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Wellcome Image Awards 2015 showcase breathtaking shots of life, death, and science up close

Wellcome Image Awards 2015 showcase breathtaking shots of life, death, and science up close | Amazing Science | Scoop.it
Most wouldn't go looking for magnified cat tongues, sheep stomachs, or parasitoid wasps in a search for gorgeous imagery. But as the finalists for the 2015 Wellcome Image Awards show, these things can be breathtakingly beautiful.


The award showcases the best in science images for the year. "The breath-taking riches of the imagery that science generates are so important in telling stories about research and helping us to understand often abstract concepts," British geneticist, author, and broadcaster Adam Rutherford, one of this year's judges, said in a statement.


"It's not just about imaging the very small either, it's about understanding life, death, sex and disease: the cornerstones of drama and art. Once again, the Wellcome Image Awards celebrate all of this and more with this year’s incredible range of winning images," Rutherford said.


The images are part of the Wellcome Images collections, which are free for non-commercial use and intended to help illustrate scientific concepts and findings.


The winner will be announced at an awards ceremony on March 18. To see previous year's winners, check out Wellcome's Web site. The 20 finalists for 2015 will be showcased at 11 science centers around Britain. Fans of beauty and science in the United States are in luck, too: MIT's Koch Institute and The University of Texas Medical Branch at Galveston will also show off the winners sometime in March.


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Charismatic Minifauna of 33 Million Things: The Secret World of Museums

Charismatic Minifauna of 33 Million Things: The Secret World of Museums | Amazing Science | Scoop.it

The American Museum of Natural History has a great new video series: Shelf Life. It features the 33,430,000 artifacts and specimens estimated to be in the museum. From their description:


Shelf Life is a collection for curious minds—opening doors, pulling out drawers, and taking the lids off some of the incredible, rarely-seen items in the American Museum of Natural History. Over the next year, Shelf Life will explore topics like specimen preparation, learn why variety is vital, and meet some of the people who work in the Museum collections.”


A lot of natural history museums are trying to make the invisible visible by turning to video and social media.  The vast majority of a museum’s collection is never seen by anyone besides a tiny group of experts. How do you convince the public that they should care about a bunch of dead stuff? The perception of a lot of people is that museums are about naming and pickling things. Travel to exotic places, find unusual species, and kill them.


This assumes that things are just warehoused in a museum, which is certainly true in one sense.  Museums are a long term, stable library of our past and our present.  But a library that stops acquiring and indexing books isn’t going to remain relevant.


What’s actually stored in a museum is change that you can touch and measure.  TheCDC is using museum specimens to track human pathogens and diseases over space and time. Ecologists are looking at Hawaiian birds collected and preserved 100 years ago (now extinct) to see if they can find a way to protect today’s Galapagos species from canarypox. Preserved insects helped us figure out dinosaurs didn’t have lice via advanced molecular techniques.


The video series also makes some of the work that goes into maintaining a collection visible. You can’t just put something in a jar and walk away; constant maintenance and care helps to make sure that we can still see insects collected by Darwin, or plants from Linnaeus’ cabinet.

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How do you build a large-scale quantum computer?

How do you build a large-scale quantum computer? | Amazing Science | Scoop.it

How do you build a universal quantum computer? Turns out, this question was addressed by theoretical physicists about 15 years ago. The answer was laid out in a research paper and has become known as the DiVincenzo criteriaThe prescription is pretty clear at a glance; yet in practice the physical implementation of a full-scale universal quantum computer remains an extraordinary challenge.


To glimpse the difficulty of this task, consider the guts of a would-be quantum computer. The computational heart is composed of multiple quantum bits, or qubits, that can each store 0 and 1 at the same time. The qubits can become “entangled,” or correlated in ways that are impossible in conventional devices. A quantum computing device must create and maintain these quantum connections in order to have a speed and storage advantage over any conventional computer. That’s the upside. The difficulty arises because harnessing entanglement for computation only works when the qubits are almost completely isolated from the outside world. Isolation and control becomes much more difficult as more and more qubits are added into the computer. Basically, as quantum systems are made bigger, they generally lose their quantum-ness.  


In pursuit of a quantum computer, scientists have gained amazing control over various quantum systems. One leading platform in this broad field of research is trapped atomic ions, where nearly 20 qubits have been juxtaposed in a single quantum register. However, scaling this or any other type of qubit to much larger numbers while still contained in a single register will become increasingly difficult, as the connections will become too numerous to be reliable.


Physicists led by ion-trapper Christopher Monroe at the JQI have now proposed a modular quantum computer architecture that promises scalability to much larger numbers of qubits. This research is described in the journal Physical Review A (reference below), a topical journal of the American Physical Society. The components of this architecture have individually been tested and are available, making it a promising approach. In the paper, the authors present expected performance and scaling calculations, demonstrating that their architecture is not only viable, but in some ways, preferable when compared to related schemes.

Individual qubit modules are at the computational center of this design, each one consisting of a small crystal of perhaps 10-100 trapped ions confined with electromagnetic fields. Qubits are stored in each atomic ion’s internal energy levels. Logical gates can be performed locally within a single module, and two or more ions can be entangled using the collective properties of the ions in a module.


One or more qubits from the ion trap modules are then networked through a second layer of optical fiber photonic interconnects. This higher-level layer hybridizes photonic and ion-trap technology, where the quantum state of the ion qubits is linked to that of the photons that the ions themselves emit. Photonics is a natural choice as an information bus as it is proven technology and already used for conventional information flow. In this design, the fibers are directed to a reconfigurable switch, so that any set of modules could be connected.


The switch system, which incorporates special micro-electromechanical mirrors (MEMs) to direct light into different fiber ports, would allow for entanglement between arbitrary modules and on-demand distribution of quantum information.


Via Szabolcs Kósa
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Andreas Pappas's curator insight, March 28, 2014 4:40 AM

This article shows how scientists can increase the scale of quantum machine while still making them behave quantum mechanically by reading the qu-bits with lasers instead of conventional wiring.

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Interactive Scientific Visualizations On The Web — D3

Interactive Scientific Visualizations On The Web — D3 | Amazing Science | Scoop.it

When D3 came out in 2011, it became clear pretty quickly that it was going to be a powerful tool for creating data visualizations. But it’s certainly not the first — or only — tool. Why did it succeed when so many other libraries have failed?


First of all, it works on the web. Data visualizations are only good if people see them, and there’s no better place to see them than on the internet, in your browser. Protovis was the first library to make any real headway in this direction, despite other libraries and services that tried. Manyeyes is cool, but it lacks graphic flexibility and the resulting visualizations can’t just live anywhere seamlessly.

Prefuse and Flare (both predecessors to D3) are nice, but neither one runs in a browser without a plugin. Quadrigram (previously Impure) has the same plugin problem.

 
Another reason it has worked so well is because of its flexibility. Since it works seamlessly with existing web technologies, and can manipulate any part of the document object model, it is as flexible as the client side web technology stack (HTML, CSS, SVG).


This gives it huge advantages over other tools because it can look like anything you want, and it isn’t limited to small regions of a webpage like Processing.jsPaper.jsRaphael.js, or other canvas or SVG-only based libraries. It also takes advantage of built in functionality that the browser has, simplifying the developer’s job, especially for mouse interaction.

 
All of these features have been timed perfectly to coincide with the rise of new browsers and a push towards documents created using open standards rather than relatively walled-in plugins. The death of Internet Explorer as the top browser plays no small role in this, and the rendering and javascript engines in other browsers have made huge strides with their newfound attention. Some of this momentum has carried over to D3 as a way to take advantage of the new features and technology buzz.

 
But snazzy new technologies that work seamlessly aren’t the only reason that D3 has become successful.


Great documentationexamplescommunity, and the accessibility of Mike Bostock have all played major roles in its rise to prominence. Without these components, D3 would likely have taken much longer to catch on.

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Brainlike Computers Are Learning From Experience

Brainlike Computers Are Learning From Experience | Amazing Science | Scoop.it

Computers have entered the age when they are able to learn from their own mistakes, a development that is about to turn the digital world on its head.


The first commercial version of the new kind of computer chip is scheduled to be released in 2014. Not only can it automate tasks that now require painstaking programming — for example, moving a robot’s arm smoothly and efficiently — but it can also sidestep and even tolerate errors, potentially making the term “computer crash” obsolete.


The new computing approach, already in use by some large technology companies, is based on the biological nervous system, specifically on how neurons react to stimuli and connect with other neurons to interpret information. It allows computers to absorb new information while carrying out a task, and adjust what they do based on the changing signals.


In coming years, the approach will make possible a new generation of artificial intelligence systems that will perform some functions that humans do with ease: see, speak, listen, navigate, manipulate and control. That can hold enormous consequences for tasks like facial and speech recognition, navigation and planning, which are still in elementary stages and rely heavily on human programming.


Designers say the computing style can clear the way for robots that can safely walk and drive in the physical world, though a thinking or conscious computer, a staple of science fiction, is still far off on the digital horizon.


“We’re moving from engineering computing systems to something that has many of the characteristics of biological computing,” said Larry Smarr, an astrophysicist who directs the California Institute for Telecommunications and Information Technology, one of many research centers devoted to developing these new kinds of computer circuits.


Until now, the design of computers was dictated by ideas originated by the mathematician John von Neumann about 65 years ago. Microprocessors perform operations at lightning speed, following instructions programmed using long strings of 1s and 0s. They generally store that information separately in what is known, colloquially, as memory, either in the processor itself, in adjacent storage chips or in higher capacity magnetic disk drives.


The data — for instance, temperatures for a climate model or letters for word processing — are shuttled in and out of the processor’s short-term memory while the computer carries out the programmed action. The result is then moved to its main memory.


The new processors consist of electronic components that can be connected by wires that mimic biological synapses. Because they are based on large groups of neuron-like elements, they are known as neuromorphic processors, a term credited to the California Institute of Technology physicist Carver Mead, who pioneered the concept in the late 1980s.


They are not “programmed.” Rather the connections between the circuits are “weighted” according to correlations in data that the processor has already “learned.” Those weights are then altered as data flows in to the chip, causing them to change their values and to “spike.” That generates a signal that travels to other components and, in reaction, changes the neural network, in essence programming the next actions much the same way that information alters human thoughts and actions.


“Instead of bringing data to computation as we do today, we can now bring computation to data,” said Dharmendra Modha, an I.B.M. computer scientist who leads the company’s cognitive computing research effort. “Sensors become the computer, and it opens up a new way to use computer chips that can be everywhere.”


Via Szabolcs Kósa
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VendorFit's curator insight, December 31, 2013 3:27 PM

Artificial intelligence is the holy grail of technological achievment, creating an entity that can learn from its own mistakes and can (independently of programmer intervention) develop new routines and programs.  The New York Times claims that the first ever "learning" computer chip is to be released in 2014, an innovation that has profound consequences for the tech market.  When these devices become cheaper, this should allow for robotics and device manufacture that incorporates more detailed sensory input and can parse real objects, like faces, from background noise. 

Laura E. Mirian, PhD's curator insight, January 10, 2014 1:16 PM

The Singularity is not far away

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Aliens, computers and synthetic biology

Our capacity to partner with biology to make useful things is limited by the tools that we can use to specify, design, prototype, test, and analyze natural or engineered biological systems. However, biology has typically been engaged as a "technology of last resort" in attempts to solve problems that other more mature technologies cannot. This lecture will examine some recent progress on virus genome redesign and hidden DNA messages from outer space, building living data storage, logic, and communication systems, and how simple but old and nearly forgotten engineering ideas are helping make biology easier to engineer.


Via Szabolcs Kósa
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An interactive map of more than 800,000 Scientific Papers that have influenced math and physics most

An interactive map of more than 800,000 Scientific Papers that have influenced math and physics most | Amazing Science | Scoop.it

ArXiv is an online archive that stores hundreds of thousands of scientific papers in physics, mathematics, and other fields. The citations in those papers link to one another, forming a web, but you're not going to see those connections just by sifting through the archive.

 

So physicist Damien George and Ph.D student Rob Knegjens took it on themselves to create Paperscape, an interactive infographic that beautifully and intuitively charts the papers.


The infographic is a mass of circles. Each circle represents a paper, and the bigger a circle is, the more highly cited it is. The papers are color-coded by discipline--pink for astrophysics, yellow for math, etc.--and papers that share many of the same citations are placed closer together.


Via Lauren Moss
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Jay Ratcliff's curator insight, September 6, 2013 1:35 PM

This is cool!  It is like the map of the Internet done last year sometime.

I lucked out and found the section about SNA in the lower left hand side of the map.  Look for Network under the Quantitative Finance section, go figure.

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Amazing Science: General Science Postings

Amazing Science: General Science Postings | Amazing Science | Scoop.it

Science (from Latin "scientia", meaning "knowledge") is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. A practitioner of science is known as a scientist. Scientific fields are commonly divided into two major groups: natural sciences, which study natural phenomena (including biological life), and social sciences, which study human behavior and societies. Mathematics, which is classified as a formal science, has both similarities and differences with the empirical sciences (the natural and social sciences).

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Amazing Science: Science and Art Postings

Amazing Science: Science and Art Postings | Amazing Science | Scoop.it

Science and art naturally overlap and there has long been a connection between both, which can be traced back to the Egyptian pyramids. History proves that the two disciplines cannot exist without each other, enduring in constantly changing and evolving relationships. Both are a means of investigation. Both involve ideas, theories, and hypotheses that are tested in places where mind and hand come together—the laboratory and studio. Artists, like scientists, study—materials, people, culture, history, religion, mythology — and learn to transform information.

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Top 13500+ Science Blogs, ranked by popularity

Top 13500+ Science Blogs, ranked by popularity | Amazing Science | Scoop.it

The top Science blogs in the Technorati Blog Directory seem to never run dry of interesting content.

 

The concept of science represents a collection of efforts put forth to expand the knowledge base of mankind, through research made in the fields of natural, formal, social, and applied sciences. The scientific method is at the core of these pursuits, with a general structure involving questions formulated leading to conducted experiments, followed by the results being analyzed and published for all to see.  The future of our understanding is based on this research, and resulting scientific discoveries are communicated through the blogosphere in a speedy fashion.

 

The rate of discovery increases daily, with various blogs reporting on items such as the  condition of the Large Hadron Collider experiment, the expanded usage of stem cells for the benefits of living organisms, and the continued development of nanotechnology for the medical and electronic advances it provides.

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Marie Rippen's curator insight, July 24, 2013 2:29 PM

Ooooo! Fun reading to distract from work!

 

 
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Mars 2023 - Inhabitants wanted! 78,000 volunteers sign up for one-way mission to Mars

Mars 2023 - Inhabitants wanted! 78,000 volunteers sign up for one-way mission to Mars | Amazing Science | Scoop.it

Mars One says it has received applications from more than 78,000 people in more than 120 countries for the Mars One astronaut selection program, in hopes of becoming a Mars settler in 2023. Most applications come from the U.S. (17324), followed by China (10241), United Kingdom (3581), Russia, Mexico, Brazil, Canada, Colombia, Argentina and India. “With 78,000 applications in two weeks, this is turning out to be the most desired job in history, said Bas Lansdorp, Mars One Co-Founder and CEO. “These numbers put us right on track for our goal of half a million applicants.”

 

A central point to Mars One's mission is the emigration of the human astronauts. Mars becomes their new home, where they will live and work for what will likely be the remainder of their lives.

 

While it is possible that, within the lifetime of the early settlers on Mars, there will be opportunity to bring one or more back to Earth, it cannot be anticipated nor expected. Consider the following fact: to return a human to Earth, a fully assembled and fueled launch vehicle (rocket) must be available, capable of escaping the gravitational field of Mars with ample, on-board life support systems and supplies for up to a seven months voyage, and the capability to either dock with a space station orbiting the Earth, or perform a safe re-entry and landing on Earth. Not one of these is a small endeavor, each requiring substantial technical capacity, weight, and cost. Furthermore, there is a point in time after which the human body will have adjusted to the 38% gravitation field of Mars, and be incapable of returning to the Earth's much stronger gravity. This is due to the total physiological change in the human body, which includes reduction in bone density, muscle strength, and circulatory system capacity. While a cosmonaut on-board the Mir was able to walk upon return to Earth after thirteen months in a weightless environment, after a prolonged stay on Mars, the human body will not be able to adjust to the higher gravity of Earth upon return.

 

By assuming human astronauts are permanent residents on Mars, the challenges are reduced to providing the astronauts with the foundations for a new life: safe living facilities, clean air and potable water, food rations until plants may be grown in green houses and hydroponic facilities, and the essentials for intellectual stimulation on a planet which is cold, desolate, and without many life giving qualities.

 

While complex, the Mars One Mission is possible now. The science and technology required to place humans on Mars exists today. Much of what we have learned from the Skylab, Mir, and the International Space Station (ISS) have given us imperative data, experience, and know-how--all of which are applicable to living on Mars.

 

In addition, the basic elements required for a viable living system are already present on Mars, resulting in the need to send more tools and machines than raw elements. For example, the location Mars One has chosen for its first settlement contains water ice in the soil, which can be extracted through the application of heat. This water may be used to drink, bathe, raise food crops, and, through electrolysis, create oxygen. In addition, Mars has ample natural sources of nitrogen, the primary element (80%) in the air we breathe.

 

Certainly, for a long time, there will be need for new supplies such as computers, clothing, specialty foods (chocolate, coffee, and tea), and complex spare parts which cannot be readily reproduced with Mars based 3D printers and computer aided mills. However, soon after the first humans arrive, it is expected the astronauts will be able to create and improve their own habitation using local materials.

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Top Science Pictures of the Year

Top Science Pictures of the Year | Amazing Science | Scoop.it
Top Science Pictures of the Year - ScienceNOW

Via Sakis Koukouvis
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Teresa Levy's curator insight, December 21, 2012 12:14 PM

top picture (science)?

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Crowdsourcing site compiles new sign language for math and science

Crowdsourcing site compiles new sign language for math and science | Amazing Science | Scoop.it

A multimedia feature published this week in the New York Times, “Pushing Science’s Limits in Sign Language Lexicon,” outlines efforts in the United States and Europe to develop sign language versions of specialized terms used in science, technology, engineering and mathematics


Via Sakis Koukouvis
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The #1 reason people die early, in each country

The #1 reason people die early, in each country | Amazing Science | Scoop.it

You're probably aware that heart disease and cancer are far and away the leading causes of death in America. But globally the picture is more complicated: The above map shows the leading cause of lost years of life by country (click to see a larger version). The data comes from the Global Burden of Disease study, whose 2013 installment was released just a few weeks ago. It's worth stressing that "cause of lost years of life" and "cause of death" aren't identical. For example, deaths from preterm births may cause more lost years of life in a country than deaths from heart disease even if heart disease is the leading cause of death. Deaths from preterm births amount to many decades of lost life, whereas heart disease tends to develop much later on.


But that makes the fact that heart disease is the leading cause of lost life in so many countries all the more striking, and indicative of those countries' successes in reducing childhood mortality. By contrast, in many lower-income countries, the leading cause is something like malaria, diarrhea, preterm birth, HIV/AIDS, or violence, which all typically afflict people earlier in life than heart disease or stroke. We've made considerable progress in fighting childhood mortality across the globe in recent decades, but there's still much work left to be done.

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The top 100 papers: NATURE magazine explores the most-cited research papers of all time

The top 100 papers: NATURE magazine explores the most-cited research papers of all time | Amazing Science | Scoop.it

The discovery of high-temperature superconductors, the determination of DNA’s double-helix structure, the first observations that the expansion of the Universe is accelerating — all of these breakthroughs won Nobel prizes and international acclaim. Yet none of the papers that announced them comes anywhere close to ranking among the 100 most highly cited papers of all time.


Citations, in which one paper refers to earlier works, are the standard means by which authors acknowledge the source of their methods, ideas and findings, and are often used as a rough measure of a paper’s importance. Fifty years ago, Eugene Garfield published the Science Citation Index (SCI), the first systematic effort to track citations in the scientific literature. To mark the anniversary, Nature asked Thomson Reuters, which now owns the SCI, to list the 100 most highly cited papers of all time. (See the full list at Web of Science Top 100.xls or the interactive graphic, below.) The search covered all of Thomson Reuter’s Web of Science, an online version of the SCI that also includes databases covering the social sciences, arts and humanities, conference proceedings and some books. It lists papers published from 1900 to the present day.


The exercise revealed some surprises, not least that it takes a staggering 12,119 citations to rank in the top 100 — and that many of the world’s most famous papers do not make the cut. A few that do, such as the first observation1 of carbon nanotubes (number 36) are indeed classic discoveries. But the vast majority describe experimental methods or software that have become essential in their fields.


The most cited work in history, for example, is a 1951 paper2 describing an assay to determine the amount of protein in a solution. It has now gathered more than 305,000 citations — a recognition that always puzzled its lead author, the late US biochemist Oliver Lowry.

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Computer science: The learning machines

Computer science: The learning machines | Amazing Science | Scoop.it

Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence. Three years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain — a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats1.


Google Brain's discovery that the Internet is full of cat videos provoked a flurry of jokes from journalists. But it was also a landmark in the resurgence of deep learning: a three-decade-old technique in which massive amounts of data and processing power help computers to crack messy problems that humans solve almost intuitively, from recognizing faces to understanding language.


Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again.


Such advances make for exciting times in artificial intelligence (AI) — the often-frustrating attempt to get computers to think like humans. In the past few years, companies such as Google, Apple and IBM have been aggressively snapping up start-up companies and researchers with deep-learning expertise. For everyday consumers, the results include software better able to sort through photos, understand spoken commands and translate text from foreign languages. For scientists and industry, deep-learning computers can search for potential drug candidates, map real neural networks in the brain or predict the functions of proteins.



Via Szabolcs Kósa
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R Schumacher & Associates LLC's curator insight, January 15, 2014 1:43 PM

The monikers such as "deep learning" may be new, but Artificial Intelligence has always been the Holy Grail of computer science.  The applications are many, and the path is becoming less of an uphill climb.  

luiy's curator insight, February 26, 2014 6:19 AM

Deep learning itself is a revival of an even older idea for computing: neural networks. These systems, loosely inspired by the densely interconnected neurons of the brain, mimic human learning by changing the strength of simulated neural connections on the basis of experience. Google Brain, with about 1 million simulated neurons and 1 billion simulated connections, was ten times larger than any deep neural network before it. Project founder Andrew Ng, now director of the Artificial Intelligence Laboratory at Stanford University in California, has gone on to make deep-learning systems ten times larger again.

 

Such advances make for exciting times in artificial intelligence (AI) — the often-frustrating attempt to get computers to think like humans. In the past few years, companies such as Google, Apple and IBM have been aggressively snapping up start-up companies and researchers with deep-learning expertise. For everyday consumers, the results include software better able to sort through photos, understand spoken commands and translate text from foreign languages. For scientists and industry, deep-learning computers can search for potential drug candidates, map real neural networks in the brain or predict the functions of proteins.

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The amazing history of the Nobel Prize, told in maps and charts

The amazing history of the Nobel Prize, told in maps and charts | Amazing Science | Scoop.it

The U.S. has 4 percent of the world's population and 34 percent of its Nobel laureates. That's the most of any country in the world, by far: next-highest ranked is Britain with 120 laureates.


Up top is a heat map showing which countries have had the most Nobel laureates in the prize's history. Most countries have zero Nobel laureates. The faint yellow countries have received exactly one Nobel in the 113 years since the first prize was given. There's a small cluster of orange countries with maybe 10 to 15 Nobel laureates. A very tiny group of dark red countries have taken most of the Nobel prizes.


Just over 1,000 Nobels have been awarded since the prize was first established in 1901. Most of those have been in sciences but there's also the literature prize and, most famously, the peace prize. We've added up every Nobel awarded since 1901 and separated them out by country. The results are fascinating – and revealing.


A stunning 83 percent of all Nobel laureates have come from Western countries (that means Western Europe, the United States, Canada, Australia or New Zealand). We'll dive into some of the statistics of the Nobel below. But first here's a map of the prizes broken down by region.

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Thomas Faltin's curator insight, December 31, 2013 6:22 AM

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The Most Amazing Science Images of 2013

The Most Amazing Science Images of 2013 | Amazing Science | Scoop.it

From slow-motion footage on YouTube to deep-space satellite imagery to weird washcloths on the International Space Station, this was a big year for science.

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Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It

Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It | Amazing Science | Scoop.it

Simon DeDeo, a research fellow in applied mathematics and complex systems at the Santa Fe Institute, had a problem. He was collaborating on a new project analyzing 300 years’ worth of data from the archives of London’s Old Bailey, the central criminal court of England and Wales. Granted, there was clean data in the usual straightforward Excel spreadsheet format, including such variables as indictment, verdict, and sentence for each case. But there were also full court transcripts, containing some 10 million words recorded during just under 200,000 trials.

 

“How the hell do you analyze that data?” DeDeo wondered. It wasn’t the size of the data set that was daunting; by big data standards, the size was quite manageable. It was the sheer complexity and lack of formal structure that posed a problem. This “big data” looked nothing like the kinds of traditional data sets the former physicist would have encountered earlier in his career, when the research paradigm involved forming a hypothesis, deciding precisely what one wished to measure, then building an apparatus to make that measurement as accurately as possible.

 

“In physics, you typically have one kind of data and you know the system really well,” said DeDeo. “Now we have this new multimodal data [gleaned] from biological systems and human social systems, and the data is gathered before we even have a hypothesis.” The data is there in all its messy, multi-dimensional glory, waiting to be queried, but how does one know which questions to ask when the scientific method has been turned on its head?


Via Ashish Umre, Complexity Digest
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Arjen ten Have's curator insight, October 9, 2013 2:48 PM

This is not as much work for math, here is where it gets interesting, where it really becomes INTERdisciplinary rather than MULTI. The same for Bioinformatics. We are developing tools to correct for instance MSAs, very simple tricks that deal with the complexity. The biologist has to explain the math guy what he wants. It is not about new math, it is about flexibility!

Mark Waser's curator insight, October 10, 2013 4:53 PM

I dislike the title and the initial thrust but the article is well worth reading by the end.

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The 20 biggest questions in science that still remain in 2013

The 20 biggest questions in science that still remain in 2013 | Amazing Science | Scoop.it

From the nature of the universe (that's if there is only one) to the purpose of dreams, there are lots of things we still don't know – but we might do soon:

 

1 What is the universe made of?

2 How and where did life begin?

3 Are we alone in the universe?

4 What makes us human?

5 What defines consciousness?

6 Why do we dream?

7 Why is there stuff?

8 Are there other universes?

9 Where do we put all the carbon?

10 How do we get more energy from the sun?

11 What's so weird about prime numbers?

12 How do we beat bacteria?

13 Can computers keep getting faster?

14 Will we ever cure cancer?

15 How will robots advance?

16 What's at the bottom of the ocean?

17 What's at the bottom of a black hole?

18 Can we live forever?

19 How do we solve the population problem?

20 What is time?

 

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Adrian Rojas's comment, September 18, 2013 9:32 PM
What is the universe made of?
2 How and where did life begin?
3 Are we alone in the universe?
4 What makes us human?
5 What defines consciousness?
6 Why do we dream?
7 Why is there stuff?
8 Are there other universes?
9 Where do we put all the carbon?
10 How do we get more energy from the sun?
11 What's so weird about prime numbers?
12 How do we beat bacteria?
13 Can computers keep getting faster?
14 Will we ever cure cancer?
15 How will robots advance?
16 What's at the bottom of the ocean?
17 What's at the bottom of a black hole?
18 Can we live forever?
19 How do we solve the population problem?
20 What is time?
All these questions can be so easily answered because you should be able to answer all of these without hesitating. Like number 1 "what is the universe made of" umm hello seriously it's made of planets,stars, and gravity. I can understand number 2 because this question can be answered on what you believe in like Jesus made us, or we originate from monkeys. But number 8 is another one of those dumb questions "are there other universes" of course there is there's hundreds if billions of universes is just a galaxy.

I like this article because its interesting to know the questions other people have. And it gives a lot of explanations of why people don't know these answers to the questions. I also like the way it doesn't change subject at all like the other article I read and this one is non-fiction. But there I something I don't understand the first paragraph on this article says "questions we don't know the answers to but soon will, but I know most of these answers. So does that mean I'm like smarter or better than most people when it comes to science?
Gerome Tadeja's comment, October 5, 2013 11:51 AM
I thought that this article was interesting because I got to see some of the questions other people had.
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Amazing Science: Science Photography Postings

Amazing Science: Science Photography Postings | Amazing Science | Scoop.it

Photography (derived from the Greek word "photos" for "light" and "graphos" for "drawing") is the art, science, and practice of creating durable images by recording light or other electromagnetic radiation, either chemically by means of a light-sensitive material such as photographic film, or electronically by means of an image sensor. Typically, a lens is used to focus the light reflected or emitted from objects into a real image on the light-sensitive surface inside a camera during a timed exposure. Photography has many uses for business, science, manufacturing, art, recreational purposes, and mass communication.

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Amazing Science : Most Popular Postings

Amazing Science : Most Popular Postings | Amazing Science | Scoop.it

Your ultimate online portal to the future. Reporting on what's new and what's next in technology, science, gadgets, astronomy, physics, math, green tech and much more. We are aggregating science news from over 1,600 international news sources and select the best science news every day, 7 days a week, 24 hours per day. A ranking algorithm preselects the postings. Dubious non-peer reviewed science is filtered out. Amazing Science is the ultimate source to stay on top of the ever changing disciplines of science and get have a scientific resource to your disposal that is unpresidented.

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Scientists, the web is becoming your oyster

Scientists, the web is becoming your oyster | Amazing Science | Scoop.it
Mozilla’s ScienceLab wants the open web to transform science as much as it’s transformed the other areas of our lives.

 

Mozilla’s Science Lab dovetails with other recent efforts to update the practice of scientific research with technology and promote what some call “science as a service.” As my colleague David Meyer reported, Berlin-based ResearchGate, which earlier this month said it raised a $35-million Series C round from Bill Gates and others, built a social network of 2.7 million researchers. Its goal is to give researchers a platform for connecting and collaborating. In April, Science Exchange, an online marketplace where scientists can list and discover experimental services from institutions around the world, said it had raised $3 million in a round led by Union Square Ventures.  Other interesting scientific researcher-focused web startups and services include Digital Science, figshareand Software Carpentry, which plans to work with Science Lab to train researchers.

 

Even though more valuable digital tools are emerging for scientists, Thaney said there’s still work to be done in raising awareness, changing incentive structures and making sure researchers have the right technical skills.

 

“There are so many different stakeholders and different people trying to push this rock up the hill in terms of modernizing the way we do science,” she said. “They’re doing incredible work but there are still gaps in terms of coordination and trying to get in front of the people doing research.”

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Expert psychologist suggests the era of genius scientists is over

Expert psychologist suggests the era of genius scientists is over | Amazing Science | Scoop.it

Dean Keith Simonton, a psychology professor at the University of California, has published a comment piece in the journal Nature, where he argues that it's unlikely mankind will ever produce another Einstein, Newton, Darwin, etc. This is because, he says, we've already discovered all the most basic ideas that describe how the natural world works. Any new work, will involve little more than adding to our knowledge base.

Simonton's comments are likely to draw a strong reaction, both in and out of the science world. It's been the geniuses among us that have driven science forward for thousands of years, after all. If no more geniuses appear to offer an entirely new way of looking at things, how will the human race ever reach new heights? Simonton has been studying geniuses and their contributions to science for more than 30 years and has even written books on them. He also writes that he hopes he is wrong in his assessment, even as he clearly doesn't think he is. Sadly, the past several decades only offer proof. Since the time of Einstein, he says, no one has really come up with anything that would mark them as a giant in the field, to be looked up to hundreds, if not thousands of years from now. Worse perhaps, he details how the way modern science is conducted is only adding to the problem. Rather than fostering lone wolves pondering the universe in isolation, the new paradigm has researchers working together as teams, efficiently going about their way, marching towards incremental increases in knowledge. That doesn't leave much room for true insight, which is of course, a necessary ingredient for genius level discoveries.


Simonton could be wrong of course – there might yet be some person that looks at all that has been discovered and compares it with his or her own observations, and finds that what we think we know, is completely wrong, and offers evidence of something truly groundbreaking as an alternative. The study of astrophysics, for example, appears ripe for a new approach. Scientists are becoming increasingly frustrated in trying to explain why the universe is not just expanding, but is doing so at an increasing rate. Perhaps most of the theories put forth over the past half-century or so, are completely off base. Modern science can't even explain gravity, after all. Isn't it possible that there is something at work that will need the intelligence, insight and courage of an Einstein to figure out? It appears we as a species are counting on it, even as we wonder if it's even possible.

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NATURE: Science of 2012 in Review

NATURE: Science of 2012 in Review | Amazing Science | Scoop.it
This epic year for science saw the discovery of the Higgs boson and Curiosity’s arrival on Mars, but researchers also felt the sting of austerity.
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