“Because nothing can protect hardware, software, applications or data from a quantum-enabled adversary, encryption keys and data will require re-encrypting with a quantum-resistant algorithm and deleting or physically securing copies and backups.” v/@preskil… See More.
To ease the disruption caused by moving away from quantum-vulnerable cryptographic code, NIST has released a draft document describing the first steps of that journey.
Physics-informed machine learning might help verify microchips.
Physicists love recreating the world in software. A simulation lets you explore many versions of reality to find patterns or to test possibilities. But if you want one that’s realistic down to individual atoms and electrons, you run out of computing juice pretty quickly.
Machine-learning models can approximate detailed simulations, but often require lots of expensive training data. A new method shows that physicists can lend their expertise to machine-learning algorithms, helping them train on a few small simulations consisting of a few atoms, then predict the behavior of system with hundreds of atoms. In the future, similar techniques might even characterize microchips with billions of atoms, predicting failures before they occur.
The researchers started with simulated units of 16 silicon and germanium atoms, two elements often used to make microchips. They employed high-performance computers to calculate the quantum-mechanical interactions between the atoms’ electrons. Given a certain arrangement of atoms, the simulation generated unit-level characteristics such as its energy bands, the energy levels available to its electrons. But “you realize that there is a big gap between the toy models that we can study using a first-principles approach and realistic structures,” says Sanghamitra Neogi, a physicist at the University of Colorado, Boulder, and the paper’s senior author. Could she and her co-author, Artem Pimachev, bridge the gap using machine learning?
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The story of humanity is progress, from the origins of humanity with slow disjointed progress to the agricultural revolution with linear progress and furthermore to the industrial revolution with exponential almost unfathomable progress.
This accelerating rate of change of progress is due to the compounding effect of technology, in which it enables countless more from 3D printing, autonomous vehicles, blockchain, batteries, remote surgeries, virtual and augmented reality, robotics – the list can go on and on. These devices in turn will lead to mass changes in society from energy generation, monetary systems, space colonization, automation and much more!
This trajectory of progress is now leading us into a time period that is, “characterized by a fusion of technologies that is blurring the lines between the physical, digital and biological spheres”, called by many the technological revolution or the 4th industrial revolution — in which everything will change, from the underlying structure and fundamental institutions of society to how we live our day-to-day lives.
00:00 Intro.
This is only the Beginning.
Quantum physicist Mario Krenn remembers sitting in a café in Vienna in early 2016, poring over computer printouts, trying to make sense of what MELVIN had found. MELVIN was a machine-learning algorithm Krenn had built, a kind of artificial intelligence. Its job was to mix and match the building blocks of standard quantum experiments and find solutions to new problems. And it did find many interesting ones. But there was one that made no sense.
“The first thing I thought was, ‘My program has a bug, because the solution cannot exist,’” Krenn says. MELVIN had seemingly solved the problem of creating highly complex entangled states involving multiple photons (entangled states being those that once made Albert Einstein invoke the specter of “spooky action at a distance”). Krenn and his colleagues had not explicitly provided MELVIN the rules needed to generate such complex states, yet it had found a way. Eventually, he realized that the algorithm had rediscovered a type of experimental arrangement that had been devised in the early 1990s. But those experiments had been much simpler. MELVIN had cracked a far more complex puzzle.
“When we understood what was going on, we were immediately able to generalize [the solution],” says Krenn, who is now at the University of Toronto. Since then, other teams have started performing the experiments identified by MELVIN, allowing them to test the conceptual underpinnings of quantum mechanics in new ways. Meanwhile Krenn, Anton Zeilinger of the University of Vienna and their colleagues have refined their machine-learning algorithms. Their latest effort, an AI called THESEUS, has upped the ante: it is orders of magnitude faster than MELVIN, and humans can readily parse its output. While it would take Krenn and his colleagues days or even weeks to understand MELVIN’s meanderings, they can almost immediately figure out what THESEUS is saying.
Below is my Answer.
“There is big confluence between AI & Social Media. It is a two way thing, AI not only affects Social Media, Social Media also plays a great role in the development of AI.
The way AI is developed is through data, large data (big data) and one of the easiest ways to generate and source for data at this scale is from the contents and interactions on social media.
Most social media platforms operate at scale, so for issues such as monitoring or censorship of what is being posted, the admin of these platforms have to use automation and AI for its management and policing.
AI algorithms such as sentiment analysis or recommendation engines (used by Facebook & Youtube to recommend posts based on the AI understanding of what you will like) are very much an integral part of any social platform architecture.
AI is integral to how and when Adverts are delivered to you on social media. AI controls the engagement levels on your posts and ensures that people who are most likely interested in the topics or communities you belong to get recommended to you as connection; this is because engagement is key goal for every social media platform.
So as you can see, AI plays a very critical role in social media. But beyond this, it is also important to mention that not all the effects of AI on social media are positive ones. For example, AI ensures a never ending supply of content recommendation (recommendation engines) that can keep you engrossed in social media, using time in an unproductive way.
Whenever there’s an issue, there’s no support. It’s you against the machine, so you don’t even try.
Amazon’s contract Flex delivery drivers already have to deal with various indignities, and you can now add the fact that they can be hired — and fired — by algorithms, according to a Bloomberg report.
To ensure same-day and other deliveries arrive on time, Amazon uses millions of subcontracted drivers for its Flex delivery program, started in 2015. Drivers sign up via a smartphone app via which they can choose shifts, coordinate deliveries and report problems. The reliance on technology doesn’t end there, though, as they’re also monitored for performance and fired by algorithms with little human intervention.
However, the system can often fire workers seemingly without good cause, according to the report. One worker said her rating (ranging from Fantastic, Great, Fair, or At Risk) fell after she was forced to halt deliveries due to a nail in her tire. She succeeded in boosting it to Great over the next several weeks, but her account was eventually terminated for violating Amazon’s terms of service. She contested the firing, but the company wouldn’t reinstate her.
Russian scientists have experimentally proved the existence of a new type of quasiparticle—previously unknown excitations of coupled pairs of photons in qubit chains. This discovery could be a step towards disorder-robust quantum metamaterials. The study was published in Physical Review B.
Superconducting qubits are a leading qubit modality today that is currently being pursued by industry and academia for quantum computing applications. However, the performance of quantum computers is largely affected by decoherence that contributes to a qubit’s extremely short lifespan and causes computational errors. Another major challenge is low controllability of large qubit arrays.
Metamaterial quantum simulators provide an alternative approach to quantum computing, as they do not require a large amount of control electronics. The idea behind this approach is to create artificial matter out of qubits, the physics of which will obey the same equations as for some real matter. Conversely, you can program the simulator in such a way as to embody matter with properties that have not yet been discovered in nature.
An outstanding idea, because for one there has been a video/ TV show/ movie, etc… showing every conceivable action a human can do; and secondly the AI could watch all of these at super high speeds.
Predicting what someone is about to do next based on their body language comes naturally to humans but not so for computers. When we meet another person, they might greet us with a hello, handshake, or even a fist bump. We may not know which gesture will be used, but we can read the situation and respond appropriately.
In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.
“Our algorithm is a step toward machines being able to make better predictions about human behavior, and thus better coordinate their actions with ours,” said Carl Vondrick, assistant professor of computer science at Columbia, who directed the study, which was presented at the International Conference on Computer Vision and Pattern Recognition on June 24, 2021. “Our results open a number of possibilities for human-robot collaboration, autonomous vehicles, and assistive technology.”
Part of the problem mirrors the rise of automation in any other industry — performers told Input that they’re nervous that game studios might try to replace them with sophisticated algorithms in order to save a few bucks. But the game modder’s decision also raises questions about the agency that performers have over their own voices, as well as the artistry involved in bringing characters to life.
“If this is true, this is just heartbreaking,” video game voice actor Jay Britton tweeted about the mod. “Yes, AI might be able to replace things but should it? We literally get to decide. Replacing actors with AI is not only a legal minefield but an utterly soulless choice.”
“Why not remove all human creativity from games and use AI…” he added.
Neil deGrasse Tyson explains the early state of our Universe. At the beginning of the universe, ordinary space and time developed out of a primeval state, where all matter and energy of the entire visible universe was contained in a hot, dense point called a gravitational singularity. A billionth the size of a nuclear particle.
While we can not imagine the entirety of the visible universe being a billion times smaller than a nuclear particle, that shouldn’t deter us from wondering about the early state of our universe. However, dealing with such extreme scales is immensely counter-intuitive and our evolved brains and senses have no capacity to grasp the depths of reality in the beginning of cosmic time. Therefore, scientists develop mathematical frameworks to describe the early universe.
Neil deGrasse Tyson also mentions that our senses are not necessarily the best tools to use in science when uncovering the mysteries of the Universe.
It is interesting to note that in the early Universe, high densities and heterogeneous conditions could have led sufficiently dense regions to undergo gravitational collapse, forming black holes. These types of Primordial black holes are hypothesized to have formed soon after the Big Bang. Going from one mystery to the next, some evidence suggests a possible Link Between Primordial Black Holes and Dark Matter.
In modern physics, antimatter is made up of elementary particles, each of which has the same mass as their corresponding matter counterparts — protons, neutrons and electrons — but the opposite charges and magnetic properties.
A collision between any particle and its anti-particle partner leads to their mutual annihilation, giving rise to various proportions of intense photons, gamma rays and neutrinos. The majority of the total energy of annihilation emerges in the form of ionizing radiation. If surrounding matter is present, the energy content of this radiation will be absorbed and converted into other forms of energy, such as heat or light. The amount of energy released is usually proportional to the total mass of the collided matter and antimatter, in accordance with Einstein’s mass–energy equivalence equation.
