By PTI July 10, 2019 15:36 IST

Scientists have developed an artificial intelligence (AI) enabled system for translating protein structures into music and converting it back to create new proteins never seen before in nature.

The method developed by researchers at Massachusetts Institute of Technology (MIT) in the US provides a systematic way of translating a protein's sequence of amino acids into a musical sequence, using the physical properties of the molecules to determine the sounds.

Although the sounds are transposed in order to bring them within the audible range for humans, the tones and their relationships are based on the actual vibrational frequencies of each amino acid molecule itself, computed using theories from quantum chemistry.

The system, described in the journal ACS Nano, translates the 20 types of amino acids, the building blocks that join together in chains to form all proteins, into a 20-tone scale.

Any protein's long sequence of amino acids then becomes a sequence of notes.

Markus Buehler, a professor at MIT, said that after listening to the resulting melodies, he is now able to differentiate certain amino acid sequences that are in tune with proteins with specific structural functions.

The entire concept is to get better insights into proteins and their vast array of variations, Buehler said.

Proteins make up the structural material of skin, bone, and muscle, but are also enzymes, signalling chemicals, molecular switches, and a host of other functional materials that make up the machinery of all living things.

Their structures, however, including the way they fold themselves into the shapes that often establish their functions, are exceedingly complicated.

"They have their own language, and we don't know how it works. We don't know what makes a silk protein a silk protein or what patterns reflect the functions found in an enzyme. We don't know the code," he said.

By Nav Dhunay

Mother Earth needs a break. From rainforest destruction to climate change to ocean acidification to air and water pollution, modern environmental threats are as numerous as they are serious. Yet with the clock ticking, humanity isn’t close to truly solving even one of these problems, yet alone to dealing with them all comprehensively and effectively.

The problem isn’t simply a lack of care; governments, organizations, and individuals across the globe are working hard to make impactful changes. And it’s also not that we don’t have the technology. Many devices and methods exist that could dramatically reduce if not eliminate our contributions to these environmental threats if we could adopt them on a large enough scale. Until we can do that, their benefits will be almost entirely theoretical.

Artificial intelligence may hold the key to finally enacting our sustainable aspirations. By gathering data on a large scale and using AI applications to interpret it, we can identify the most effective solution or set of solutions to each environmental issue. We can then apply those solutions strategically in order to deliver the most substantial, enduring improvements.

To better understand how AI can play a key role in sustainability, consider its potential to…

1. Enhance green energy

By Omesh Tickoo, principal engineer and artificial intelligence research manager, Intel. on JULY 12, 2019Cloud Computing, Network Infrastructure, Reader Forum

Intelligence at the edge is exciting. The edge allows devices to compute and analyze data closer to the user as opposed to a centralized data center far away, which benefits the end user in many ways.

Intelligence at the edge is exciting. The edge allows devices to compute and analyze data closer to the user as opposed to a centralized data center far away, which benefits the end user in many ways. It promises low latency as the brains of the system are close by, rather than thousands of miles away in the cloud; it functions with a local network connection rather than with an internet connection, which may not always be available; and it offers a stronger guarantee of privacy because a user’s information is not transmitted and shared to remote servers. We will soon be able to process data closer to (or even inside) endpoint devices, so that we can reap the full potential of the intelligent analytics and decision-making.

But the computing power, storage and memory required to run current AIalgorithms at the end point is hampering our ability to optimize processing there. These are serious limitations, especially when the operations are time-critical.

To make intelligence at the edge a reality, being able to understand, represent and handle context is most critical.

What does that mean? It means that we give computing systems the tools to identify and learn what is needed, and only what is needed . Why generate and analyze useless or low-priority data? Capture what is needed for the purpose required and move on. Intelligent machines at the edge should be able to “learn” new concepts needed to perform their tasks efficiently and they should also be able to “systematically forget” the concepts not needed for their tasks.

André Blair — July 9, 2019

Galaxy clusters are among the most massive structures in the universe, also complicated to identify. To make things easier, scientists have now come with the idea to use artificial intelligence along with their research. The technology they plan to use is called Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), deep learning machinery that speeds up the search for galaxy clusters.

The research suggesting the method and the software were presented by Matthew Chan, a doctoral researcher at Lancaster University, at the Royal Astronomical Society’s National Astronomy meeting on July the 4th.

Galaxy clusters are pretty rare in the cosmos. The majority of the galaxies are located in low-density habitats, also known as ‘the fields,’ or in small groups, just like the Milky Way and the Andromeda. Even though the galaxy clusters are rare, they hold the most severe environments for star systems to thrive in. Observing them could open some of the world’s most intangible hypothesizes about the universe, dark matter, and dark energy.

Deep-CEE Artificial Intelligence Helps Astronomers Spot Galaxy Clusters

A high-tech AI model has been trained as a virtual astronomer to observe color images and particularly select galaxy clusters from the bunch. The machinery uses neural networks, which mirror the way human brain identifies objects, using particular neurons that can see patterns and colors.

In a first study which examined the AI’s abilities, the technology managed to identify and label galaxy clusters in images that include many other cosmic bodies. The results were promising, Royal Astronomy Society stated. This new automation of the identification process enables researchers to rapidly scan several images and receive accurate predictions with minimal human work.