Chemists officially add new elements to the periodic table

Jav hdKids have to memorize four new elements, now that the powers that be in the world of chemistry have expanded the periodic table. The International Union of Pure and Applied Chemistry has accepted the official names for elements 113, 115, 117 and 118, which it recognized in late December 2015. Previously known by their unappealing placeholder names ununtrium, ununpentium, ununseptium and ununoctium, the elements have been christened Nihonium (Nh), Moscovium (Mc), Tennessine (Ts) and Oganesson (Og), respectively.

These four superheavy elements -- they break down into lighter elements quickly -- were synthesized between 2002 and 2010 by Japanese, Russian and American teams. The scientists sent in their name proposals in June but had to go through a five-month waiting period.

Wondering how the teams came up with those names? The Japanese researchers who created element 113 based Nihonium from the word "Nihon," which means Japan in the country's language. Element 115's Russian creators named it Moscovium after Moscow, while their countrymen who made element 118 (with the help of American scientists) named theirs Oganesson after Yuri Oganessian. He's a Russian nuclear physicist who helped discover the heaviest elements in the periodic table. Finally, the Oak Ridge National Laboratory scientists, who also helped create element 115, named element 117 Tennessine after their home state of Tennessee.

It appears machines may already be catching up to humans, at least in the world of computational biology. A team of researcher's at the MIT-based Center for Brains, Minds and Machines (CBMM) found that the system they designed to recognize faces had spontaneously come up with a step that can identify portraits regardless of the rotation of the face. This adds credence to a previous theory about how humans recognize faces that was based on studies of MRIs of primate brains.
Jav Oppai

The as-yet-unnamed system is a computational model of how the human brain recognizes faces, and was trained to identify particular visages from a battery of sample images it was fed. In the process of learning to spot faces, the program created an intermediate processing step that looked at "a face's degree of rotation - say 45 degrees from center - but not the direction."

In layman's terms, this means the system, which was looking for invariance (or non-difference) between faces, was able to do so regardless of whether a face was flipped, as long as it was rotated in the same angle. That property is known as "mirror symmetry."

This discovery excites scientists because it duplicates a previously observed feature of how primates process faces, indicating that the system might be doing something similar to the brain. However, it's not for sure. "This is not a proof that we understand what's going on," says Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the CBMM.

The researchers' machine-learning system in this case is a neural network, which has been employed by tech giants such as Microsoft, Google and Facebook. These companies all have their own facial recognition systems in place, and have been investing in machine-learning to enhance their tools.
Jav Uncensored
Understanding how we recognize people could help facial recognition systems get significantly better and more accurate, which has vast applications in tech. Face unlock is an increasingly popular feature of phones and laptops, and identifying people in photos lets companies like Facebook, Apple and Google better sort your photos. The downside, if you choose to see it that way, is that surveillance systems could also get pictures accurate at finding the exact individuals they wish to seek from the endless amount of security camera footage and DMV photos they have. While this is clearly in early stages, and a tiny step towards implementing human-level facial recognition in machines, it certainly is a sign that artificial intelligence is capable of replicating specific functions of the human brain.