Russia is holding its largest naval drills in the Pacific in recent memory as the U.S. Navy’s big RIMPAC wargames are underway.
Big initiatives to understand the workings of the brain
Neuroscience has a data problem. In our efforts to understand the brain researchers are generating ever greater amounts of data. The problem is, how can they gain meaning from it?
A recent study, that used functional magnetic resonance imaging (fMRI) data to produce the most detailed map to date of the human cortex, looked at data from 210 peoples’ brains. The researchers estimated that equated to 30 gigabytes of data per participant. Meaning over 6 terabytes of data were analyzed to form the map, and this was a study of a large section of cortex, not the whole brain.
Rotary cell phone
Posted in futurism, mobile phones
Tiny battery for micro robots
Posted in futurism, robotics/AI
Abstract: Advances in high speed imaging techniques have opened new possibilities for capturing ultrafast phenomena such as light propagation in air or through media. Capturing light-in-flight in 3-dimensional xyt-space has been reported based on various types of imaging systems, whereas reconstruction of light-in-flight information in the fourth dimension z has been a challenge. We demonstrate the first 4-dimensional light-in-flight imaging based on the observation of a superluminal motion captured by a new time-gated megapixel single-photon avalanche diode camera. A high resolution light-in-flight video is generated with no laser scanning, camera translation, interpolation, nor dark noise subtraction. A machine learning technique is applied to analyze the measured spatio-temporal data set. A theoretical formula is introduced to perform least-square regression, and extra-dimensional information is recovered without prior knowledge. The algorithm relies on the mathematical formulation equivalent to the superluminal motion in astrophysics, which is scaled by a factor of a quadrillionth. The reconstructed light-in-flight trajectory shows a good agreement with the actual geometry of the light path. Our approach could potentially provide novel functionalities to high speed imaging applications such as non-line-of-sight imaging and time-resolved optical tomography.
One of the biggest impediments to adoption of new technologies is trust in AI.
Now, a new tool developed by USC Viterbi Engineering researchers generates automatic indicators if data and predictions generated by AI algorithms are trustworthy. Their research paper, “There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks” by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC Cyber Physical Systems Group, was featured in Frontiers in Artificial Intelligence.
Neural networks are a type of artificial intelligence that are modeled after the brain and generate predictions. But can the predictions these neural networks generate be trusted? One of the key barriers to adoption of self-driving cars is that the vehicles need to act as independent decision-makers on auto-pilot and quickly decipher and recognize objects on the road—whether an object is a speed bump, an inanimate object, a pet or a child—and make decisions on how to act if another vehicle is swerving towards it.
The quantum properties underlying crystal formation can be replicated and investigated with the help of ultracold atoms. A team led by Dr. Axel U. J. Lode from the University of Freiburg’s Institute of Physics has now described in the journal Physical Review Letters how the use of dipolar atoms enables even the realization and precise measurement of structures that have not yet been observed in any material. The theoretical study was a collaboration involving scientists from the University of Freiburg, the University of Vienna and the Technical University of Vienna in Austria, and the Indian Institute of Technology in Kanpur, India.
Crystals are ubiquitous in nature. They are formed by many different materials—from mineral salts to heavy metals like bismuth. Their structures emerge because a particular regular ordering of atoms or molecules is favorable, because it requires the smallest amount of energy. A cube with one constituent on each of its eight corners, for instance, is a crystal structure that is very common in nature. A crystal’s structure determines many of its physical properties, such as how well it conducts a current or heat or how it cracks and behaves when it is illuminated by light. But what determines these crystal structures? They emerge as a consequence of the quantum properties of and the interactions between their constituents, which, however, are often scientifically hard to understand and also hard measure.
To nevertheless get to the bottom of the quantum properties of the formation of crystal structures, scientists can simulate the process using Bose-Einstein condensates—trapped ultracold atoms cooled down to temperatures close to absolute zero or minus 273.15 degrees Celsius. The atoms in these highly artificial and highly fragile systems are extremely well under control.
DARPA announces a new type of cryptography to protect the Big Tech firm profits from the dawn of quantum computers and allow backdoor access into 3 trillion internet-connected devices.
by Raul Diego
The U.S. Military-Industrial complex is sprinting on a chariot to shore up the encryption space before the next era of computation upends the entire digital edifice built on semiconductors and transistors. But, the core of the effort is protecting markets for Big Tech and all of its tentacles, which stand to lose years or even decades of profits should the new tech be rolled out too quickly.