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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A portrait of Earth’s dirty atmosphere

Here’s a mind-blowing view of the Earth that you’ve probably never seen — or even thought of — before. Dubbed “Portrait of Global Aerosols” by NASA, this is the kind of imagery that climate scientists use to analyze the Earth’s atmosphere, the weather, and trends such as global climate change.

Now, first things first: The Earth doesn’t actually look like this from space (alas). Rather, this is an image output by the Goddard Earth Observation System Model, version 5 (GEOS-5). GEOS-5 is an almighty piece of software that runs on a supercomputer at NASA’s Center for Climate Simulation in Maryland.

In the case of this image, GEOS-5 is modeling the presence of aerosols (solid or liquid particles suspended in gas) across the Earth’s atmosphere. Each of the colors represents a different aerosol: Red is dust (swept up from deserts, like the Sahara); Blue is sea salt, swirling inside cyclones; Green is smoke from forest fires; and white is sulfates, which bubble forth from volcanoes — and from burning fossil fuels. The full-size version of the image is particularly mesmerizing, with beautiful swirls of Saharan sand in the Atlantic, and perhaps the tail end of the Gulf Stream circling around Iceland.

It’s hard to be certain, but it seems like the US east coast, central Europe, and east Asia are burning a lot of fossil fuels. Japan, of course, sits on the edge of the Pacific Ring of Fire, so the sulfates there could be from volcanoes. The smoke in Australia is probably from forest fires — but the large volume of smoke from the Amazon rain forest and sub-Saharan Africa is curious. Are these forest fires, or the large-scale burning of wood for heat and power?

As you can imagine, the amount of raw data required to produce such imagery is immense. Weather modeling is still one of the primary uses of supercomputers. To create the Portrait of Global Aerosols, GEOS-5 will have aggregated the measurements from hundreds of weather stations across Earth, along with data from the four NASA/NOAA GOES weather satellites. So you have some idea of the complexity of the GEOS-5 model, the resolution of this image is 10 kilometers (6 miles) — meaning the Earth has been split into regions (“pixels”) of 10km², and then the atmospheric conditions are simulated for each region. The surface area of the Earth is 510,072,000km², which means the total number of regions is around 5 million.

Each of these 5 million pixels might have megabytes or gigabytes of weather data associated with it — and of course, in any given area, the weather in each pixel interacts with those around it. This gives you some idea of how much data needs to be processed and moved around — and it only becomes exponentially more complex as sensors improve (producing more data) and as you increase the depth of your analysis. In the case of climate change, for example, scientists are modeling decades or even centuries of data to try and divine some kind of pattern — a task that taxes even the most powerful supercomputers. If you’ve ever wondered why we keep building faster and faster supercomputers, now you know why.

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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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NASA combats the conspiracy theorists: How Curiosity took a self-portrait on Mars

Back when NASA released Curiosity’s first self-portrait from the surface of Mars, there was asurprisingly large number of people who doubted the veracity of the image. Most of these doubts revolved around the 7-foot robotic arm that captured the photo, but which isn’t actually visible. As with the Moon landing photos, there were conspiracy theorists who claimed that the self-portrait was actually shot here on Earth. “It’s impossible to shoot a photo and not have your arm visible,” the tinfoil-hat-wearing fruitcakes decried. “A third party must’ve taken the photo… or it’s CGI!”

NASA, which is rightfully rather proud of its ability to land a one-ton rover on another planetafter a 352-million-mile journey, seemingly took these attacks to heart and has nowpublished information about how the self-portrait was taken.

As we originally reported, the self-portrait is a mosaic of 55 images captured by the Mars Hand Lens Imager (MAHLI), which sits on the end of Curiosity’s 7-foot robotic arm. These 55 images were taken in such a way that the arm was mostly out of shot — and where that wasn’t possible, data from other images (other angles) was used to fill in the gaps.

As you can see in the animation above, the moves that MAHLI makes as it traverses Curiosity are very exact. According to National Geographic, these movements were planned out last year by none other than James Cameron, and Michael Malin and Michael Ravine of MSSS, the company that manufactured Curiosity’s cameras. “Actually, there weren’t that many images with the arm in them because of how we positioned the arm,” Ravine explains. “It’s like if you hold a camera out in front of you with your elbow crooked and shoot—what you’ll probably get is your face and top of your body including your shoulder, but most of your arm is out of the frame.”

The exact movements to shoot all 55 images were then programmed into NASA’s Rover Sequencing and Visualization Program (RSVP), and then tested on the Curiosity’s stunt double — the Vehicle System Test Bed at the Jet Propulsion Lab in Pasadena (pictured above). Before trying out new maneuvers, just as an extra precaution, NASA generally tests them on the VSTB first. Finally, to take the actual self-portrait on Mars, NASA simply presses “play” in RSVP, transmitting the commands to NASA’s Deep Space Network antennae, which squirts them across 200 million miles of deep space to the rover.

In other news, Curiosity has now reached the region of Glenelg, and is powering along to Yellowknife Bay, where the rover will hopefully try out its percussive hammer for the first time (by drilling into an interesting rock). The photo below was taken at Shaler, where Curiosity briefly stopped to take some photos and other scientific measurements. Curiosity will likely spend a few weeks/months in the Glenelg region before turning south to Mount Sharp — the final destination of the rover’s primary mission.

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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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Is it worth investing in a high-efficiency power supply?

If you’ve gone shopping for a power supply any time over the last few years, you’ve probably noticed the explosive proliferation of various 80 Plus ratings. As initially conceived, an 80 Plus certification was a way for PSU manufacturers to validate that their power supply units were at least 80% efficient at 25%, 50%, 75%, and 100% of full load.

The 80 Plus program has expanded significantly since the first specification was adopted. Valid levels now include Bronze, Silver, Gold, Platinum, and a currently unused Titanium specification level. The chart below lists the requirements a PSU must meet to be certified.

In the pre-80 Plus days, PSU prices normally clustered around a given wattage output. The advent of the various 80 Plus levels has created a second variable that can have a significant impact on unit price. This leads us to three important questions: How much power can you save by moving to a higher-efficiency supply, what’s the premium of doing so, and how long does it take to make back your initial investment?

Power supply pricing and premiums

First, here’s an overview of 80 Plus PSU pricing at various wattages. We created this data from NewEgg results, but only picked units from well-known vendors. Generic products from companies like CoolMax aren’t a part of these results. When we priced units, we opted for the lowest-cost unit from the same manufacturer.

Basic 400W-600W units are quite cheap these days, even from top vendors like Antec, Corsair, OCZ, and Silverstone. Prices start to climb by the 700W range; 1200W units are several hundred dollars.

The price premium for greater-than-80 Plus certification can be substantial. Below 800W, Bronze certification adds 4-20% to the list price of an 80 Plus unit. 80 Plus Gold PSUs are 35-61% more expensive within the same wattage category. Platinum-level power supplies are 90-100% more expensive; twice the price of a standard 80 Plus unit.

By way of example: Antec and Rosewill have $50-$60 80 Plus PSUs in the 501-600W category, while the 80 Plus Platinum products are $139 and $110 respectively. In the 701-800W division, Corsair has 80 Plus Bronze units for $84.95, and 80 Plus Platinum priced at $179.

At the highest end of the market, this changes slightly. Power supplies in the 1kW and greater category don’t put much of a premium on high-efficiency units. An 80 Plus 1200W PSU is $229; 80 Plus Gold is ~$258. 80 Plus Platinum is still significantly more expensive at ~$332.

You can’t save power that you aren’t using

Power supply efficiency is defined as the amount of power actually provided to the internal components, divided by the amount of power drawn at the wall. A 50% efficient PSU that’s tasked with providing 50W of power to a system will draw 100W from the grid. The extra 50W is lost as heat. A 90% efficient PSU would draw 56W in the same circumstances.

Even generic PSUs are far more than 50% efficient; in fact, 75-77% is fairly common. This means the amount of money you save from upgrading to a high-efficiency PSU is minimal if you don’t actually draw much power to start with. Electricity rates are charged by the kWh — if your system only uses 80W at idle, and idles 20 hours a day, you won’t see much benefit from an 80 Plus Platinum PSU as opposed to a regular 80 Plus.

How we tested

We’ve tested two pairs of PSUs from the same manufacturer and with the same rated power output (or close as we could get). Our first testbed was outfitted with two 750W power supplies from PC Power & Cooling. The first is a red Silencer with an 80 Plus certification. Overall listed efficiency for the unit is 83%. The manual breaks this down further, specifying that efficiency ranges from 82-85% depending on exact load.

The other 750W is a Silencer Mark II. It’s certified as 80 Plus Silver with an average efficiency of 85%. Efficiency isn’t broken down by overall load for this model.

The second testbed was configured with a brace of Thermaltake Toughpowers. The first is a 1200W Toughpower 1200A, the second is a 1275W Toughpower XT Platinum. The first unit is certified as 80 Plus, with a listed efficiency of up to 87%; the second’s efficiency is listed as up to 94%. Thermaltake doesn’t provide any additional clarity for either unit, so it’s not initially clear if those figures are for 115V or 220V operation.

Note: The 750W and 1200W figures cannot be cross-compared. We built two entirely different testbeds for this project. Putting a moderate load on a 750W PSU isn’t particularly difficult, while stretching the legs of a 1200W PSU took a bit more work.

Our test methodology was simple: We plugged in a Kill-A-Watt wall meter and measured the power consumption of each unit over 2.5 hours at both load and idle. The meter was reset in between each test for each PSU. Our wattage figures are the average load while the system was in each state, not spot checks on the meter. It’s true that this is a relatively simple, broad-spectrum test, but our goal is to compare simple, real-world savings; not metrics you can’t measure without expensive equipment.

Results

First, here are the idle figures for the four solutions:

The idle figures illustrate what we said earlier regarding the limited impact of increased idle efficiency as far as total power costs are concerned. Gains here are in line with claimed figures. Moving to the 80 Plus Silver 750W cuts idle consumption by roughly 3.6%; the 80 Plus Platinum reduces power consumption by 9.2%.

Load tests show the same gaps at higher power consumption. The 80 Plus Silver 750W Silencer Mark II is 4.5% more efficient than the original Silencer; the 1200W Toughpower XT Platinum is 8.8% more efficient than the 1200A power supply. Again, it matters where you start from. Saving 25W between the 80 Plus and 80 Plus Silver isn’t bad, but the XT Platinum knocks almost three times as much wattage off the 1200A’s main draw.

Clearly the efficiency of a top-end PSU can save you some scratch over the long term. Exactly how much depends on what you’re doing.

How much can you save?

Here, we’ve taken our data from all four power supplies and plugged it into various use equations over an entire year. Our first two graphs assume that the system is either in idle or under full load 24/7/365. Two different costs per kilowatt-hour are included: The US average, at 12.5 cents per kWh, and the current New York State average of 18.7 cents. These are simplistic assumptions, but they ballpark the maximum and minimum savings you’ll see if you never turn the system off.

At constant idle, the 750W 80 Plus Silver saves $4.38 to $6.56 over the course of a year. Upgrading to the 80 Plus Platinum drops between $18.63 and $27.87 back in your pocket.

At constant load, even the modest upgrade offered by the 750W 80 Plus Silver is worth $27-$40. The Toughpower XT 1275W saves you $80-$120 in power costs per year.

Granted, very few people are going to need a power supply under this type of continuous load, but there is a financial benefit to upgrading if you use this much power. At some point, however, we need to address the fact that the best way to save power is to turn the machine off or put it into hibernation.

Here are power usage figures and costs if we assume that the system is idle eight hours a day, under load for four hours, and off/hibernating for the remaining 12.

Heavy workers may still see an advantage from an upgrade; the Thermaltake 1275 XT Platinum will save from $19.57 to $29.38 a year. The smaller 750W upgrade is worth $6 to $9.

A dubious investment

The good news is that power supplies with better 80 Plus ratings really do deliver what they claim — there is a net reduction in total power consumption. If you burn a lot of power, Platinum units could be good investments and pay back their premiums in a year or two. Similarly, if you’re trying to minimize every last watt of consumption, this is one way to do it. The cost premiums, however, don’t add up anywhere but at the highest end. If you’re buying a 1200W unit, Gold is scarcely more expensive and Platinum will still pay back its initial up-front cost in a year or two.

Most of us, however, would be best served by turning the machine off or dropping into hibernation. The best way to save power is simply not to use it, and manufacturers currently charge huge premiums for marginal performance gains. If you’re upgrading from a cheap piece of junk (anything with words like Sparkle, Max, Tech, Sun, Bright, or Beam in the name is virtually guaranteed to be garbage), the premium is easier to justify. If you’ve already got an 80 Plus PSU, it’s a much harder sell.

The flip side is that PSU units go on sale fairly frequently, and a gold or silver unit can be trusted to provide an upgrade. It may not make much sense to buy a unit at a significant premium, but if you get a good deal, we recommend taking it.

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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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MIT creates tiny, 22nm transistor without silicon

Researchers at MIT’s Microsystems Technology Lab (MTL) have created the smallest transistor fashioned from indium gallium arsenide, a material that is being positioned as an eventual successor to silicon. MIT’s indium gallium arsenide (InGaAs) transistor has a gate length of just 22nm — roughly the same size as the smallest features on Intel’s 22nm FinFET Ivy Bridge chips.

This tiny InGaAs transistor was mostly fashioned from normal semiconductor processes — molecular beam epitaxy, electron beam lithography, and so on. The breakthrough here is using an exotic, compound material, rather than straight-up silicon. In this case, the MIT researchers allow evaporated indium, gallium, and arsenic atoms to react, forming a very thin crystal of InGaAs that will become the transistor’s channel (the thin, lighter line at the tip of the inverted V). Molybdenum is then deposited at the source and drain, oxide is deposited at the gate (the inverted V) — and voila, a tiny, exotic transistor. MIT says it “performs well,” but its exact performance characteristics aren’t given.

It is fairly well understood at this point that silicon — the fundamental building block of almost every computer chip, and much of modern society — will eventually run out of steam. No one quite agrees when this will occur, but the general consensus is within 10-20 years. Basically, at some point in the future, as CMOS components continue to shrink, silicon simply won’t function as a semiconductor any more. When this happens, we’ll need to replace silicon with something else.

ITRS’s table for emerging silicon replacement technologies

As we’ve discussed before on ExtremeTech, the ITRS (International Technology Roadmap for Semiconductors) currently pegs III-V semiconductors such as gallium arsenide (GaAs) as one of the only short-term alternatives to silicon. “Short-term” is relative, though; we’re talking at least five to 10 years until GaAs (or MIT’s InGaAs) finds its way into commercial memory or logic chips. In MIT’s case, the researchers have managed to build a singleInGaAs transistor — scaling that up to the billions of transistors that will be in CPUs of the future will verge on the impossible.

The problem with GaAs, InGaAs, carbon nanotubes, graphene, and any number of exotic materials that we cover on ExtremeTech, is that they’re trying to replace the most advanced technology in the world. It is not hyperbolic to state that hundreds of billions of dollars have been poured into CMOS R&D; maybe trillions. For these silicon replacements to even stand a chance, a similar investment will need to be made — and put simply, there is probably only one group in the world who has the requisite time or resources: Intel. We don’t even have definitive proof that the new materials will scale much further than silicon — so we’d be plowing billions of dollars into something that might only get us another few years of Moore’s law.

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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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The first flexible, fiber-optic solar cell that can be woven into clothes

An international team of engineers, physicists, and chemists have created the first fiber-optic solar cell. These fibers are thinner than human hair, flexible, and yet they produce electricity, just like a normal solar cell. The US military is already interested in weaving these threads into clothing, to provide a wearable power source for soldiers.

In essence, the research team started with optical fibers made from glass — and then, using high-pressure chemical vapor deposition, injected n-, i-, and p-type silicon into the fiber, turning it into a solar cell. Functionally, these silicon-doped fiber-optic threads are identical to conventional solar cells, generating electricity from the photovoltaic effect. Whereas almost every solar cell on the market is crafted out of 2D, planar amorphous silicon on a rigid/brittle glass substrate, though, these fiber-optic solar cells have a 3D cross-section and retain the glass fiber’s intrinsic flexibility.

The lead researcher, John Badding of Penn State University, says the team has already produced “meters-long fiber,” and that their new technique could be used to create “bendable silicon solar-cell fibers of over 10 meters in length.” From there, it’s simply a matter of weaving the thread into a fabric. Badding says that the military is “interested in designing wearable power sources for soldiers in the field,” but unfortunately he falls short of actually demonstrating some woven fabric. As we can see in the picture above, the solar cell fiber certainly looks flexible — but we’ll have to take Badding’s word for it that it can turn right angles, and withstand everyday garment stresses, without shattering.

Moving forward, the potential for flexible, woven solar cells is enormous. On the most basic, immediate level, you can imagine a baseball cap or t-shirt that can recharge your smartphone. As we move towards bionic implants and other biomedical devices, though, there is a very pressing need to develop a wearable power source — and fiber-optic solar cells could certainly be it.

These fibers also have two other intriguing properties that still need to be investigated. Due to their three-dimensional cross-section, they can absorb sunlight from any direction — unlike their conventional, 2D siblings that lose much of their efficiency when the sun sinks below a certain angle. Further, according to Pier Sazio, another member of the research team, they used the same silicon injection method to embed photodetectors inside the fiber. Sazio doesn’t extrapolate on what this might lead to, but it’s fun to speculate: Awearable computer with built-in solar charging and high-speed networking? Neat.

Now read: LG produces the first flexible cable-type lithium-ion battery, or Creating cheap solar panels with an ion cannon

Research paper: DOI: 10.1002/adma.201203879 – “Silicon p-i-n Junction Fibers”

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