designed to streamline home construction projects.

No matter the size, location, style, or chosen building material–when it comes to constructing houses, it can be a dangerous job. Even with bulky construction vehicles, building homes requires a lot of finesse and attention. As our technological worlds evolve, so do our tools and that includes those used for home construction. Home-building robotics company Hausbots developed an automated, climbing construction robot called HB1 to help get home projects done.

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The research study of Spanish clinical neuropsychologist Gabriel G. De la Torre, Does artificial intelligence dream of non-terrestrial techno-signatures?, suggests that one of the “potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general.”

“Our form of life and intelligence,” observed Silvano P. Colombano at NASA’s Ames Research Center who was not involved in the study, “may just be a tiny first step in a continuing evolution that may well produce forms of intelligence that are far superior to ours and no longer based on carbon ” machinery.”

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Automating repetitive tasks with loops and functions.

Many R users get into R programming from a statistics background rather than a programming/software engineering background, having previously used software such as SPSS, Excel etc. As such they may not have an understanding of some of the programming techniques that can be leveraged to improve code. This can include making the code more modular which in turn makes it easier to find and resolve bugs, but also can be used to automate repetitive tasks, such as producing tables and plots etc.

This short post in c ludes some of the basic programming techniques that can be used to improve the quality and maintainability of R scripts. This will also save you a whole lot of time if you are carrying out repetitive tasks that are only marginally different. We assume that you have a basic understanding of writing simple scripts in R.

Let’s start with a simple example. Let’s say we have some data from several different groups. In this case 3 animals (tigers, swans and badgers) and we have collected some data on relating to this (a score and value of some kind).

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Let there be darkness.

That is the potential catchphrase for those that are concerned about nighttime light pollution.

More formerly known as Artificial Light At Night (ALAN), there is an ongoing bruhaha that our modern way of living is generating way too much light during the evening darkness. It is an ongoing issue and the amount of such pollution is likely to keep on increasing due to further industrialization and expansion of societies into additional geographical areas.

In short, you can expect more light to be emitted in existing populated areas, along with nighttime light being unleashed in regions that had so far not been especially well lit due to insufficient means or lack of a light-producing populace. When you start adding more office buildings, more homes, more cars, more lampposts, and the like, this all translates into a tsunami of unbridled light at night.

You might be puzzled as to why the mere shepherding of artificial light is considered a pollution monstrosity.

One obvious facet is that you cannot see the stars at nighttime, or they are otherwise generally blotted out of view by the abundance of overwhelming artificial light.

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Continue reading “Be Resolute To Reduce That Nighttime Artificial Light Pollution, Which AI Self-Driving Cars Can Help Solve (But Doing So Will Be Tricky)” | >

Tang Jie, the Tsinghua University professor leading the Wu Dao project, said in a recent interview that the group built an even bigger, 100 trillion-parameter model in June, though it has not trained it to “convergence,” the point at which the model stops improving. “We just wanted to prove that we have the ability to do that,” Tang said.

Ironically, China is a competitor that the United States abetted. It’s well known that the U.S. consumer market fed China’s export engine, itself outfitted with U.S. machines, and led to the fastest-growing economy in the world since the 1980s. What’s less well-known is how a handful of technology companies transferred the know-how and trained the experts now giving the United States a run for its money in AI.

Blame Bill Gates, for one. In 1992, Gates led Microsoft into China’s fledgling software market. Six years later, he established Microsoft Research Asia, the company’s largest basic and applied computer-research institute outside the United States. People from that organization have gone on to found or lead many of China’s top technology institutions.

China is a competitor that the United States abetted. A handful of U.S. tech companies transferred their know-how and trained some of China’s top AI experts.

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