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, 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.”
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.
Monday, January 04. 2016
Via Tech Crunch
As humanity debates the threats and opportunities of advanced artificial intelligence, we are simultaneously enabling that technology through the increasing use of personalization that is understanding and anticipating our needs through sophisticated machine learning solutions.
In effect, while using personalization technologies in our everyday lives, we are contributing in a real way to the development of the intelligent systems we purport to fear.
Perhaps uncovering the currently inaccessible personalization systems is crucial for creating a sustainable relationship between humans and super–intelligent machines?
From Machines Learning About You…
Industry giants are currently racing to develop more intelligent and lucrative AI solutions. Google is extending the ways machine learning can be applied in search, and beyond. Facebook’s messenger assistant M is combining deep learning and human curators to achieve the next level in personalization.
With your iPhone you’re carrying Apple’s digital assistant Siri with you everywhere; Microsoft’s counterpart Cortana can live in your smartphone, too. IBM’s Watson has highlighted its diverse features, varying from computer vision and natural language processing to cooking skills and business analytics.
At the same time, your data and personalized experiences are used to develop and train the machine learning systems that are powering the Siris, Watsons, Ms and Cortanas. Be it a speech recognition solution or a recommendation algorithm, your actions and personal data affect how these sophisticated systems learn more about you and the world around you.
The less explicit fact is that your diverse interactions — your likes, photos, locations, tags, videos, comments, route selections, recommendations and ratings — feed learning systems that could someday transform into super–intelligent AIs with unpredictable consequences.
As of today, you can’t directly affect how your personal data is used in these systems.
In these times, when we’re starting to use serious resources to contemplate the creation of ethical frameworks for super–intelligent AIs-to-be, we also should focus on creating ethical terms for the use of personal data and the personalization technologies that are powering the development of such systems.
To make sure that you as an individual continue to have a meaningful agency in the emerging algorithmic reality, we need learning algorithms that are on your side and solutions that augment and extend your abilities. How could this happen?
…To Machines That Learn For You
Smart devices extend and augment your memory (no forgotten birthdays) and brain processing power (no calculating in your head anymore). And they augment your senses by letting you experience things beyond your immediate environment (think AR and VR).
The web itself gives you access to a huge amount of diverse information and collective knowledge. The next step would be that smart devices and systems enhance and expand your abilities even more. What is required for that to happen in a human-centric way?
Data Awareness And Algorithmic Accountability
Algorithmic systems and personal data are too often seen as something abstract, incomprehensible and uncontrollable. Concretely, how many really stopped using Facebook or Google after PRISM came out in the open? Or after we learned that we are exposed to continuous A/B testing that is used to develop even more powerful algorithms?
More and more people are getting interested in data ethics and algorithmic accountability. Academics are already analyzing the effects of current data policies and algorithmic systems. Educational organizations are starting to emphasize the importance of coding and digital literacy.
Initiatives such as VRM, Indie Web and MyData are raising awareness on alternative data ecosystems and data management practices. Big companies like Apple and various upcoming startups are bringing personal data issues to the mainstream discussion.
Yet we still need new tools and techniques to become more data aware and to see how algorithms can be more beneficial for us as unique individuals. We need apps and data visualizations with great user experience to illuminate the possibilities of more human-centric personalization.
It’s time to create systems that evaluate algorithmic biases and keep them in check. More accessible algorithms and transparent data policies are created only through wider collaboration that brings together companies, developers, designers, users and scientists alike.
Personal Machine Learning Systems
Personalization technologies are already augmenting your decision making and future thinking by learning from you and recommending to you what to see and do next. However, not on your own terms. Rather than letting someone else and their motives and values dictate how the algorithms work and affect your life, it’s time to create solutions, such as algorithmic angels, that let you develop and customize your own algorithms and choose how they use your data.
When you’re in control, you can let your personal learning system access previously hidden data and surface intimate insights about your own behavior, thus increasing your self-awareness in an actionable way.
Personal learners could help you develop skills related to work or personal life, augmenting and expanding your abilities. For example, learning languages, writing or playing new games. Fitness or mediation apps powered by your personal algorithms would know you better than any personal trainer.
Google’s experiments with deep learning and image manipulation showed us how machine learning could be used to augment creative output. Systems capable of combining your data with different materials like images, text, sound and video could expand your abilities to see and utilize new and unexpected connections around you.
In effect, your personal algorithm can take a mind-expanding “trip” on your behalf, letting you see music or sense other dimensions beyond normal human abilities. By knowing you, personal algorithms can expose you to new diverse information, thus breaking your existing filter bubbles.
Additionally, people tinkering with their personal algorithms would create more “citizen algorithm experts,” like “citizen scientists,” coming up with new ideas, solutions and observations, stemming from real live situations and experiences.
However, personally adjustable algorithms for the general public are not happening overnight, even though Google recently open-sourced parts of its machine learning framework. But it’s possible to see how today’s personalization experiences can someday evolve into customizable algorithms that strengthen your agency and capacity to deal with other algorithmic systems.
The next step is that your personal algorithms become a more concrete part of you, continuously evolving with you by learning from your interactions both in digital and physical environments. Your algorithmic self combines your personal abilities and knowledge with machine learning systems that adapt to you and work for you. Be it your smartwatch, self-driving car or an intelligent home system, they can all be spirited by your algorithmic self.
Your algorithmic self also can connect with other algorithmic selves, thus empowering you with the accumulating collective knowledge and intelligence. To expand your existing skills and faculties, your algorithmic self also starts to learn and act on its own, filtering information, making online transactions and comparing best options on your behalf. It makes you more resourceful, and even a better person, when you can concentrate on things that really require your human presence and attention.
Partly algorithmic humans are not bound by existing human capabilities; new skills and abilities emerge when human intelligence is extended with algorithmic selves. For example, your algorithmic self can multiply to execute different actions simultaneously. Algorithmic selves could also create simple simulations by playing out different scenarios involving your real-life choices and their consequences, helping you to make better decisions in the future.
Algorithmic selves — tuned by your data and personal learners — also could be the key when creating invasive human-computer interfaces that connect digital systems directly to your brain, expanding human brain concretely beyond the “wetware.”
But to ensure that your algorithmic self works for your benefit, could you trust someone building that for you without you participating in the process?
Machine learning expert Pedro Domingos says in his new book “The Master Algorithm” that “[m]achine learning will not single-handedly determine the future… it’s what we decide to do with it that counts.”
Machines are still far from human intelligence. No one knows exactly when super–intelligent AIs will become concrete reality. But developing personal machine learning systems could enable us to interact with any algorithmic entities, be it an obtrusive recommendation algorithm or a super–intelligent AI.
In general, being more transparent on how learning algorithms work and use our data could be crucial for creating ethical and sustainable artificial intelligence. And potentially, maybe we wouldn’t need to fear being overpowered by our own creations.
Friday, January 01. 2016
what3words is a geocoding system for the simple communication of precise locations. what3words encodes geographic co-ordinates into 3 dictionary words (for example, the Statue of Liberty is located at planet.inches.most). what3words is different from other alphanumeric location systems and GPS coordinates in that it displays 3 words rather than long strings of numbers or random letters or numbers. what3words has an iOS App, Android App, a website and an API that enables bi-directional conversion of what3words address and latitude/longitude co-ordinates.
Tuesday, November 24. 2015
Via The Daily Dot
People with Android devices might be a bit frustrated with Google after a report from the New York District Attorney's office provided detailed information about smartphone security, and Google's power to access devices when asked to by law enforcement. The report went viral on Reddit over the weekend.
Google can unlock many Android phones remotely when given a search warrant, bypassing lock codes on particular devices. The report reads:
Forensic examiners are able to bypass passcodes on some of those [Android] devices using a variety of forensic techniques. For some other types of Android devices, Google can reset the passcodes when served with a search warrant and an order instructing them to assist law enforcement to extract data from the device. This process can be done by Google remotely and allows forensic examiners to view the contents of a device.
When compared to Apple devices, which encrypt by default on iOS 8 and later, Google's seemingly lax protection is irksome. The report continues:
For Android devices running operating systems Lollipop 5.0 and above, however, Google plans to use default full-disk encryption, like that being used by Apple, that will make it impossible for Google to comply with search warrants and orders instructing them to assist with device data extraction. Generally, users have the option to enable full-disk encryption on their current Android devices, whether or not the device is running Lollipop 5.0, but doing so causes certain inconveniences, risks, and performance issues, which are likely to exist until OEMs are required to standardize certain features.
In October, Google announced that new devices that ship with the Marshmallow 6.0 operating system (the most recent version of Android) must enable full-disk encryption by default. Nexus devices running Lollipop 5.0 are encrypted by default as well. This means that Google is unable to bypass lock codes on those devices. However, because of the massive fragmentation of Android devices and operating systems, Google can still access lots of Android devices running older versions when asked to by law enforcement.
And despite the encryption updates to the Android compatibility documentation, a number of devices are exempt from full-disk encryption, including older devices, devices without a lock screen, and those that don't meet the minimum security requirements.
The number of devices that actually have full-disk encryption appears to be low. Just 0.3 percent of Android devices are running Marshmallow and more than 25 percent of Android devices are running Lollipop 5.0, but most of those aren't Nexus, according to ZDNet.
When compared to Apple, Google's security appears lacking. Apple made encryption mandatory in iOS 8 back in 2014, which of course extends to iOS 9, its most recent mobile OS update. Data shows that 67 percent of Apple users are on iOS 9, and 24 percent of devices are still on iOS 8. Just nine percent of devices run an older version of iOS.
Android users are often at the mercy of carriers who decide when to roll out Android updates, which is an obstacle for some Android owners who want the latest OS.
If you do have a compatible device and want to enable encryption, head over to your security settings and select "encrypt device."
Tuesday, October 06. 2015
Binaural audio consists in recording audio sounds in the exact same way as humans are naturally perceiving it. Listening to binaural audio may only be performed by the use of a headset, making the resulting experience hyper-realistic. Many more details may be found on the binaural recording wikipedia page.
Wednesday, September 09. 2015
We invariably imagine electronic devices to be made from silicon chips, with which computers store and process information as binary digits (zeros and ones) represented by tiny electrical charges. But it need not be this way: among the alternatives to silicon are organic mediums such as DNA.
DNA computing was first demonstrated in 1994 by Leonard Adleman who encoded and solved the travelling salesman problem, a maths problem to find the most efficient route for a salesman to take between hypothetical cities, entirely in DNA.
Deoxyribonucleaic acid, DNA, can store vast amounts of information encoded as sequences of the molecules, known as nucleotides, cytosine (C), guanine (G), adenine (A), or thymine (T). The complexity and enormous variance of different species’ genetic codes demonstrates how much information can be stored within DNA encoded using CGAT, and this capacity can be put to use in computing. DNA molecules can be used to process information, using a bonding process between DNA pairs known as hybridisation. This takes single strands of DNA as input and produces subsequent strands of DNA through transformation as output.
Since Adleman’s experiment, many DNA-based “circuits” have been proposed that implement computational methods such as Boolean logic, arithmetical formulas, and neural network computation. Called molecular programming, this approach applies concepts and designs customary to computing to nano-scale approaches appropriate for working with DNA.
It’s circuitry, but not as we know it. Caltech/Lulu Qian, CC BY
In this sense “programming” is really biochemistry. The “programs” created are in fact methods of selecting molecules that interact in a way that achieves a specific result through the process of DNA self-assembly, where disordered collections of molecules will spontaneously interact to form the desired arrangement of strands of DNA.
DNA can also be used to control motion, allowing for DNA-based nano-mechanical devices. This was first achieved by Bernard Yurke and colleagues in 2000, who created from DNA strands a pair of tweezers that opened and pinched. Later experiments such as by Shelley Wickham and colleagues in 2011 and at Andrew Turberfield’s lab at Oxford demonstrated nano-molecular walking machines made entirely from DNA that could traverse set routes.
One possible application is that such a nano-robot DNA walker could progress along tracks making decisions and signal when reaching the end of the track, indicating computation has finished. Just as electronic circuits are printed onto circuit boards, DNA molecules could be used to print similar tracks arranged into logical decision trees on a DNA tile, with enzymes used to control the decision branching along the tree, causing the walker to take one track or another.
DNA walkers can also carry molecular cargo, and so could be used to deliver drugs inside the body.
Why DNA Computing?
DNA molecules’ many appealing features include their size (2nm width), programmability and high storage capacity – much greater than their silicon counterparts. DNA is also versatile, cheap and easy to synthesise, and computing with DNA requires much less energy than electric powered silicon processors.
Its drawback is speed: it currently takes several hours to compute the square root of a four digit number, something that a traditional computer could compute in a hundredth of a second. Another drawback is that DNA circuits are single-use, and need to be recreated to run the same computation again.
Perhaps the greatest advantage of DNA over electronic circuits is that it can interact with its biochemical environment. Computing with molecules involves recognising the presence or absence of certain molecules, and so a natural application of DNA computing is to bring such programmability into the realm of environmental biosensing, or delivering medicines and therapies inside living organisms.
DNA programs have already been put to medical uses, such as diagnosing tuberculosis. Another proposed use is a nano-biological “program” by Ehud Shapiro of the Weizmann Institute of Science in Israel, termed the “doctor in the cell” that targets cancer molecules. Other DNA programs for medical applications target lymphocytes (a type of white blood cell), which are defined by the presence or absence of certain cell markers and so can be naturally detected with true/false Boolean logic. However, more effort is required before we can inject smart drugs directly into living organisms.
Future of DNA Computing
Taken broadly, DNA computation has enormous future potential. Its huge storage capacity, low energy cost, ease of manufacturing that exploits the power of self-assembly and its easy affinity with the natural world are an entry to nanoscale computing, possibly through designs that incorporate both molecular and electronic components. Since its inception, the technology has progressed at great speed, delivering point-of-care diagnostics and proof-of-concept smart drugs – those that can make diagnostic decisions about the type of therapy to deliver.
There are many challenges, of course, that need to be addressed so that the technology can move forward from the proof-of-concept to real smart drugs: the reliability of the DNA walkers, the robustness of DNA self-assembly, and improving drug delivery. But a century of traditional computer science research is well placed to contribute to developing DNA computing through new programming languages, abstractions, and formal verification techniques – techniques that have already revolutionised silicon circuit design, and can help launch organic computing down the same path.
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