Basic economics always taught us that ‘supply and demand’ was a central feature of understanding a market. However, Bill Gates wrote in his blogrecently that “supply and demand is over” and he argued that it simply doesn’t apply to today’s economy. He also stated that politicians aren’t paying enough attention to this economic shift.
Why does he make this claim?
His reasoning is based on the fact that companies are no longer only making money by selling tangible products. Companies that supply software being one example. To develop new software, Gates points out, all of the cost is upfront, whereas a traditional manufacturer has to pay for parts and labour. When Microsoft launches a new version of a software programme, it can be copied, sold, and downloaded indefinitely for the relatively minimal costs of distribution and server space.
Gates claims that more large companies are operating without tangible products and says that digital products, which are a so-called “intangible investment,” carry newrisks for businesses and investors and that this is not being accounted for in economic thinking, which still relies too much on an old model.
Capitalism without Capital
In his book “Capitalism without Capital” Gates presents the idea that developing software is a “sunk cost” because developers can’t recoup their losses the way other companies might. If you manufacture tangible products and go bust, you can sell off machinery, but a tech company doesn’t have any such assets to sell.
Gates also points out, the Gross Domestic Product (GDP), the sum of all goods and services sold in a country that is often used as a benchmark for an economy’s well-being doesn’t factor in the investment in intangible elements needed to make a product marketable, such as research and development or market research. He also suggests that didn’t matter two decades ago, but now it does because tech companies make up a bigger slice of a country’s GDP these days. And governments haven’t caught up with this fact.
Gates doesn’t offer a new economic model, but as he says: “The idea today that anyone would need to be pitched on why software is a legitimate investment seems unimaginable, but a lot has changed since the 1980s. It’s time the way we think about the economy does, too.”
Somebody may have said to you at sometime: “I’ve only got two hands,” indicating that whatever you asked them to do just isn’t possible. But in the field of prosthetics and what are called “enhanced humans” it may well be possible now to have additional limbs to carry out tasks.
We have devices that wearers can control with their thoughts — these help people with prosthetic limbs to feel more whole, but now researchers are setting out to see if such devices could make humans more than whole.
Advanced Telecommunications Research Institute in Japan wanted to know if giving someone a supernumerary robotic limb (SRL), a mind-controlled robotic limb that worked alongside the person’s two biological ones, could give that person multitasking abilities beyond those of the average human.
The study
The researchers asked 15 volunteers to sit in a chair with an SRL positioned as if it were a third arm coming from their own body. Each volunteer wore a cap that that tracked the brain’s electrical activity and the cap transmitted this activity to a computer that then turned it into movement in the SRL. All the volunteers had to do to get the SRL to work was think about what they wanted it to do.
The next stage was to ask the volunteers to complete two tasks. The first one — balancing a ball on a board –they did using their natural arms and hands. The second one — picking up and putting down a bottle –they did using the SRL. They then asked the volunteers to do both the tasks separately and simultaneously.
The results
The results of 20 trials showed that the volunteers were able to complete both tasks successfully using the three limbs about 75 percent of the time. What this means is that they were able to complete two tasks simultaneously that they couldn’t have done if they had been limited to using their natural limbs.
The researchers also think that by operating this brain-machine interface, we have an idea that we may be able to train the brain itself and their future research will try to establish if we might be able to enhance our minds by temporarily enhancing our bodies.
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.
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 