Monday, March 20. 2017
Via New York Times (Mike Isaac)
SAN FRANCISCO — Uber has for years engaged in a worldwide program to deceive the authorities in markets where its low-cost ride-hailing service was resisted by law enforcement or, in some instances, had been banned.
The program, involving a tool called Greyball, uses data collected from the Uber app and other techniques to identify and circumvent officials who were trying to clamp down on the ride-hailing service. Uber used these methods to evade the authorities in cities like Boston, Paris and Las Vegas, and in countries like Australia, China and South Korea.
Greyball was part of a program called VTOS, short for “violation of terms of service,” which Uber created to root out people it thought were using or targeting its service improperly. The program, including Greyball, began as early as 2014 and remains in use, predominantly outside the United States. Greyball was approved by Uber’s legal team.
Greyball and the VTOS program were described to The New York Times by four current and former Uber employees, who also provided documents. The four spoke on the condition of anonymity because the tools and their use are confidential and because of fear of retaliation by Uber.
Uber’s use of Greyball was recorded on video in late 2014, when Erich England, a code enforcement inspector in Portland, Ore., tried to hail an Uber car downtown in a sting operation against the company.
At the time, Uber had just started its ride-hailing service in Portland without seeking permission from the city, which later declared the service illegal. To build a case against the company, officers like Mr. England posed as riders, opening the Uber app to hail a car and watching as miniature vehicles on the screen made their way toward the potential fares.
But unknown to Mr. England and other authorities, some of the digital cars they saw in the app did not represent actual vehicles. And the Uber drivers they were able to hail also quickly canceled. That was because Uber had tagged Mr. England and his colleagues — essentially Greyballing them as city officials — based on data collected from the app and in other ways. The company then served up a fake version of the app, populated with ghost cars, to evade capture.
At a time when Uber is already under scrutiny for its boundary-pushing workplace culture, its use of the Greyball tool underscores the lengths to which the company will go to dominate its market. Uber has long flouted laws and regulations to gain an edge against entrenched transportation providers, a modus operandi that has helped propel it into more than 70 countries and to a valuation close to $70 billion.
Yet using its app to identify and sidestep the authorities where regulators said Uber was breaking the law goes further toward skirting ethical lines — and, potentially, legal ones. Some at Uber who knew of the VTOS program and how the Greyball tool was being used were troubled by it.
In a statement, Uber said, “This program denies ride requests to users who are violating our terms of service — whether that’s people aiming to physically harm drivers, competitors looking to disrupt our operations, or opponents who collude with officials on secret ‘stings’ meant to entrap drivers.”
The mayor of Portland, Ted Wheeler, said in a statement, “I am very concerned that Uber may have purposefully worked to thwart the city’s job to protect the public.”
Uber, which lets people hail rides using a smartphone app, operates multiple types of services, including a luxury Black Car offering in which drivers are commercially licensed. But an Uber service that many regulators have had problems with is the lower-cost version, known in the United States as UberX.
UberX essentially lets people who have passed a background check and vehicle inspection become Uber drivers quickly. In the past, many cities have banned the service and declared it illegal.
That is because the ability to summon a noncommercial driver — which is how UberX drivers using private vehicles are typically categorized — was often unregulated. In barreling into new markets, Uber capitalized on this lack of regulation to quickly enlist UberX drivers and put them to work before local regulators could stop them.
After the authorities caught on to what was happening, Uber and local officials often clashed. Uber has encountered legal problems over UberX in cities including Austin, Tex., Philadelphia and Tampa, Fla., as well as internationally. Eventually, agreements were reached under which regulators developed a legal framework for the low-cost service.
That approach has been costly. Law enforcement officials in some cities have impounded vehicles or issued tickets to UberX drivers, with Uber generally picking up those costs on the drivers’ behalf. The company has estimated thousands of dollars in lost revenue for every vehicle impounded and ticket received.
This is where the VTOS program and the use of the Greyball tool came in. When Uber moved into a new city, it appointed a general manager to lead the charge. This person, using various technologies and techniques, would try to spot enforcement officers.
One technique involved drawing a digital perimeter, or “geofence,” around the government offices on a digital map of a city that Uber was monitoring. The company watched which people were frequently opening and closing the app — a process known internally as eyeballing — near such locations as evidence that the users might be associated with city agencies.
Other techniques included looking at a user’s credit card information and determining whether the card was tied directly to an institution like a police credit union.
Enforcement officials involved in large-scale sting operations meant to catch Uber drivers would sometimes buy dozens of cellphones to create different accounts. To circumvent that tactic, Uber employees would go to local electronics stores to look up device numbers of the cheapest mobile phones for sale, which were often the ones bought by city officials working with budgets that were not large.
In all, there were at least a dozen or so signifiers in the VTOS program that Uber employees could use to assess whether users were regular new riders or probably city officials.
If such clues did not confirm a user’s identity, Uber employees would search social media profiles and other information available online. If users were identified as being linked to law enforcement, Uber Greyballed them by tagging them with a small piece of code that read “Greyball” followed by a string of numbers.
When someone tagged this way called a car, Uber could scramble a set of ghost cars in a fake version of the app for that person to see, or show that no cars were available. Occasionally, if a driver accidentally picked up someone tagged as an officer, Uber called the driver with instructions to end the ride.
Uber employees said the practices and tools were born in part out of safety measures meant to protect drivers in some countries. In France, India and Kenya, for instance, taxi companies and workers targeted and attacked new Uber drivers.
“They’re beating the cars with metal bats,” the singer Courtney Love posted on Twitter from an Uber car in Paris at a time of clashes between the company and taxi drivers in 2015. Ms. Love said that protesters had ambushed her Uber ride and had held her driver hostage. “This is France? I’m safer in Baghdad.”
Uber has said it was also at risk from tactics used by taxi and limousine companies in some markets. In Tampa, for instance, Uber cited collusion between the local transportation authority and taxi companies in fighting ride-hailing services.
In those areas, Greyballing started as a way to scramble the locations of UberX drivers to prevent competitors from finding them. Uber said that was still the tool’s primary use.
But as Uber moved into new markets, its engineers saw that the same methods could be used to evade law enforcement. Once the Greyball tool was put in place and tested, Uber engineers created a playbook with a list of tactics and distributed it to general managers in more than a dozen countries on five continents.
At least 50 people inside Uber knew about Greyball, and some had qualms about whether it was ethical or legal. Greyball was approved by Uber’s legal team, led by Salle Yoo, the company’s general counsel. Ryan Graves, an early hire who became senior vice president of global operations and a board member, was also aware of the program.
Ms. Yoo and Mr. Graves did not respond to requests for comment.
Outside legal specialists said they were uncertain about the legality of the program. Greyball could be considered a violation of the federal Computer Fraud and Abuse Act, or possibly intentional obstruction of justice, depending on local laws and jurisdictions, said Peter Henning, a law professor at Wayne State University who also writes for The New York Times.
“With any type of systematic thwarting of the law, you’re flirting with disaster,” Professor Henning said. “We all take our foot off the gas when we see the police car at the intersection up ahead, and there’s nothing wrong with that. But this goes far beyond avoiding a speed trap.”
On Friday, Marietje Schaake, a member of the European Parliament for the Dutch Democratic Party in the Netherlands, wrote that she had written to the European Commission asking, among other things, if it planned to investigate the legality of Greyball.
To date, Greyballing has been effective. In Portland on that day in late 2014, Mr. England, the enforcement officer, did not catch an Uber, according to local reports.
And two weeks after Uber began dispatching drivers in Portland, the company reached an agreement with local officials that said that after a three-month suspension, UberX would eventually be legally available in the city.
Monday, February 27. 2017
Samsung has scrapped its Raspberry Pi 3 competitor called Artik 10 as it moves to smaller and more powerful boards to create gadgets, robots, drones, and IoT devices.
Monday, February 20. 2017
Via IEEE Spectrum
Photo: University of Michigan and TSMC One of several varieties of University of Michigan micromotes. This one incorporates 1 megabyte of flash memory.
Computer scientist David Blaauw pulls a small plastic box from his bag. He carefully uses his fingernail to pick up the tiny black speck inside and place it on the hotel café table. At 1 cubic millimeter, this is one of a line of the world’s smallest computers. I had to be careful not to cough or sneeze lest it blow away and be swept into the trash.
Blaauw and his colleague Dennis Sylvester, both IEEE Fellows and computer scientists at the University of Michigan, were in San Francisco this week to present 10 papers related to these “micromote” computers at the IEEE International Solid-State Circuits Conference (ISSCC). They’ve been presenting different variations on the tiny devices for a few years.
Their broader goal is to make smarter, smaller sensors for medical devices and the Internet of Things—sensors that can do more with less energy. Many of the microphones, cameras, and other sensors that make up the eyes and ears of smart devices are always on alert, and frequently beam personal data into the cloud because they can’t analyze it themselves. Some have predicted that by 2035, there will be 1 trillion such devices. “If you’ve got a trillion devices producing readings constantly, we’re going to drown in data,” says Blaauw. By developing tiny, energy-efficient computing sensors that can do analysis on board, Blaauw and Sylvester hope to make these devices more secure, while also saving energy.
At the conference, they described micromote designs that use only a few nanowatts of power to perform tasks such as distinguishing the sound of a passing car and measuring temperature and light levels. They showed off a compact radio that can send data from the small computers to receivers 20 meters away—a considerable boost compared to the 50-centimeter range they reported last year at ISSCC. They also described their work with TSMC (Taiwan Semiconductor Manufacturing Company) on embedding flash memory into the devices, and a project to bring on board dedicated, low-power hardware for running artificial intelligence algorithms called deep neural networks.
Blaauw and Sylvester say they take a holistic approach to adding new features without ramping up power consumption. “There’s no one answer” to how the group does it, says Sylvester. If anything, it’s “smart circuit design,” Blaauw adds. (They pass ideas back and forth rapidly, not finishing each other’s sentences but something close to it.)
The memory research is a good example of how the right trade-offs can improve performance, says Sylvester. Previous versions of the micromotes used 8 kilobytes of SRAM (static RAM), which makes for a pretty low-performance computer. To record video and sound, the tiny computers need more memory. So the group worked with TSMC to bring flash memory on board. Now they can make tiny computers with 1 megabyte of storage.
Flash can store more data in a smaller footprint than SRAM, but it takes a big burst of power to write to the memory. With TSMC, the group designed a new memory array that uses a more efficient charge pump for the writing process. The memory arrays are a bit less dense than TSMC’s commercial products, for example, but still much better than SRAM. “We were able to get huge gains with small trade-offs,” says Sylvester.
Another micromote they presented at the ISSCC incorporates a deep-learning processor that can operate a neural network while using just 288 microwatts. Neural networks are artificial intelligence algorithms that perform well at tasks such as face and voice recognition. They typically demand both large memory banks and intense processing power, and so they’re usually run on banks of servers often powered by advanced GPUs. Some researchers have been trying to lessen the size and power demands of deep-learning AI with dedicated hardware that’s specially designed to run these algorithms. But even those processors still use over 50 milliwatts of power—far too much for a micromote. The Michigan group brought down the power requirements by redesigning the chip architecture, for example by situating four processing elements within the memory (in this case, SRAM) to minimize data movement.
The idea is to bring neural networks to the Internet of Things. “A lot of motion detection cameras take pictures of branches moving in the wind—that’s not very helpful,” says Blaauw. Security cameras and other connected devices are not smart enough to tell the difference between a burglar and a tree, so they waste energy sending uninteresting footage to the cloud for analysis. Onboard deep-learning processors could make better decisions, but only if they don’t use too much power. The Michigan group imagine that deep-learning processors could be integrated into many other Internet-connected things besides security systems. For example, an HVAC system could decide to turn the air-conditioning down if it sees multiple people putting on their coats.
After demonstrating many variations on these micromotes in an
academic setting, the Michigan group hopes they will be ready for market
in a few years. Blaauw and Sylvester say their startup company, CubeWorks,
is currently prototyping devices and researching markets. The company
was quietly incorporated in late 2013. Last October, Intel Capital announced they had invested an undisclosed amount in the tiny computer company.
Monday, February 13. 2017
HyperFace is being developed for Hyphen Labs NeuroSpeculative AfroFeminism project at Sundance Film Festival and is a collaboration with Hyphen Labs members Ashley Baccus-Clark, Carmen Aguilar y Wedge, Ece Tankal, Nitzan Bartov, and JB Rubinovitz.
NeuroSpeculative AfroFeminism is a transmedia exploration of black women and the roles they play in technology, society and culture—including speculative products, immersive experiences and neurocognitive impact research. Using fashion, cosmetics and the economy of beauty as entry points, the project illuminates issues of privacy, transparency, identity and perception.
HyperFace is a new kind of camouflage that aims to reduce the confidence score of facial detection and recognition by providing false faces that distract computer vision algorithms. HyperFace development began in 2013 and was first presented at 33c3 in Hamburg, Germany on December 30th, 2016. HyperFace will launch as a textile print at Sundance Film Festival on January 16, 2017.
Together HyperFace and NeuroSpeculative AfroFeminism will explore an Afrocentric countersurveillance aesthetic.
For more information about NeuroSpeculative AfroFeminism visit nsaf.space
How Does HyperFace Work?
HyperFace works by providing maximally activated false faces based on ideal algorithmic representations of a human face. These maximal activations are targeted for specific algorithms. The prototype above is specific to OpenCV’s default frontalface profile. Other patterns target convolutional nueral networks and HoG/SVM detectors. The technical concept is an extension of earlier work on CV Dazzle. The difference between the two projects is that HyperFace aims to alter the surrounding area (ground) while CV Dazzle targets the facial area (figure). In camouflage, the objective is often to minimize the difference between figure and ground. HyperFace reduces the confidence score of the true face (figure) by redirecting more attention to the nearby false face regions (ground).
Conceptually, HyperFace recognizes that completely concealing a face to facial detection algorithms remains a technical and aesthetic challenge. Instead of seeking computer vision anonymity through minimizing the confidence score of a true face (i.e. CV Dazzle), HyperFace offers a higher confidence score for a nearby false face by exploiting a common algorithmic preference for the highest confidence facial region. In other words, if a computer vision algorithm is expecting a face, give it what it wants.
How Well Does This Work?
The patterns are still under development and are expected to change. Please check back towards the end of January for more information.
Please check back towards the end of January for product photos
If you’re interested in purchasing one of the first commercially available HyperFace textiles, please add yourself to my mailing list at Undisclosed.studio
Monday, February 06. 2017
Via IEEE Spectrum
Today’s mobile users want faster data speeds and more reliable service. The next generation of wireless networks—5G—promises to deliver that, and much more. With 5G, users should be able to download a high-definition film in under a second (a task that could take 10 minutes on 4G LTE). And wireless engineers say these networks will boost the development of other new technologies, too, such as autonomous vehicles, virtual reality, and the Internet of Things.
If all goes well, telecommunications companies hope to debut the first commercial 5G networks in the early 2020s. Right now, though, 5G is still in the planning stages, and companies and industry groups are working together to figure out exactly what it will be. But they all agree on one matter: As the number of mobile users and their demand for data rises, 5G must handle far more traffic at much higher speeds than the base stations that make up today’s cellular networks.
To achieve this, wireless engineers are designing a suite of brand-new technologies. Together, these technologies will deliver data with less than a millisecond of delay (compared to about 70 ms on today’s 4G networks) and bring peak download speeds of 20 gigabits per second (compared to 1 Gb/s on 4G) to users.
At the moment, it’s not yet clear which technologies will do the most for 5G in the long run, but a few early favorites have emerged. The front-runners include millimeter waves, small cells, massive MIMO, full duplex, and beamforming. To understand how 5G will differ from today’s 4G networks, it’s helpful to walk through these five technologies and consider what each will mean for wireless users.
Today’s wireless networks have run into a problem: More people and devices are consuming more data than ever before, but it remains crammed on the same bands of the radio-frequency spectrum that mobile providers have always used. That means less bandwidth for everyone, causing slower service and more dropped connections.
One way to get around that problem is to simply transmit signals on a whole new swath of the spectrum, one that’s never been used for mobile service before. That’s why providers are experimenting with broadcasting on millimeter waves, which use higher frequencies than the radio waves that have long been used for mobile phones.
Millimeter waves are broadcast at frequencies between 30 and 300 gigahertz, compared to the bands below 6 GHz that were used for mobile devices in the past. They are called millimeter waves because they vary in length from 1 to 10 mm, compared to the radio waves that serve today’s smartphones, which measure tens of centimeters in length.
Until now, only operators of satellites and radar systems used millimeter waves for real-world applications. Now, some cellular providers have begun to use them to send data between stationary points, such as two base stations. But using millimeter waves to connect mobile users with a nearby base station is an entirely new approach.
There is one major drawback to millimeter waves, though—they can’t easily travel through buildings or obstacles and they can be absorbed by foliage and rain. That’s why 5G networks will likely augment traditional cellular towers with another new technology, called small cells.
Small cells are portable miniature base stations that require minimal power to operate and can be placed every 250 meters or so throughout cities. To prevent signals from being dropped, carriers could install thousands of these stations in a city to form a dense network that acts like a relay team, receiving signals from other base stations and sending data to users at any location.
While traditional cell networks have also come to rely on an increasing number of base stations, achieving 5G performance will require an even greater infrastructure. Luckily, antennas on small cells can be much smaller than traditional antennas if they are transmitting tiny millimeter waves. This size difference makes it even easier to stick cells on light poles and atop buildings.
This radically different network structure should provide more targeted and efficient use of spectrum. Having more stations means the frequencies that one station uses to connect with devices in one area can be reused by another station in a different area to serve another customer. There is a problem, though—the sheer number of small cells required to build a 5G network may make it hard to set up in rural areas.
In addition to broadcasting over millimeter waves, 5G base stations will also have many more antennas than the base stations of today’s cellular networks—to take advantage of another new technology: massive MIMO.
Today’s 4G base stations have a dozen ports for antennas that handle all cellular traffic: eight for transmitters and four for receivers. But 5G base stations can support about a hundred ports, which means many more antennas can fit on a single array. That capability means a base station could send and receive signals from many more users at once, increasing the capacity of mobile networks by a factor of 22 or greater.
This technology is called massive MIMO. It all starts with MIMO, which stands for multiple-input multiple-output. MIMO describes wireless systems that use two or more transmitters and receivers to send and receive more data at once. Massive MIMO takes this concept to a new level by featuring dozens of antennas on a single array.
MIMO is already found on some 4G base stations. But so far, massive MIMO has only been tested in labs and a few field trials. In early tests, it has set new records for spectrum efficiency, which is a measure of how many bits of data can be transmitted to a certain number of users per second.
Massive MIMO looks very promising for the future of 5G. However, installing so many more antennas to handle cellular traffic also causes more interference if those signals cross. That’s why 5G stations must incorporate beamforming.
Beamforming is a traffic-signaling system for cellular base stations that identifies the most efficient data-delivery route to a particular user, and it reduces interference for nearby users in the process. Depending on the situation and the technology, there are several ways for 5G networks to implement it.
Beamforming can help massive MIMO arrays make more efficient use of the spectrum around them. The primary challenge for massive MIMO is to reduce interference while transmitting more information from many more antennas at once. At massive MIMO base stations, signal-processing algorithms plot the best transmission route through the air to each user. Then they can send individual data packets in many different directions, bouncing them off buildings and other objects in a precisely coordinated pattern. By choreographing the packets’ movements and arrival time, beamforming allows many users and antennas on a massive MIMO array to exchange much more information at once.
For millimeter waves, beamforming is primarily used to address a different set of problems: Cellular signals are easily blocked by objects and tend to weaken over long distances. In this case, beamforming can help by focusing a signal in a concentrated beam that points only in the direction of a user, rather than broadcasting in many directions at once. This approach can strengthen the signal’s chances of arriving intact and reduce interference for everyone else.
Besides boosting data rates by broadcasting over millimeter waves and beefing up spectrum efficiency with massive MIMO, wireless engineers are also trying to achieve the high throughput and low latency required for 5G through a technology called full duplex, which modifies the way antennas deliver and receive data.
Today's base stations and cellphones rely on transceivers that must take turns if transmitting and receiving information over the same frequency, or operate on different frequencies if a user wishes to transmit and receive information at the same time.
With 5G, a transceiver will be able to transmit and receive data at the same time, on the same frequency. This technology is known as full duplex, and it could double the capacity of wireless networks at their most fundamental physical layer: Picture two people talking at the same time but still able to understand one another—which means their conversation could take half as long and their next discussion could start sooner.
Some militaries already use full duplex technology that relies on bulky equipment. To achieve full duplex in personal devices, researchers must design a circuit that can route incoming and outgoing signals so they don’t collide while an antenna is transmitting and receiving data at the same time.
This is especially hard because of the tendency of radio waves to travel both forward and backward on the same frequency—a principle known as reciprocity. But recently, experts have assembled silicon transistors that act like high-speed switches to halt the backward roll of these waves, enabling them to transmit and receive signals on the same frequency at once.
One drawback to full duplex is that it also creates more signal interference, through a pesky echo. When a transmitter emits a signal, that signal is much closer to the device’s antenna and therefore more powerful than any signal it receives. Expecting an antenna to both speak and listen at the same time is possible only with special echo-canceling technology.
With these and other 5G technologies, engineers hope to build the wireless network that future smartphone users, VR gamers, and autonomous cars will rely on every day. Already, researchers and companies have set high expectations for 5G by promising ultralow latency and record-breaking data speeds for consumers. If they can solve the remaining challenges, and figure out how to make all these systems work together, ultrafast 5G service could reach consumers in the next five years.
Art Direction and Illustrations:
Monday, January 30. 2017
Hungry penguins have inspired a novel way of making sure computer code in smart cars does not crash.
Tools based on the way the birds co-operatively hunt for fish are being developed to test different ways of organising in-car software.
The tools look for safe ways to organise code in the same way that penguins seek food sources in the open ocean.
Experts said such testing systems would be vital as cars get more connected.
Engineers have often turned to nature for good solutions to tricky problems, said Prof Yiannis Papadopoulos, a computer scientist at the University of Hull who developed the penguin-inspired testing system.
The way ants pass messages among nest-mates has helped telecoms firms keep telephone networks running, and many robots get around using methods of locomotion based on the ways animals move.
Penguins were another candidate, said Prof Papadopoulos, because millions of years of evolution has helped them develop very efficient hunting strategies.
This was useful behaviour to copy, he said, because it showed that penguins had solved a tricky optimisation problem – how to ensure as many penguins as possible get enough to eat.
“Penguins are social birds and we know they live in colonies that are often very large and can include hundreds of thousands of birds. This raises the question of how can they sustain this kind of big society given that together they need a vast amount of food.
“There must be something special about their hunting strategy,” he said, adding that an inefficient strategy would mean many birds starved.
Prof Papadopoulos said many problems in software engineering could be framed as a search among all hypothetical solutions for the one that produces the best results. Evolution, through penguins and many other creatures, has already searched through and discarded a lot of bad solutions.
Studies of hunting penguins have hinted at how they organised themselves.
“They forage in groups and have been observed to synchronise their dives to get fish,” said Prof Papadopoulos. “They also have the ability to communicate using vocalisations and possibly convey information about food resources.”
The communal, co-ordinated action helps the penguins get the most out of a hunting expedition. Groups of birds are regularly reconfigured to match the shoals of fish and squid they find. It helps the colony as a whole optimise the amount of energy they have to expend to catch food.
“This solution has generic elements which can be abstracted and be used to solve other problems,” he said, “such as determining the integrity of software components needed to reach the high safety requirements of a modern car.”
Integrity in this sense means ensuring the software does what is intended, handles data well, and does not introduce errors or crash.
By mimicking penguin behaviour in a testing system which seeks the safest ways to arrange code instead of shoals of fish, it becomes possible to slowly zero in on the best way for that software to be structured.
The Hull researchers, in conjunction with Dr Youcef Gheraibia, a postdoctoral researcher from Algeria, turned to search tools based on the collaborative foraging behaviour of penguins.
The foraging-based system helped to quickly search through the many possible ways software can be specified to home in on the most optimal solutions in terms of safety and cost.
Currently, complex software was put together and tested manually, with only experience and engineering judgement to guide it, said Prof Papadopoulos. While this could produce decent results it could consider only a small fraction of all possible good solutions.
The penguin-based system could crank through more solutions and do a better job of assessing which was best, he said.
Mike Ahmadi, global director of critical systems security at Synopsys, which helps vehicle-makers secure code, said modern car manufacturing methods made optimisation necessary.
“When you look at a car today, it’s essentially something that’s put together from a vast and extended supply chain,” he said.
Building a car was about getting sub-systems made by different manufacturers to work together well, rather than being something made wholly in one place.
That was a tricky task given how much code was present in modern cars, he added.
“There’s about a million lines of code in the average car today and there’s far more in connected cars.”
Carmakers were under pressure, said Mr Ahmadi, to adapt cars quickly so they could interface with smartphones and act as mobile entertainment hubs, as well as make them more autonomous.
“From a performance point of view carmakers have gone as far as they can,” he said. “What they have discovered is that the way to offer features now is through software.”
Security would become a priority as cars got smarter and started taking in and using data from other cars, traffic lights and online sources, said Nick Cook from software firm Intercede, which is working with carmakers on safe in-car software.
“If somebody wants to interfere with a car today then generally they have to go to the car itself,” he said. “But as soon as it’s connected they can be anywhere in the world.
“Your threat landscape is quite significantly different and the opportunity for a hack is much higher.”
Monday, January 16. 2017
Via IEEE Spectrum
By Robert Ubell
In 2011, when Stanford computer scientists Sebastian Thrun and Peter Norvig came up with the bright idea of streaming their robotics lectures over the Internet, they knew it was an inventive departure from the usual college course. For hundreds of years, professors had lectured to groups of no more than a few hundred students. But MOOCs—massive open online courses—made it possible to reach many thousands at once. Through the extraordinary reach of the Internet, learners could log on to lectures streamed to wherever they happened to be. To date, about 58 million people have signed up for a MOOC.
Familiar with the technical elements required for a MOOC—video streaming, IT infrastructure, the Internet—MOOC developers put code together to send their lectures into cyberspace. When more than 160,000 enrolled in Thrun and Norvig’s introduction to artificial intelligence MOOC, the professors thought they held a tiger by the tail. Not long after, Thrun cofounded Udacity to commercialize MOOCs. He predicted that in 50 years, streaming lectures would so subvert face-to-face education that only 10 higher-education institutions would remain. Our quaint campuses would become obsolete, replaced by star faculty streaming lectures on computer screens all over the world. Thrun and other MOOC evangelists imagined they had inspired a revolution, overthrowing a thousand years of classroom teaching.
These MOOC pioneers were therefore stunned when their online courses didn’t perform anything like they had expected. At first, the average completion rate for MOOCs was less than 7 percent. Completion rates have since gone up a bit, to a median of about 12.6 percent, although there’s considerable variation from course to course. While a number of factors contribute to the completion rate, my own observation is that students who have to pay a fee to enroll tend to be more committed to finishing the course.
Looking closer at students’ MOOC habits, researchers found that some people quit watching within the first few minutes. Many others were merely “grazing,” taking advantage of the technology to quickly log in, absorb just the morsel they were hunting for, and then log off as soon as their appetite was satisfied. Most of those who did finish a MOOC were accomplished learners, many with advanced degrees.
What accounts for MOOCs’ modest performance? While the technological solution they devised was novel, most MOOC innovators were unfamiliar with key trends in education. That is, they knew a lot about computers and networks, but they hadn’t really thought through how people learn.
It’s unsurprising then that the first MOOCs merely replicated the standard lecture, an uninspiring teaching style but one with which the computer scientists were most familiar. As the education technology consultant Phil Hill recently observed in the Chronicle of Higher Education, “The big MOOCs mostly employed smooth-functioning but basic video recording of lectures, multiple-choice quizzes, and unruly discussion forums. They were big, but they did not break new ground in pedagogy.”
Indeed, most MOOC founders were unaware that a pedagogical revolution was already under way at the nation’s universities: The traditional lecture was being rejected by many scholars, practitioners, and, most tellingly, tech-savvy students. MOOC advocates also failed to appreciate the existing body of knowledge about learning online, built over the last couple of decades by adventurous faculty who were attracted to online teaching for its innovative potential, such as peer-to-peer learning, virtual teamwork, and interactive exercises. These modes of instruction, known collectively as “active” learning, encourage student engagement, in stark contrast to passive listening in lectures. Indeed, even as the first MOOCs were being unveiled, traditional lectures were on their way out.
The impact of active learning can be significant. In a 2014 meta-analysis published in Proceedings of the National Academy of Sciences [PDF], researchers looked at 225 studies in which standard lectures were compared with active learning for undergraduate science, math, and engineering. The results were unambiguous: Average test scores went up about 6 percent in active-learning sections, while students in traditional lecture classes were 1.5 times more likely to fail than their peers in active-learning classes.
Even lectures by “star” faculty were no match for active-learning sections taught by novice instructors: Students still performed better in active classes. “We’ve yet to see any evidence that celebrated lecturers can help students more than even first-generation active learning does,” Scott Freeman, the lead author of the study, told Wired.
Unfortunately, early MOOCs failed to incorporate active learning approaches or any of the other innovations in teaching and learning common in other online courses. The three principal MOOC providers—Coursera, Udacity, and edX—wandered into a territory they thought was uninhabited. Yet it was a place that was already well occupied by accomplished practitioners who had thought deeply and productively over the last couple of decades about how students learn online. Like poor, baffled Columbus, MOOC makers believed they had “discovered” a new world. It’s telling that in their latest offerings, these vendors have introduced a number of active-learning innovations.
To be sure, MOOCs have been wildly successful in giving millions of people all over the world access to a wide range of subjects presented by eminent scholars at the world’s elite schools. Some courses attract so many students that a 7 percent completion rate still translates into several thousand students finishing—greater than the total enrollment of many colleges.
But MOOC pioneers were presumptuous to imagine they could not only
topple the university—an institution that has successfully withstood
revolutions far more devastating than the Web—but also ignore common
experience. They erroneously assumed they could open the minds of
millions who were unprepared to tackle sophisticated curriculum. MOOCs
will never sweep away face-to-face classrooms, nor can they take the
place of more intensive and intimate online degree programs. The real
contribution of MOOCs is likely to be much more modest, as yet another
digital education option.
Tuesday, January 10. 2017
In the closing weeks of 2016, Google published an article that quietly sailed under most people’s radars. Which is a shame, because it may just be the most astonishing article about machine learning that I read last year.
Don’t feel bad if you missed it. Not only was the article competing with the pre-Christmas rush that most of us were navigating — it was also tucked away on Google’s Research Blog, beneath the geektastic headline Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System.
This doesn’t exactly scream must read, does it? Especially when you’ve got projects to wind up, gifts to buy, and family feuds to be resolved — all while the advent calendar relentlessly counts down the days until Christmas like some kind of chocolate-filled Yuletide doomsday clock.
Luckily, I’m here to bring you up to speed. Here’s the deal.
Up until September of last year, Google Translate used phrase-based translation. It basically did the same thing you and I do when we look up key words and phrases in our Lonely Planet language guides. It’s effective enough, and blisteringly fast compared to awkwardly thumbing your way through a bunch of pages looking for the French equivalent of “please bring me all of your cheese and don’t stop until I fall over.” But it lacks nuance.
Phrase-based translation is a blunt instrument. It does the job well enough to get by. But mapping roughly equivalent words and phrases without an understanding of linguistic structures can only produce crude results.
This approach is also limited by the extent of an available vocabulary. Phrase-based translation has no capacity to make educated guesses at words it doesn’t recognize, and can’t learn from new input.
All that changed in September, when Google gave their translation tool a new engine: the Google Neural Machine Translation system (GNMT). This new engine comes fully loaded with all the hot 2016 buzzwords, like neural network and machine learning.
The short version is that Google Translate got smart. It developed the ability to learn from the people who used it. It learned how to make educated guesses about the content, tone, and meaning of phrases based on the context of other words and phrases around them. And — here’s the bit that should make your brain explode — it got creative.
Google Translate invented its own language to help it translate more effectively.
What’s more, nobody told it to. It didn’t develop a language (or interlingua, as Google call it) because it was coded to. It developed a new language because the software determined over time that this was the most efficient way to solve the problem of translation.
Stop and think about that for a moment. Let it sink in. A neural computing system designed to translate content from one human language into another developed its own internal language to make the task more efficient. Without being told to do so. In a matter of weeks.
To understand what’s going on, we need to understand what zero-shot translation capability is. Here’s Google’s Mike Schuster, Nikhil Thorat, and Melvin Johnson from the original blog post:
Let’s say we train a multilingual system with Japanese⇄English and Korean⇄English examples. Our multilingual system, with the same size as a single GNMT system, shares its parameters to translate between these four different language pairs. This sharing enables the system to transfer the “translation knowledge” from one language pair to the others. This transfer learning and the need to translate between multiple languages forces the system to better use its modeling power.
This inspired us to ask the following question: Can we translate between a language pair which the system has never seen before? An example of this would be translations between Korean and Japanese where Korean⇄Japanese examples were not shown to the system. Impressively, the answer is yes — it can generate reasonable Korean⇄Japanese translations, even though it has never been taught to do so.
Here you can see an advantage of Google’s new neural machine over the old phrase-based approach. The GMNT is able to learn how to translate between two languages without being explicitly taught. This wouldn’t be possible in a phrase-based model, where translation is dependent upon an explicit dictionary to map words and phrases between each pair of languages being translated.
And this leads the Google engineers onto that truly astonishing discovery of creation:
The success of the zero-shot translation raises another important question: Is the system learning a common representation in which sentences with the same meaning are represented in similar ways regardless of language — i.e. an “interlingua”? Using a 3-dimensional representation of internal network data, we were able to take a peek into the system as it translates a set of sentences between all possible pairs of the Japanese, Korean, and English languages.
Within a single group, we see a sentence with the same meaning but from three different languages. This means the network must be encoding something about the semantics of the sentence rather than simply memorizing phrase-to-phrase translations. We interpret this as a sign of existence of an interlingua in the network.
So there you have it. In the last weeks of 2016, as journos around the world started penning their “was this the worst year in living memory” thinkpieces, Google engineers were quietly documenting a genuinely astonishing breakthrough in software engineering and linguistics.
I just thought maybe you’d want to know.
Ok, to really understand what’s going on we probably need multiple computer science and linguistics degrees. I’m just barely scraping the surface here. If you’ve got time to get a few degrees (or if you’ve already got them) please drop me a line and explain it all me to. Slowly.
Update 1: in my excitement, it’s fair to say that I’ve exaggerated the idea of this as an ‘intelligent’ system — at least so far as we would think about human intelligence and decision making. Make sure you read Chris McDonald’s comment after the article for a more sober perspective.
Update 2: Nafrondel’s excellent, detailed reply is also a must read for an expert explanation of how neural networks function.
Monday, January 18. 2016
Researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) have created an algorithm they claim can predict how memorable or forgettable an image is almost as accurately as a human — which is to say that their tech can predict how likely a person would be to remember or forget a particular photo.
The algorithm performed 30 per cent better than existing algorithms and was within a few percentage points of the average human performance, according to the researchers.
The team has put a demo of their tool online here, where you can upload your selfie to get a memorability score and view a heat map showing areas the algorithm considers more or less memorable. They have also published a paper on the research which can be found here.
Here are some examples of images I ran through their MemNet algorithm, with resulting memorability scores and most and least forgettable areas depicted via heat map:
Potential applications for the algorithm are very broad indeed when you consider how photos and photo-sharing remains the currency of the social web. Anything that helps improve understanding of how people process visual information and the impact of that information on memory has clear utility.
The team says it plans to release an app in future to allow users to tweak images to improve their impact. So the research could be used to underpin future photo filters that do more than airbrush facial features to make a shot more photogenic — but maybe tweak some of the elements to make the image more memorable too.
Beyond helping people create a more lasting impression with their selfies, the team envisages applications for the algorithm to enhance ad/marketing content, improve teaching resources and even power health-related applications aimed at improving a person’s capacity to remember or even as a way to diagnose errors in memory and perhaps identify particular medical conditions.
The MemNet algorithm was created using deep learning AI techniques, and specifically trained on tens of thousands of tagged images from several different datasets all developed at CSAIL — including LaMem, which contains 60,000 images each annotated with detailed metadata about qualities such as popularity and emotional impact.
Publishing the LaMem database alongside their paper is part of the team’s effort to encourage further research into what they say has often been an under-studied topic in computer vision.
Asked to explain what kind of patterns the deep-learning algorithm is trying to identify in order to predict memorability/forgettability, PhD candidate at MIT CSAIL, Aditya Khosla, who was lead author on a related paper, tells TechCrunch: “This is a very difficult question and active area of research. While the deep learning algorithms are extremely powerful and are able to identify patterns in images that make them more or less memorable, it is rather challenging to look under the hood to identify the precise characteristics the algorithm is identifying.
“In general, the algorithm makes use of the objects and scenes in the image but exactly how it does so is difficult to explain. Some initial analysis shows that (exposed) body parts and faces tend to be highly memorable while images showing outdoor scenes such as beaches or the horizon tend to be rather forgettable.”
The research involved showing people images, one after another, and asking them to press a key when they encounter an image they had seen before to create a memorability score for images used to train the algorithm. The team had about 5,000 people from the Amazon Mechanical Turk crowdsourcing platform view a subset of its images, with each image in their LaMem dataset viewed on average by 80 unique individuals, according to Khosla.
In terms of shortcomings, the algorithm does less well on types of images it has not been trained on so far, as you’d expect — so it’s better on natural images and less good on logos or line drawings right now.
“It has not seen how variations in colors, fonts, etc affect the memorability of logos, so it would have a limited understanding of these,” says Khosla. “But addressing this is a matter of capturing such data, and this is something we hope to explore in the near future — capturing specialized data for specific domains in order to better understand them and potentially allow for commercial applications there. One of those domains we’re focusing on at the moment is faces.”
The team has previously developed a similar algorithm for face memorability.
Discussing how the planned MemNet app might work, Khosla says there are various options for how images could be tweaked based on algorithmic input, although ensuring a pleasing end photo is part of the challenge here. “The simple approach would be to use the heat map to blur out regions that are not memorable to emphasize the regions of high memorability, or simply applying an Instagram-like filter or cropping the image a particular way,” he notes.
“The complex approach would involve adding or removing objects from images automatically to change the memorability of the image — but as you can imagine, this is pretty hard — we would have to ensure that the object size, shape, pose and so on match the scene they are being added to, to avoid looking like a photoshop job gone bad.”
Looking ahead, the next step for the researchers will be to try to update their system to be able to predict the memory of a specific person. They also want to be able to better tailor it for individual “expert industries” such as retail clothing and logo-design.
How many training images they’d need to show an individual person before being able to algorithmically predict their capacity to remember images in future is not yet clear. “This is something we are still investigating,” says Khosla.
Wednesday, January 06. 2016
Via Slash Gear
Everyone on the internet has come across at least couple error codes, the most well-known being 404, for page not found, while other common ones include 500, for internal server error, or 403, for a "forbidden" page. However, with latter, there's the growing issue of why a certain webpage has become forbidden, or who made it so. In an effort to address things like censorship or "legal obstacles," a new code has been published, to be used when legal demands require access to a page be blocked: error 451.
The number is a knowing reference Fahrenheit 451, the novel by Ray Bradbury that depicted a dystopian future where books are banned for spreading dissenting ideas, and in burned as a way censor the spread of information. The code itself was approved for use by the Internet Engineering Steering Group (IESG), which helps maintain internet standards.
The idea for code 451 originally came about around 3 years ago, when a UK court ruling required some websites to block The Pirate Bay. Most sites in turn used the 403 "forbidden" code, making it unclear to users about what the issue was. The goal of 451 is to eliminate some of the confusion around why sites may be blocked.
The use of the code is completely voluntary, however, and requires developers to begin adopting it. But if widely implemented, it should be able to communicate to users that some information has been taken down because of a legal demand, or is being censored by a national government.
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