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Novel memristors to overcome AI’s ‘catastrophic forgetting’

So-called “memristors” consume extremely little power and behave similarly to brain cells. Researchers from Jülich, led by Ilia Valov, have now introduced novel memristive components that offer significant advantages over previous versions: they are more robust, function across a wider voltage range, and can operate in both analog and digital modes. These properties could help address the problem of “catastrophic forgetting,” where artificial neural networks abruptly forget previously learned information.

The problem of catastrophic forgetting occurs when deep neural networks are trained for a new task. This is because a new optimization simply overwrites a previous one. The brain does not have this problem because it can apparently adjust the degree of synaptic change; something experts call “metaplasticity.”

They suspect that it is only through these different degrees of plasticity that our brain can permanently learn new tasks without forgetting old content. The new memristor accomplishes something similar.

New materials could deliver ultrathin solar panel

A race is on in solar engineering to create almost impossibly-thin, flexible solar panels. Engineers imagine them used in mobile applications, from self-powered wearable devices and sensors to lightweight aircraft and electric vehicles. Against that backdrop, researchers at Stanford University have achieved record efficiencies in a promising group of photovoltaic materials.

Chief among the benefits of these transition metal dichalcogenides – or TMDs – is that they absorb ultrahigh levels of the sunlight that strikes their surface compared to other solar materials.

“Imagine an autonomous drone that powers itself with a solar array atop its wing that is 15 times thinner than a piece of paper,” said Koosha Nassiri Nazif, a doctoral scholar in electrical engineering at Stanford and co-lead author of a study published in the Dec. 9 edition of Nature Communications. “That is the promise of TMDs.”

The search for new materials is necessary because the reigning king of solar materials, silicon, is much too heavy, bulky and rigid for applications where flexibility, lightweight and high power are preeminent, such as wearable devices and sensors or aerospace and electric vehicles.


New, ultrathin photovoltaic materials could eventually be used in mobile applications, from self-powered wearable devices and sensors to lightweight aircraft and electric vehicles.

Artificial Intelligence in Neuroscience

Artificial Intelligence (AI) and Neuroscience are two fields, but they are closely related to each other. Artificial intelligence can provide powerful tools for neuroscience research, and its application in neurological diseases is of great importance. The convergence of AI and neuroscience has sparked a paradigm shift in our understanding of the brain and its intricate mechanisms.

Here, Creative Biolabs explores the remarkable impact of AI in neuroscience research, highlighting its potential to unlock new frontiers in our quest to unravel the mysteries of the brain.

Neuroscience research generates vast amounts of complex data, ranging from molecular and cellular information to data generated by large-scale brain activity. For researchers, analyzing and decoding this wealth of data is a major challenge. AI technology steps in to address just this problem.

Fifty and out: How Gen X became the biggest work losers (with 10 years still to go)

As the heyday of the creative economy boom becomes a distant memory, Stephen Armstrong looks at the generation who were once the high-rollers and are now facing an uncertain future as younger workers and AI come for their jobs, just as their children and parents are relying on them more than ever

Danish supercomputer to drive innovation

The Gefion AI Supercomputer (GAIS) project, which delivers Denmark’s first artificial intelligence (AI) turbo-charged supercomputer, has positioned Denmark as the most advanced of the Nordic region’s quantum computing investing nations.

It also serves to accelerate the use of AI to drive innovation across Denmark’s business and industrial sectors.

Built on the Nvidia DGX SuperPOD AI supercomputer, GAIS is powered by 1,528 Nvidia H100 Tensor Core graphics processing units (GPUs) and interconnected using Nvidia Quantum-2 InfiniBand networking.