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An abandoned wheelhouse shed sits atop an old mine shaft high in the San Juan Mountains north of Silverton on Aug. 14, 2015. The mines that settlers built in the booms of the 19th century are an ever-present part of the landscape in this mineral-rich part of Colorado.
Associated Press file.
Latest on 3D printed Synthetic hair.
Makeup brushes haven’t changed all that much over the last century. Sure, brands have figured out how to create synthetic fibers and played around with handle placement, but otherwise, there hasn’t been a whole lot of innovation, especially compared with the developments we’ve seen in skin care and cosmetics. But that could all change thanks to the creation and testing of 3D-printed hair by researchers at the Massachusetts Institute of Technology’s Tangible Media Group.
3D-printed hair isn’t technically a new innovation; MIT unveiled the first 3D-printed hair about a year ago. What’s new is that since then, the researchers have explored the exciting possibilities of the technology. In a recently released paper, the Tangible Media Group details the creation of its Cillia program, which allows for the 3D printing of both flat and curved surfaces covered in extremely fine, tightly packed, artificial hairs. What’s so cool is just how small they can make the hairs—as tiny as 50 microns across—giving them the ability to create highly dense hairy or furry surfaces that were previously only possible in nature. And because they can get the hair that small, it allows the company to control a whole bunch of things like the length, thickness, and density of each individual hair that’s printed.
Anyone who has heard of Bitcoin knows that it is built on a mechanism called The Blockchain. Most of us who follow the topic are also aware that Bitcoin and the blockchain were unveiled—together—in a whitepaper by a mysterious developer, under the pseudonym Satoshi Nakamoto.
That was eight years ago. Bitcoin is still the granddaddy of all blockchain-based networks, and most of the others deal with alternate payment coins of one type or another. Since Bitcoin is king, the others are collectively referred to as ‘Altcoins’.
But the blockchain can power so much more than coins and payments. And so—as you might expect—investors are paying lots of attention to blockchain startups or blockchain integration into existing services. Not just for payments, but for everything under the sun.
Think of Bitcoin as a product and the blockchain as a clever network architecture that enables Bitcoin and a great many future products and institutions to do more things—or to do these things better, cheaper, more robust and more 
secure than products and institutions built upon legacy architectures.
When blockchain developers talk about permissionless, peer-to-peer ledgers, or decentralized trust, or mining and “the halving event”, eyes glaze over. That’s not surprising. These things refer to advantages and minutiae in abstract ways, using a lexicon of the art. But—for many—they don’t sum up the benefits or provide a simple listing of products that can be improved, and how they will be better.
I am often asked “What can the Blockchain be used for—other than digital currency?” It may surprise some readers to learn that the blockchain is already redefining the way we do banking and accounting, voting, land deeds and property registration, health care proxies, genetic research, copyright & patents, ticket sales, and many proof-of-work platforms. All of these things existed in the past, but they are about to serve society better because of the blockchain. And this impromptu list barely scratches the surface.
I address the question of non-coin blockchain applications in other articles. But today, I will focus on a subtle but important tangent. I call it “A blockchain in name only”…
Question: Can a blockchain be a blockchain if it is controlled by the issuing authority? That is, can we admire the purpose and utility, if it was released in a fashion that is not is open-source, fully distributed—and permissionless to all users and data originators?
Answer: Unmask the Charlatans
Many of the blockchains gaining attention from users and investors are “blockchains” in name only. So, what makes a blockchain a blockchain?
Everyone knows that it entails distributed storage of a transaction ledger. But this fact alone could be handled by a geographically redundant, cloud storage service. The really beneficial magic relies on other traits. Each one applies to Bitcoin, which is the original blockchain implementation:
▪Open-source
▪Fully distributed among all users.
▪ Any user can also be a node to the ledger
▪Permissionless to all users and data originators
▪Access from anywhere data is generated or analyzed
A blockchain designed and used within Santander Bank, the US Post Office, or even MasterCard might be a nifty tool to increase internal redundancy or immunity from hackers. These potential benefits over the legacy mechanism are barely worth mentioning. But if a blockchain pretender lacks the golden facets listed above, then it lacks the critical and noteworthy benefits that make it a hot topic at the dinner table and in the boardroom of VCs that understand what they are investing in.
Some venture financiers realize this, of course. But, I wonder how many Wall Street pundits stay laser-focused on what makes a blockchain special, and know how to ascertain which ventures have a leg up in their implementations.
Perhaps more interesting and insipid is that even for users and investors who are versed in this radical and significant new methodology—and even for me—there is a subtle bias to assume a need for some overseer; a nexus; a trusted party. 
After all, doesn’t there have to be someone who authenticates a transaction, guarantees redemption, or at least someone who enforces a level playing field?
That bias comes from our tendency to revert to a comfort zone. We are comfortable with certain trusted institutions and we feel assured when they validate or guarantee a process that involves value or financial risk, especially when we deal with strangers. A reputable intermediary is one solution to the problem of trust. It’s natural to look for one.
So, back to the question. True or False?…
In a complex value exchange with strangers and at a distance, there must be someone or some institution who authenticates a transaction, guarantees redemption, or at least enforces the rules of engagement (a contract arbiter).
Absolutely False!
No one sits at the middle of a blockchain transaction, nor does any institution guarantee the value exchange. Instead, trust is conveyed by math and by the number of eyeballs. Each transaction is personal and validation is crowd-sourced. More importantly, with a dispersed, permissionless and popular blockchain, transactions are more provably accurate, more robust, and more immune from hacking or government interference.
What about the protections that are commonly associated with a bank-brokered transaction? (For example: right of rescission, right to return a product and get a refund, a shipping guaranty, etc). These can be built into a blockchain transaction. That’s what the Cryptocurrency Standards Association is working on right now. Their standards and practices are completely voluntary. Any missing protection that might be expected by one party or the other is easily revealed during the exchange set up.
For complex or high value transactions, some of the added protections involve a trusted authority. 
But not the transaction itself. (Ah-hah!). These outside authorities only become involved (and only tax the system), when there is a dispute.
Sure! The architecture must be continuously tested and verified—and Yes: Mechanisms facilitating updates and scalability need organizational protocol—perhaps even a hierarchy. Bitcoin is a great example of this. With ongoing growing pains, we are still figuring out how to manage disputes among the small percentage of users who seek to guide network evolution.
But, without a network that is fully distributed among its users as well as permissionless, open-source and readily accessible, a blockchain becomes a blockchain in name only. It bestows few benefits to its creator, none to its users—certainly none of the dramatic perks that have generated media buzz from the day Satoshi hit the headlines.
Related:
Philip Raymond is co-chair of The Cryptocurrency Standards Association,
host & MC for The Bitcoin Event and editor at A Wild Duck.
The same laser system being developed to blast tiny spacecraft between the stars could also launch human missions to Mars, protect Earth from dangerous asteroids and help get rid of space junk, project leaders say.
Last month, famed physicist Stephen Hawking and other researchers announced Breakthrough Starshot, a $100 million project that aims to build prototype light-propelled “wafersats” that could reach the nearby Alpha Centauri star system just 20 years after launch.
The basic idea behind Breakthrough Starshot has been developed primarily by astrophysicist Philip Lubin of the University of California, Santa Barbara, who has twice received funding from the NASA Innovative Advanced Concepts (NIAC) program to develop the laser propulsion system. [Stephen Hawking Video: ‘Transcending Our Limits’ with Breakthrough Starshot].
Nice.
/EINPresswire.com/ — SAN JOSE, CA — (Marketwired) — 05/24/16 — UltraMemory Inc. (UltraMemory) has selected NanoSpice™ and NanoSpice Giga™ from ProPlus Design Solutions, Inc., the leading technology provider of giga-scale parallel SPICE simulation, SPICE modeling solutions and Design-for-Yield (DFY) applications, to simulate its super-broadband, super large-scale memory design.
UltraMemory is developing innovative 3D DRAM chip, which includes Through Chip Interface (TCI), enabling low-cost and low-power wireless communication between stacked DARM when compared to TSV technology.
Highly accurate and high-capacity SPICE simulation was necessary because it needed to simulate several DRAM chips with analog functions. UltraMemory’s decision to adopt NanoSpice, a high-performance parallel SPICE simulator, and NanoSpice Giga, the industry’s only GigaSpice simulator, came after an extensive evaluation of commercial SPICE and FastSPICE circuit simulators. NanoSpice and NanoSpice Giga have been integrated in UltraMemory’s existing design flows to replace other SPICE and FastSPICE simulators to provide full circuit simulation solutions from small block simulation to full-chip verification.
“We have developed a hydrogel based rapid E. coli detection system that will turn red when E. coli is present,” says Professor Sushanta Mitra, Lassonde School of Engineering. “It will detect the bacteria right at the water source before people start drinking contaminated water.”
The new technology has cut down the time taken to detect E. coli from a few days to just a couple of hours. It is also an inexpensive way to test drinking water (C$3 per test estimated), which is a boon for many developing countries, as much as it is for remote areas of Canada’s North.
“This is a significant improvement over the earlier version of the device, the Mobile Water Kit, that required more steps, handling of liquid chemicals and so on,” says Mitra, Associate Vice-President of Research at York U. “The entire system is developed using a readily available plunger-tube assembly. It’s so user-friendly that even an untrained person can do the test using this kit.”
Visit Singularity Hub for the latest from the frontiers of manufacturing and technology as we bring you coverage of Singularity University’s Exponential Manufacturing conference. Watch all the talks from the first day here and second day here.
The software startup launching out of a garage or a dorm room is now the stuff of legend. We can all name the stories of people who got together in a garage with a few computers and ended up disrupting massive, established corporations — or creating something the world never even knew it wanted.
Until now, this hasn’t really been as true for physical things you build from the ground up. The cost of tools and production has been too high, and for top quality, you still had to go at it the traditional manufacturing route.
If you’ve ever seen a “recommended item” on eBay or Amazon that was just what you were looking for (or maybe didn’t know you were looking for), it’s likely the suggestion was powered by a recommendation engine. In a recent interview, Co-founder of machine learning startup Delvv, Inc., Raefer Gabriel, said these applications for recommendation engines and collaborative filtering algorithms are just the beginning of a powerful and broad-reaching technology.
Gabriel noted that content discovery on services like Netflix, Pandora, and Spotify are most familiar to people because of the way they seem to “speak” to one’s preferences in movies, games, and music. Their relatively narrow focus of entertainment is a common thread that has made them successful as constrained domains. The challenge lies in developing recommendation engines for unbounded domains, like the internet, where there is more or less unlimited information.
“Some of the more unbounded domains, like web content, have struggled a little bit more to make good use of the technology that’s out there. Because there is so much unbounded information, it is hard to represent well, and to match well with other kinds of things people are considering,” Gabriel said. “Most of the collaborative filtering algorithms are built around some kind of matrix factorization technique and they definitely tend to work better if you bound the domain.”
Of all the recommendation engines and collaborative filters on the web, Gabriel cites Amazon as the most ambitious. The eCommerce giant utilizes a number of strategies to make item-to-item recommendations, complementary purchases, user preferences, and more. The key to developing those recommendations is more about the value of the data that Amazon is able to feed into the algorithm initially, hence reaching a critical mass of data on user preferences, which makes it much easier to create recommendations for new users.
“In order to handle those fresh users coming into the system, you need to have some way of modeling what their interest may be based on that first click that you’re able to extract out of them,” Gabriel said. “I think that intersection point between data warehousing and machine learning problems is actually a pretty critical intersection point, because machine learning doesn’t do much without data. So, you definitely need good systems to collect the data, good systems to manage the flow of data, and then good systems to apply models that you’ve built.”
Beyond consumer-oriented uses, Gabriel has seen recommendation engines and collaborative filter systems used in a narrow scope for medical applications and in manufacturing. In healthcare for example, he cited recommendations based on treatment preferences, doctor specialties, and other relevant decision-based suggestions; however, anything you can transform into a “model of relationships between items and item preferences” can map directly onto some form of recommendation engine or collaborative filter.
One of the most important elements that has driven the development of recommendation engines and collaborative filtering algorithms is the Netflix Prize, Gabriel said. The competition, which offered a $1 million prize to anyone who could design an algorithm to improve upon the proprietary Netflix’s recommendation engine, allowed entrants to use pieces of the company’s own user data to develop a better algorithm. The competition spurred a great deal of interest in the potential applications of collaborative filtering and recommendation engines, he said.
In addition, relative ease of access to an abundant amount of cheap memory is another driving force behind the development of recommendation engines. An eCommerce company like Amazon with millions of items needs plenty of memory to store millions of different of pieces of item and correlation data while also storing user data in potentially large blocks.
“You have to think about a lot of matrix data in memory. And it’s a matrix, because you’re looking at relationships between items and other items and, obviously, the problems that get interesting are ones where you have lots and lots of different items,” Gabriel said. “All of the fitting and the data storage does need quite a bit of memory to work with. Cheap and plentiful memory has been very helpful in the development of these things at the commercial scale.”
Looking forward, Gabriel sees recommendation engines and collaborative filtering systems evolving more toward predictive analytics and getting a handle on the unbounded domain of the internet. While those efforts may ultimately be driven by the Google Now platform, he foresees a time when recommendation-driven data will merge with search data to provide search results before you even search for them.
“I think there will be a lot more going on at that intersection between the search and recommendation space over the next couple years. It’s sort of inevitable,” Gabriel said. “You can look ahead to what someone is going to be searching for next, and you can certainly help refine and tune into the right information with less effort.”
While “mind-reading” search engines may still seem a bit like science fiction at present, the capabilities are evolving at a rapid pace, with predictive analytics at the bow.
