Lifeboat Foundation

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.

French manufacturer used 100% recycled material, green energy.

Adriano V. Autino presents his book “A greater world is possible!”

Mon, May 16 at 2 PM CDT.

Interested.

With drone-in-a-box (DiaB) solutions emerging as a growth segment within the drone industry, Norway-based Nordic Unmanned has entered into an acquisition agreement with DiaB specialist DroneMatrix.

This partnership will enable both companies to scale their commercial offerings through new services and solutions.