You may have seen a photo of a dress circulating on social media last year. People were asked what colour the dress was. Some saw white and gold, some lilac and gold, and others saw blue and black. The post demonstrated that perception is not universal, and the same can be said about cryptocurrency and blockchain technology, which can be viewed through two lenses.
Data and code first
There are those who perceive the technology to be the most important aspect of this new space and the one that will outlast all other aspects. Some people see the blockchain as a gigantic network of global computers working on the decentralised principle. Most often these ‘believers’ are software designers and developers who are focused on code and data. They see lots of potential in the blockchain for implementing new forms of software with new capabilities. It offers them data storage that is resistant to censorship and is immutable. It can also be audited and the code can’t be changed once it’s in use. This is one group, but there is another.
New money
Another group perceives the technology as merely a tool that is necessary to create a new form of money. This group is more likely to be made up of people from backgrounds in economics and finance. They look at it from a perspective of the history of money and bring the idea that all forms of money have specific properties: resistant to forgery, secure, durable, measurable and divisible. So this group sees cryptocurrency as a new version of ‘sound money’. Some of them are sceptical about fiat currencies and aren’t fans of centrally controlled monetary policies. They see cryptocurrencies as a revolutionary new form of global money and the antidote to what they see as the questionable modern experiment of fiat currency.
The bigger picture
What does this leave us with? One group see crytpocurrency as the single useful purpose of the blockchain, while the other sees cryptocurrency as just one component in what the blockchain can do.
The ‘new money’ group actively buy cryptocurrencies and want to encourage mass adoption so the value of the coins increases. The blockchain-focused group is more enthusiastic about projects that experiment, add features, and explore the blockchain tradeoff-space.
But, what we can take away from this is that both views complement each other and one keeps the other in check. There is room for both perspectives and those working in the crypto space would do well to take a step back and take in a wider perspective that includes the views of both these groups to see exactly where the crypto space is heading.
The 3D-printed artificial neural network can be used in medicine, robotics and security
The network, composed of a series of polymer layers, works using light that travels through it. Each layer is 8 centimeters square.
A team of UCLA electrical and computer engineers has created a physical artificial neural network — a device modeled on how the human brain works — that can analyze large volumes of data and identify objects at the actual speed of light. The device was created using a 3D printer at the UCLA Samueli School of Engineering.
Numerous devices in everyday life today use computerized cameras to identify objects — think of automated teller machines that can “read” handwritten dollar amounts when you deposit a check, or internet search engines that can quickly match photos to other similar images in their databases. But those systems rely on a piece of equipment to image the object, first by “seeing” it with a camera or optical sensor, then processing what it sees into data, and finally using computing programs to figure out what it is.
The UCLA-developed device gets a head start. Called a “diffractive deep neural network,” it uses the light bouncing from the object itself to identify that object in as little time as it would take for a computer to simply “see” the object. The UCLA device does not need advanced computing programs to process an image of the object and decide what the object is after its optical sensors pick it up. And no energy is consumed to run the device because it only uses diffraction of light.
New technologies based on the device could be used to speed up data-intensive tasks that involve sorting and identifying objects. For example, a driverless car using the technology could react instantaneously — even faster than it does using current technology — to a stop sign. With a device based on the UCLA system, the car would “read” the sign as soon as the light from the sign hits it, as opposed to having to “wait” for the car’s camera to image the object and then use its computers to figure out what the object is.
Technology based on the invention could also be used in microscopic imaging and medicine, for example, to sort through millions of cells for signs of disease.
“This work opens up fundamentally new opportunities to use an artificial intelligence-based passive device to instantaneously analyze data, images and classify objects,” said Aydogan Ozcan, the study’s principal investigator and the UCLA Chancellor’s Professor of Electrical and Computer Engineering. “This optical artificial neural network device is intuitively modeled on how the brain processes information. It could be scaled up to enable new camera designs and unique optical components that work passively in medical technologies, robotics, security or any application where image and video data are essential.”
The process of creating the artificial neural network began with a computer-simulated design. Then, the researchers used a 3D printer to create very thin, 8 centimeter-square polymer wafers. Each wafer has uneven surfaces, which help diffract light coming from the object in different directions. The layers look opaque to the eye but submillimeter-wavelength terahertz frequencies of light used in the experiments can travel through them. And each layer is composed of tens of thousands of artificial neurons — in this case, tiny pixels that the light travels through.
Together, a series of pixelated layers functions as an “optical network” that shapes how incoming light from the object travels through them. The network identifies an object because the light coming from the object is mostly diffracted toward a single pixel that is assigned to that type of object.
The researchers then trained the network using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produces as the light from that object passes through the device. The “training” used a branch of artificial intelligence called deep learning, in which machines “learn” through repetition and over time as patterns emerge.
“This is intuitively like a very complex maze of glass and mirrors,” Ozcan said. “The light enters a diffractive network and bounces around the maze until it exits. The system determines what the object is by where most of the light ends up exiting.”
In their experiments, the researchers demonstrated that the device could accurately identify handwritten numbers and items of clothing — both of which are commonly used tests in artificial intelligence studies. To do that, they placed images in front of a terahertz light source and let the device “see” those images through optical diffraction.
They also trained the device to act as a lens that projects the image of an object placed in front of the optical network to the other side of it — much like how a typical camera lens works, but using artificial intelligence instead of physics.
Because its components can be created by a 3D printer, the artificial neural network can be made with larger and additional layers, resulting in a device with hundreds of millions of artificial neurons. Those bigger devices could identify many more objects at the same time or perform more complex data analysis. And the components can be made inexpensively — the device created by the UCLA team could be reproduced for less than $50.
While the study used light in the terahertz frequencies, Ozcan said it would also be possible to create neural networks that use visible, infrared or other frequencies of light. A network could also be made using lithography or other printing techniques, he said.
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The study’s others authors, all from UCLA Samueli, are postdoctoral scholars Xing Lin, Yair Rivenson, and Nezih Yardimci; graduate students Muhammed Veli and Yi Luo; and Mona Jarrahi, UCLA professor of electrical and computer engineering.
The research was supported by the National Science Foundation and the Howard Hughes Medical Institute. Ozcan also has UCLA faculty appointments in bioengineering and in surgery at the David Geffen School of Medicine at UCLA. He is the associate director of the UCLA California NanoSystems Institute and an HHMI professor.
Facebook has not had a good year. First there was the Cambridge Analytica scandal and then its share price fell like a stone from a skyscraper. And yet, just a few days ago The Wall Street Journal reported that the social media megalith is asking major US banks to share detailed financial information about how customers spend their money, apparently to increase user engagement. What could possibly go wrong, as we like to say when we can see all kinds of catastrophes likely to emerge from what is made to appear simple and innocuous?
Hand over your data!
The banks it has approached are well known to all, even if we don’t live in the United States. They are Wells Fargo, JP Morgan Chase and Citigroup. Facebook has, according to the WSJ’s interview with banking insiders, asked them to hand over data, including “everything from customers’ account balances to their credit card transactions.”
What’s in it for the banks?
Why might the banks agree to collaborate? The answer lies in the mobile commerce where apps like PayPal and Venmo dominate the scene. The banks would like a slice of this action and Facebook could help them achieve it. The article reports that Facebook is offering the banks “a presence on its Messenger app”, which has around 1.3 billion users. Messenger users can send and receive money via the app, BUT, at the moment, if a user wants to connect the Messenger app with their bank account they have to ‘opt-in’ to do that. They can also use the app to get in direct contact with Facebook’s credit card partners. The suggestion is that Messenger could also offer the same kind of direct contact with the banks.
Trapped by Facebook’s Messenger app
It’s easy to see the benefit to the banks and to Facebook, which is hoping that such a service will mean users conduct all their financial transactions through Messenger. Apparently the fact that many users leave the app to go and check their account balances at their bank’s online service is annoying Zuckerberg & Co who would prefer its users never wander off. Facebook also promises that it would never use customers’ financial data to improve its ad targeting. There are probably a lot of people reading that and thinking, “If you believe that, you’ll believe anything.”
So far, the banks have declined Facebook’s offer, citing customer privacy as a concern. And they are right to be concerned about it, because Facebook’s track record on use of customer data is covered in mud and it has stuck.
Knowing what you know about Facebook, how would you feel about your bank handing over your data to such a company?
The Tech Giants Growing Behind China’s Great Firewall
The Chart of the Week is a weekly Visual Capitalist feature on Fridays.
Every day, your feeds are likely dominated by the latest news about Silicon Valley’s biggest tech giants.
Whether it’s Facebook’s newest algorithm changes, Amazon’s announcement to enter the healthcare market, a new acquisition by Alphabet, or the buzz about the latest iPhone – the big four tech giants in the U.S. are covered extensively by the media, and we’re all very familiar with what they do.
However, what is less commonly talked about is the alternate universe that exists on the other side of China’s Great Firewall. It’s there that four Chinese tech giants are taking advantage of a lack of foreign competition to post explosive growth numbers – some which compare favorably even to their American peers.
BIZARRO WORLD
Like the “Bizarro Jerry” episode of Seinfeld, the Chinese-based tech giants look recognizably familiar – but markedly different – to the ones we know so well.
ALIBABA
Likely the best known of China’s tech giants, Alibaba is the dominant online retailer in the country. The company had revenues of $25.1 billion in 2017 and is seeing that revenue grow at impressive speeds. In its most recent quarterly results (Q3, 2017), the company noted a 56% jump in revenue.
Amazon’s tough sell: Amazon does exist in the Chinese market, but it just has trouble competing with Jack Ma’s creation. Amazon has less than a 1% share of the e-commerce space in China, after a decade of trying to get a foothold. Further, Alibaba also runs AliCloud, which provides direct competition to Amazon’s AWS.
BAIDU
Baidu is the largest search engine in China and also a leading player in AI. It’s the most visited website in China, and ranks #4 globally. The company will announce 2017 annual results in the coming weeks, after reporting a 29% jump in revenue in Q3 2017.
Google’s searching for a way in: Google was blocked in China in 2010 after refusing to filter search requests. However, since then, the giant has been able to take very small steps in entering the Chinese market – even though its signature search engine is still blocked, Google now has at least three offices in the country.
TENCENT
Tencent has recently been in the news for its rapidly surging stock. The company, which owns the dominant social platform in China (WeChat), is now valued at over $500 billion. For those keeping tabs, Facebook is currently worth $550 billion.
It’s complicated: Facebook remains blocked by China, meaning that Zuckerberg and company can’t take advantage of a 1 billion plus market of people with growing buying power. Even if it found its way in, there are multiple social platforms in China and competition would be stiff.
XIAOMI
Dubbed as “China’s Apple”, Xiaomi is one of the world’s most valuable private companies. Things have been hot and cold for the ambitious smartphone manufacturer, but recently reports have surfaced that Xiaomi will IPO in the second half of 2018 for upwards of $50 billion.
Mehran Muslimi 