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Coelacanth: The Fish That Time Forgot

Often called a living fossil, the coelacanth was long believed to have fallen extinct 70 million years ago, until a specimen was recognized in a fish market in South Africa in 1938. The coelacanth has fleshy, lobed fins that look somewhat like limbs, as does the lungfish, an air-breathing freshwater fish.

 

The coelacanth and the lungfish have long been battling for the honor of which is closer to the ancestral fish that first used fins to walk on land and give rise to the tetrapods, meaning all the original vertebrates and their descendants, from reptiles and birds to mammals.

 

The decoding of the coelacanth genome results in a victory for the lungfish as the closer relative to the first tetrapod. But the coelacanth may have the last laugh because its genome — which, at 2.8 billion bp of DNA, about the same size as a human genome — is decodable, whereas the lungfish genome, a remarkable 100 billion DNA units in length, cannot be cracked with present methods. The coelacanth genome is therefore more likely to shed light on the central evolutionary question of what genetic alterations were needed to change a lobe-finned fish into the first land-dwelling tetrapod.

 

The idea of decoding the coelacanth genome began six years ago when Chris Amemiya, a biologist at the University of Washington in Seattle, acquired some samples of coelacanth tissue. He asked the Broad Institute of Harvard and M.I.T., a biological research institute in Cambridge, Mass., to decode the DNA and invited experts in evolutionary and developmental biology to help interpret the results.

 

Dr. Amemiya’s team has sifted through the coelacanth’s genome for genes that might have helped its cousin species, the ancestor to the first tetrapod, invade dry land some 400 million years ago. They have found one gene that is related to those that, in animal species, build the placenta. Coelacanths have no placenta, but they produce extremely large eggs, with a good blood supply, that hatch inside the mother’s body. This gene could have been developed by land animals into a way of constructing the placenta.

 

Another helpful preadaptation is a snippet of DNA that enhances the activity of the genes that drive the formation of limbs in the embryo. The Amemiya team focused on the enhancer DNA sequence because it occurred in the coelacanth and animals but not in ordinary fish. They then inserted the coelacanth enhancer DNA into mice.

 

“It lit up right away and made an almost normal limb,” said Neil Shubin, meaning that the coelacanth gene enhancer successfully encouraged the mouse genes to make a limb. Dr. Shubin, a member of the team, is a paleontologist at the University of Chicago.

 

Present-day coelacanths are ferocious predators that live in a twilight zone about 500 feet deep where light barely penetrates. They lurk in caves during the day and emerge at night to attack surface fish as they descend and deep-sea fish as they rise to the surface. They have no evident need of fins that might help them walk on land.

 

“This is probably an unusual habitat for this lineage,” said Axel Meyer, an evolutionary biologist at the University of Konstanz in Germany and a member of the team. “Other coelacanths lived in more shallow, estuarylike environments 400 million years ago, and you can envisage them using the fins more like walking legs.”

 

The Amemiya team reports evidence that the coelacanth’s genes have been evolving more slowly than those of mammals, possibly because of “a static habitat and lack of predators.” But its environment must have changed quite considerably over the last 400 million years, Dr. Meyer said. Its principal habitat at present is the caves beneath the Comoro Islands in the Indian Ocean, but since these are extinct volcanoes a mere 5 million to 10 million years old, they must be a quite recent home for the coelacanth.

 

The Amemiya team does not possess a full coelacanth — these are endangered species — and decoded the genome from tissues obtained from Rosemary Dorrington of Rhodes University in South Africa. Dr. Dorrington supplied DNA kits to the Comoro Islands fishermen who occasionally snag coelacanths by accident. When a coelacanth was captured in 2003, they preserved blood and tissues, which were given to Dr. Dorrington and kept frozen, Dr. Amemiya said.

 

The specimen was preserved in Moroni, the capital of the Comoro Islands, but Dr. Amemiya has been unable to find out where it is now because of the constant state of civil war in the islands, he said.

 

Can he be certain, then, that the tissue came from a coelacanth? “Oh, no question,” Dr. Amemiya said. “We have DNA from several other coelacanths, from Africa and Indonesia, which is very similar to this one.” The one caught in 2003 was identified as a coelacanth by Said Ahamada, a South African expert, Dr. Amemiya said.

 

Because the original specimen is not available and the DNA sequencing is incomplete, the Amemiya team does not know its sex.

 
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How Rocks Move at Racetrack Playa in Death Valley

Scripps Oceanography paleooceanographer Richard Norris describes the phenomenon of sliding rocks in Death Valley.
https://scripps.ucsd.edu/news/mystery...

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Continued Fractions: What are they good for and what unexpected properties do they possess?

What are continued fractions? How can they tell us what is the most irrational number? What are they good for and what unexpected properties do they possess? How did Ramanujan make good use of their odd features to make striking discoveries? We will look at how they have played a role in the study of numbers, chaos, gears and astronomical motions.

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What Caused the Big Bang? What Happened Before the Big Bang?

Discovering Alien Worlds:https://youtu.be/snHMxbdel_M

Cosmic Voyage:https://youtu.be/rZFLi1TSOow

Sucked Inside a Black Hole:https://youtu.be/mpODXb6O6-s

Hubbles Amazing Discoveries:https://youtu.be/gCAmywvyajM

Mars Curiosity Rover Landing:https://youtu.be/4MbcqUL4FfM

Mars pioneers:https://youtu.be/M4JER61EJRo

Earth From Space Real Footage:https://youtu.be/CJxkV8y0zlM

The Mystery of the Milkyway:https://youtu.be/R8PQ9zZpZJo

The Ever Expanding Universe:https://youtu.be/7bWvo-dZgFg

Voyager`s Interstellar Mission:https://youtu.be/ume2XEgqW7o

If the Earth Stops Spinning:https://youtu.be/-W21ZlACv3w

The Dark Side of the Universe:https://youtu.be/HWnlLFo5DBk

Black Hole:Monster in the Milkyway:https://youtu.be/X5Z49Zas59o

Pluto Mysteries:https://youtu.be/v-xbetSNoAQ

Revisiting The Moon Landing:https://youtu.be/zmGqT1_iAac

Pluto:https://youtu.be/9ql2i3EyzS4

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Supersymmetry, Grand Unification and String Theory Documentary Lecture

Supersymmetry, Grand Unification and String Theory - A revolutionary new concepts about elementary particles, space and time, and the structure of matter began to emerge in the mid-1970s.

Theory got far ahead of experiment with radical new ideas, but the concepts have never been experimentally tested. Now all that is about to change. The LHC — the Large Hadron Collider — has finally been built and is about to confront theory with experiment.

The final quarter of this ongoing physics series with Leonard Susskind is devoted to supersymmetry, grand unification, and string theory. This course was originally presented in Stanford's Continuing Studies program.

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Matias Zaldarriaga: The Latest News from the Cosmos

On March 21, 2013, the most detailed map of the infant universe to date was publicly released, showing relic radiation from the Big Bang, imprinted when the universe was just 380,000 years old. This was the first release of cosmological data from the Planck satellite, a mission of the European space agency that was initiated in 1996 and involved hundreds of scientists in over thirteen countries. In this lecture, Matias Zaldarriaga, Professor in the School of Natural Sciences, reviews the new results and explains where they fit in our broader understanding of the beginnings and evolution of the universe.

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The Future of Robotics Technology [Video Documentary]

Though robotics seems the technology of the future, it actually has its roots in ancient history. The study and field of robotics can be traced back to approximately 300 B.C. in Ancient China. The Muslim inventor Al-Jazari is credited with inventing a humanoid robot in 1206. By the 20th century, the field of robotics was one that held great fascination and offered virtually limitless possibilities. As technology began to focus on the development of computers, so too did the study of robotics continue to move forward. The 60s was a time that both computers and robotics saw great advancement. In 1961, the first industrial robot was used at the General Motors plant. By the 21st century, the automotive industry and manufacturing plants saw widespread use of robots for commercial production.

When it comes to pinpointing the first robot created; there is a bit of confusion. In addition to the robot created in 1206 by Al-Jazari, Leonardo da Vinci is credited with building a mechanical man referred to as the anthrobat. In 1921, Karel Capek used the word “robota” to describe slave like labor in his play titled Rossum’s Universal Robots. The word became associated with the humanoid prototypes. During the 1940s, Missouri native, William Grey Walter developed a robot named Elise the tortoise. In 1961, while at MIT, Heinrich Ernst created a computer that operated a mechanical hand called the MH-1. By 1962, General Motors used the first industrial arm robot that would ensure people remained safe while performing difficult tasks on the assembly line. Several other robots and computer programs were created that helped advance the field of robotics.
t was not until the 1950s that industrial robotics really took off. As technological advancements were made in areas such as electronics and computers, so too, did robotics make vast strides. During the 50s, Alan Turing released the “Turing Test” which attempted to measure whether or not machines or robots could think for themselves.

In 1961, General Motors utilized the first industrial robot. The robot was named Unimate and Devol and Engelberger created it. The robot performed welding and die casting work at the New Jersey plant. As robotics made an impact on manufacturing and industry, its uses to assist humans were being explored. In 1963, the “Rancho Arm” was created to assist handicapped people. Today, robotics has revolutionized the way handicapped people can reclaim the use of lost limbs. More robotic arms were developed and by 1969, the Stanford Arm marked the first robotic arm controlled electrically via a computer. By 1973, industrial robots were controlled by computers and the T3, created by Richard Hohn was available for commercial sale. In 1976, robotic arms were used by both Viking 1 and Viking 2 for space exploration. By 1980, the official robotic age was underway. More robots were developed and perfected during the last part of the 20th century, with robots finding their way to the silver screen, in hospital rooms, in space, and in industries such as automotive and manufacturing. By the 21st century, the science behind Humanoid robots was developed with companies such as Hanson Robotics that developed the humanoid “Jules” and a realistic looking “Albert Einstein” that walks and talks.

 

The 21st century sees robotics in everyday use. The automotive industry is full of robots that complete tasks often too difficult for humans to accomplish. Many assembly lines and manufacturing companies are manned by robots instead of people. Television stations use robotics for video production and filming. Where once man stood behind a camera and filmed inside a studio, many of these tasks are now accomplished by robots. Robotics has revolutionized the medical industry, as robotic surgery is now a staple in hospital rooms. Amputees are now experiencing the power of robotics with newly designed limbs that can respond to sensations and pressure as human limbs complete with nerves would. The field of robotics continues to advance brining new technological advances to many factors of society.

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Video Collection about Cloning Methods

Video Collection about Cloning Methods | Science-Videos | Scoop.it

There are two types of gene cloning: in vivo, which involves the use of restriction enzymes and ligases using vectors and cloning the fragments into host cells (as can be seen in the image above). The other type is in vitro which is using the polymerase chain reaction (PCR) method to create copies of fragments of DNA.

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David Deutsch: Chemical scum that dream of distant quasars

Legendary scientist David Deutsch puts theoretical physics on the back burner to discuss a more urgent matter: the survival of our species. The first step toward solving global warming, he says, is to admit that we have a problem.

TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers are invited to give the talk of their lives in 18 minutes -- including speakers such as Jill Bolte Taylor, Sir Ken Robinson, Hans Rosling, Al Gore and Arthur Benjamin. TED stands for Technology, Entertainment, and Design, and TEDTalks cover these topics as well as science, business, politics and the arts. Watch the Top 10 TEDTalks on TED.com, at http://www.ted.com/index.php/talks/top10

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Eric Ladizinsky: Evolving Scalable Quantum Computers

Eric Ladizinsky visited the Quantum AI Lab at Google LA to give a talk "Evolving Scalable Quantum Computers." This talk took place on March 5, 2014.

"The nineteenth century was known as the machine age, the twentieth century will go down in history as the information age. I believe the twenty-first century will be the quantum age". Paul Davies

Quantum computation represents a fundamental paradigm shift in information processing. By harnessing strange, counterintuitive quantum phenomenon, quantum computers promise computational capabilities far exceeding any conceivable classical computing systems for certain applications. These applications may include the core hard problems in machine learning and artificial intelligence, complex optimization, and simulation of molecular dynamics .. the solutions of which could provide huge benefits to humanity. 

Realizing this potential requires a concerted scientific and technological effort combining multiple disciplines and institutions ... and rapidly evolving quantum processor designs and algorithms as learning evolves. D-Wave Systems has built such a mini-Manhattan project like effort and in just a under a decade, created the first, special purpose, quantum computers in a scalable architecture that can begin to address real world problems. D-Wave's first generation quantum processors (now being explored in conjunction with Google/NASA as well as Lockheed and USC) are showing encouraging signs of being at a "tipping point" .. matching state of the art solvers for some benchmark problems (and sometimes exceeding them) ... portending the exciting possibility that in a few years D-Wave processors could exceed the capabilities of any existing classical computing systems for certain classes of important problems in the areas of machine learning and optimization. 

In this lecture, Eric Ladizinsky, Co-Founder and Chief Scientist at D-Wave will describe the basic ideas behind quantum computation , Dwave's unique approach, and the current status and future development of D-Wave's processors. Included will be answers to some frequently asked questions about the D-Wave processors, clarifying some common misconceptions about quantum mechanics, quantum computing, and D-Wave quantum computers.

Speaker Info: Eric Ladizinsky is a physicist, Co-founder, and Chief Scientist of D-Wave Systems. Prior to his involvement with D-Wave, Mr. Ladizinsky was a senior member of the technical staff at TRW's Superconducting Electronics Organization (SCEO) in which he contributed to building the world's most advanced Superconducting Integrated Circuit capability intended to enable superconducting supercomputers to extend Moore's Law beyond CMOS. In 2000, with the idea of creating a quantum computing mini -Manhattan-project like effort, he conceived, proposed, won and ran a multi-million dollar, multi-institutional DARPA program to develop a prototype quantum computer using (macroscopic quantum) superconducting circuits. Frustrated with the pace of that effort Mr. Ladizinsky, in 2004, teamed with D-Wave's original founder (Geordie Rose) to transform the then primarily IP based company to a technology development company modeled on his mini-Manhattan-project vision. He is also responsible for designing the superconducting (SC) IC process that underlies the D-Wave quantum processors ... and transferring that process to state of art semiconductor production facilities to create the most advanced SC IC process in the world.

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The Global Brain: The Internet Become Smarter and Connecting the Planet

The Global Brain: The Internet Become Smarter and Connecting the Planet | Science-Videos | Scoop.it

The Global Brain can be defined as the self-organizing network formed by all people on this planet together with the information and communication technologies that connect and support them. As the Internet becomes faster, smarter, and more encompassing, it increasingly links its users into a single information processing system, which functions like a nervous system for the planet Earth.


The intelligence of this system is collective and distributed: it is not localized in any particular individual, organization or computer system. It rather emerges from the interactions between all its components—a property characteristic of a complex adaptive system. Such a distributed intelligence may be able to tackle current and emerging global problems that have eluded more traditional approaches. Yet, at the same time it will create technological and social challenges that are still difficult to imagine, transforming our society in all aspects.


The Global Brain Conference Track took place a couple of weeks ago. We have streamed it live to the web and, additionally, edited and uploaded videos of most of talks to the GBI YouTube channel, including:


Via Complexity Digest
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Physicist Stephen Hawking reflects on the Earth's chances of sustaining life

Physicist Stephen Hawking reflects on the Earth's chances of sustaining life | Science-Videos | Scoop.it

Physicist and cosmologist Prof. Stephen Hawking, at his first Australian public lecture, appears at the Sydney Opera House from Cambridge University in England via hologram technology. Hawking reflects on the state of the universe and why he believes we need to set up colonies in outer space. Before his BBC Reith Lecture on black holes, Hawking discusses the danger inherent in progress and the chances of disaster on Earth.


Via CineversityTV
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Caltech Lectures: Einstein's General Relativity, from 1905 to 2005 - Kip Thorne

instein struggled from 1905 to 1915 to formulate a new theory of gravity—his general relativity. He announced his theory 90 years ago, on November 25, 1915; it describes gravity as a consequence of a warping of space and time. Since 1915, physicists have struggled to understand and test the predictions. This struggle led to black holes, gravitational waves, and the acceleration of the universe; and atCaltech/JPL, to powerful tools for probing warped spacetime.

The full title of this lecture is Einstein's General Relativity, from 1905 to 2005: Warped Spacetime, Black Holes, Gravitational Waves, and the Accelerating Universe.


Kip Thorne is the Richard P. Feynman Professor of Theoretical Physics at Caltech.

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The True Nature Of Time - New Documentary 2015

Theories of science have ignored time… until now. A new idea reveals how it created the Universe – and you, writes Robert Matthews.

Time: it rules our lives, and we all wish we had more of it. Businesses make money out of it, and scientists can measure it with astonishing accuracy. Earlier this year, American researchers unveiled an atomic clock accurate to better than one second since the Big Bang 14 billion years ago.

But what, exactly, is time? Despite its familiarity, its ineffability has defied even the greatest thinkers. Over 1,600 years ago the philosopher Augustine of Hippo admitted defeat with words that still resonate: “If no-one asks me, I know what it is. If I wish to explain it to him who asks, I do not know.”


Yet according to theoretical physicist Lee Smolin, the time has come to grapple with this ancient conundrum: “Understanding the nature of time is the single most important problem facing science,” he says.

As one of the founders of the Perimeter Institute for Theoretical Physics in Ontario, Canada, which specialises in tackling fundamental questions in physics, Professor Smolin has spent more time pondering deep questions than most. So why does he think the nature of time is so important? Because, says Smolin, it is central to the success of attempts to understand reality itself.

To most people, this may sound a bit overblown. Since reality in all its forms, from the Big Bang to the Sunday roast, depends on time, isn’t it obvious that we should take time seriously? And didn’t scientists sort out its mysteries centuries ago?

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Genome editing and stem cell engineering for disease modeling [VIDEO]

The ability to create accurate disease models of human monogenic and complex genetic disorders is very important for the understanding of disease pathogenesis and the development of new therapeutics. Although proof of principle using adult stem cells for disease modeling has been established, induced pluripotent stem cells (iPSCs) have been demonstrated to have the greatest utility for modeling human diseases. Additionally, the latest advances in programmable nucleases have empowered researchers with genome editing tools, such as CRISPR/Cas9, that substantially improve their ability to make precise changes at a defined genomic locus in a broad array of cell types including stem cells. While the utility of these tools is improving, there are several key factors, including design and delivery that should be taken into account to ensure maximum editing efficiency and specificity. Already, these tools have allowed us to efficiently knock out genes and generate single nucleotide polymorphism (SNP) iPSCs. This ability to modify target genomic loci with high efficiency will facilitate the generation of novel genetically modified stem cells for research and therapeutic applications.

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David Haussler: Large-Scale Cancer Genomics

UCSC has built the Cancer Genomics Hub (CGHub) for the US National Cancer Institute, designed to hold data for all major NCI projects. To date it has served more than more than 10 petabytes of data to more than 320 research labs. Cancer is exceedingly complex, with thousands of subtypes involving an immense number of different combinations of mutations. The only way we will understand it is to gather together DNA data from many thousands of cancer genomes so that we have the statistical power to distinguish between recurring combinations of mutations that drive cancer progression and "passenger" mutations that occur by random chance. Currently, with the exception of a few international research projects, most cancer genomics research is taking place in research silos, with little opportunity for data sharing. If this trend continues, we lose an incredible opportunity. Soon cancer genome sequencing will be widespread in clinical practice, making it possible in principle to study as many as a million cancer genomes. For these data to also have impact on understanding cancer, we must begin soon to move data into a network of compatible global cloud storage and computing systems, and design mech- anisms that allow genome and clinical data to be used in research with appropriate patient consent.

 

The Global Alliance for Genomics and Health was created to address this problem. Our Data Working Group is designing the future of large-scale genomics for cancer and other diseases. This is an opportunity we cannot turn away from, but involves both social and technical challenges.

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Better, Stronger, Faster: The Future of the Bionic Body

In the future, a woman with a spinal cord injury could make a full recovery; a baby with a weak heart could pump his own blood. How close are we today to the bold promise of bionics—and could this technology be used to improve normal human functions, as well as to repair us? Join Bill Blakemore, John Donoghue, Jennifer French, Joseph J. Fins, and P. Hunter Peckham at "Better, Stronger, Faster," part of the Big Ideas Series, as they explore the unfolding future of embedded technology.
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Most Advanced Robotic Technology:  Rise of the Humanoid Robots

Are intelligent robots just around the corner? This video answers some of the most crucial questions.

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Peter Diamandis - The World in 2050

Where are we headed? What will the world look like in the near future? Dr. Peter Diamandis gives some stunning examples and answers.

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Brain upload, pattern survival, matter independent minds [Video collection]

Brain upload, pattern survival, matter independent minds [Video collection] | Science-Videos | Scoop.it

Pattern survival in humans is currently being driven by gene-survival, even though the evolution of humans is itself merely a byproduct of the competition for gene survival (Dawkins, 1976). So how can one motivate pattern survival without gene survival? How can one separate the desire to procreate thought characteristics that support specific memes from the desire to procreate genes in humans?

http://tinyurl.com/7nlujx6

 

Variety of singularity videos: http://tinyurl.com/7y6mb83

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Deep Learning in Action: How to learn an algorithm

Deep Learning in Action: How to learn an algorithm | Science-Videos | Scoop.it

Deep Learning in Action | A talk by Juergen Schmidhuber, PhD at the Deep Learning in Action talk series in October 2015. He is professor in computer science at the Dalle Molle Institute for Artificial Intelligence Research, part of the University of Applied Sciences and Arts of Southern Switzerland.


Juergen Schmidhuber, PhD: " I review 3 decades of our research on both gradient based and more general problem solvers that search the space of algorithms running on general purpose computers with internal memory."


Architectures include traditional computers, Turing machines, recurrent neural networks, fast weight networks, stack machines, and others. Some of our algorithm searchers are based on algorithmic information theory and are optimal in asymptotic or other senses.


Most can learn to direct internal and external spotlights of attention. Some of them are self-referential and can even learn the learning algorithm itself, recursive self-improvement. Without a teacher, some of them can reinforcement learn to solve very deep algorithmic problems — involving billions of steps — not feasible for more recent memory based deep learners.


And algorithms learned by our long short term memory recurrent networks defined the state of the art in handwriting recognition, speech recognition, natural language processing, machine translation, image caption generation, etc. Google and other companies made them available to over a billion users.

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Visualizing and Understanding Deep Neural Networks [by Matt Zeiler]

Matthew Zeiler, PhD, Founder and CEO of Clarifai Inc, speaks about large convolutional neural networks. These networks have recently demonstrated impressive object recognition performance making real world applications possible. However, there was no clear understanding of why they perform so well, or how they might be improved. In this talk, Matt covers a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the overall classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that perform exceedingly well.


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Kurzweil Interview of Marvin Minsky: Is Singularity Near?

Kurzweil Interview of Marvin Minsky: Is Singularity Near? | Science-Videos | Scoop.it
  • Understanding how the brain works: “The brain is 400 different computers.”
  • Modeling and building a brain: “You can’t do it from the bottom up.”
  • Backing up the brain: “There’s no ‘you.’ … I’m not exactly like I was five minutes ago….”
  • Is the brain “just” a machine? “There’s no person in here … identity is an illusion.”
  • Emotional intelligence: “‘Emotions are different from thinking’: that’s nonsense.”
  • Human-level artificial intelligence: “We will need AIs because longevity is increasing. … There will be no one to do the work. … We’ll need to find something else to do.”
  • Unfriendly AI: “Machines may re-compile themselves. … People say, ‘Scientists should be more responsible for what they do.’ The fact is, the scientist is no better and possibly worse than the average person at deciding what’s good and what’s bad, and if you ask scientists to spend a lot of time deciding what to invent or not, all you can get from that is that they won’t invent some things that might be wonderful.”
  • Is the Singularity near? “Yes, depending on what you mean by ‘near’ … It may well be, within our lifetimes.”
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The future of flying robots

The future of flying robots | Science-Videos | Scoop.it

At his lab at the University of Pennsylvania, Vijay Kumar and his team have created autonomous aerial robots inspired by honeybees. Their latest breakthrough: Precision Farming, in which swarms of robots map, reconstruct and analyze every plant and piece of fruit in an orchard, providing vital information to farmers that can help improve yields and make water management smarter.


http://go.ted.com/CCaq 


Via Complexity Digest
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Alien Life: Will We Know It When We See It?

What are scientists looking for when searching for alien life? A lot, it turns out: the search for extraterrestrials requires the help from astronomers, planetary scientists, chemists, computer scientists, and geneticists, just to name a few. But are we barking up the wrong carbon-based tree? Could alien life develop in ways we haven't dreamed of here on Earth? Hear Paul Davies, Sara Seager, Jack Szostak, and Dimitar Sasselov give updates on the search for life outside our planet in "Alien Life: Will We Know It When We See It?" part of the Big Ideas series at the 2014 World Science Festival.


NASA: We will find alien life within 20 years

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2013 Record deepest zoom E1500 or 2^5000 magnification non-trivial location Mandelbrot Set zoom

Deepest Mandelbrot set zoom to date, but soon to be surpassed
And it's already surpassed as of 30 june 2013:
http://www.youtube.com/watch?v=nVZ5Pd...

Comparison of scale:
The universe has a volume of 3,5*10^80 m^3 while a planck space is 1,62*10^-35 m^3. So the number of space quantums that fit inside the observable universe is already very large, but now consider the number of spacetime quantums in the observable universe and its entire history too. The universe is 13,8 billion years old, while a plancktime is 5,39*10^-44 sec. That's 1,75*10^174 spacetime quantums. Take this number to the power of 8, then you're getting somewhere close to the final magnification of this zoom video.

You can also download the original video file from either 180upload or rapidshare which is 60 frames per second (youtube allows only 30).
https://mega.co.nz/#!9oNESYCC!qFkPGps...

Final magnification:
2^5000
E1500
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