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Category: information science – Page 257

Stephen Hawking –“Treating AI as Science Fiction Would Potentially Be Our Worst Mistake Ever“

“We should plan ahead,” warned physicist Stephen Hawking who died last March, 2018, and was buried next to Isaac Newton. “If a superior alien civilization sent us a text message saying, ‘We’ll arrive in a few decades,’ would we just reply, ‘OK, call us when you get here, we’ll leave the lights on’? Probably not, but this is more or less what has happened with AI.”

The memorial stone placed on top of Hawking’s grave included his most famous equation describing the entropy of a black hole. “Here Lies What Was Mortal Of Stephen Hawking,” read the words on the stone, which included an image of a black hole.

“I regard the brain as a computer,” observed Hawking, “which will stop working when its components fail. There is no heaven or afterlife for broken down computers; that is a fairy story for people afraid of the dark.”

Drone plays dodgeball to demo fast new obstacle detection system

Obstacle avoidance is a crucial piece of technology for drones, but commercially-available systems just aren’t fast enough for some situations. Now, engineers at the University of Zurich have developed a new system that gives drones such fast reflexes that they can play – and win at – dodgeball.

According to the researchers, most current obstacle avoidance systems take about 20 to 40 milliseconds to process changes in their surroundings. That’s fine for a drone gently approaching a building and finding its way inside, but it’s no match for fast-moving obstacles like birds or other drones. That makes navigation a problem in certain situations, like when there are a lot of drones together or in dynamic environments like disaster zones, or when a drone just needs to move fast.

So for the new study, the researchers kitted out a quadcopter drone with cameras specially designed to detect fast movement, as well as new algorithms that made them even faster. This cut the reaction time down to just 3.5 milliseconds.

The imitation game: Scientists describe and emulate new quantum state of entangled photons

:oooo.

A research team from ITMO University, with the help of colleagues from MIPT (Russia) and Politecnico di Torino (Italy), has predicted a novel type of topological quantum state of two photons. Scientists have also applied a new, affordable experimental method for testing this prediction. The method relies on an analogy: Instead of expensive experiments with quantum systems of two or more entangled photons, the researchers have used resonant electric circuits of higher dimensionality described by similar equations. The obtained results can be useful for the engineering of optical chips and quantum computers without the need for expensive experiments. The research was published in Nature Communications.

Light plays a key role in modern information technologies: With its help, information is transmitted over large distances via optical fibers. In the future, scientists anticipate the invention of optical chips and computers that process information with the help of photons—light quanta—instead of electrons, as it is done today. This will decrease energy consumption, while also increasing the capabilities of computers. However, to turn these predictions into reality, fundamental and applied research of light behavior at the micro- and nanoscale is needed.

In the new study, the researchers have theoretically predicted the formation of a new quantum state of photons: Two photons propagating in the array of quantum microresonators (qubits) can form a bound pair and settle down on the edge of the array. A proper experiment demands special nanostructures, as well as special devices to create such quantum state of photons and detect it. Currently, such capabilities are available only to very few research teams worldwide.

Russian Scientists Break Google’s Quantum Algorithm

Clause density is something new to me but seems interesting as I know shores algorithm is the only thing that can hack systems.

Google is racing to develop quantum-enhanced processors that utilize quantum mechanical effects to one day dramatically increase the speed at which data can be processed.

In the near term, Google has devised new quantum-enhanced algorithms that operate in the presence of realistic noise. The so-called quantum approximate optimization algorithm, or QAOA for short, is the cornerstone of a modern drive towards noise-tolerant quantum-enhanced algorithm development.

The celebrated approach taken by Google in QAOA has sparked vast commercial interest and ignited a global research community to explore novel applications. Yet, little actually remains known about the ultimate performance limitations of Google’s QAOA algorithm.

A flower pollination algorithm for efficient robot path planning

Over the past decade or so, researchers worldwide have developed increasingly advanced techniques to enable robot navigation in a variety of environments, including on land, in the air, underwater or on particularly rough terrains. To be effective, these techniques should allow robots to move around in their surroundings both safely and efficiently, saving as much energy as possible.

Researchers at the Indian Institute of Technology Kharagpur in India have recently developed a new approach to achieve efficient path planning in mobile robots. Their method, presented in a paper published in Springer Link’s Nature-Inspired Computation in Navigation and Routine Problems, is based on the use of a flower pollination (FPA), a soft computing-based tool that can identify ideal solutions to a given problem by considering a number of factors and criteria.

“Flower pollination algorithms (FPAs) have shown their potential in various engineering fields,” Atul Mishra, one of the researchers who carried out the study, told TechXplore. “In our study, we used the algorithm to solve the problem of path planning for mobile robots. Our prime objective was to plan, in the least time possible, the most optimal path in terms of minimum path length and energy consumption, with maximum safety.”

Google releases quantum computing library

Google announced Monday that it is making available an open-source library for quantum machine-learning applications.

TensorFlow Quantum, a free library of applications, is an add-on to the widely-used TensorFlow toolkit, which has helped to bring the world of machine learning to developers across the globe.

“We hope this framework provides the necessary tools for the and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage,” a report posted by members of Google’s X unit on the AI Blog states.

Intel AI gives women career advice for Int’l Women’s Day

Intel Israel announced that the project is the first of its kind which uses AI to create “female intelligence.” The experts who worked on the project, led by data scientist and researcher Shira Guskin, analyzed thousands of insights from “veteran career women.” Once the initial advice was submitted by many women across the Israeli work force, the researchers passed the data through three algorithm models: Topic Extraction, Grouping and Summarization. This led to an algorithm which “processed the tips pool and extracted the key tips and guidelines.”

The AI said that women should fully invest in their careers, be confident, network, love, and trust their guts.

Scientists break Google’s quantum algorithm

Google is racing to develop quantum-enhanced processors that use quantum mechanical effects to increase the speed at which data can be processed. In the near term, Google has devised new quantum-enhanced algorithms that operate in the presence of realistic noise. The so-called quantum approximate optimisation algorithm, or QAOA for short, is the cornerstone of a modern drive toward noise-tolerant quantum-enhanced algorithm development.

The celebrated approach taken by Google in QAOA has sparked vast commercial interest and ignited a global research community to explore novel applications. Yet, little is known about the ultimate performance limitations of Google’s QAOA .

A team of scientists from Skoltech’s Deep Quantum Laboratory took up this contemporary challenge. The all-Skoltech team led by Prof. Jacob Biamonte discovered and quantified what appears to be a fundamental limitation in the widely adopted approach initiated by Google.