Add idea


«    August 2019    »










DateDate: 9-07-2019, 05:15

Scientists have developed an apparatus capable of distinguishing certain vegetables on the field.
A group of British engineers designed a robot that distinguishes between ripe cabbages and carefully cuts them off. This was reported by the Journal of Field Robotics.
According to the results of field tests, the device showed an efficiency of about 85%. It is this proportion of ripe vegetables that it recognized.
Now the agriculture of the entire planet reaches three to four percent of world GDP. Companies are engaged in the development and testing of automated systems for harvesting and other activities within the industry.
The team of engineers led by Fumiya Iida. They created a manipulator on a wheeled platform. Its width is comparable to the width of one bed. The end of the “hand” is crowned with a grip, a knife and a camera. A second lens is mounted at the top of the platform.
Experts assumed to develop only a test version of a potentially massively distributed robot. For this reason, they decided to simplify some elements. For example, the device is on a passive platform, which the operator must manually move.
The robot is able to recognize heads with the help of localization based on the YOLO v3 convolutional neural network. The convolutional neural network technology Darknet Object Classification Network classifies vegetables into four types - ripe, unripe, infected, and the latter is designed to recognize the earth.
For learning the first network, one and a half thousand photos were used, and the second - 665.

DateDate: 8-07-2019, 05:55

The Danish company Mita-Teknik presented the new “smart” system Mita Pitch, which controls the rotation of the blades of wind power plants. The system is designed to control land and offshore wind turbines with a capacity of up to 12 MW.
This is reported by ElektroVesti, reports Business Censor.
The life of the system is 30 years. It will be produced with a minimum of parts that wear out in a short time. Due to this, maintenance of the structure will not require extra large expenses.
The developers designed Mita Pitch so that it could be easily integrated into the original software of the wind power plant, and it could work using the built-in analysis algorithms with the available information. With the help of a “smart” system, it is possible in real time to evaluate the performance of turbines, the load on them, and to minimize downtime by optimizing the speed of revolutions of wind turbines and the angles of inclination of their blades.
Another difference in the performance characteristics of Mita Pitch is its ability to work in different weather conditions - both in the cold and in the heat (from -30 C to +60 C). In addition, the aluminum hull construction does not allow seawater to have a devastating impact. Source:

DateDate: 7-07-2019, 08:41

Scientists from Harvard University in the United States and the University of Dalhousie in Canada stated that they created an algorithm that determines the authorship of the songs of The Beatles. AI has already managed to analyze eight songs. The neural network was taught on 70 songs of the group, written from 1962 to 1966 - their authorship was unequivocally established, - the LAW transmits. They identified 137 unique musical patterns, a sequence of chords and melodic patterns, characteristic of either John Lennon or Paul McCartney. When the AI was able to determine the authorship of the downloaded songs with an accuracy of 76%, he was offered controversial compositions, which were signed by two musicians. The algorithm determined that the music for the songs “Ask Me Why”, “Do You Want to Know a Secret” and the loss to “A Hard Days Night” with a probability above 90% was written by Lennon, and the loss in the song “In My Life”, with 57% probability - the work of McCartney. Research director Mark Glikman hopes that the development will be useful for specialists in the history of music. The representative of the British fan club The Beatles Ernie Sutton said that AI is very interesting to fans of the group, but even the technology they can not trust to the end.

DateDate: 6-07-2019, 06:18

Biometrics is developing in leaps and bounds. It seems that only recently began to use a fingerprint to unlock smartphones, as has already appeared facial recognition technology. What will be the next step?
Recently, the Pentagon announced a new development for personal identification. Researchers and engineers working for the US Department of Defense created a prototype device using an infrared laser. With it, you can "recognize" a person by his heartbeat.
The device was called Jetson. Its principle of operation is based on laser vibrometry. The device detects micromovements of the surface of the human body, which creates the beating of his heart.
This is a very promising approach. The fact is that each of us has unique features of the heartbeat, the so-called cardiac signature. It is unchangeable and impossible to fake.
So far, the most common method of biometric identification at a distance has been face recognition. The main disadvantage of this method is the need for a good frontal view. Catching the right angle is not so easy (especially if the drone is used for identification).
In addition, face recognition is not the most reliable way. A beard, sunglasses or, say, scarves and kerchiefs are easily “confused” by the system.
The new promising device "reads" a person's heartbeat and, comparing the figures with the database, identifies the person with 95% accuracy.
However, while the system works only under strictly defined conditions. Thus, a laser, “triggered” already at an impressive distance of 200 meters, can fix the heart rate only through thin material (for example, a T-shirt). More dense layers of clothing interfere with the operation of the device.
In addition, the system takes about 30 seconds to receive data. This means that the object must remain motionless during this time.
However, American experts believe that it is quite possible to reduce the response time of the mechanism to five seconds or even less.
Despite the "capriciousness" of the device, the method based on the use of a laser has a huge and incontestable advantage: the system cannot be deceived, such as, for example, face or fingerprint recognition technology.
"Compared to the face-based approach, cardiac biometrics is more reliable and can achieve 98% accuracy," said one of the developers of the new device Wenyao Xu from the University of Buffalo.
The Pentagon is considering several options for applying the new laser detector in the future. This is the fight against terrorism, and military objectives, and improving the work of the police.
In addition, the technology may be embedded in mobile devices. It is possible that in everyday medical practice such laser technology will be a good help. For example, it will provide an opportunity to conduct research of the heart, without resorting to a traditional stethoscope or an electrocardiograph.
Another area of application Jetson experts see security systems at the entrance to protected areas of enterprises and industries. So experts hope to significantly simplify the control system.
By the way, the authors of "Vesti.Nauka" ( previously told about other methods of personal identification and the fight against them.
We will report on the latest discoveries and inventions, and further, but for now we offer to find out how artificial intelligence taught us to distinguish between men and women by a smile and even determine their sexual orientation from a photo.

DateDate: 5-07-2019, 05:55

Artificial intelligence, which did not contain any knowledge of chemistry, rediscovered the periodic table and prompted scientists to new promising materials. For this, he analyzed 3.3 million annotations of scientific papers.
The achievement is described in a scientific article published in Nature magazine by a group led by Anubhav Jain from Lawrence Berkeley National Laboratory, USA.
Today, there is a sad joke among scientists that it is easier to rediscover it than to find information about it. An estimated five years ago, the Internet was available 114 million (!) Of scientific publications in English. And every day a lot of new ones are added to this array.
Even in narrow areas of science, be it the study of the solar wind or thermoelectric materials, the number of outgoing articles is such that the researcher is not able to read them all, even if he will only do this every day from morning to evening.
The scientific community is trying to solve this problem by creating more sophisticated search engines, databases and automatic information analysis tools. But at the moment the task of being aware of everything that is happening in your field still requires overwork from a scientist.
The Jaina team contributed to solving this problem by creating an artificial intelligence system based on Word2vec technology.
This method is inherently purely linguistic. Each word is represented as a set of n numbers (coordinates). In other words, it becomes a point in n-dimensional space.
The computer calculates how often certain words are found nearby from each other. On this basis, he assigns them the values of the coordinates. It is assumed that words with close coordinates have a similar meaning.
Jaina and colleagues were interested in how this approach would cope with the analysis of the scientific literature on materials science. They were specifically interested in thermoelectric materials that convert temperature differences into electrical voltage (or vice versa).
The researchers fed the system 3.3 million annotations of scientific articles published in more than a thousand journals between 1922 and 2018. Artificial intelligence has identified in them about half a million different words. He presented each word as a set of two hundred coordinates.
The authors emphasize that the program was not incorporated any information on chemistry or physics. The system learned all its “knowledge” from annotations of scientific articles. All the more surprising were the results.
For example, the researchers found out what the coordinates in the 200-dimensional space got the name of each chemical element. Having projected this picture onto a plane, they got a kind of periodic table. The elements were grouped by nature: inert gases separately, alkali metals separately, diatomic non-metals separately, and so on.
Left: elements grouped by artificial intelligence. Right: the same groups in the periodic table.
Berkeley Lab illustration.
“Without knowing anything about materials science, [the program] studied such concepts as the periodic table [of Mendeleev] and the crystal structure of metals,” says Jain.
If computer intelligence has mastered materials science so well, can it identify effective thermoelectrics among numerous materials? The authors checked this by specifying a search for the names of substances, in their coordinates as close as possible to the word "thermoelectric".
The program has formed the top 10 materials. For each of them, the researchers calculated the power factor (power factor), which determines its effectiveness as a thermoelectric.
It turned out that for all selected substances this value was higher than the average for all known thermoelectric compounds. The materials from the top 3 had more than 95% of the known thermoelectrics.
But for a prospective thermoelectric, not only the power factor is important. It is necessary that the substance was inexpensive, safe and easy to manufacture. Does artificial intelligence take into account these features? And can he predict which substances should experts pay attention to in future studies? According to the authors, yes.
For example, scientists gave the system the same task: to find thermoelectrics, but they did it twice. For the first time they provided her with publications published before 2008, and for the second time - until 2018.
For the first time, the system selected the top 5 materials. Three of them were in the top and in publications in 2018, and the other two contained rare or toxic elements.
According to the calculations of researchers, the system guessed the material, to which experts will pay attention in the next ten years, four times more often than if she called them by chance.
"This study shows that if this algorithm were used earlier, some materials could have been discovered many years earlier," says Jain.
In other words, information about promising compounds was contained in scientific articles, but the community did not notice it in time.
Together with the results of such "after-orders", the authors publish