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New robot hand for amputees combines human and robot control

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A group of researchers from the Ecole Polytechnique Fédérale de Lausanne has created a new prosthesis for the hand, which allows the control of the fingers and automation thanks to machine learning software. This ensures a better grip and better handling of objects.

The study, published in Nature Machine Intelligence, describes how this robot hand is constructed by combining two concepts that belong to two different sectors: one comes from neuro-engineering, the other related to robotics.

As Aude Billard, EPFL Learning Algorithms and Systems Laboratory, manager of the study, explains: “The robot hand can react within 400 milliseconds. It can react and stabilize the object before the brain can actually perceive that the object is slipping.”

In order to move the hand and thus the objects, the amputee has to perform a series of movements as soon as he has applied the correct structure. In this way, the software-automatic learning algorithm is trained and used by the robot hand to move. After training, the algorithm can understand how to move the brain, something that allows the robot to perform many more movements and more natural ways.

“Because muscle signals can be noisy, we need a machine learning algorithm that extracts meaningful activities from muscles and interprets them in movements,” says Katie Zhuang, first author of the study, suggesting how much software’s approach to artificial intelligence was necessary to obtain such functions.


See also:

https://actu.epfl.ch/news/a-smart-artificial-hand-for-amputees-merges-user-a/

https://www.nature.com/articles/s42256-019-0093-5

Image source:

https://i.ytimg.com/vi/doSynaDdEBA/maxresdefault.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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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
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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

Science

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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