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Category: information science – Page 304

Let’s say closer to 7yrs or less.

Whether quantum computing is 10 years away — or is already here — it promises to make current encryption methods obsolete, so enterprises need to start laying the groundwork for new encryption methods.

A quantum computer uses qubits instead of bits. A bit can be a zero or a one, but a qubit can be both simultaneously, which is weird and hard to program but once folks get it working, it has the potential to be significantly more powerful than any of today’s computers.

And it will make many of today’s public key algorithms obsolete, said Kevin Curran, IEEE senior member and a professor at the University of Ulster, where he heads up the Ambient Intelligence Research Group.

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According to her profile, “She is a comforting character that is great to those living alone. She will always do all she can just for the owner.” How thoughtful and sweet. Except she comes with a $2,600 price tag (and her US version will be sold for $3,000). So, caring for her “owner” is the least she can do, right?

The hologram bot is based on a Japanese anime character, but she isn’t going to be the only character for Gatebox. From the looks of the website, the company is going to make other characters available, presumably also from anime.

Azuma’s hologram appears inside the main tube body of Gatebox, projected at a 1280 × 720 resolution. The hardware itself weighs 5kg, has stereo speakers, a microphone, and a camera mounted on top. Azuma is built with a machine learning algorithm, that helps her recognize her “master’s” voice, learn his sleeping habits, and send him messages through Gatebox’s native chat app.

Perhaps Azuma will work for some, but she might not cut it for others — going home to a cartoonish AI hologram could take some getting used to. Anyway, Gatebox is certainly trying to disrupt the virtual assistant space. However, we still seem to be far from that holographic projection that we’re really be looking for.

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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.

Continue reading “Evolution’s Brutally Simple Rules Can Make Machines More Creative” | >

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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