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#437491 3.2 Billion Images and 720,000 Hours of ...
Twitter over the weekend “tagged” as manipulated a video showing US Democratic presidential candidate Joe Biden supposedly forgetting which state he’s in while addressing a crowd.
Biden’s “hello Minnesota” greeting contrasted with prominent signage reading “Tampa, Florida” and “Text FL to 30330.”
The Associated Press’s fact check confirmed the signs were added digitally and the original footage was indeed from a Minnesota rally. But by the time the misleading video was removed it already had more than one million views, The Guardian reports.
A FALSE video claiming Biden forgot what state he was in was viewed more than 1 million times on Twitter in the past 24 hours
In the video, Biden says “Hello, Minnesota.”
The event did indeed happen in MN — signs on stage read MN
But false video edited signs to read Florida pic.twitter.com/LdHQVaky8v
— Donie O'Sullivan (@donie) November 1, 2020
If you use social media, the chances are you see (and forward) some of the more than 3.2 billion images and 720,000 hours of video shared daily. When faced with such a glut of content, how can we know what’s real and what’s not?
While one part of the solution is an increased use of content verification tools, it’s equally important we all boost our digital media literacy. Ultimately, one of the best lines of defense—and the only one you can control—is you.
Seeing Shouldn’t Always Be Believing
Misinformation (when you accidentally share false content) and disinformation (when you intentionally share it) in any medium can erode trust in civil institutions such as news organizations, coalitions and social movements. However, fake photos and videos are often the most potent.
For those with a vested political interest, creating, sharing and/or editing false images can distract, confuse and manipulate viewers to sow discord and uncertainty (especially in already polarized environments). Posters and platforms can also make money from the sharing of fake, sensationalist content.
Only 11-25 percent of journalists globally use social media content verification tools, according to the International Centre for Journalists.
Could You Spot a Doctored Image?
Consider this photo of Martin Luther King Jr.
Dr. Martin Luther King Jr. Giving the middle finger #DopeHistoricPics pic.twitter.com/5W38DRaLHr
— Dope Historic Pics (@dopehistoricpic) December 20, 2013
This altered image clones part of the background over King Jr’s finger, so it looks like he’s flipping off the camera. It has been shared as genuine on Twitter, Reddit, and white supremacist websites.
In the original 1964 photo, King flashed the “V for victory” sign after learning the US Senate had passed the civil rights bill.
“Those who love peace must learn to organize as effectively as those who love war.”
Dr. Martin Luther King Jr.
This photo was taken on June 19th, 1964, showing Dr King giving a peace sign after hearing that the civil rights bill had passed the senate. @snopes pic.twitter.com/LXHmwMYZS5
— Willie's Reserve (@WilliesReserve) January 21, 2019
Beyond adding or removing elements, there’s a whole category of photo manipulation in which images are fused together.
Earlier this year, a photo of an armed man was photoshopped by Fox News, which overlaid the man onto other scenes without disclosing the edits, the Seattle Times reported.
You mean this guy who’s been photoshopped into three separate photos released by Fox News? pic.twitter.com/fAXpIKu77a
— Zander Yates ザンダーイェーツ (@ZanderYates) June 13, 2020
Similarly, the image below was shared thousands of times on social media in January, during Australia’s Black Summer bushfires. The AFP’s fact check confirmed it is not authentic and is actually a combination of several separate photos.
Image is more powerful than screams of Greta. A silent girl is holding a koala. She looks straight at you from the waters of the ocean where they found a refuge. She is wearing a breathing mask. A wall of fire is behind them. I do not know the name of the photographer #Australia pic.twitter.com/CrTX3lltdh
— EVC Music (@EVCMusicUK) January 6, 2020
Fully and Partially Synthetic Content
Online, you’ll also find sophisticated “deepfake” videos showing (usually famous) people saying or doing things they never did. Less advanced versions can be created using apps such as Zao and Reface.
Or, if you don’t want to use your photo for a profile picture, you can default to one of several websites offering hundreds of thousands of AI-generated, photorealistic images of people.
These people don’t exist, they’re just images generated by artificial intelligence. Generated Photos, CC BY
Editing Pixel Values and the (not so) Simple Crop
Cropping can greatly alter the context of a photo, too.
We saw this in 2017, when a US government employee edited official pictures of Donald Trump’s inauguration to make the crowd appear bigger, according to The Guardian. The staffer cropped out the empty space “where the crowd ended” for a set of pictures for Trump.
Views of the crowds at the inaugurations of former US President Barack Obama in 2009 (left) and President Donald Trump in 2017 (right). AP
But what about edits that only alter pixel values such as color, saturation, or contrast?
One historical example illustrates the consequences of this. In 1994, Time magazine’s cover of OJ Simpson considerably “darkened” Simpson in his police mugshot. This added fuel to a case already plagued by racial tension, to which the magazine responded, “No racial implication was intended, by Time or by the artist.”
Tools for Debunking Digital Fakery
For those of us who don’t want to be duped by visual mis/disinformation, there are tools available—although each comes with its own limitations (something we discuss in our recent paper).
Invisible digital watermarking has been proposed as a solution. However, it isn’t widespread and requires buy-in from both content publishers and distributors.
Reverse image search (such as Google’s) is often free and can be helpful for identifying earlier, potentially more authentic copies of images online. That said, it’s not foolproof because it:
Relies on unedited copies of the media already being online.
Doesn’t search the entire web.
Doesn’t always allow filtering by publication time. Some reverse image search services such as TinEye support this function, but Google’s doesn’t.
Returns only exact matches or near-matches, so it’s not thorough. For instance, editing an image and then flipping its orientation can fool Google into thinking it’s an entirely different one.
Most Reliable Tools Are Sophisticated
Meanwhile, manual forensic detection methods for visual mis/disinformation focus mostly on edits visible to the naked eye, or rely on examining features that aren’t included in every image (such as shadows). They’re also time-consuming, expensive, and need specialized expertise.
Still, you can access work in this field by visiting sites such as Snopes.com—which has a growing repository of “fauxtography.”
Computer vision and machine learning also offer relatively advanced detection capabilities for images and videos. But they too require technical expertise to operate and understand.
Moreover, improving them involves using large volumes of “training data,” but the image repositories used for this usually don’t contain the real-world images seen in the news.
If you use an image verification tool such as the REVEAL project’s image verification assistant, you might need an expert to help interpret the results.
The good news, however, is that before turning to any of the above tools, there are some simple questions you can ask yourself to potentially figure out whether a photo or video on social media is fake. Think:
Was it originally made for social media?
How widely and for how long was it circulated?
What responses did it receive?
Who were the intended audiences?
Quite often, the logical conclusions drawn from the answers will be enough to weed out inauthentic visuals. You can access the full list of questions, put together by Manchester Metropolitan University experts, here.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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#436944 Is Digital Learning Still Second Best?
As Covid-19 continues to spread, the world has gone digital on an unprecedented scale. Tens of thousands of employees are working from home, and huge conferences, like the Google I/O and Apple WWDC software extravaganzas, plan to experiment with digital events.
Universities too are sending students home. This might have meant an extended break from school not too long ago. But no more. As lecture halls go empty, an experiment into digital learning at scale is ramping up. In the US alone, over 100 universities, from Harvard to Duke, are offering online classes to students to keep the semester going.
While digital learning has been improving for some time, Covid-19 may not only tip us further into a more digitally connected reality, but also help us better appreciate its benefits. This is important because historically, digital learning has been viewed as inferior to traditional learning. But that may be changing.
The Inversion
We often think about digital technologies as ways to reach people without access to traditional services—online learning for children who don’t have schools nearby or telemedicine for patients with no access to doctors. And while these solutions have helped millions of people, they’re often viewed as “second best” and “better than nothing.” Even in more resource-rich environments, there’s an assumption one should pay more to attend an event in person—a concert, a football game, an exercise class—while digital equivalents are extremely cheap or free. Why is this? And is the situation about to change?
Take the case of Dr. Sanjeev Arora, a professor of medicine at the University of New Mexico. Arora started Project Echo because he was frustrated by how many late-stage cases of hepatitis C he encountered in rural New Mexico. He realized that if he had reached patients sooner, he could have prevented needless deaths. The solution? Digital learning for local health workers.
Project Echo connects rural healthcare practitioners to specialists at top health centers by video. The approach is collaborative: Specialists share best practices and work through cases with participants to apply them in the real world and learn from edge cases. Added to expert presentations, there are lots of opportunities to ask questions and interact with specialists.
The method forms a digital loop of learning, practice, assessment, and adjustment.
Since 2003, Project Echo has scaled to 800 locations in 39 countries and trained over 90,000 healthcare providers. Most notably, a study in The New England Journal of Medicine found that the outcomes of hepatitis C treatment given by Project Echo trained healthcare workers in rural and underserved areas were similar to outcomes at university medical centers. That is, digital learning in this context was equivalent to high quality in-person learning.
If that is possible today, with simple tools, will they surpass traditional medical centers and schools in the future? Can digital learning more generally follow suit and have the same success? Perhaps. Going digital brings its own special toolset to the table too.
The Benefits of Digital
If you’re training people online, you can record the session to better understand their engagement levels—or even add artificial intelligence to analyze it in real time. Ahura AI, for example, founded by Bryan Talebi, aims to upskill workers through online training. Early study of their method suggests they can significantly speed up learning by analyzing users’ real-time emotions—like frustration or distraction—and adjusting the lesson plan or difficulty on the fly.
Other benefits of digital learning include the near-instantaneous download of course materials—rather than printing and shipping books—and being able to more easily report grades and other results, a requirement for many schools and social services organizations. And of course, as other digitized industries show, digital learning can grow and scale further at much lower costs.
To that last point, 360ed, a digital learning startup founded in 2016 by Hla Hla Win, now serves millions of children in Myanmar with augmented reality lesson plans. And Global Startup Ecosystem, founded by Christine Souffrant Ntim and Einstein Kofi Ntim in 2015, is the world’s first and largest digital accelerator program. Their entirely online programs support over 1,000 companies in 90 countries. It’s astonishing how fast both of these organizations have grown.
Notably, both examples include offline experiences too. Many of the 360ed lesson plans come with paper flashcards children use with their smartphones because the online-offline interaction improves learning. The Global Startup Ecosystem also hosts about 10 additional in-person tech summits around the world on various topics through a related initiative.
Looking further ahead, probably the most important benefit of online learning will be its potential to integrate with other digital systems in the workplace.
Imagine a medical center that has perfect information about every patient and treatment in real time and that this information is (anonymously and privately) centralized, analyzed, and shared with medical centers, research labs, pharmaceutical companies, clinical trials, policy makers, and medical students around the world. Just as self-driving cars can learn to drive better by having access to the experiences of other self-driving cars, so too can any group working to solve complex, time-sensitive challenges learn from and build on each other’s experiences.
Why This Matters
While in the long term the world will likely end up combining the best aspects of traditional and digital learning, it’s important in the near term to be more aware of the assumptions we make about digital technologies. Some of the most pioneering work in education, healthcare, and other industries may not be highly visible right now because it is in a virtual setting. Most people are unaware, for example, that the busiest emergency room in rural America is already virtual.
Once they start converging with other digital technologies, these innovations will likely become the mainstream system for all of us. Which raises more questions: What is the best business model for these virtual services? If they start delivering better healthcare and educational outcomes than traditional institutions, should they charge more? Hopefully, we will see an even bigger shift occurring, in which technology allows us to provide high quality education, healthcare, and other services to everyone at more affordable prices than today.
These are some of the topics we can consider as Covid-19 forces us into uncharted territory.
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#436484 If Machines Want to Make Art, Will ...
Assuming that the emergence of consciousness in artificial minds is possible, those minds will feel the urge to create art. But will we be able to understand it? To answer this question, we need to consider two subquestions: when does the machine become an author of an artwork? And how can we form an understanding of the art that it makes?
Empathy, we argue, is the force behind our capacity to understand works of art. Think of what happens when you are confronted with an artwork. We maintain that, to understand the piece, you use your own conscious experience to ask what could possibly motivate you to make such an artwork yourself—and then you use that first-person perspective to try to come to a plausible explanation that allows you to relate to the artwork. Your interpretation of the work will be personal and could differ significantly from the artist’s own reasons, but if we share sufficient experiences and cultural references, it might be a plausible one, even for the artist. This is why we can relate so differently to a work of art after learning that it is a forgery or imitation: the artist’s intent to deceive or imitate is very different from the attempt to express something original. Gathering contextual information before jumping to conclusions about other people’s actions—in art, as in life—can enable us to relate better to their intentions.
But the artist and you share something far more important than cultural references: you share a similar kind of body and, with it, a similar kind of embodied perspective. Our subjective human experience stems, among many other things, from being born and slowly educated within a society of fellow humans, from fighting the inevitability of our own death, from cherishing memories, from the lonely curiosity of our own mind, from the omnipresence of the needs and quirks of our biological body, and from the way it dictates the space- and time-scales we can grasp. All conscious machines will have embodied experiences of their own, but in bodies that will be entirely alien to us.
We are able to empathize with nonhuman characters or intelligent machines in human-made fiction because they have been conceived by other human beings from the only subjective perspective accessible to us: “What would it be like for a human to behave as x?” In order to understand machinic art as such—and assuming that we stand a chance of even recognizing it in the first place—we would need a way to conceive a first-person experience of what it is like to be that machine. That is something we cannot do even for beings that are much closer to us. It might very well happen that we understand some actions or artifacts created by machines of their own volition as art, but in doing so we will inevitably anthropomorphize the machine’s intentions. Art made by a machine can be meaningfully interpreted in a way that is plausible only from the perspective of that machine, and any coherent anthropomorphized interpretation will be implausibly alien from the machine perspective. As such, it will be a misinterpretation of the artwork.
But what if we grant the machine privileged access to our ways of reasoning, to the peculiarities of our perception apparatus, to endless examples of human culture? Wouldn’t that enable the machine to make art that a human could understand? Our answer is yes, but this would also make the artworks human—not authentically machinic. All examples so far of “art made by machines” are actually just straightforward examples of human art made with computers, with the artists being the computer programmers. It might seem like a strange claim: how can the programmers be the authors of the artwork if, most of the time, they can’t control—or even anticipate—the actual materializations of the artwork? It turns out that this is a long-standing artistic practice.
Suppose that your local orchestra is playing Beethoven’s Symphony No 7 (1812). Even though Beethoven will not be directly responsible for any of the sounds produced there, you would still say that you are listening to Beethoven. Your experience might depend considerably on the interpretation of the performers, the acoustics of the room, the behavior of fellow audience members or your state of mind. Those and other aspects are the result of choices made by specific individuals or of accidents happening to them. But the author of the music? Ludwig van Beethoven. Let’s say that, as a somewhat odd choice for the program, John Cage’s Imaginary Landscape No 4 (March No 2) (1951) is also played, with 24 performers controlling 12 radios according to a musical score. In this case, the responsibility for the sounds being heard should be attributed to unsuspecting radio hosts, or even to electromagnetic fields. Yet, the shaping of sounds over time—the composition—should be credited to Cage. Each performance of this piece will vary immensely in its sonic materialization, but it will always be a performance of Imaginary Landscape No 4.
Why should we change these principles when artists use computers if, in these respects at least, computer art does not bring anything new to the table? The (human) artists might not be in direct control of the final materializations, or even be able to predict them but, despite that, they are the authors of the work. Various materializations of the same idea—in this case formalized as an algorithm—are instantiations of the same work manifesting different contextual conditions. In fact, a common use of computation in the arts is the production of variations of a process, and artists make extensive use of systems that are sensitive to initial conditions, external inputs, or pseudo-randomness to deliberately avoid repetition of outputs. Having a computer executing a procedure to build an artwork, even if using pseudo-random processes or machine-learning algorithms, is no different than throwing dice to arrange a piece of music, or to pursuing innumerable variations of the same formula. After all, the idea of machines that make art has an artistic tradition that long predates the current trend of artworks made by artificial intelligence.
Machinic art is a term that we believe should be reserved for art made by an artificial mind’s own volition, not for that based on (or directed towards) an anthropocentric view of art. From a human point of view, machinic artworks will still be procedural, algorithmic, and computational. They will be generative, because they will be autonomous from a human artist. And they might be interactive, with humans or other systems. But they will not be the result of a human deferring decisions to a machine, because the first of those—the decision to make art—needs to be the result of a machine’s volition, intentions, and decisions. Only then will we no longer have human art made with computers, but proper machinic art.
The problem is not whether machines will or will not develop a sense of self that leads to an eagerness to create art. The problem is that if—or when—they do, they will have such a different Umwelt that we will be completely unable to relate to it from our own subjective, embodied perspective. Machinic art will always lie beyond our ability to understand it because the boundaries of our comprehension—in art, as in life—are those of the human experience.
This article was originally published at Aeon and has been republished under Creative Commons.
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