Archive for the ‘information science’ category: Page 11

Jul 26, 2019

Virginia Tech researchers lead breakthrough in quantum computing

Posted by in categories: chemistry, computing, information science, quantum physics

Abstract: The large, error-correcting quantum computers envisioned today could be decades away, yet experts are vigorously trying to come up with ways to use existing and near-term quantum processors to solve useful problems despite limitations due to errors or “noise.”

A key envisioned use is simulating molecular properties. In the long run, this can lead to advances in materials improvement and drug discovery. But not with noisy calculations confusing the results.

Now, a team of Virginia Tech chemistry and physics researchers have advanced quantum simulation by devising an algorithm that can more efficiently calculate the properties of molecules on a noisy quantum computer. Virginia Tech College of Science faculty members Ed Barnes, Sophia Economou, and Nick Mayhall recently published a paper in Nature Communications detailing the advancement.

Jul 25, 2019

Why ‘upgrading’ humanity is a transhumanist myth

Posted by in categories: information science, robotics/AI, transhumanism

Click on photo to start video.

Though some computer engineers claim to know what human consciousness is, many neuroscientists say that we’re nowhere close to understanding what it is — or its source.

In this video, bestselling author Douglas Rushkoff gives the “transhumanist myth” — the belief that A.I. will replace humans — a reality check. Is it hubristic to upload people’s minds to silicon chips, or re-create their consciousness with algorithms, when we still know so little about what it means to be human?

Continue reading “Why ‘upgrading’ humanity is a transhumanist myth” »

Jul 25, 2019

The Algorithm That Changed Quantum Machine Learning

Posted by in categories: information science, quantum physics, robotics/AI

It’s not every day that an 18-year-old college student catches the eye of the computing world, but when Ewin Tang took aim at recommendation algorithms similar to those commonly used by the likes of Amazon and Netflix, the University of Texas at Austin mathematics and computer science undergraduate blew up an established belief: that classical computers cannot perform these types of calculations at the speed of quantum computers.

In a July 2018 paper, which Tang wrote for a senior honors thesis under the supervision of computer science professor Scott Aaronson, a leading researcher in quantum computing algorithms, she discovered an algorithm that showed classical computers can indeed tackle predictive recommendations at a speed previously thought possible only with quantum computers. “I actually set out to demonstrate that quantum machine learning algorithms are faster,” she explains. “But, along the way, I realized this was not the case.”

Figure. Ewin Tang set out to show that quantum machine learning algorithms are faster than classical algorithms, “but … I realized this was not the case.”

Jul 24, 2019

Microsoft, Google and the Artificial Intelligence Race

Posted by in categories: Elon Musk, information science, robotics/AI

Microsoft and Google companies want to be central to the development of the thinking machine.

The decision by Microsoft to invest $1 billion in OpenAI, a company jointly founded by Elon Musk, brings closer the time when machines threaten to replace humans in any tasks that humans do today.

OpenAI, which was founded just four years ago, has pioneered a range of technologies which have pushed the frontiers of massive data processing in defiance of the physical and computer capabilities that governed such developments for generations.

Continue reading “Microsoft, Google and the Artificial Intelligence Race” »

Jul 24, 2019

Reprogrammable self-assembly makes molecular computer

Posted by in categories: biotech/medical, computing, information science

Researchers have designed a tile set of DNA molecules that can carry out robust reprogrammable computations to execute six-bit algorithms and perform a variety of simple tasks. The system, which works thanks to the self-assembly of DNA strands designed to fit together in different ways while executing the algorithm, is an important milestone in constructing a universal DNA-based computing device.

The new system makes use of DNA’s ability to be programmed through the arrangement of its molecules. Each strand of DNA consists of a backbone and four types of molecules known as nucleotide bases – adenine, thymine, cytosine, and guanine (A, T, C, and G) – that can be arranged in any order. This order represents information that can be used by biological cells or, as in this case, by artificially engineered DNA molecules. The A, T, C, and G have a natural tendency to pair up with their counterparts: A base pairs with T, and C pairs with G. And a sequence of bases pairs up with a complementary sequence: ATTAGCA pairs up with TGCTAAT (in the reverse orientation), for example.

The DNA tile.

Jul 24, 2019

AI protein-folding algorithms solve structures faster than ever

Posted by in categories: biotech/medical, information science, robotics/AI

More broadly, biologists are wondering how else deep learning — the AI technique used by both approaches — might be applied to the prediction of protein arrangements, which ultimately dictate a protein’s function. These approaches are cheaper and faster than existing lab techniques such as X-ray crystallography, and the knowledge could help researchers to better understand diseases and design drugs. “There’s a lot of excitement about where things might go now,” says John Moult, a biologist at the University of Maryland in College Park and the founder of the biennial competition, called Critical Assessment of protein Structure Prediction (CASP), where teams are challenged to design computer programs that predict protein structures from sequences.

Deep learning makes its mark on protein-structure prediction.

Jul 23, 2019

Microsoft Invests $1 Billion to Create a World-Saving AI

Posted by in categories: information science, robotics/AI

Whether or not creating an AGI is even possible remains up for debate. Meanwhile, others may cringe at the thought of an AI with the intellect to match and exceed humanity. However, OpenAI has been bullish on the prospect. The company points to the breakthroughs researchers have made in last decade in getting AI algorithms to recognize images, translate languages, and control robots. One of OpenAI’s own AI projects can write fiction like a human can (sort of).

However, creating new AI-based technologies costs a lot of money. Not only does it require programming, but also renting access to thousands of servers. So OpenAI has been seeking funding. “The most obvious way to cover costs is to build a product, but that would mean changing our focus. Instead, we intend to license some of our pre-AGI technologies, with Microsoft becoming our preferred partner for commercializing them,” Altman wrote in a separate blog post.

  • The AI Breakthrough Will Require Researchers Burying Their Hatchets.

Jul 21, 2019

The First Complete Brain Wiring Diagram of Any Species Is Here

Posted by in categories: biotech/medical, information science, robotics/AI, sex

For a humble, microscopic worm with only 302 neurons, C. elegans has had a lot of firsts. It was the first multicellular animal to have its whole genome sequenced. It was also the spark that lit the connectome fire—the revolutionary idea that mapping the entirety of connections among neurons will unveil secrets of our minds, memory, and consciousness. And if the connectomists are to be believed, a map of individual brains may be the blueprint that will one day hurtle AI into human-level intelligence, or reconstruct an entire human mind in digital form.

More than 30 years ago, a pioneering group of scientists painstakingly traced and reconstructed the roundworm’s neural wiring by hand. The “heroic” effort, unaided by modern computers and brain-mapping algorithms, resulted in the first connectome in 1986.

Yet the “mind of the worm” map had significant lapses. For one, it only focused on one sex, the hermaphrodite—a “female” equivalent that can self-fertilize. This makes it hard to tell which connections are universal for the species, and which are dependent on sex and reproduction. For another, because the effort relied entirely on human beings who get tired, bored, and mess up, the map wasn’t entirely accurate. Even with multiple rounds of subsequent refinements, errors could linger, which would royally screw up any interpretation of results using these maps.

Jul 20, 2019

What if you were immune to chronic pain?

Posted by in categories: biotech/medical, information science, neuroscience

Our current approach to treating chronic pain is drug-based, but a vaccine-based approach can cut addiction out of the equation. In this video, Big Think contributor Lou Reese, co-founder of United Neuroscience, explains how soon we may soon be able to vaccinate people, en masse, against pain!

Jul 20, 2019

A new set of images that fool AI could help make it more hacker-proof

Posted by in categories: information science, robotics/AI

Squirrels mislabeled as sea lions and dragonflies confused with manhole covers are challenging algorithms to be more resilient to attacks.

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