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Researchers train artificial intelligence to recognize smells

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A team of Google Brain researchers published a new study on arXiv in which they explain how they train artificial intelligence software to recognize smells. They first created a set of more than 5,000 molecules and then labeled these molecules with descriptions that identified the type of odor.

The researchers used a special artificial intelligence called graphical neural network (GNN) so that these molecules were associated with their descriptions based on their structures. This is not a software that can be compared to the sensitivity of the human sense of smell, because the latter is very difficult to define. For example, there are scents that can appear in one way for one person and in another.

Moreover, some molecules sometimes have the same atoms and the same bonds, but they are arranged as mirror images: these molecules, usually recognized by the same software as practically the same, can have completely different smells. And this without mentioning the fragrances that are the result of the fragrances combined.

Despite these undeniable difficulties, Google researchers think that this is an important first step, a step that can also be useful in the fields of chemistry, sensory neuroscience and the production of synthetic fragrances.

This is not the first team of researchers to attempt to mimic or imitate the characteristics of an olfactory system based on artificial intelligence. For example, a team of scientists from the Barbican Centre in London used machine learning techniques to “recreate” the smell of an extinct flower. In addition, IBM is conducting experiments to create new fragrances generated by artificial intelligence.


See also:

https://arxiv.org/abs/1910.10685

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Martin Hill

An accomplished journalist and freelancer, Martin has held a long career in media and has worked for numerous different agencies. He was an editor for the Arizona Business Gazette for over 10 years before joining the Tucson Weekly (tucsonweekly.com) and founding Hplex Science News, a new publication with the aim of reporting on science news over the internet. Beyond having extensive writing and research experience, Martin is also a science enthusiast with a passion for science and technology. In his younger life, he had studied mechanical engineering before moving on to journalism.
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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:

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Martin Hill

An accomplished journalist and freelancer, Martin has held a long career in media and has worked for numerous different agencies. He was an editor for the Arizona Business Gazette for over 10 years before joining the Tucson Weekly (tucsonweekly.com) and founding Hplex Science News, a new publication with the aim of reporting on science news over the internet. Beyond having extensive writing and research experience, Martin is also a science enthusiast with a passion for science and technology. In his younger life, he had studied mechanical engineering before moving on to journalism.
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602-769-4516
[email protected]
Martin Hill
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Science

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. ”

Martin Hill

An accomplished journalist and freelancer, Martin has held a long career in media and has worked for numerous different agencies. He was an editor for the Arizona Business Gazette for over 10 years before joining the Tucson Weekly (tucsonweekly.com) and founding Hplex Science News, a new publication with the aim of reporting on science news over the internet. Beyond having extensive writing and research experience, Martin is also a science enthusiast with a passion for science and technology. In his younger life, he had studied mechanical engineering before moving on to journalism.
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602-769-4516
[email protected]
Martin Hill
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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

Martin Hill

An accomplished journalist and freelancer, Martin has held a long career in media and has worked for numerous different agencies. He was an editor for the Arizona Business Gazette for over 10 years before joining the Tucson Weekly (tucsonweekly.com) and founding Hplex Science News, a new publication with the aim of reporting on science news over the internet. Beyond having extensive writing and research experience, Martin is also a science enthusiast with a passion for science and technology. In his younger life, he had studied mechanical engineering before moving on to journalism.
---
602-769-4516
[email protected]
Martin Hill
Continue Reading
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