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Category: information science

Researchers from the Tyndall National Institute in Cork have created micro-structures shaped like small pyramids that can create entangled photons. Does this mean that quantum computers are closer than we realize?

Quantum computers have been the stuff of science fiction for the past few decades. In recent times, quantum computers have slowly become more of a reality with some machines successfully solving real world problems such as games and path finding algorithms.

But why are quantum computers so desired by tech firms and why is there so much research into the field? Silicon has been incredibly loyal to the tech world for the past 50 years, giving us the point contact transistor in 1947. Now, silicon is at the center of technology with computers, tablets, smartphones, the IoT, and even everyday items. In fact, you cannot walk down a city street without being in range of some Wi-Fi network or influence from a small silicon device.

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Now that’s an idea; education for systems. I can see the online university advertisements now showing an autonomous car beeping and flashing its lights over the enjoyment of graduating.

What if I told you to tie your shoes, but you had no laces? Or to cook dinner, but you had no pots or pans.

There are certain tools we need to succeed, which we often don’t have access to or are held back by a gatekeeper.

Dozens of AI / Machine Learning startups experience this same problem because they don’t have enough data to properly train their AI algorithm. Startups that aim to eliminate the error involved in judging cancerous tumors. Startups that aim to improve personalized medicine and create a healthier future.

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Creative Machines; however, are they truly without a built in bias due to their own creator/s?

Despite nature’s bewildering complexity, the driving force behind it is incredibly simple. ‘Survival of the fittest’ is an uncomplicated but brutally effective optimization strategy that has allowed life to solve complex problems, like vision and flight, and colonize the harshest of environments.

Researchers are now trying to harness this optimization process to find solutions to a host of science and engineering problems. The idea of using evolutionary principles in computation dates back to the 1950s, but it wasn’t until the 1960s that the idea really took off. By the 1980s the approach had crossed over from academic curiosities into real-world fields like engineering and economics.

Applying natural selection to computing

Evolutionary algorithms are numerous and diverse, but they all seek to replicate key features of biological evolution, such as natural selection, reproduction and mutation. Typically these methods rely on a kind of trial and error — a large population of potential solutions to a problem are randomly generated and tested against a so-called “fitness function.” This lets the system rank the solutions in order of how well they solve the problem.

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Quantum computing might be closer than we thought, thanks to a series of newly developed scientific methods. Furthermore, a new implementation of Shor’s algorithm increases the urgency of getting Bitcoin ready for the advent of quantum computing.

Also read: NIST Starts Developing Quantum-Resistant Cryptography Standards.

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Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.

By James Le, New Story Charity.

Blackboard header

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.

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It is not often that a scientist walks the red carpet at a Silicon Valley party and has Morgan Freeman award them millions of dollars while Alicia Keys performs on stage and other A-listers rub shoulders with NASA astronauts.

But the guest list for the Breakthrough prize ceremony is intended to make it an occasion. At the fifth such event in California last night, a handful of the world’s top researchers left their labs behind for the limelight. Honoured for their work on black holes and string theory, DNA repair and rare diseases, and unfathomable modifications to Schrödinger’s equation, they went home to newly recharged bank accounts.

Founded by Yuri Milner, the billionaire tech investor, with Facebook’s Mark Zuckerberg and Google’s Sergey Brin, the Breakthrough prizes aim to right a perceived wrong: that scientists and engineers are not appreciated by society. With lucrative prizes and a lavish party dubbed “the Oscars of science”, Milner and his companions want to elevate scientists to rock star status.

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The goal of roboticists has long been to make A.I. as efficient as the human brain, and researchers at the Massachusetts Institute of Technology just brought them one step closer.

In a recent paper, published in the journal Biology, scientists were able to successfully train a neural network to recognize faces at different angles by feeding it a set of different orientations for several face templates. Although this only initially gave the neural network the ability to roughly reach invariance — the ability to process data regardless of form — over time, the network taught itself to achieve full “mirror symmetry. Through mathematical algorithms, the neural network was able to mimic the human brain’s ability to understand objects are the same despite orientation or rotation.

The brain requires three different layers to process image orientation.

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What if a simple algorithm were all it took to program tomorrow’s artificial intelligence to think like humans?

According to a paper published in the journal Frontiers in Systems Neuroscience, it may be that easy — or difficult. Are you a glass-half-full or half-empty kind of person?

Researchers behind the theory presented experimental evidence for the Theory of Connectivity — the theory that all of the brains processes are interconnected (massive oversimplification alert) — “that a simple mathematical logic underlies brain computation.” Simply put, an algorithm could map how the brain processes information. The painfully-long research paper describes groups of similar neurons forming multiple attachments meant to handle basic ideas or information. These groupings form what researchers call “functional connectivity motifs” (FCM), which are responsible for every possible combination of ideas.

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When you see a photo of a dog bounding across the lawn, it’s pretty easy for us humans to imagine how the following moments played out. Well, scientists at MIT have just trained machines to do the same thing, with artificial intelligence software that can take a single image and use it to to create a short video of the seconds that followed. The technology is still bare-bones, but could one day make for smarter self-driving cars that are better prepared for the unexpected, among other applications.

The software uses a deep-learning algorithm that was trained on two million unlabeled videos amounting to a year’s worth of screen time. It actually consists of two separate neural networks that compete with one another. The first has been taught to separate the foreground and the background and to identify the object in the image, which allows the model to then determine what is moving and what isn’t.

According to the scientists, this approach improves on other computer vision technologies under development that can also create video of the future. These involve taking the information available in existing videos and stretching them out with computer-generated vision, by building each frame one at a time. The new software is claimed to be more accurate, by producing up to 32 frames per second and building out entire scenes in one go.

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