Programmer creates 800,000 books algorithmically, starts selling them on Amazon

When we think about writing books, especially the technical kind, we think about a person or small group of people hunched over their keyboards typing away. There’s a good reason for that mental image: that’s how the majority of books are written. That’s not the way it has to be, though. Philip M. Parker, a marketing professor at INSEAD, has a patented system for algorithmically compiling data into book form. Thanks to Parker’s system, Amazon now has over 800,000 books for sale from his company. Other organizations pay for this service to compile data for their reports, so the system clearly has flexibility.

In a fascinating piece covering the news the sheer power of this system was revealed. Countless topics can be listed on sites like Amazon — everything you’d ever want to know. The funny part is that the books don’t even have to be written yet. Thanks to digital distribution and print-on-demand solutions, a whole new book can be generated on an incredibly obscure topic as soon as someone buys it. The system will be able to compile an entire book on the subject in the range of ten minutes to a few hours. It’s that simple.

This video below features Parker himself explaining how the process works, and why it’s useful. Because of his specialty in marketing, it’s easy to assume that these books are designed for spam-like purposes, but it does also have benefits to traditional writing outside of the amazing speed. Specifically, he points out that in the case of very rare diseases, it’s unlikely that any books would be written in the first place. Especially when you’re looking at statistics and data, having a computer compile and find potentially significant data is very useful. While the books won’t be particularly creative, they absolutely do have a place.

The technology isn’t just for books. Videos and games can be generated as well. When you’re focusing on areas like developing and distributing content all over the world in dozens of languages, traditional manpower isn’t exactly efficient. Humans just don’t have the ability to translate content to that many languages in a time and cost effective manner. Computers can knock that out during a long lunch. Using this system, it is possible to spread information to places that used to be impossible to reach. Computers won’t be replacing humans for writing the great American novel or entertaining the masses on TV, but it is obvious that computers will be an increasing fixture in the analysis and translation of content. This is a perfect complement to human creativity — not something for creatives, researchers, or consumers to fear.

[Image credit: Willi Heidelbach]

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Fusion power at home, or, how small science will defeat big science

Fusion research is known for its huge projects — and its huge lack of tangible success. Big machines like the Princeton tokamak and theLivermore laser have indeed managed to fuse a few nuclei, but have required too much energy to get too little in return. A Brooklyn web developer named Mark Suppes recently created fusion in in his own home, using a much simpler device called a Farnsworth fusor. Accessing declassified experiments, and using open-source software, open-source hardware and crowdsourced funding, he has turned the traditional approach to scientific research on its head — and he makes it look easy.

In his early teenage years, Philo Farnsworth presented a concept for the all-electronic “image dissector,” and soon developed it into the first functioning television set. He successfully defended his rights to the design against larger corporations like RCA, which tried to claim it in a patent, and in the process became a legend and inspiration for private inventors and DIYers everywhere. Farnsworth’s skill at controlling electrons with electric fields later led him to develop a small nuclear fusion device. The device used inertial electrostatic confinement, as opposed to magnetic confinement which is used to fuse charged particles in the larger and more complex machines.

Suppes first heard about the Farnesworth fusor from Robert Brussard’s Google Tech Talk.With DARPA’s permission, Brussard described his work on Polywell reactors. The Polywell is a refinement of the Farnesworth fusor, but has the potential for significant net energy production. Suppes knew little of physics, but decided that with a little help from the open source community, he could make a fusor for himself. His blog
and Github repository show step-by-step exactly how he did it. In the video below, you can see a talk that Suppes gave at Wired 2012.

Can you really create fusion at home?

The biggest challenge to homebrew fusion is creating a spot where the conditions are just right. Typically a vacuum chamber that can tolerate some heat is needed. In university and industrial research labs a vacuum system is built using standard erector set pieces called“conflat flange” mounts. Prior to Ebay, the best way to get value out of an old vacuum system was to recycle it for the nickel and chrome in the steel. Today however, passing these systems on to someone who can use them is just a matter of a few clicks.

Another thing Suppes had going for him was the capability to design and 3D print heat resistant parts in the complex geometry needed for the Polywell device. The Polywell is basically a set of electromagnetic coils positioned in a precise geometry that enables charged particles to be confined. Ceramic is needed because other heat resistant materials, like metals, would perturb the field and let particles escape.

The most important a tool for Suppes was the willingness of skilled individuals to help him at every turn. As the 38th person to build a working fusor, there was a lot of technical know-how floating around. Suppes was able to collect that information into one place and package it in a way anyone can understand. His approach of publish first, then review, has been catching on as the new way to do science. Not every person cares about the research that their tax dollars fund, but those who do care have demanded access to it — and are getting it.

A cautionary note is perhaps in order. David Hahn, also known as the radioactive boy scout, was a child prodigy who built a subcritical fission reactor in his backyard using tiny amounts of radioactive material from many smoke detectors. He eventually became obsessed with his hobby and landed himself in the hospital for treatment of radiation injuries, and then in jail for larceny. The risks from radiation are not the same with fission as with fusion. High energy X-rays and neutrons are created in a fusor and need need to be respected accordingly.

The fire that Farnsworth lit years ago continues to burn bright. The untimely death of Brussard, just a year after his Google Talk and initial results with the Polywell device offered the torch, and Suppes and others have run with it. Big science concentrates all the money and knowledge on large projects that can’t fail, but it is slowly yielding to small science, where nimble, crowd-funded and -sourced projects can gracefully die if they don’t yield productive results. Not every scientist is compelled to fuse atoms, nor every layperson, but with enough people working on the problem and communicating their results and techniques openly, humankind will one day harness the power of the Sun (perhaps through a Sun-encompassing Dyson sphere, hm?)

Now read: Inside California’s star power fusion facility

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Father-daughter duo have the world’s first brain-to-brain ‘telepathic’ conversation

It should be fairly obvious why, all technological considerations aside, there has been much more research into letting machines extract our thoughts, rather than insert them. Mind reading is a scary-enough concept all on its own — but mindwriting? It calls to mind the hacker deities of cyber punk novels; skinny, trench-swathed Neos projecting e-thoughts into the skulls of passing civilians. With such basic issues of privacy on the line, it took the trusting relationship between UK scientist Christopher James and his adventurous young daughter to give us our first stab at developing real telepathic, brain-to-brain communication technology.

James’ process of telepathic communication is rough, its results shaky, but the principle of brain-to-brain (B2B) communication is unquestionably met. It begins with the by-now standard collection of mental information, achieved in this case with electrodes placed against the skull. “I only used scalp electrodes on my daughter, since my wife wouldn’t let me drill holes in my daughter’s head,” James told the Times of India.

In the experiment, the sender imagined a series of binary digits, broadcasting their choices by imagining movement in their right arm or their left. The resulting patterns of brain activity were recorded and expressed by an LED — one frequency to represent a one, another to represent a zero. The patterns are simply too arcane to be useful to the conscious mind, too quick and complex, but they’re not meant to be read like Morse code, in any case.

Dr. James conducting a preceding experiment in 2009.

When the LED signal travels to the recipient, it flashes into a very specific part of the eye (which part doesn’t matter much) and so the resulting optical signal is sent to a predictable section of the visual cortex. Surface electrodes just like those that originally recorded the signal are much better than people at making sense of the quick-flash LED language, seeing in the recipient’s brain more data than does the recipient themselves.

Once the pattern has been reverse-engineered from LED back to arm-waving, the telepathic process is said to have concluded. “The key idea to grasp,” said Dr. James, “is that a person’s eyes cannot distinguish between the different frequencies of flashing lights but a part of his brain, [the] visual cortex, can.” For more serious results, the electrodes would have to be implanted on the surface of the brain, a procedure for which he had neither governmental nor spousal approval.

All in all, this advance will take some time to spawn any dystopian mind flayers or Inception-style dreamscapes. This advance has to do with the translation of thought to binary data, and the ability to technically induce that data in the brain of another person. The glaringly absent piece of the puzzle is any ability to induce much more sophisticated visual images; multi-pixel messages that appear in the mind’s eye, as opposed to the physical one.

That sort of sophistication could come through a better understanding of just how stimulation of the visual cortex influences images in the mind, or in teaching brains the language of light bulbs. With LED technology now finding its way into contact lenses, this technology seems well-suited to the (possibly) upcoming brain-machine revolution. It’s unclear was uses this tech might find in such a future, especially when it steps beyond the constraints of fatherly affection.

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Vector vengeance: British researchers claim they can kill the pixel within five years

The humble pixel — the 2D picture element that has formed the foundation of just about every kind of digital media for the last 50 years — may soon meet its maker. Believe it or not, if a team of British researchers have their way, the pixel, within five short years, will be replaced with… vectors.

If you know about computer graphics, or if you’ve ever edited or drawn an image on your computer, you know that there are two primary ways of storing image data: As a bitmap, or as vectors. A bitmap is quite simply a giant grid of pixels, with the arrangement and color of the pixels dictating what the image looks like. Vectors are an entirely different beast: In vector graphics, the image is described as a series of mathematical equations. To draw a bitmap shape you just color in a block of pixels; with vector graphics, you would describe the shape in terms of height, width, radius, and so on.

These two methods are very different, and they fulfill very different needs. Vector graphics, because they’re made out of geometric primitives, are infinitely scalable, making them the ideal image format for illustrations, clipart, maps, typography, Flash animations, and so on. For everything else, we use pixel bitmaps. Streaming videos, digital cameras, movie editing, video game textures — all bitmaps. There might be different file formats involved (PNG, MOV, JPG), but they’re all ultimately converted into pixel bitmaps when it comes to displaying them on your monitor, TV, or cinema screen.

Pixel bitmaps have their problems, though. As display (and camera and cinema) resolution increases, so does the number of pixels. The obvious problem with this is that larger bitmaps are computationally more expensive to process, resulting in a slower (or more expensive) workflow. Pixel bitmaps also don’t scale very gracefully; reduction is okay, but enlargement is a no-no. There is always the issue of a master format, too: With pixel bitmaps, conversions from one format to another, or changing frame rates, is messy, lossy business.

Which finally leads us back to the innovation at hand: Philip Willis and John Patterson of the University of Bath in England have devised a video codec that replaces pixel bitmaps with vectors. In a conventional digital camera, images (or videos) are captured as pixel bitmaps and compressed using a codec such as JPEG or H.264. Willis and Patterson have devised a codec called Vectorized Streaming Video (VSV) that converts the bitmap image into vectors. This builds on their previous work with VPI — vectorized photographic images [PDF] — which deals with converting bitmap images into perfect, vectorized copies.

At the moment there’s very little information about VSV, only that the Bath researchers are working with Root6 Technology (a company that specializes in transcoding) and Smoke & Mirrors (a post-processing studio) to bring the codec to market. According to Smoke & Mirrors, there should be working demonstrations of VSV within the next three to six months — and then, within five years, according to the University of Bath, the pixel will simply… die.

An example of the VPI bitmap-to-vector conversion. Bitmap (left) vs. vectorized (right)

Looking at the sample images in the VPI paper (above), Bath’s vectorizing algorithm is certainly quite impressive. Performance is awful — but the algorithm is apparently very parallelizable, so this is unlikely to be an insurmountable issue. A brief look through the paper suggests that the algorithm is fairly similar to the auto-vectorization tools, such as Adobe Live Trace. The biggest issue with photorealistic vector graphics is the coloring of spaces between the geometric shapes — but apparently Willis and Patterson have solved this.

Ultimately, though, I think it will take a lot more than a new codec to kill the pixel. There has been no shortage of new codecs over the last few years, but it has so far proved to be very, very difficult to unseat entrenched favorites such as JPEG, GIF, and PNG. Even WebP, which promised to be better than JPEG in every way, failed to gain traction — and that was with the might of Google behind it.

Who knows: A bona fide, high-performance vector video codec would be very, very exciting. If anything could shake up the tools and industry that has built up around the bitmap, it would be a vector video codec, with vector masters that can be scaled and resized infinitely in any direction. “This is a significant breakthrough which will revolutionise the way visual media is produced,” says co-inventor Willis. We shall see.

[Image credit]

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Smartphones become capable of sensing human emotion

Smartphones are amazing. They tell us where we’re going, let us know if it’s going to rain, and even act like personal assistants. Now, a new research project out of the University of Rochester aims to make your phone capable of sensing your emotions just from measuring how you’re speaking — not based on what you’re saying.

This research, titled the Bridge Project, focuses on small changes in the human voice. Rather than using the traditional methodology of self-reporting and monitoring body language, this new method is based on automatic passive emotion detection. This technology can always be listening and monitoring a patient’s emotional state without any work on his or her part — providing a bigger picture on the patient’s entire status.

The basics of the system involve measuring twelve different aspects of speech, and then mapping the data onto six different emotions. Wendi Heinzelman, professor of electrical and computer engineering, said that the project analyzed completely emotionless phrases of speech, such as saying dates of the month. Impressively, they are able to reach an 81% accuracy rating with this model while previous attempts were only around 55%. By having actors read scripts with certain performances, the researchers are able to tweak their algorithm to associate certain pitches, volumes, and harmonics to a specific emotional state.

While this is undoubtedly an invaluable tool for psychologists and medical researchers, it also has huge potential for consumers. Take a look at Apple’s Siri. It’s designed to appear more human-like by offering humorous answers, apologizing, and using more realistic speech, like “Let’s hear some Beatles,” instead of something with less flare, like “Now playing: The Beatles, track one.” This gives us a better experience because it mimics human interaction. Now, think about this technology integrated into Siri. When you’re getting frustrated, it could offer simple hints on how to interact better. When you’re sad, it could throw in compliments.

In Dr. Oliver Sacks’s book The Man Who Mistook His Wife For A Hat, he tells a story about a group of patients suffering from aphasia — an inability to understand words. In the story, he details how very capable these patients are in detecting emotion through speech. In fact, they are able to use sound cues to effectively communicate with their loved ones and doctors despite not being able to understand the words directly. He even notes that it is extremely difficult to execute a lie in front of an aphasiac because they are so adept at picking up the hidden emotion. This story truly illustrates how much of our emotional states are expressed verbally, and just how useful this research really is.

[Image credit: Lara604 & William Gunn]

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How to pirate Windows 8 Metro apps, bypass in-app purchases, and more

The principal engineer for Nokia’s WP7 and WP8 devices has demonstrated, in rather frank detail, how to pirate Windows 8 Metro apps, how to bypass in-app purchases, and how to remove in-game ads. These hacks aren’t exactly easy, but more worryingly they’re not exactly hard either.

On his blog (Google cache), Justin Angel shows that turning a trial version of a Metro app into the full version — i.e. pirating an app — is scarily simple. It’s just a matter of downloading a free, open-source tool, and then using it to change a Metro app’s XML attribute from “Trial” to “Full.” Likewise, a quick change to a XAML file can remove an app’s ads.

Bypassing in-app purchases is a little trickier, involving some reverse engineering of some DLLs and and decryption of database files, but Angel still makes it look fairly easy. Angel gives himself one million credits in Soulcraft, an RPG game — something that would cost you over a thousand dollars, if you performed a legitimate in-app purchase. Angel also demonstrates a way to bypass in-app purchases in WinJS (Metro/JavaScript) apps, by injecting scripts into IE10 (the rendering engine for WinJS apps).

Ultimately, all of these hacks represent ways of getting stuff for free. This is obviously bad news for developers, who probably don’t realize that by allowing trial downloads they are opening themselves up to piracy. In-app ads and purchases are massive revenue streams for developers, and yet we now see that it’s very easy to circumvent both.

You can protect these files with encryption — and indeed, some of them are — but that’s no good if you have access to the code that performs the encryption. As Angel says, “We have the algorithm used for encryption, we have the hash key and we have the encrypted data. Once we have all of those it’s pretty simple to decrypt anything.” Angel notes that there are some security mechanisms in place that stopped him from directly editing app DLL and JS files, but, as we can see, that didn’t stop him from pirating apps or bypassing in-app purchases.

It’s easy to blame Microsoft for this, but really this is an issue that is intrinsic to all installed applications. The fact is, Windows 8 Metro apps are stored on your hard drive — and this means that you have access to the code and data. In general, every installed application is vulnerable to these kinds of attacks. Hex editors, save game editors, bypassing Adobe’s 30-day trials by replacing DLL files, pirating Windows 8 apps — these are all just different incarnations of the same attack vectors.

The only real solution is to provide some kind of server-side sanity checking: You hack the software from Trial to Full — but when you log in, the server knows that you haven’t bought the software, and so it reverts you back to Trial mode. You give yourself one million credits — but the server checks your purchase history, knows that you cheated, and so resets your credits back to zero. The problem with this route, of course, is that it requires you to be online — and you know how we feel about always-on DRM. Plus, it’s very easy to disable server-side checks with a little Hosts file hacking.

In short, Windows 8 Metro apps have been hacked, and it’s now just a matter of time until some enterprising developer creates a one-button tool that pirates trial apps, unlocks every in-app purchase, and removes in-app ads. There are certainly changes that Microsoft could make to shore up the security of Metro apps, but it would only delay the inevitable. Really, this is just a natural part of Windows 8′s evolution.

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The past, present, and future of bionic eyes

Next-generation bionic eyes are practically here today. Imagine a blind person’s real-world conundrum trying to shop for one — they could schedule surgery for Nano Retina’s implant today and see their daughter’s wedding in 576-pixel clarity, but it would cost them their life’s savings. The Nano Retina 5000-pixel device could be ready tomorrow, or in another six months… and would be much more affordable. When the procedure involves assimilation of an electrode pincushion into the ganglionic tentacles of your retina, hardware upgrades are not as simple as popping in more RAM. What kind of decision matrix could be offered under such critical circumstances?

Cochlear implants, used to restore hearing, work phenomenally well when properly tuned and fitted. Most are refinements of the basic piece of hardware one might have sitting on their bookshelf — the graphic equalizer. The implant processes a single audio stream into bins of various sizes according to frequency, and then applies current to the corresponding frequency location in the cochlea, typically with a 16-spot linear electrode. The main function of these devices is to capture speech formants — the peaks in the frequency spectrum of the voice. The toughest challenge for the cochlear implant is to provide sound localization and source separation in noisy environments like a cocktail party.

Vision implants are much more complex. As any practiced photographer knows, the eye is more than a camera. The optic nerve does not feed the brain pixels. If you imagine your camera responding to auto-selected targets several times a second, gathering the full spectrum of light through its entire range of settings at each pause, and compressing the data onto a bandwidth- and energy-limited channel ideally matched to its receiver, you have some idea of what the retina accomplishes routinely.

The reason cochlear implants work so well is that the brain is just that good at making sense out of virtually any kind of signal it is given. If presented only with noise, or with nothing at all, the brain will eventually begin to manufacture hallucinations. If the implant signal contains even some distorted fragment of the original signal, it can be made to work convincingly. This is also the reason why retina implants can work without incorporating any knowledge of what the retina actually does in the healthy state.

These days researchers are trying to do a little better than the grainy images provided through our current implants. Signal processing techniques were developed in the Cold War era to track and target incoming missiles by extracting signals from noisy radar data. These same techniques are now used to convert the activity of groups of neurons in the motor cortex into a set of commands for moving a cursor, prosthetic device, or de-enervated limb in brain machine interfaces (BCIs). These methods and derivations of them can also be applied to incoming sensory data and can approximate what the retina actually does, without doing it in the same way.

Unfortunately, videos and TED talks are not the places where this kind of knowledge is typically transmitted in much depth. For that, one needs to look back to the work of the founding father of cybernetics, Norbert Wiener, and his eminently practical inspiration, Vito Volterra. After suggesting that helium be used instead of hydrogen in airships, to great success, Volterra shifted gears and came up with some methods to characterize complex systems. Wiener simplified Volterra’s equations and they are now widely used today in statistical techniques like linear regression analysis, and analysis of spike trains from neurons.

A single neuron in the brain of a blow fly can read input from its photoreceptors and command a wing muscle to change its flight path within about 30 milliseconds. That’s just time enough for a few spikes on a one-neuron chain, so the temporal structure of those spikes contains real information relevant both to the stimulus and motor imperative. It is therefore not just a coarse pulse frequency code. These Wiener equations, or more precisely, kernels, have been used to accurately represent the information in these spike trains and replace the neuron in simulated systems. To do so — even for a few spikes — requires intensive computation using reiterative numerical methods.

To attempt such a process for the million or more axons (the long connections between neurons) that constitute the output of the retina would be prohibitive. To get around this, researchers have further simplified the equations and can now do a decent job of reconstructing a stimulus, as long as the number of pixels or other kind of input chosen is limited. Rather than directly representing pixels, the processed responses of the ganglion cells in the retina can better be understood in terms of standard image-processing concepts like edge detection, and center-surround inhibition. These filters are built into the physical structure of the neuron’s dendritic tree. A project to create a connectome for the retina, known as Eyewire, is now looking to create a rough map of these details through a crowdsourced, online gaming effort.

Ultimately, this kind of analysis is a top-down approach and has its limitations. For the present time, it is the best we have. Neuromorphic chips and artificial neural networkscould replace these methods in the interim time until actual biologic equivalents can be grown for replacement retinas. Research on stem cell replacements for the “hair” cells in the cochlea, which do the actual sound mechano-transduction into electrical nerve activity, is making astounding progress that will hopefully soon be transferred to visual and motor systems.

MIT Technology Review has reported on a couple projects still in the early stages of development. At the Society of Neuroscience this meeting this November, Massoud Khraiche proposed using silicon nanowires to replace damaged photoreceptors. These nanowires could allow for both light detection and neuron stimulation. Another group, atCarnegie Mellon, is also making inside-the-eye devices even smaller. Their device would provide detail comparable to that of the fovea, the part of the eye with the highest density of photoreceptors.

Under some conditions, photoreceptors, like a dark-adapted rod cell, can detect a single photon. More impressively, that single rod cell can inform its owner of the event with some statistical reliability. In other words, the person can guess whether they saw the photon or not with significance better than chance. Considering that the same cell can also function on the reflective sands of a sunny beach, that gives us some appreciation for the dynamic range through which the retina can operate. Capturing this full complement of skill with a prosthetic bionic eye will certainly take time.

As far as choosing a go-to implant manufacturer, is hard to know what technologies and algorithms various developers may eventually employ. If your implant allows new vision apps to be installed over-the-air, that might be a good sign. Operating system/firmware upgrades should be provided for as well. Ultimately, if your implant permits actual hardware upgradeability by including a spare FPGA, that would be preferable. People with disabilities are learning today to temper their expectations when news reports announce medical breakthroughs. The day will come soon enough when lack of technology won’t be the biggest problem, rather they might simply be too expensive for the mass market to acquire. Hopefully, equitable systems for the disbursement of these new products will be found, and they can be enjoyed in the spirit of the best for the most.

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Qubits and binary data successfully squirted down the same fiber, at the same time: Here comes quantum cryptography

Toshiba Research and Cambridge University in England are reporting that they have succeeded in building the first conventional fiber-optic network that’s capable of transmitting and receiving both quantum data (for encryption) and normal high-speed binary data. This breakthrough means that the world’s fiber networks can now be secured with theoretically unbreakable encryption.

The basic concept behind quantum cryptography is that Alice sends a crypto key to Bob via a stream of single photons (quantum key distribution, QKD). If a man-in-the-middle attacker somehow manages to intercept the photons, this interrupts the transmission in such a way that can be detected. In theory, quantum crypto should result in totally secure communications. (In reality, there are other attack vectors that bypass the inherent security provided by the photons.)

Now, quantum-secured networks aren’t particularly new, but until now the single photons (qubits) have required their own dedicated optical fiber for transmission. In conventional fiber-optic networks, the transmission of data is very intense, with over 1 million photons carrying a single bit of binary data. Sending single photons down the same fiber simply wasn’t feasible; it was impossible to extract that single photon at the other end. Until now!

Toshiba/Cambridge QKD + fiber data network diagram. A = the overall setup. B = the quantum transmitter. C = the quantum receiver.

The Toshiba and Cambridge researchers have overcome this restriction by transmitting the quantum photons and data signals at different wavelengths, and by using a special photodetector at the receiving end that turns on for just 100 millionth of a microsecond (a few hundred femtoseconds). The different wavelengths mean that the signals don’t clash, and the photodetector only turns on when it expects to receive a single photon.

The end result is a fiber network that can transmit binary data at 1Gbps in both directions, and perform quantum key distribution at 500Kbps at the same time, over a 90km (56mi) length. This is apparently 50,000 times faster than the previous best QKD over a fiber network of this length. In theory, this means that the next generation of fiber networks — assuming this femtosecond photodetector can be implemented commercially — could be secured with quantum cryptography. Most importantly, we’re only talking about new routers — this method of QKD could be performed on existing (and very expensive) fiber networks.

The immediate benefit will be for military and police/security networks, and domestic infrastructure (think secure communications in smart cities). With fiber steadily rolling out to consumers, though, you and I might soon be downloading torrents and watching cat videos that have been encrypted with quantum cryptography.

Now read: Quantum teleportation lays the foundation for a global quantum internet — or check out our popular story on the secret world of submarine fiber-optic cables.

Research paper: 10.1103/PhysRevX.2.041010 – “Coexistence of High-Bit-Rate Quantum Key Distribution and Data on Optical Fiber”

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Harvard creates self-assembling DNA nanostructures from tiny DNA ‘Lego bricks’

Harvard’s Wyss Institute, which brought us 700-terabytes-per-gram-of-DNA data storage earlier in the year, has now produced DNA Lego bricks — three-dimensional DNA building blocks that self-assemble into more than 100 different, three-dimensional structures (pictured above).

These DNA Lego bricks are short strands of DNA that have been specially crafted to join with other DNA bricks at a 90-degree angle — just as if you had pushed two eight-stud Lego bricks on top of each other at 90 degrees. By joining more and more of these DNA bricks together, a 3D structure emerges. In this case, the DNA Legos are built into 25-nanometer cubes, which consist of around 1,000 voxels, with each voxel consisting of DNA strands that are just 2.5nm. A voxel (volumetric pixels) is a term borrowed from graphics; it’s essential the 3D equivalent of a 2D pixel.

The Wyss Institute call these cubes the “master molecular canvas.” By restricting which DNA bricks are available during self-assembly, 102 distinct 3D shapes were formed. In the image at the top of the story you can see the simulated 3D models of these 102 shapes, and below is an actual microscopic view from above. As you can see, the level of detail is really quite astonishing — and even better, some of the shapes include intricately detailed tunnels and cavities. “This is a simple, versatile and robust method,” says Peng Yin, who led the project.

Essentially, Peng Yin is now an architect of, quite possibly, the world’s smallest building blocks. Intel alters features that are perhaps 30 nanometers in size, while Yin has the power to alter a single 2.5nm voxel. This is important and exciting because changing a single voxel could alter the function of the DNA cube, much as moving a single transistor alters the function of a computer chip.

As for what these self-assembled DNA cubes will actually be used for, the answer is probably medicine. DNA molecules are (obviously) biocompatible, and Harvard’s Wyss Institute is generally oriented towards medical research. The general idea is that you could somehow fashion a DNA structure that interacts with the human body in a curative or preventative way — or, more simply, you might fashion a DNA cube that can carry medicine to a specific region of the body.

Moving forward, there could be non-medical applications too. In much the same way thatHarvard’s DNA data storage could be used for storage in computer systems, these DNA building blocks might one day form the basis of biological (or digital-biological hybrid) computers.

Now read: Living organ-on-a-chip could soon replace animal testing

Research paper: DOI: 10.1126/science.1227268 – “Three-Dimensional Structures Self-Assembled from DNA Bricks”

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Spaun, the most realistic artificial human brain yet

A group of neuroscientists and software engineers at the University of Waterloo in Canada are claiming to have built the world’s most complex, large-scale model simulation of the human brain. The simulated brain, which runs on a supercomputer, has a digital eye which it uses for visual input, a robotic arm that it uses to draw its responses — and it can pass the basic elements of an IQ test.

The brain, called Spaun (Semantic Pointer Architecture Unified Network), consists of 2.5 million simulated neurons, allowing it to perform eight different tasks. These tasks range from copy drawing to counting, to question answering and fluid reasoning. At this point, you should watch the video below to get a rough idea of how Spaun works — and then read on to find out why Spaun is so interesting.

Now, the nitty-gritty details. Spaun has a 28×28 (784-pixel) digital eye, and a robotic arm which can write on some paper. Every interaction with Spaun is through its 784-pixel eye. The scientists flash up a bunch of numbers and letters, which Spaun reads into memory, and then another letter or symbol acts as the command, telling Spaun what to do with its memory. The output of the task is then inscribed by the robotic arm.

Spaun’s brain consists of 2.5 million neurons that are broken down into a bunch of simulated cranial subsystems, including the prefrontal cortex, basal ganglia, and thalamus, which are wired together with simulated neurons that very accurately mimic the wiring of a real human brain. The basic idea is that these subsystems behave very similarly to a real brain: Visual input is processed by the thalamus, the data is stored in the neurons, and then the basal ganglia fires off a task to a part of the cortex that’s designed to handle that task.

All of this computation is performed in a physiologically accurate way, with simulated voltage spikes and neurotransmitters. Even the limitations of the human brain are simulated, as you can see in the video below, with Spaun struggling to store more than a few numbers in its short-term memory.

The end result is a brain that is mechanistically simple (2.5 million neurons isn’t really much to write home about), but which is surprisingly flexible. By implementing just a handful of very basic tasks, it’s interesting to see how complex behavior begins to emerge. There are some tantalizing hints as to how the brain evolved: starting with simple tasks, and then building upon and weaving them together to build complex functionality. In the video below, Spaun recognizes the pattern of a number sequence — the kind of question you would find on an actual IQ test.

Moving forward, the research team, led by Chris Eliasmith, wants to imbue Spaun with adaptive plasticity — the ability to rewire its neurons and learn new tasks simply by doing, rather than being pre-programmed. As for the ultimate end goal, Eliasmith is excited about Spaun’s prospects. “It lets us understand how the brain, the biological substrate, and behavior relate. That’s important for all sorts of health applications,” he says in an interview with PopSci. In testing he has “killed” synthetic neurons and watched performance degrade, which could provide an interesting insight into natural aging and degenerative disorders.

Spaun is built upon Nengo, a graphical open-source software package for building simulated neural systems. You can actually download the Spaun neural model, if you want to simulate your own brain — though I suspect it might require a little more processing power than your desktop PC.

Now read: Hackers backdoor the human brain, successfully extract sensitive data

Research paper: DOI: 10.1126/science.1225266 – “A Large-Scale Model of the Functioning Brain”

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