Lifeboat Foundation

Artificial intelligence has mostly been focusing on a technique called deep learning. It might be time to reconsider.

Imagine driving home after a long day at work. Suddenly, a car careens out of an obscured side street and turns right in front of you. Luckily, your autonomous car saw this vehicle long before it came within your line of sight and slowed to avoid a crash. This might seem like magic, but a novel technique developed at Caltech could bring it closer to a reality.

With the advent of autonomous vehicles, advanced spacecraft, and other technologies that rely on sensors for navigation, there is an ever-increasing need for advanced technologies that can scan for obstacles, pedestrians, or other objects. But what if something is hidden behind another object?

In a paper recently published in the journal Nature Photonics, Caltech researchers and their colleagues describe a new method that essentially transforms nearby surfaces into lenses that can be used to indirectly image previously obscured objects.

A team of researchers from Nanjing University of Posts and Telecommunications and the Chinese Academy of Sciences in China and Nanyang Technological University and the Agency for Science Technology and Research in Singapore developed an artificial neuron that is able to communicate using the neurotransmitter dopamine. They published their creation and expected uses for it in the journal Nature Electronics.

As the researchers note, most machine-brain interfaces rely on electrical signals as a communications medium, and those signals are generally one-way. Electrical signals generated by the brain are read and interpreted; signals are not sent to the brain. In this new effort, the researchers have taken a step toward making a brain-machine interface that can communicate in both directions, and it is not based on electrical signals. Instead, it is chemically mediated.

Continue reading “An artificial neuron that can receive and release dopamine” »

Simulations of a quantum computer made of six rubidium atoms suggest it could run a simple brain-inspired algorithm that can learn to remember and make simple decisions.

A pair of UCLA bioengineers and a former postdoctoral scholar have developed a new class of bionic 3D camera systems that can mimic flies’ multiview vision and bats’ natural sonar sensing, resulting in multidimensional imaging with extraordinary depth range that can also scan through blind spots.

Powered by computational image processing, the camera can decipher the size and shape of objects hidden around corners or behind other items. The technology could be incorporated into autonomous vehicles or medical imaging tools with sensing capabilities far beyond what is considered state of the art today. This research has been published in Nature Communications.

Continue reading “Bug eyes and bat sonar: Bioengineers turn to animal kingdom for creation of bionic super 3D cameras” »

Researchers have observed the formation of 2D ice on gold surfaces that were thought to be too hydrophilic and too rough to support this type of ice.

Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology.

Mobile devices use facial recognition technology to help users quickly and securely unlock their phones, make a financial transaction or access medical records. But facial recognition technologies that employ a specific user-detection method are highly vulnerable to deepfake-based attacks that could lead to significant security concerns for users and applications, according to new research involving the Penn State College of Information Sciences and Technology.

The researchers found that most application programming interfaces that use facial liveness verification—a feature of facial recognition technology that uses computer vision to confirm the presence of a live user—don’t always detect digitally altered photos or videos of individuals made to look like a live version of someone else, also known as deepfakes. Applications that do use these detection measures are also significantly less effective at identifying deepfakes than what the app provider has claimed.

“In recent years we have observed significant development of facial authentication and verification technologies, which have been deployed in many security-critical applications,” said Ting Wang, associate professor of information sciences and technology and one principal investigator on the project. “Meanwhile, we have also seen substantial advances in deepfake technologies, making it fairly easy to synthesize live-looking facial images and video at little cost. We thus ask the interesting question: Is it possible for malicious attackers to misuse deepfakes to fool the facial verification systems?”

Scientists in Germany and the US have predicted the most topologically complex knot ever found in a protein using AlphaFold, the artificial intelligence (AI) system developed by Google’s DeepMind. Their complete analysis of the data produced by AlphaFold also revealed the first composite knots in proteins: topological structures containing two separate knots on the same string. If the discovered protein knots can be recreated experimentally it will serve to verify the accuracy of predictions made by AlphaFold.

Proteins can fold to form complex topological structures. The most intriguing of these are protein knots – shapes that would not disentangle if the protein were pulled from both ends. Peter Virnau, a theoretical physicist at Johannes Gutenberg University Mainz, tells Physics World that there are currently around 20 to 30 known knotted proteins. These structures, Virnau explains, raise interesting questions around how they fold and why they exist.

A protein’s shape can be closely linked with its function, but while there are a few theories on the functionality and purpose of protein knots there is little hard evidence to back these up. Virnau says that they might help to keep the proteins stable, by being particularly resistant to thermal fluctuations, for instance, but these are open questions. While protein knots are rare, they also appear to be highly preserved by evolution.

Nuclear power plants provide large amounts of electricity without releasing planet-warming pollution. But the expense of running these plants has made it difficult for them to stay open. If nuclear is to play a role in the U.S. clean energy economy, costs must come down. Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are devising systems that could make nuclear energy more competitive using artificial intelligence.

Nuclear power plants are expensive in part because they demand constant monitoring and maintenance to ensure consistent power flow and safety. Argonne is midway through a $1 million, three-year project to explore how smart, computerized systems could change the economics.

“Operation and maintenance costs are quite relevant for nuclear units, which currently require large site crews and extensive upkeep,” said Roberto Ponciroli, a principal nuclear engineer at Argonne. “We think that autonomous operation can help to improve their profitability and also benefit the deployment of advanced reactor concepts.”