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DateDate: 13-04-2019, 06:39

The device is based on the principle of absorption and heat during deformation of the metal.
A group of German researchers at Saarland University (Saarland University) has created an unusual heating-cooling system, which, when used in refrigeration, can at least be three times more efficient than modern similar devices, not to mention its other advantages, reports with referring to Esoreiter.
If we speak in a somewhat simplified language, then the essence of this refrigeration generator lies in a rotating cylinder, on which the nitinol wire is wound, which stretches in one direction of rotation, and in another is compressed. Nitinol (a unique alloy) is capable of absorbing heat when it is expanded, and when it returns to its original position, it can give off. Experiments have shown that the temperature difference in this case can reach, even at the minimum value, 20 degrees.
This is what one of the co-authors of this invention, Professor Stefan Seeleke, says about this:
We were able to calculate the diameter and size of the nitinol wire, the speed of the drum, the parameters of the air flow from the fan and so on, as a result of which we achieved not only an impressive temperature difference of 20 degrees Celsius, but also the system power 30 times greater than the effort required by the operation of this installation.
It is three times more efficient than a modern refrigerator. But in principle, in the future when creating industrial units of this type, the indicators can be improved several times. Moreover, this refrigerator does not consume toxic substances (environmentally friendly), does not require hermetic pipes, heat exchangers and other intermediate links.
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There is only one very important question: if the nitinol wire will constantly expand and shrink again, how much is enough? But it turns out that this alloy differs from many others in its fantastic fatigue resistance, which makes nitinol, for example, used in arterial stents, which allows such an implant to shrink, curl, stretch and so on along with the blood vessel for an infinitely long time. In addition, this metal is relatively cheap, which, together with countless expansion-compression cycles, makes it optimal for the cooling system in question.

DateDate: 12-04-2019, 05:56

In Switzerland, completed the first house, which was built by robots, designed computers, and parts made 3D-printers. So far, however, with the participation of man. In Switzerland, they put into operation a house that was built by Digital Manufacturing and Living robots - this is how its name (Digital Fabrication and Living) stands for. DFAB HOUSE is a joint demonstration of the digital production of the Swiss National Center for Competence in Research (NCCR) in the NEST (Nest) building. As part of a full-featured construction project, researchers from Zurich (ETH Zurich), Switzerland, have gathered together with industry experts and planners to explore and test how digital production can change our design and construction methods.
The “house on the roof” space is planned to be used not only as housing, but also as a testing ground, on which construction and technological innovations will be tested in real conditions. Within this project, for the first time, six new digital construction processes were transferred from research to architectural applications:
In situ Fabricator, the universal autonomous building robot;
Mesh Mold, a non-formwork robotic process for reinforced concrete structures;
Smart Dynamic Casting, an automated concrete molding process;
Smart Slab, built-in ceiling tiles made with 3D-printed formwork;
Spatial assembly of wooden structures;
Robotic construction of wood.
Combining these new processes in a single constructed facility allows you to rethink the entire planning and construction process and use the advantages inherent in the digital chain of design, planning and manufacturing: design flexibility, saving material, saving time and cost and improving quality. It is already clear that the architectural potential of digital technology is enormous, but is almost never used on construction sites. Experimental projects like DFAB should speed up the transition from theory to practice, says ETH Zurich professor Matthias Kohler. The house turned out to be futuristic: the blinds are raised on command and the water in the kettle starts to boil; It has a multi-stage security and lighting control system.
The work of the smart home is provided by digitalSTROM equipment. The “smart” technologies of this house work not only at the household level, they also help to control energy consumption. Solar cells on the roof provide energy - on average, one and a half times more than is necessary to maintain the house, and the “intelligent” control system controls its consumption and smoothes the load peaks. That is, it is an "active home". Heat from wastewater is not wasted, but transferred further through heat exchangers installed in shower trays. Unused hot water is returned through the pipes back to the boiler, which allows not only to save energy and water, but also to prevent the growth of bacteria in the pipes.

DateDate: 11-04-2019, 05:56

The academic publishing house Springer Nature presented the first research book created using machine learning. Reported by The Verge.
The book "Lithium-Ion Batteries: A Machine-Generated Summary of Current Research" contains a summary of peer-reviewed articles published on this topic, including citations, hyperlinks and automatically generated links to the content. The tutorial is available for free download.
In his opening remarks, Henning Schoenenberger of Springer Nature said that such books are capable of starting "a new era in scientific publications, automating routine work."
Schoenberger notes that in the last three years alone more than 53,000 scientific papers on lithium-ion batteries have been published. This is a huge problem for scientists who are trying to keep abreast of developments. But using AI to automatically scan and summarize these results, scientists can save time and continue important research.
"This method allows readers to speed up the process of mastering the literature in this field of research. At the same time, if necessary, readers can always identify the source and go to it for further study of the subject," said Schönenberger.
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Although the recent boom in machine learning has greatly improved the ability of computers to generate the written word, the performance of these bots is still very limited. They cannot struggle with the long-term consistency and structure that human writers create, and therefore such efforts as fiction or poetry generated by AI are more likely to be formatted.
What an AI can do is compose formula texts when loading a library. For example, in journalism, machine learning is used by organizations such as The Associated Press to create reports of football matches, earthquakes, and financial news. These are topics where creativity is an obstacle.
According to associate professor at the Institute of Human Computer Interaction Jeff Bigham, the book was not the most impressive feat of AI. “It’s enough just to take a high-quality introductory text, briefly summarize the main conclusions and make the material look connected. In fact, the very nature of the extract means that it will be consistent in parts if the input texts are coherent. would call it valuable, "commented Bigham.
Earlier, InternetUA has already reported on AI journalism: the world's largest media uses robot authors to create articles. Moreover, AI journalists are already writing fake news - computers that have already been used to create short news reports from press releases can be taught to read and write long false texts.

DateDate: 10-04-2019, 05:55

Scientists have conducted experiments to test how much artificial intelligence is smarter than man.
As an experimental, the researchers took the DeepMind neural network and decided to offer it to independently examine the school curriculum in mathematics, and also to perform tasks from the tenth grade level. Reports the edition about it "Today".
As a result, DeepMind failed the school test with disgrace, gaining 14 points out of 40. Most British students receive marks much higher.
Artificial intelligence was unable to adequately evaluate test assignments. If an ordinary schoolchild can easily recognize a huge number of numbers, letters and formulas, then this is given to a neural network with difficulty. Most of the time, DeepMind spent on character recognition, as well as translating information into a readable form for its own analysis spent a lot of energy.
Meizu got into a big scandal: what happened
Meizu got into a big scandal: what happened
But still, artificial intelligence is able to solve a complex mathematical problem. For this, it must be presented in a special form so that the neural network does not spend its resources on recognition. In the meantime, the "visual" information is processed by neural networks with difficulty, which promises such distressing results.
Earlier it was reported that artificial intelligence has learned to predict death to the nearest second.
Thus, the case histories of half a million Britons aged 40 to 69 years were chosen as the material for the study.
Artificial Intelligence
Researchers from the University of Nottingham began testing a self-learning algorithm that they had independently developed. This algorithm is able to assess the risks of premature death of a person suffering from chronic diseases. And also knows how to name almost the exact date of departure from a person’s life.
Half a million Britons aged 40 to 69 years of age were chosen for the study.
After comparing the data, it turned out that the neural network predicted human deaths more accurately than the system used by humans.

DateDate: 9-04-2019, 05:57

Machine learning was used to create very tasty basil bushes - you probably know this plant with an unusual taste, the main ingredient of pesto sauce. Although, unfortunately, we cannot convey the taste of this herb, it only remains for scientists to take the word. However, these results reflect a broader trend that includes the use of a scientific approach in data and machine learning to improve agriculture. What makes basil so tasty? In some cases - artificial intelligence.
Machine learning makes products better
Scientists who have grown optimized basil used machine learning to determine growing conditions that would maximize the concentration of volatile compounds responsible for the taste of basil. A study published in the journal PLOS One.
Basil was grown on hydroponic farms in modified transport containers in Middleton, Massachusetts. Temperature, light, humidity and other environmental factors inside the containers can be controlled automatically. Scientists tested the taste of plants by searching for certain compounds using gas chromatography and mass spectrometry. And they used the data in machine learning algorithms developed by the Massachusetts Institute of Technology and Cognizant.
Strangely, the study showed that the effect of light on plants for 24 hours a day gives the best taste. Now scientists are planning to explore how technology can improve the ability of plants to fight diseases, as well as how different flora reacts to the effects of climate change.
“We are really interested in creating networking tools that can take into account the experience of the plant, its phenotype, a set of environmental stresses and its genetics, and digitizing it all so that you can understand the interaction of the plant and the environment,” says Caleb Harper, head of the OpenAg group at Media Lab MIT. His lab worked with colleagues at the University of Texas at Austin.
The idea of using machine learning to optimize yields and plant properties is rapidly gaining momentum in agriculture. Last year, the Wageningen University in the Netherlands organized the “Autonomous Greenhouse” competition in which various teams competed to develop algorithms that increase the yield of cucumber while minimizing the necessary resources. They worked with greenhouses in which computer systems control various factors.
A similar technology is already being used in some commercial farms, says Nawin Single, who heads a group of data scientists who deal with yield at Bayer, a German multinational company that acquired Monsanto last year. “Taste is one of the areas where we intensively use machine learning,” he says. And he adds that machine learning is a powerful tool for growing in greenhouses, but less useful for open fields. In “field conditions,” scientists are still looking for ways to narrow the gap.
Harper added that in the future his group will consider the genetic structure of plants (just what Bayer introduces into their algorithms) and will try to spread the technology. Their goal is to develop open source technology at the interface of data collection, sensing and machine learning, and so on.