Lifeboat Foundation: Safeguarding Humanity
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Category: information science

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.

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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.”

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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.

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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.

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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.

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Another important question is the extent to which continued increases in computational capacity are economically viable. The Stanford Index reports a 300,000-fold increase in capacity since 2012. But in the same month that the Report was issued, Jerome Pesenti, Facebook’s AI head, warned that “The rate of progress is not sustainable…If you look at top experiments, each year the cost is going up 10-fold. Right now, an experiment might be in seven figures but it’s not going to go to nine or 10 figures, it’s not possible, nobody can afford that.”

AI has feasted on low-hanging fruit, like search engines and board games. Now comes the hard part — distinguishing causal relationships from coincidences, making high-level decisions in the face of unfamiliar ambiguity, and matching the wisdom and commonsense that humans acquire by living in the real world. These are the capabilities that are needed in complex applications such as driverless vehicles, health care, accounting, law, and engineering.

Despite the hype, AI has had very little measurable effect on the economy. Yes, people spend a lot of time on social media and playing ultra-realistic video games. But does that boost or diminish productivity? Technology in general and AI in particular are supposed to be creating a new New Economy, where algorithms and robots do all our work for us, increasing productivity by unheard-of amounts. The reality has been the opposite. For decades, U.S. productivity grew by about 3% a year. Then, after 1970, it slowed to 1.5% a year, then 1%, now about 0.5%. Perhaps we are spending too much time on our smartphones.

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Autonomous and semi-autonomous systems need active illumination to navigate at night or underground. Switching on visible headlights or some other emitting system like lidar, however, has a significant drawback: It allows adversaries to detect a vehicle’s presence, in some cases from long distances away.

To eliminate this vulnerability, DARPA announced the Invisible Headlights program. The fundamental research effort seeks to discover and quantify information contained in ambient thermal emissions in a wide variety of environments and to create new passive 3D sensors and algorithms to exploit that information.

“We’re aiming to make completely passive navigation in pitch dark conditions possible,” said Joe Altepeter, program manager in DARPA’s Defense Sciences Office. “In the depths of a cave or in the dark of a moonless, starless night with dense fog, current autonomous systems can’t make sense of the environment without radiating some signal—whether it’s a laser pulse, radar or visible light beam—all of which we want to avoid. If it involves emitting a signal, it’s not invisible for the sake of this program.”

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