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Superconductors are among the most bizarre and exciting materials yet discovered. Counterintuitive quantum-mechanical effects mean that, below a critical temperature, they have zero electrical resistance. This property alone is more than enough to spark the imagination.
A current that could flow forever without losing any energy means transmission of power with virtually no losses in the cables. When renewable energy sources start to dominate the grid and high-voltage transmission across continents becomes important to overcome intermittency, lossless cables will result in substantial savings.
What’s more, a superconducting wire carrying a current that never, ever diminishes would act as a perfect store of electrical energy. Unlike batteries, which degrade over time, if the resistance is truly zero, you could return to the superconductor in a billion years and find that same old current flowing through it. Energy could be captured and stored indefinitely!
With no resistance, a huge current could be passed through the superconducting wire and, in turn, produce magnetic fields of incredible power.
You could use them to levitate trains and produce astonishing accelerations, thereby revolutionizing the transport system. You could use them in power plants—replacing conventional methods which spin turbines in magnetic fields to generate electricity—and in quantum computers as the two-level system required for a “qubit,” in which the zeros and ones are replaced by current flowing clockwise or counterclockwise in a superconductor.
Arthur C. Clarke famously said that any sufficiently advanced technology is indistinguishable from magic; superconductors can certainly seem like magical devices. So, why aren’t they busy remaking the world? There’s a problem—that critical temperature.
For all known materials, it’s hundreds of degrees below freezing. Superconductors also have a critical magnetic field; beyond a certain magnetic field strength, they cease to work. There’s a tradeoff: materials with an intrinsically high critical temperature can also often provide the largest magnetic fields when cooled well below that temperature.
This has meant that superconductor applications so far have been limited to situations where you can afford to cool the components of your system to close to absolute zero: in particle accelerators and experimental nuclear fusion reactors, for example.
But even as some aspects of superconductor technology become mature in limited applications, the search for higher temperature superconductors moves on. Many physicists still believe a room-temperature superconductor could exist. Such a discovery would unleash amazing new technologies.
The Quest for Room-Temperature Superconductors
After Heike Kamerlingh Onnes discovered superconductivity by accident while attempting to prove Lord Kelvin’s theory that resistance would increase with decreasing temperature, theorists scrambled to explain the new property in the hope that understanding it might allow for room-temperature superconductors to be synthesized.
They came up with the BCS theory, which explained some of the properties of superconductors. It also predicted that the dream of technologists, a room-temperature superconductor, could not exist; the maximum temperature for superconductivity according to BCS theory was just 30 K.
Then, in the 1980s, the field changed again with the discovery of unconventional, or high-temperature, superconductivity. “High temperature” is still very cold: the highest temperature for superconductivity achieved was -70°C for hydrogen sulphide at extremely high pressures. For normal pressures, -140°C is near the upper limit. Unfortunately, high-temperature superconductors—which require relatively cheap liquid nitrogen, rather than liquid helium, to cool—are mostly brittle ceramics, which are expensive to form into wires and have limited application.
Given the limitations of high-temperature superconductors, researchers continue to believe there’s a better option awaiting discovery—an incredible new material that checks boxes like superconductivity approaching room temperature, affordability, and practicality.
Without a detailed theoretical understanding of how this phenomenon occurs—although incremental progress happens all the time—scientists can occasionally feel like they’re taking educated guesses at materials that might be likely candidates. It’s a little like trying to guess a phone number, but with the periodic table of elements instead of digits.
Yet the prospect remains, in the words of one researcher, tantalizing. A Nobel Prize and potentially changing the world of energy and electricity is not bad for a day’s work.
Some research focuses on cuprates, complex crystals that contain layers of copper and oxygen atoms. Doping cuprates with various different elements, such exotic compounds as mercury barium calcium copper oxide, are amongst the best superconductors known today.
Research also continues into some anomalous but unexplained reports that graphite soaked in water can act as a room-temperature superconductor, but there’s no indication that this could be used for technological applications yet.
In early 2017, as part of the ongoing effort to explore the most extreme and exotic forms of matter we can create on Earth, researchers managed to compress hydrogen into a metal.
The pressure required to do this was more than that at the core of the Earth and thousands of times higher than that at the bottom of the ocean. Some researchers in the field, called condensed-matter physics, doubt that metallic hydrogen was produced at all.
It’s considered possible that metallic hydrogen could be a room-temperature superconductor. But getting the samples to stick around long enough for detailed testing has proved tricky, with the diamonds containing the metallic hydrogen suffering a “catastrophic failure” under the pressure.
Superconductivity—or behavior that strongly resembles it—was also observed in yttrium barium copper oxide (YBCO) at room temperature in 2014. The only catch was that this electron transport lasted for a tiny fraction of a second and required the material to be bombarded with pulsed lasers.
Not very practical, you might say, but tantalizing nonetheless.
Other new materials display enticing properties too. The 2016 Nobel Prize in Physics was awarded for the theoretical work that characterizes topological insulators—materials that exhibit similarly strange quantum behaviors. They can be considered perfect insulators for the bulk of the material but extraordinarily good conductors in a thin layer on the surface.
Microsoft is betting on topological insulators as the key component in their attempt at a quantum computer. They’ve also been considered potentially important components in miniaturized circuitry.
A number of remarkable electronic transport properties have also been observed in new, “2D” structures—like graphene, these are materials synthesized to be as thick as a single atom or molecule. And research continues into how we can utilize the superconductors we’ve already discovered; for example, some teams are trying to develop insulating material that prevents superconducting HVDC cable from overheating.
Room-temperature superconductivity remains as elusive and exciting as it has been for over a century. It is unclear whether a room-temperature superconductor can exist, but the discovery of high-temperature superconductors is a promising indicator that unconventional and highly useful quantum effects may be discovered in completely unexpected materials.
Perhaps in the future—through artificial intelligence simulations or the serendipitous discoveries of a 21st century Kamerlingh Onnes—this little piece of magic could move into the realm of reality.
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Pedro Domingos on the Arms Race in Artificial Intelligence
Christoph Scheuermann and Bernhard Zand | Spiegel Online
“AI lowers the cost of knowledge by orders of magnitude. One good, effective machine learning system can do the work of a million people, whether it’s for commercial purposes or for cyberespionage. Imagine a country that produces a thousand times more knowledge than another. This is the challenge we are facing.”
Gene Therapy Could Free Some People From a Lifetime of Blood Transfusions
Emily Mullin | MIT Technology Review
“A one-time, experimental treatment for an inherited blood disorder has shown dramatic results in a small study. …[Lead author Alexis Thompson] says the effect on patients has been remarkable. ‘They have been tied to this ongoing medical therapy that is burdensome and expensive for their whole lives,’ she says. ‘Gene therapy has allowed people to have aspirations and really pursue them.’ ”
The Revolutionary Giant Ocean Cleanup Machine Is About to Set Sail
Adele Peters | Fast Company
“By the end of 2018, the nonprofit says it will bring back its first harvest of ocean plastic from the North Pacific Gyre, along with concrete proof that the design works. The organization expects to bring 5,000 kilograms of plastic ashore per month with its first system. With a full fleet of systems deployed, it believes that it can collect half of the plastic trash in the Great Pacific Garbage Patch—around 40,000 metric tons—within five years.”
Autonomous Boats Will Be on the Market Sooner Than Self-Driving Cars
Tracey Lindeman | Motherboard
“Some unmanned watercraft…may be at sea commercially before 2020. That’s partly because automating all ships could generate a ridiculous amount of revenue. According to the United Nations, 90 percent of the world’s trade is carried by sea and 10.3 billion tons of products were shipped in 2016.”
Style Is an Algorithm
Kyle Chayka | Racked
“Confronting the Echo Look’s opaque statements on my fashion sense, I realize that all of these algorithmic experiences are matters of taste: the question of what we like and why we like it, and what it means that taste is increasingly dictated by black-box robots like the camera on my shelf.”
How Apple Will Use AR to Reinvent the Human-Computer Interface
Tim Bajarin | Fast Company
“It’s in Apple’s DNA to continually deliver the ‘next’ major advancement to the personal computing experience. Its innovation in man-machine interfaces started with the Mac and then extended to the iPod, the iPhone, the iPad, and most recently, the Apple Watch. Now, get ready for the next chapter, as Apple tackles augmented reality, in a way that could fundamentally transform the human-computer interface.”
Advanced Microscope Shows Cells at Work in Incredible Detail
Steve Dent | Engadget
“For the first time, scientists have peered into living cells and created videos showing how they function with unprecedented 3D detail. Using a special microscope and new lighting techniques, a team from Harvard and the Howard Hughes Medical Institute captured zebrafish immune cell interactions with unheard-of 3D detail and resolution.”
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Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.
Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.
That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.
Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.
Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.
This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.
The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.
The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.
This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.
“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.
“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”
It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.
Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.
The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.
Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.
Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.
Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.
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You might be really pleased with the camera technology in your latest smartphone, which can recognize your face and take slow-mo video in ultra-high definition. But these technological feats are just the start of a larger revolution that is underway.
The latest camera research is shifting away from increasing the number of mega-pixels towards fusing camera data with computational processing. By that, we don’t mean the Photoshop style of processing where effects and filters are added to a picture, but rather a radical new approach where the incoming data may not actually look like at an image at all. It only becomes an image after a series of computational steps that often involve complex mathematics and modeling how light travels through the scene or the camera.
This additional layer of computational processing magically frees us from the chains of conventional imaging techniques. One day we may not even need cameras in the conventional sense any more. Instead we will use light detectors that only a few years ago we would never have considered any use for imaging. And they will be able to do incredible things, like see through fog, inside the human body and even behind walls.
Single Pixel Cameras
One extreme example is the single pixel camera, which relies on a beautifully simple principle. Typical cameras use lots of pixels (tiny sensor elements) to capture a scene that is likely illuminated by a single light source. But you can also do things the other way around, capturing information from many light sources with a single pixel.
To do this you need a controlled light source, for example a simple data projector that illuminates the scene one spot at a time or with a series of different patterns. For each illumination spot or pattern, you then measure the amount of light reflected and add everything together to create the final image.
Clearly the disadvantage of taking a photo in this is way is that you have to send out lots of illumination spots or patterns in order to produce one image (which would take just one snapshot with a regular camera). But this form of imaging would allow you to create otherwise impossible cameras, for example that work at wavelengths of light beyond the visible spectrum, where good detectors cannot be made into cameras.
These cameras could be used to take photos through fog or thick falling snow. Or they could mimic the eyes of some animals and automatically increase an image’s resolution (the amount of detail it captures) depending on what’s in the scene.
It is even possible to capture images from light particles that have never even interacted with the object we want to photograph. This would take advantage of the idea of “quantum entanglement,” that two particles can be connected in a way that means whatever happens to one happens to the other, even if they are a long distance apart. This has intriguing possibilities for looking at objects whose properties might change when lit up, such as the eye. For example, does a retina look the same when in darkness as in light?
Single-pixel imaging is just one of the simplest innovations in upcoming camera technology and relies, on the face of it, on the traditional concept of what forms a picture. But we are currently witnessing a surge of interest for systems that use lots of information but traditional techniques only collect a small part of it.
This is where we could use multi-sensor approaches that involve many different detectors pointed at the same scene. The Hubble telescope was a pioneering example of this, producing pictures made from combinations of many different images taken at different wavelengths. But now you can buy commercial versions of this kind of technology, such as the Lytro camera that collects information about light intensity and direction on the same sensor, to produce images that can be refocused after the image has been taken.
The next generation camera will probably look something like the Light L16 camera, which features ground-breaking technology based on more than ten different sensors. Their data are combined using a computer to provide a 50 MB, re-focusable and re-zoomable, professional-quality image. The camera itself looks like a very exciting Picasso interpretation of a crazy cell-phone camera.
Yet these are just the first steps towards a new generation of cameras that will change the way in which we think of and take images. Researchers are also working hard on the problem of seeing through fog, seeing behind walls, and even imaging deep inside the human body and brain.
All of these techniques rely on combining images with models that explain how light travels through through or around different substances.
Another interesting approach that is gaining ground relies on artificial intelligence to “learn” to recognize objects from the data. These techniques are inspired by learning processes in the human brain and are likely to play a major role in future imaging systems.
Single photon and quantum imaging technologies are also maturing to the point that they can take pictures with incredibly low light levels and videos with incredibly fast speeds reaching a trillion frames per second. This is enough to even capture images of light itself traveling across as scene.
Some of these applications might require a little time to fully develop, but we now know that the underlying physics should allow us to solve these and other problems through a clever combination of new technology and computational ingenuity.
This article was originally published on The Conversation. Read the original article.
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