Category: TECH_Technology

University of Wisconsin-Madison and Oak Ridge National Laboratory researchers just trained artificial intelligence to consistently and quickly analyze and detect microscopic radiation damage in materials considered for nuclear reactors better than human experts.

“Machine learning has great potential to transform the current, human-involved approach of image analysis in microscopy,” said Wei Li, who participated in the research.

“In the future, I believe images from many instruments will pass through a machine learning algorithm for initial analysis before being considered by humans,” said engineering professor Dane Morgan, Li’s graduate school advisor.

The job in question is crucial for the development of safe nuclear materials and could make the time-consuming process more effective and efficient.

“Human detection and identification is error-prone, inconsistent and inefficient. Perhaps most importantly, it’s not scalable,” Morgan said. “Newer imaging technologies are outstripping human capabilities to analyze the data we can produce.”

After training the machine with 270 images, the neural network, in combination with a cascade object detector machine learning algorithm, was able to identify and classify about 86 percent of dislocation loops in a set of sample pictures. In comparison, human experts only found 80 percent of the defects.

“When we got the final result, everyone was surprised, not only by the accuracy of the approach, but the speed,” said Oak Ridge staff scientist Kevin Field. “We can now detect these loops like humans while doing it in a fraction of the time on a standard home computer.”

“This is just the beginning,” Morgan said. “Machine learning tools will help create a cyber infrastructure that scientists can utilize in ways we are just beginning to understand.”

Researchers just created a new material that is able to morph into pre-programmed shapes via temperature and light stimuli, allowing a square peg to transform and fit into a round hole before reverting to its original state.

The unique shape-shifting material was created by University of Colorado Boulder engineers and could have broad applications for robotics, manufacturing, artificial muscles, and biomedical devices.

“The ability to form materials that can repeatedly oscillate back and forth between two independent shapes by exposing them to light will open up a wide range of new applications and approaches to areas such as additive manufacturing, robotics and biomaterials”, said Christopher Bowman, senior author of the new study from the University of Colorado Boulder.

Previous studies have attempted to change an object’s size, texture, or shape with programmable stimuli. However, these materials were limited in their size or extent and the state changes were difficult to reverse.

The new material is easily programmable due to the use of liquid crystal elastomers (LCEs), which is the technology the underlies modern televisions displays. Their unique arrangement makes them susceptible to dynamic change via light and heat.

The material has a wide range of unique applications, especially in the field of biomedical devices, which could become more adaptable.

“We view this as an elegant foundational system for transforming an object’s properties,” said Matthew McBride, lead author of the new study. “We plan to continue optimizing and exploring the possibilities of this technology.”

The findings were published in Science Advances.

A University of Bristol study revealed that light can be used to create a multi-functional quantum processor, which could be used to perform numerous quantum information experiments. In addition, the data highlights how fully functional computers could be created from large-scale fabrication processes.

The team accomplished the experiment by creating a silicon chip that guides photons down waveguides in order to encode quantum-bits of information, also known as “qubits.”

Instead of using bits in the form of a “0” or “1,” qubits can be a superposition of both 1 and 0 states. Not only that, qubits can be connected in a unique way called quantum entanglement. Together, these physical quantum properties power quantum computers.

“What we’ve demonstrated is a programmable machine that can do lots of different tasks,” said Xiaogang Qiang, lead author of the study.

“It’s a very primitive processor, because it only works on two qubits, which means there is still a long way before we can do useful computations with this technology,” he added. “But what is exciting is that it the different properties of silicon photonics that can be used for making a quantum computer have been combined together in one device.”

“This is just too complicated to physically implement with light using previous approaches.”

Jonathan Matthews believes that scientists need to continue examining the potential scalable technology that can be used to create quantum computers, including technology that can be creataed precisely on a large scale.

“We think silicon is a promising material to do this, partly because of all the investment that has already gone into developing silicon for the micro-electronics and photonics industries,” he said. “And the types of devices developed in Bristol, such as the one presented today, are showing just how well quantum devices can be engineered.”

The findings were published in Nature Photonics.

A new study reveals that artificial intelligence can predict your personality type by the way that your eyes move. The team behind it used state-of-the-art machine-learning algorithms to shed light on the connection between eye movements and personality.

The researchers showed that eye movements are connected to curiosity, conscientiousness, and sociability by using an algorithm software that recognizes four of the Big Five personality traits: extroversion, neuroticism, conscientiousness, and agreeableness.

The team examined the eye movements of 42 participants as they conducted everyday tasks around a school campus and analyzed their personality traits using questionnaires.

“There’s certainly the potential for these findings to improve human-machine interactions,” said co-author Tobias Loetscher. “People are always looking for improved, personalised services. However, today’s robots and computers are not socially aware, so they cannot adapt to non-verbal cues.”

“This research provides opportunities to develop robots and computers so that they can become more natural, and better at interpreting human social signals,” he added.

Loetscher said that the new findings could connect the examination of natural eye movements in real-world environments and controlled laboratory studies.

“This research has tracked and measured the visual behaviour of people going about their everyday tasks, providing more natural responses than if they were in a lab,” he said.

“And thanks to our machine-learning approach, we not only validate the role of personality in explaining eye movement in everyday life, but also reveal new eye movement characteristics as predictors of personality traits,” he added.

The findings were published in Frontiers in Human Neuroscience.