Lifeboat Foundation

The researchers tested their algorithm on a replica of a US Army combat ground vehicle and found it was 99% effective in preventing a malicious attack.

Australian researchers have developed an artificial intelligence algorithm to detect and stop a cyberattack on a military robot in seconds.

The research was conducted by Professor Anthony Finn from the University of South Australia (UniSA) and Dr Fendy Santoso from Charles Sturt University in collaboration with the US Army Futures Command. They simulated a MitM attack on a GVT-BOT ground vehicle and trained its operating system to respond to it, according to the press release.

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Australian researchers have designed an algorithm that can intercept a man-in-the-middle (MitM) cyberattack on an unmanned military robot and shut it down in seconds.

In an experiment using deep learning neural networks to simulate the behavior of the human brain, artificial intelligence experts from Charles Sturt University and the University of South Australia (UniSA) trained the robot’s operating system to learn the signature of a MitM eavesdropping cyberattack. This is where attackers interrupt an existing conversation or data transfer.

The algorithm, tested in real time on a replica of a United States army combat ground vehicle, was 99% successful in preventing a malicious attack. False positive rates of less than 2% validated the system, demonstrating its effectiveness.

Summary: Unveiling the neurological enigma of traumatic memory formation, researchers harnessed innovative optical and machine-learning methodologies to decode the brain’s neuronal networks engaged during trauma memory creation.

The team identified a neural population encoding fear memory, revealing the synchronous activation and crucial role of the dorsal part of the medial prefrontal cortex (dmPFC) in associative fear memory retrieval in mice.

Groundbreaking analytical approaches, including the ‘elastic net’ machine-learning algorithm, pinpointed specific neurons and their functional connectivity within the spatial and functional fear-memory neural network.

“ The Matrix is everywhere. It is all around us. Even now in this very room. ”

So says Laurence Fishburne’s Morpheus in sci-fi classic ‘ The Matrix ’ as he offers Keanu Reeves’s Neo the choice to find out just how “deep the rabbit hole goes”.

Now, just as Neo discovered that the “life” he’d been living was little more than an algorithmic construct, scientists and philosophers are arguing that we could be stuck inside a simulation ourselves.

Researchers developed ‘HistoAge,’ an algorithm that unravels brain aging and neurodegenerative disorders.

As we age, our brains undergo structural and cellular changes influenced by intrinsic and external factors. Accelerated aging in the brain can result in an increased risk of neurodegenerative conditions, bipolar disorder, and mortality. In a bid to deeply understand how an aging brain works, researchers say they have built a powerful AI tool that can identify regions in the brain vulnerable to age-related changes.

The team used AI to develop an algorithm called ‘HistoAge,’ which predicts age at death based on the cellular composition of human brain tissue specimens with an average accuracy… More.

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This is the first algorithm that can design robots that actually work in the real world.

Edge AI isn’t a type of algorithm in and of itself, but it’s about where AI happens, regardless of the algorithm in question.

If new particles are out there, the Large Hadron Collider (LHC) is the ideal place to search for them. The theory of supersymmetry suggests that a whole new family of partner particles exists for each of the known fundamental particles. While this might seem extravagant, these partner particles could address various shortcomings in current scientific knowledge, such as the source of the mysterious dark matter in the universe, the “unnaturally” small mass of the Higgs boson, the anomalous way that the muon spins and even the relationship between the various forces of nature. But if these supersymmetric particles exist, where might they be hiding?

This is what physicists at the LHC have been trying to find out, and in a recent study of proton–proton collision data from Run 2 of the LHC (2015–2018), the ATLAS collaboration provides the most comprehensive overview yet of its searches for some of the most elusive types of supersymmetric particles—those that would only rarely be produced through the “weak” nuclear force or the electromagnetic force. The lightest of these weakly interacting supersymmetric particles could be the source of dark matter.

The increased collision energy and the higher collision rate provided by Run 2, as well as new search algorithms and machine-learning techniques, have allowed for deeper exploration into this difficult-to-reach territory of supersymmetry.

Researcher show that n-bit integers can be factorized by independently running a quantum circuit with orders of magnitude fewer qubits many times. It then use polynomial-time classical post-processing. The correctness of the algorithm relies on a number-theoretic heuristic assumption reminiscent of those used in subexponential classical factorization algorithms. It is currently not clear if the algorithm can lead to improved physical implementations in practice.

Shor’s celebrated algorithm allows to factorize n-bit integers using a quantum circuit of size O(n^2). For factoring to be feasible in practice, however, it is desirable to reduce this number further. Indeed, all else being equal, the fewer quantum gates there are in a circuit, the likelier it is that it can be implemented without noise and decoherence destroying the quantum effects.

The new algorithm can be thought of as a multidimensional analogue of Shor’s algorithm. At the core of the algorithm is a quantum procedure.