Lifeboat Foundation

Mastercard has announced that it has developed an in-house generative AI to help combat fraud on its payment processing network.

Instead of relying on textual inputs, Mastercard’s algorithm uses a cardholder’s merchant visit history as a prompt to determine whether a transaction involves a business that the customer would likely visit. The algorithm generates pathways through Mastercard’s network, akin to heat-sensing radar, to provide a score as an answer.

Continue reading “Mastercard’s new anti-fraud AI has a success rate of up to 300%” »

An AI algorithm outperformed other screening methods in identifying cervical precancer. The approach could be especially valuable in low-resource settings.

There has been significant progress in the field of quantum computing. Big global players, such as Google and IBM, are already offering cloud-based quantum computing services. However, quantum computers cannot yet help with problems that occur when standard computers reach the limits of their capacities because the availability of qubits or quantum bits, i.e., the basic units of quantum information, is still insufficient.

One of the reasons for this is that bare qubits are not of immediate use for running a quantum algorithm. While the binary bits of customary computers store information in the form of fixed values of either 0 or 1, qubits can represent 0 and 1 at one and the same time, bringing probability as to their value into play. This is known as quantum superposition.

This makes them very susceptible to external influences, which means that the information they store can readily be lost. In order to ensure that quantum computers supply reliable results, it is necessary to generate a genuine entanglement to join together several physical qubits to form a logical qubit. Should one of these physical qubits fail, the other qubits will retain the information. However, one of the main difficulties preventing the development of functional quantum computers is the large number of physical qubits required.

Window to the soul? Maybe, but the eyes are also a flashing neon sign for a new artificial intelligence-based system that can read them to predict what you’ll do next.

A University of Maryland researcher and two colleagues have used eye-tracking technology and a new deep-learning AI algorithm to predict study participants’ choices while they viewed a comparison website with rows and columns of products and their features.

The algorithm, known as RETINA (Raw Eye Tracking and Image Ncoder Architecture), could accurately zero in on selections before people had even made their decisions.

Researchers mapped over 10,000 mouse hippocampal #neurons, creating the world’s most comprehensive database of single-neuron #connectivity #patterns.

Summary: Researchers unveiled the most extensive single-neuron projectome database to date, featuring over 10,000 mouse hippocampal neurons.

The study provides an unprecedented view of the spatial connectivity patterns at the mesoscopic level, crucial for understanding learning, memory, and emotional processing in the hippocampus. By employing machine learning algorithms for categorizing axonal trajectories and integrating spatial transcriptome data, researchers identified 43 distinct projectome cell types, revealing intricate projection patterns and soma locations’ correspondence to projection targets.

Continue reading “Mapping the Brain: The Largest Neuron Projectome Unveiled” »

Why is Adam the most popular optimizer in Deep Learning? Let’s understand it by diving into its math, and recreating the algorithm.

A consensus has arisen in the astronomical community that familiar matter made of atoms is not the dominant form of matter in the Universe. Instead, an invisible form of matter, called dark matter, is thought to be far more prevalent. However, a small group of researchers deny the existence of dark matter, instead saying our understanding of how objects move is incomplete. A recent paper in the Monthly Notices of the Royal Astronomical Society seems to have ruled this out definitively.

Stars, planets, and galaxies move under the direction of the force of gravity, and Isaac Newton worked out the laws that govern that motion, which we now call Newtonian dynamics. However, despite the enormous success of Newtonian dynamics, this success is not universal. Indeed, when Newton’s equations are applied to certain astronomical phenomena, they do not make the correct predictions. One such example is the speed at which galaxies rotate. When astronomers measure the speed of stars in the periphery of a galaxy, they move faster than can be explained by accepted theory. Instead, the galaxies should fly apart.

Continue reading “Scientists rule out a popular alternative theory to dark matter” »

In the world of quantum computing, the spotlight often lands on the hardware: qubits, superconducting circuits, and the like. But it’s time to shift our focus to the unsung hero of this tale – the quantum software, the silent maestro orchestrating the symphony of qubits. From turning abstract quantum algorithms into executable code to optimizing circuit designs, quantum software plays a pivotal role.

Here, we’ll explore the foundations of quantum programming, draw comparisons to classical computing, delve into the role of quantum languages, and forecast the transformational impact of this nascent technology. Welcome to a beginner’s guide to quantum software – a journey to the heart of quantum computing.

Quantum vs. Classical Programming: The Core Differences.

Scientists identified 18 new Tidal Disruption Events (TDEs), instances where a nearby black hole violently tears apart a neighboring star.

The powerful gravitational force of the black holes rips apart the star in its vicinity, resulting in a substantial release of energy across the entire electromagnetic spectrum.

The new catalog of TDEs was found by combing through the archival data of the satellite telescope NEOWISE. The team identified infrared patterns associated with these intense, transient bursts using a novel algorithm.