Lifeboat Foundation

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.

Continue reading “Physicists Teach AI to Simulate Atomic Clusters” »

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Continue reading “The Technological Revolution (The 4th Industrial Revolution Explained)” »

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.

Continue reading “AI Designs Quantum Physics Experiments Beyond What Any Human Has Conceived” »

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.

Continue reading “Someone recently asked the question on Quora, How does AI affect social media?” »

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.

Continue reading “Amazon is reportedly using algorithms to fire Flex delivery drivers” »

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.

Continue reading “AI learns to predict human behavior from videos” »

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.

Continue reading “The Early Universe Explained by Neil deGrasse Tyson” »