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Scientists model new superhard materials with artificial intelligence

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A group of scientists from the University of Buffalo automated 43 new forms of carbon, some of which could be even tougher than diamonds.

Each computer-modeled carbon variety is made of carbon atoms placed with a certain pattern in a kind of crystal lattice. The researchers then published the study on npj Computational Materials, a study that also confirms how artificial intelligence, and in particular the machine learning technique, can also be important for the research of new materials and in general in all fields of materials science.

Among other things, superhard materials can be very useful because they can cut, drill or even grind other materials and objects and can also be used to create scratch-resistant coatings. Currently there is no harder material than diamond, but the latter is also very expensive, as Eva Zurek, a chemist at the University of Buffalo, who was involved in the research, recalled.

Precisely for this reason, there are many laboratories around the world that try to synthesize materials, at least by modeling them, which are harder than diamonds and possibly cheaper. However, these are often long and laborious processes, and this is where the computer and the new and increasingly powerful artificial intelligence algorithms play a role.

With the computer, you can get modeled materials that can exhibit other interesting properties, such as certain interactions with heat or electricity or other properties that diamonds don’t have.
“Few superhard materials are known, so it’s interesting to find new ones,” says the researcher, who suggests that the open-source algorithm they used, called XtalOpt, to generate random and crystalline structures containing carbon, can also be used to discover new structures and new materials in an increasingly efficient and fast way and that some of them can reserve a pleasant surprise.


See also:

https://www.nature.com/articles/s41524-019-0226-8

Image source:

https://scx2.b-cdn.net/gfx/news/hires/2019/5c51be3b91d37.jpg

Kelly Owen

Kelly majored in English Literature and is responsible for assisting in proofreading, editing and research, as well as for web design and the maintenance of this website. Beyond her outstanding writing skills, she has like the rest of us a passion for science and science reporting. She is an avid reader of many scientific journals and magazines, especially Scientific American. In her spare time she also enjoys reading fiction and hopes to complete her own novel in 2020.
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Science

Artificial intelligence passes the third grade scientific test for the first time

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A software based on artificial intelligence has passed an eighth American school test (comparable to the third year of high school), according to an article in the New York Times. It is the first time that artificial intelligence has passed a test of this level.

It’s been a few years since hundreds of computer scientists entered a competition to create artificial intelligence that can pass a test of this level, but the only one to pass seems to have been the Allen Institute for Artificial Intelligence.

In fact, this Seattle Institute has created a new artificial intelligence system that seems to have passed the scientific test by correctly answering more than 90% of the questions. The software, called Aristo, is designed to mimic the logic of human decision-making.

And it is perhaps precisely for this reason that he managed to overcome not only the questions that made a “simple” information search possible (something that even Google can do now if the questions are very simple), but also questions that needed a real reasoning, essentially the classic and simple “problems” that primary or secondary school students have to solve, issues that, however, require the use of logic.

The standardized scientific tests used in schools are increasingly being used to assess the level of artificial intelligence and the manufacturers themselves see them as excellent benchmarks for understanding the progress and level their software achieves. These types of tests are considered more important than the classic tests based on games such as chess or backgammon.

The latter may, in fact, be governed by the rules to learn, but a scientific test, a series of questions that also includes the use of logic, is more difficult to overcome. Jingjing Liu, one of the Microsoft researchers who has also worked on various Allen Institute initiatives based on artificial intelligence, seems to be cautious and openly declares that it is not yet possible to compare such technology with real human students of the third degree: their ability to reason, at least for the moment, is still superior.

However, the progress that has been made with Aristo can already be used in the short term in a range of different services, ranging from the answers that an Internet search engine can provide to the various tasks that a digital assistant can perform. However, the progress made in artificial intelligence, especially in neural networks that can understand the natural language thanks to models built on the basis of huge amounts of data, does not seem to deny it.


See also:

https://allenai.org/aristo/

Image source:

https://miro.medium.com/max/4000/1*DcHlT-ImdvYaJZL7LWDUUA.jpeg

Kelly Owen

Kelly majored in English Literature and is responsible for assisting in proofreading, editing and research, as well as for web design and the maintenance of this website. Beyond her outstanding writing skills, she has like the rest of us a passion for science and science reporting. She is an avid reader of many scientific journals and magazines, especially Scientific American. In her spare time she also enjoys reading fiction and hopes to complete her own novel in 2020.
---
520-557-5143
[email protected]
Kelly Owen
Continue Reading

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Scientists discover new geometric models that are more resistant to shocks and explosions

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A method to make materials more resistant to vibration and shock, for example during earthquakes, was developed by a group of engineers at the University of California in San Diego.

Professor Veronica Eliasson and her colleagues have discovered during several experiments that have seen the use of a particular device that generates powerful explosions in the laboratory, a particular structural conformation that can reduce the energy of shock waves and therefore to reduce the total damage.

In particular, they discovered that certain grooves in the geometric models used reduced the impact of the so-called “reflected shock wave.” “This research can also be used in military and civil applications to design materials and buildings to better withstand high-intensity explosions,” says Christina Scafidi, one of the researchers working on the project.

Another researcher involved in the research, aerospace engineer Alexander Ivanov, says in the press release: “The coal industry has had many fatal accidents and we believe this research is a valid reason to protect workers from eruptions that can easily spread throughout an entire coal mine. If the entire coal wall could be covered with these solid geometric obstacles, this would be an economical way to protect all miners. ”

Kelly Owen

Kelly majored in English Literature and is responsible for assisting in proofreading, editing and research, as well as for web design and the maintenance of this website. Beyond her outstanding writing skills, she has like the rest of us a passion for science and science reporting. She is an avid reader of many scientific journals and magazines, especially Scientific American. In her spare time she also enjoys reading fiction and hopes to complete her own novel in 2020.
---
520-557-5143
[email protected]
Kelly Owen
Continue Reading

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Algorithm recognizes bullies and molesters on Twitter with an accuracy of 90%

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A new algorithm that recognizes bullies and online attackers has been developed by a group of researchers from the University of Binghamton. Specifically, the researchers developed an algorithm that, with an accuracy of 90% according to the press release, recognizes the bullies on Twitter.

More and more IT and Artificial Intelligence laboratories and researchers are spending their time trying to develop methods for automatically recognizing bullying and aggression on the internet, in order, quite clearly, to benefit even large companies, they keep the social networks that, at least for the time being, mostly use human moderators.

Jeremy Blackburn, an American university computer scientist, is trying to bridge this gap by analyzing the behavioral patterns of “bullies” on Twitter and comparing them to those of “normal” users. It is precisely for this reason that the researcher, together with his colleagues, has created special crawlers to collect data from Twitter faster and more efficiently.

He then relied on natural language processing algorithms and other tools already available for social network analysis and was able to develop an algorithm that automatically classifies two models of offensive behavior online: cyberbullying and cyber aggression.

The accuracy of the algorithm would be 90%. The algorithm is able to identify tricky behavior, for example users who launch threats make racist comments to other users.


See also:

https://www.researchgate.net/publication/334624617_Detecting_Cyberbullying_and_Cyberaggression_in_Social_Media

Image source:

https://marketingland.com/wp-content/ml-loads/2014/07/twitter-logo-small-1920.png

Kelly Owen

Kelly majored in English Literature and is responsible for assisting in proofreading, editing and research, as well as for web design and the maintenance of this website. Beyond her outstanding writing skills, she has like the rest of us a passion for science and science reporting. She is an avid reader of many scientific journals and magazines, especially Scientific American. In her spare time she also enjoys reading fiction and hopes to complete her own novel in 2020.
---
520-557-5143
[email protected]
Kelly Owen
Continue Reading
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