Lifeboat Foundation

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.

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The artificial intelligence revolution is just getting started. But it is already transforming conflict. Militaries all the way from the superpowers to tiny states are seizing on autonomous weapons as essential to surviving the wars of the future. But this mounting arms-race dynamic could lead the world to dangerous places, with algorithms interacting so fast that they are beyond human control. Uncontrolled escalation, even wars that erupt without any human input at all.

DW maps out the future of autonomous warfare, based on conflicts we have already seen – and predictions from experts of what will come next.

Continue reading “How AI is driving a future of autonomous warfare | DW Analysis” »

MIT researchers demonstrate a way to sharply reduce errors in two-qubit gates, a significant advance toward fully realizing quantum computation.

MIT researchers have made a significant advance on the road toward the full realization of quantum computation, demonstrating a technique that eliminates common errors in the most essential operation of quantum algorithms, the two-qubit operation or “gate.”

“Despite tremendous progress toward being able to perform computations with low error rates with superconducting quantum bits (qubits), errors in two-qubit gates, one of the building blocks of quantum computation, persist,” says Youngkyu Sung, an MIT graduate student in electrical engineering and computer science who is the lead author of a paper on this topic published on June 16, 2021, in Physical Review X. “We have demonstrated a way to sharply reduce those errors.”

Advanced uses of time in image rendering and reconstruction have been the focus of much scientific research in recent years. The motivation comes from the equivalence between space and time given by the finite speed of light c. This equivalence leads to correlations between the time evolution of electromagnetic fields at different points in space. Applications exploiting such correlations, known as time-of-flight (ToF)¹ and light-in-flight (LiF)² cameras, operate at various regimes from radio^3,4 to optical⁵ frequencies. Time-of-flight imaging focuses on reconstructing a scene by measuring delayed stimulus responses via continuous wave, impulses or pseudo-random binary sequence (PRBS) codes¹. Light-in-flight imaging, also known as transient imaging⁶, explores light transport and detection^2,7. The combination of ToF and LiF has recently yielded higher accuracy and detail to the reconstruction process, especially in non-line-of-sight images with the inclusion of higher-order scattering and physical processes such as Rayleigh–Sommerfeld diffraction⁸ in the modeling. However, these methods require experimental characterization of the scene followed by large computational overheads that produce images at low frame rates in the optical regime. In the radio-frequency (RF) regime, 3D images at frame rates of 30 Hz have been produced with an array of 256 wide-band transceivers³. Microwave imaging has the additional capability of sensing through optically opaque media such as walls. Nonetheless, synthetic aperture radar reconstruction algorithms such as the one proposed in ref. ³ required each transceiver in the array to operate individually thus leaving room for improvements in image frame rates from continuous transmit-receive captures. Constructions using beamforming have similar challenges⁹ where a narrow focused beam scans a scene using an array of antennas and frequency modulated continuous wave (FMCW) techniques.

In this article, we develop an inverse light transport model¹⁰ for microwave signals. The model uses a spatiotemporal mask generated by multiple sources, each emitting different PRBS codes, and a single detector, all operating in continuous synchronous transmit-receive mode. This model allows image reconstructions with capture times of the order of microseconds and no prior scene knowledge. For first-order reflections, the algorithm reduces to a single dot product between the reconstruction matrix and captured signal, and can be executed in a few milliseconds. We demonstrate this algorithm through simulations and measurements performed using realistic scenes in a laboratory setting. We then use the second-order terms of the light transport model to reconstruct scene details not captured by the first-order terms.

We start by estimating the information capacity of the scene and develop the light transport equation for the transient imaging model with arguments borrowed from basic information and electromagnetic field theory. Next, we describe the image reconstruction algorithm as a series of approximations corresponding to multiple scatterings of the spatiotemporal illumination matrix. Specifically, we show that in the first-order approximation, the value of each pixel is the dot product between the captured time series and a unique time signature generated by the spatiotemporal electromagnetic field mask. Next, we show how the second-order approximation generates hidden features not accessible in the first-order image. Finally, we apply the reconstruction algorithm to simulated and experimental data and discuss the performance, strengths, and limitations of this technique.