Lifeboat Foundation

Deep learning models have proved to be highly promising tools for analyzing large numbers of images. Over the past decade or so, they have thus been introduced in a variety of settings, including research laboratories.

In the field of biology, deep learning models could potentially facilitate the quantitative analysis of microscopy images, allowing researchers to extract meaningful information from these images and interpret their observations. Training models to do this, however, can be very challenging, as it often requires the extraction of features (i.e., number of cells, area of cells, etc.) from microscopy images and the manual annotation of training data.

Researchers at CERVO Brain Research Center, the Institute for Intelligence and Data, and Université Laval in Canada have recently developed an artificial neural network that could perform in-depth analyses of microscopy images using simpler, image-level annotations. This model, dubbed MICRA-Net (MICRoscopy Analysis neural network), was introduced in a paper published in Nature Machine Intelligence.

Crafty hackers can make a tool to eavesdrop on some 6G wireless signals in as little as five minutes using office paper, an inkjet printer, a metallic foil transfer and a laminator.

The wireless security hack was discovered by engineering researchers from Rice University and Brown University, who will present their findings and demonstrate the attack this week in San Antonio at ACM WiSec 2022, the Association for Computing Machinery’s annual conference on security and privacy in wireless and mobile networks.

“Awareness of a future threat is the first step to counter that threat,” said study co-author Edward Knightly, Rice’s Sheafor-Lindsay Professor of Electrical and Computer Engineering. “The frequencies that are vulnerable to this attack aren’t in use yet, but they are coming and we need to be prepared.”

A team of astronomers led by University of Michigan’s Ian Roederer and including Carnegie’s Erika Holmbeck have identified the widest range of elements yet observed in a star beyond our own Sun. Their findings will be published in The Astrophysical Journal Supplement Series.

The researchers identified 65 elements in the star, which is called HD 222925. Of these, 42 are from the bottom of the periodic table. Their identification will help astronomers better understand rapid neutron capture process — one of the main methods by which the universe’s heavy elements were created.

“To the best of my knowledge, that’s a record for any object beyond our Solar System. And what makes this star so unique is that it has a very high relative proportion of the elements listed along the bottom two-thirds of the periodic table. We even detected gold,” explained Roederer, a former Carnegie postdoc. “These elements were made by the rapid neutron capture process. That’s really the thing we’re trying to study: the physics in understanding how, where and when those elements were made.”

A team of international scientists have performed difficult machine learning computations using a nano-scale device, named an “optomemristor.”

The chalcogenide thin-film device uses both light and electrical signals to interact and emulate multi-factor biological computations of the mammalian brain while consuming very little energy.

To date, research on hardware for artificial intelligence and machine learning applications has concentrated mainly on developing electronic or photonic synapses and neurons, and combining these to carry out basic forms of neural-type processing.

Machine learning techniques are designed to mathematically emulate the functions and structure of neurons and neural networks in the brain. However, biological neurons are very complex, which makes artificially replicating them particularly challenging.

Researchers at Korea University have recently tried to reproduce the complexity of biological neurons more effectively by approximating the function of individual neurons and synapses. Their paper, published in Nature Machine Intelligence, introduces a network of evolvable neural units (ENUs) that can adapt to mimic specific neurons and mechanisms of synaptic plasticity.

“The inspiration for our paper comes from the observation of the complexity of biological neurons, and the fact that it seems almost impossible to model all of that complexity produced by nature mathematically,” Paul Bertens, one of the researchers who carried out the study, told TechXplore. “Current artificial neural networks used in deep learning are very powerful in many ways, but they do not really match biological neural network behavior. Our idea was to use these existing artificial neural networks not to model the entire brain, but to model each individual neuron and synapse.”

Evolution, the process by which living organisms adapt to their surrounding environment over time, has been widely studied over the years. As first hypothesized by Darwin in the mid 1800s, research evidence suggests that most biological species, including humans, continuously adapt to new environmental circumstances and that this ultimately enables their survival.

In recent years, researchers have been developing advanced computational techniques based on artificial neural networks, which are architectures inspired by biological neural networks in the human brain. Models based on artificial neural networks are trained to optimize millions of synaptic weights over millions of observations in order to make accurate predictions or classify data.

Researchers at Princeton University have recently carried out a study investigating the similarities and differences between artificial and biological neural networks from an evolutionary standpoint. Their paper, published in Neuron, compares the evolution of biological neural networks with that of artificial ones using psychology theory.

A research group from Politecnico di Milano has developed a new computing circuit that can execute advanced operations, typical of neural networks for artificial intelligence, in one single operation.

The circuit performance in terms of speed and energy consumption paves the way for a new generation of artificial intelligence computing accelerators that are more energy efficient and more sustainable on a global scale. The study has been recently published in the prestigious Science Advances.

Recognizing a face or an object, or correctly interpreting a word or a musical tune are operations that are today possible on the most common electronic gadgets, such as smartphones and tablets, thanks to artificial intelligence. For this to happen, complicated neural networks needs to be appropriately trained, which is so energetically demanding that, according to some studies, the carbon footprint that derives from the training of a complex neural network can equal the emission of 5 cars throughout their whole life cycle.

Robin Murphy, a roboticist at Texas A&M University has published a Focus piece in the journal Science Robotics outlining her views on the robots portrayed in “Star Wars,” most particularly those featured in “The Mandalorian” and “The Book of Boba Fett.” In her article, she says she believes that the portrayals of robots in both movies are quite creative, but suggests they are not wild enough to compete with robots that are made and used in the real world today.

Murphy begins by noting that one robot in particular, IG-11 in the Mandalorian, makes for good viewing with a rotating head that allows for shooting at targets in any direction, but she also notes that such a robot would very likely be overly susceptible to joint failure and would be saddled with huge computational demands. She suggests a more practical design would involve the use of fixed-array sensors.

Murphy also notes that robots in “Star Wars” movies do fail on occasion, generally during suspenseful scenes, which she further notes might explain why the empire met with its demise. As just one example, she wonders why the stormtroopers so often miss their targets. She also notes that in some ways, droids in “Star Wars” movies tend to be far more advanced than droids in the real world, allowing them to hold human-like jobs such as bartending, teaching or translating. In so doing, she points out, producers of the movies have shied away from showing them doing more mundane work, like mining.

As transportation becomes autonomous, maritime navigation is also set for a major change. Systems like Orca AI will help in rapid transitions.