# data[].attributes.body
Artwork by Ryan Blackwell in collaboration with Aleksi Halttunen and Joseph Scalera exploring digital uncanny valley.

# data[].attributes.attachment_link.title
Psyche | Art Installation 2021 | Ryan Blackwell  & Aleksi Haltunnen

# data[].attributes.body
Free booklet exploring the uncanny in relation to the Psyche Project

# data[].attributes.attachment_link.title
Exploring Kyoto's Sagano Bamboo Forest - CNN.com

# data[].attributes.attachment_link.description
Facial Recognition is a free zine using AI to create imagery. This zine is part of the Psyche art project developed by Ryan Blackwell and Aleksi Halttunen

# data[].attributes.attachment_link.title
Uncanny Valley: Being Human in the Age of AI - Announcements - e-flux

# data[].attributes.attachment_link.description
Uncanny Valley: Being Human in the Age of AI at the de Young Museum, San Francisco examines the current applications of AI as a challenge to traditional understandings of the human–machine relationship

# data[].attributes.attachment_link.readable_text
Mick Walters opens a door in his lab and points his computer's camera towards the small, blurry, tan-coloured object he has just revealed. "This is Kaspar Two," he says. As the Skype connection catches up, an image of a robot in a baseball hat, a blue button-down shirt and striped socks appears. Kaspar Two is a robot child. He's not even on, just sitting slumped over. Even though the image is somewhat fuzzy, Kaspar Two is able to give me that feeling, that nagging sense of unease. "I must admit," says Walters, "when I first actually built Kaspar, I did think he was a bit uncanny."

Kaspar has been created at University of Hertfordshire, UK to help children with autism understand how to read emotions and engage with other people, but it falls into what's often called "the uncanny valley". From humanoid robot heads to super-realistic prosthetic hands, the uncanny valley is where robots that give us the creeps live. It is the range between obvious cartoons and discernibly real people, where things look almost lifelike, and yet not quite believable. Peering into the uncanny valley is an uncomfortable experience. Its residents, like Kaspar, have a way of eliciting feelings of disgust, fear or dread.

For almost 30 years, the concept of the uncanny valley has acted as a golden rule for roboticists and animators. From Pixar to puppets, creating characters that are too lifelike was thought to be the kiss of death for any project. But now the concept itself is coming under scrutiny like never before. What exactly we are feeling and why we feel this way are questions that have finally found their way under the microscope. And some researchers are asking whether the uncanny valley exists at all.
What's in a name?
The first time many people encountered the concept of the uncanny valley was in 2001 with the movie Final Fantasy: The Spirits Within. Today, it is known as one of the first photorealistic computer animated films, but at the time not everyone was impressed. The groundbreaking graphics made many movie-goers uncomfortable, and the film flopped, losing Columbia Pictures $52 million. The faces were too human, too close to real life. "At first it's fun to watch the characters," film critic Peter Travers wrote in Rolling Stone. "But then you notice a coldness in the eyes, a mechanical quality in the movements."

A link between what is almost human and what is creepy was proposed long before Final Fantasy, however. The phrase "uncanny valley" is widely accepted to have originated in 1970, with the publication of an academic paper by roboticist Masahiro Mori in an obscure journal called Energy. Mori's original paper was in Japanese. Contrary to popular belief, his original title "Bukimi No Tani" only roughly translates into the phrase it has made famous. A more accurate translation is "valley of eeriness".

This matters because it demonstrates the problem with the uncanny valley: it is an inherently woolly idea. When researchers try to study the phenomenon, they often have a hard time pinning down what an uncanny response actually looks like. The main graph in Mori's paper has been mistranslated many times, leaving many people unsure what he really meant. Mori used the Japanese word "shinwakan" on the y-axis, a word that has no direct translation into English. The most common interpretation is "likeability", but not all translators agree about that. Other suggestions include "familiarity", "affinity", and "comfort level".

Perhaps the most surprising thing about the concept's history, though, isn't the translation troubles, nor the debate over what is being represented on his graph, but how long it took for that debate to arise. Mori's paper didn't include any measurements. It was more an essay than a study. Yet, despite broad dissemination, the uncanny valley avoided scientific scrutiny until the early 2000's, when graphics and animatronics like Final Fantasy started giving people the creeps. As scientists started to explore Mori's graph, they began to ask whether real data would reveal the same pattern.
Spot of bother
A few studies have asserted that the whole thing doesn't exist. In one study, David Hanson of Hanson Robotics, in Plano, Texas, and his colleagues showed participants images of two different robots that were animated to simulate human-like facial expressions. The survey simply asked the participants what they thought of the experience. The vast majority (73%) liked the human-like robots. In fact, not one person stated that these robots disturbed them.

Hanson and his team then showed the participants a continuum of images, starting with a picture of Princess Jasmine taken from the Disney movie Aladdin. Over the course of six images, Jasmine's face slowly morphed into that of actress Jennifer Love Hewitt. The idea of these facial progression studies is to try to observe the dip in likeability that Mori predicted between an obviously cartoon image and an obviously human one. The participants were asked to rank the acceptability of each picture in the series. But, again, rather than see a dip in the scores in the middle of the range - as the uncanny valley would predict - none of the images seemed to bother anyone.

Why this happened isn't clear, and not everyone thinks Hanson's experiment is robust. Many other studies have shown the opposite. For example, Edward Schneider's lab at SUNY Potsdam in New York collected 75 existing characters from video games and animation, including Hello Kitty, Mickey Mouse, Snoopy and Lara Croft. They asked participants how human and how attractive (or repulsive) they perceived each character to be. In this case, the researchers did find a dip in likeability in the middle of the series, roughly where the ogres from World of Warcraft sit.

Moreover, a team lead by Karl MacDorman at Indiana University conducted an experiment similar to Hanson's, using a progression of images in which a robot face slowly morphs into a human one. They, too, found a U-shaped dip in likeability in the middle of their 11-image series.

However, among the labs that have observed the uncanny valley, there is strong debate about its shape. Christoph Bartneck, a robotics researcher at Canterbury University in New Zealand says that, based on his studies, a valley might be the wrong geological metaphor altogether. "As far as we can tell," he says, "it looks more like a cliff." Essentially, he says, at the point where robots achieve extreme human-likeness, but remain discernibly un-human, their likeability plummets. And people only start to like them again when they become so human-like that they escape detection.

To make things even more complicated, there's nothing that proves the uncanny valley reflects gradations of the same reaction. It could be a handful of reactions to different aspects of having a varying degree of human-likeness. When MacDorman showed his subjects videos of many different robots, the responses followed no clear pattern. "The results do not indicate a single uncanny valley for a particular range of human-likeness," MacDorman's wrote in the paper. "Rather, they suggest that human-likeness is only one of perhaps many factors influencing the extent to which a robot is perceived as being strange, familiar, or eerie."
Basic instincts
Questions about what reaction (or reactions) cause the uncanny valley (or, indeed, the uncanny cliff) quickly lead to other questions about why we react at all.

There are a few explanations that might account for our strange aversion to humanoid robots. One is that not being able to tell whether something is human or not can be a deeply unsettling feeling in itself. Artists and directors take advantage of this all the time for dramatic effect. The dread that viewers feel while trying to figure out who is a zombie, or Cylon, or alien might be the very same dread they feel when faced with a very realistic robot.

Another explanation focuses on the disconnect between how realistic something looks, and how well it moves. There's always been a lag time between how quickly designers can make things look like people, and how quickly engineers can make them move like us. If a figure that you thought was human started to move jerkily, you would recoil. Similarly, if you were to shake a robot's hand while expecting a human touch, but instead felt cold rubber, you would be caught of guard. An unexpected break in humanness can be an unpleasant shock, one that sets off fearful and distrustful instincts. "Whenever we see something move, and we're not familiar with the mechanism of movement, it grabs our attention," says Andrew Olney, a psychologist at the University of Memphis who works on designing intelligent robots. "If your coffee cup started slowly moving across the table, that would kind of freak you out a bit."

Finally, a third theory turns to evolution. It suggests that if a robot looks like a human, but moves unnaturally, our brains subconsciously classify what we're seeing as someone with a disease. This is the same explanation proposed for most feelings of disgust. When we stand near something like faeces, rotting flesh, or a jerking robot, we experience a sudden urge to get away from it so as to avoid catching the infections it may harbour. Some preliminary research in rhesus monkeys suggests that these animals share an uncanny valley-like response, indicating that they have perhaps adapted to the same evolutionary pressure in the same way as us.
Ultimate challenge
We are, of course, becoming more accustomed to robots and avatars in everyday life. Between games like Last of Us and movies like Avatar, we see computer-generated images of people all the time. Mori's original examples of uncanny objects, like a wooden prosthetic hand, probably wouldn't raise an eyebrow today because they are so obviously fake. Final Fantasy no longer triggers unsettling feelings among younger viewers, who are used to games like Crysis and Witcher 2. The shift in expectations has been going on all along - and might well continue until technology is good enough to fool us.

This trend sets up a roboticist's ultimate challenge: to be the first person to build a robot that is indistinguishably human to other humans. It is a challenge that Hiroshi Ishiguro, one of the world's leading humanoid roboticists, believes he will one day meet. Some of his humanoid robots already interact with people, and some robot designers treat them as if they were human. When one roboticist named Peter Kahn visited Karl MacDorman's human-computer interaction lab at Indiana University and wanted to take apart Ishiguro's Repliee Q1Expo, a petite Japanese humanoid-woman in a pink blazer, he first turned to his wife and asked, "May I touch her?"

But not everyone is convinced that we'll engineer our way out of the uncanny valley, or that it is a good thing if we do. While what makes us uncomfortable is likely to shift, the presence of discomfort won't, they argue. Potentially, it could get worse. "You can imagine cases, for example, maybe 50 years down the road, someone might be in a relationship with an android and not know it," says MacDorman. "But if there were an accident and some of the mechanical underpinnings are exposed, that would be uncanny. It would be uncanny in a different way."
If you would like to comment on this article or anything else you have seen on Future, head over to our Facebook page or message us on .

# data[].attributes.attachment_link.description
Mick Walters opens a door in his lab and points his computer's camera towards the small, blurry, tan-coloured object he has just revealed. "This is Kaspar Two," he says. As the Skype connection catches up, an image of a robot in a baseball hat, a blue button-down shirt and striped socks appears.

# data[].attributes.body
SYNOPSIS:

Psyche explores the uncanny nature of the digital sphere by injecting it into the physical world. The complex algorithmic processes that make up the digital sphere are often masked by comforting user interfaces and speech synthesis mimicking the human form. This representation that we identify as ‘the internet' can quickly fall into the uncanny valley when questioning what processes lurk beneath this pleasurable aesthetic which users engage with. Psyche contextualises the fear of digital exposure through a hyperrealist personification of a computer vision algorithm if it were some kind of 80s horror monster. Using a generative video manipulation method, artificial subjects are projected onto synthetic plastics and step foot into the real world.

In 21st century western society, it is seemingly impossible not to adopt digital technology for the majority of social communication, information sourcing, and entertainment. Now the first generation of people who don’t know a world without internet have reached adulthood. As we become increasingly reliant on such a way of existing, the internet continues to become a comforting extension of our collective and individual psyche in day-to-day life. The internet’s growing impact on society raises many fears; doxxing, hacking, fraud, stalkers, polarised political forces and so much more. Psyche confronts the viewer by bringing this tech monster into the physical world, the digital sphere falls into the uncanny valley by questioning our individual safety from this monster which knows more about us, than we do of it.

TEAM:

Ryan Blackwell: Director

Aleksi Haltunnen: Creative Coder

Joseph Scalera: Sound Design

Ganesha Lock Hart: Cast

Alina zum Hebel: Cast

# data[].attributes.attachment_link.title
Why Artists Love the Eerie Sensation of Characters That Look Almost Human

# data[].attributes.attachment_link.description
Something strange happens when we see a creature that is almost—but not quite—human. Here’s how artists have delighted in what’s known as the “uncanny valley.”

# data[].attributes.body
Heidegger draws attention to technology's place in bringing about our decline by constricting our experience of things as they are. He argues that we now view nature, and increasingly human beings too, only technologically - that is, we see nature and people only as raw material for technical operations. Heidegger seeks to illuminate this phenomenon and to find a way of thinking by which we might be saved from its controlling power, to which, he believes, modern civilisation both in the communist East and the democratic West has been shackled. We might escape this bondage, Heidegger argues, not by rejecting technology, but by perceiving its danger.

# data[].attributes.attachment_link.title
Understanding Heidegger on Technology on JSTOR

# data[].attributes.attachment_link.description
Journal Information The New Atlantis is a quarterly journal about the social, ethical, and political dimensions of modern science and technology. Since its founding in 2003, the journal has been at the center of debates about how to govern science and how to live well and wisely with technological change.

# data[].attributes.attachment_link.readable_text
Uncanny valley: artistic visions of our AI future

As algorithms and data-sets increasingly shape our lives, artists are exploring our collective concerns around artificial intelligence

Our current situation has ushered us into a new, technology-driven relationship with the art world. The virtual exhibitions and augmented reality experiences from museums and galleries across the globe have accelerated our path into a digitised future, albeit a temporary one.

But as our world relies further on technology and algorithms to shape every aspect of our lives, how will our relationship with machines evolve? Where will our humanity begin and end? And how might this data-driven reality risk reinforcing our worst fears about the future?

These are some of the questions posed by 'Uncanny Valley: Being Human in the Age of AI', the first major contemporary art exhibition exploring A.I. at the de Young museum in San Francisco and currently accessible via its Instagram page.

Bringing together 13 artists and activist collectives including Zach Blas, Ian Cheng, Stephanie Dinkins, Forensic Architecture, Lynn Hershman Leeson, Trevor Paglen and Martine Syms, the works leverage immersive technology to explore how machine learning, data sets, cloud infrastructure and search engines are transforming our existence as we know it.

'Technology is changing our world, with artificial intelligence both a new frontier of possibility but also a development fraught with anxiety'

'Uncanny Valley' was a term first coined fifty years ago by Masahiro Mori, a robotics professor at the Tokyo Institute of Technology in an essay where he examined the apprehensive reaction humans have to robots who look and act just like us. It has taken on a new meaning in today's world of digital alter egos, deep fakes and AR influencers, where the lines between man and machine have become increasingly blurred.

'Technology is changing our world, with artificial intelligence both a new frontier of possibility but also a development fraught with anxiety' says Thomas P. Campbell, Director and CEO of the Fine Arts Museums of San Francisco. The socio-economic and moral implications of A.I. technologies are explored throughout several of the works.

Based on an unrealised design by Amazon to contain human workers in its warehouses, Simon Denny's Amazon worker cage patent drawing as virtual King Island Brown Thornbill cage, US 9,280,157 B2: System and method for transporting personnel within an active workspace, (2016) stands at seven feet tall. With just enough space to turn around, the cage-like structure houses an augmented reality application that displays a bird that is almost extinct, the King Island brown thornbill. Featuring a joystick control and a metal claw to pick up orders, the piece questions the future of labour, and whether the human worker is not too long from finding themselves rendered obsolete.

Silicon Valley's crowdsourcing marketplaces and their invisible armies of human labour are critiqued in Agnieszka Kurant's A.A.I. (artificial artificial intelligence), an installation of multiple termite mounds of coloured sand, gold, glitter, and crystals that shines a light on the layers of human beings hidden behind these advancements.

Zach Blas has created an artificial garden inspired by those found on tech campuses to highlight how these companies appropriate ideas from counterculture to drive their commercial ambitions. The garden space is surrounded by video screens that play out perception-altering visuals - a kind of algorithmic psychedelia.

Elsewhere, the risks that artificial intelligence poses in reinforcing racial, gender and class divisions are explored in Lynn Hershman Leeson's Shadow Stalker (2019). Allowing viewers to see their digital footprints and urging them to take control of their data, it looks at the reliance on algorithmic systems in policing that categorise individuals based on inaccurate 'embodied metrics'.

Stephanie Dinkins' Bina48 is a robot that has no understanding of race, gender or class. Her conversations with it illuminate the darker side of A.I. programming - how can a machine be used to make decisions if it has no awareness or sensitivity to history or social equity?

A dystopian future is not the only prediction offered here though. In Martine Syms' Mythiccbeing, the possibilities for A.I. to be used to redress these same imbalances are suggested. Syms has created an Alexa-like avatar who listens and responds to instances of racial inequality and social injustice in everyday life, a 'woke' companion who challenges its human subjects to aim towards fairness.

No longer the stuff of science-fiction movies or a far-off moment in time, the 'Uncanny Valley' that Masahiro Mori foresaw in 1970, is now a place that we inhabit. Our friends are electric. Do they control us or do we control them? The fast-moving path to an A.I. future must be powered not only by data and neural networks but by humanity, ethics and heart. §

# data[].attributes.attachment_link.title
Uncanny valley: artistic visions of our AI future

# data[].attributes.attachment_link.description
Uncanny valley: artistic visions of our AI future As algorithms and data-sets increasingly shape our lives, artists are exploring our collective concerns around artificial intelligence Our current situation has ushered us into a new, technology-driven relationship with the art world.

# data[].attributes.body
"A world awash in AI-generated content is a classic case of a utopia that is also a dystopia. It’s messy, it’s beautiful, and it’s already here." - Hrybyk

# data[].attributes.attachment_link.readable_text
There's a revolution afoot, and you will know it by the stripes.

Earlier this year, a group of Berkeley researchers released a pair of videos. In one, a horse trots behind a chain link fence. In the second video, the horse is suddenly sporting a zebra's black-and-white pattern. The execution isn't flawless, but the stripes fit the horse so neatly that it throws the equine family tree into chaos.

Turning a horse into a zebra is a nice stunt, but that's not all it is. It is also a sign of the growing power of machine learning algorithms to rewrite reality. Other tinkerers, for example, have used the zebrafication tool to turn shots of black bears into believable photos of pandas, apples into oranges, and cats into dogs. A Redditor used a different machine learning algorithm to edit porn videos to feature the faces of celebrities. At a new startup called Lyrebird, machine learning experts are synthesizing convincing audio from one-minute samples of a person's voice. And the engineers developing Adobe's artificial intelligence platform, called Sensei, are infusing machine learning into a variety of groundbreaking video, photo, and audio editing tools. These projects are wildly different in origin and intent, yet they have one thing in common: They are producing artificial scenes and sounds that look stunningly close to actual footage of the physical world. Unlike earlier experiments with AI-generated media, these look and sound real.

Sandra Upson is Backchannel's executive editor.

Sign up to get Backchannel's weekly newsletter, and follow us on Facebook, Twitter, and Instagram.

The technologies underlying this shift will soon push us into new creative realms, amplifying the capabilities of today's artists and elevating amateurs to the level of seasoned pros. We will search for new definitions of creativity that extend the umbrella to the output of machines. But this boom will have a dark side, too. Some AI-generated content will be used to deceive, kicking off fears of an avalanche of algorithmic fake news. Old debates about whether an image was doctored will give way to new ones about the pedigree of all kinds of content, including text. You'll find yourself wondering, if you haven't yet: What role did humans play, if any, in the creation of that album/TV series/clickbait article?

A world awash in AI-generated content is a classic case of a utopia that is also a dystopia. It's messy, it's beautiful, and it's already here.

Currently there are two ways to produce audio or video that resembles the real world. The first is to use cameras and microphones to record a moment in time, such as the original Moon landing. The second is to leverage human talent, often at great expense, to commission a facsimile. So if the Moon descent had been a hoax, a skilled film team would have had to carefully stage Neil Armstrong's lunar gambol. Machine learning algorithms now offer a third option, by letting anyone with a modicum of technical knowledge algorithmically remix existing content to generate new material.

At first, deep-learning-generated content wasn't geared toward photorealism. Google's Deep Dreams, released in 2015, was an early example of using deep learning to crank out psychedelic landscapes and many-eyed grotesques. In 2016, a popular photo editing app called Prisma used deep learning to power artistic photo filters, for example turning snapshots into an homage to Mondrian or Munch. The technique underlying Prisma is known as style transfer: take the style of one image (such as The Scream) and apply it to a second shot.

Now the algorithms powering style transfer are gaining precision, signalling the end of the Uncanny Valley-the sense of unease that realistic computer-generated humans typically elicit. In contrast to the previous somewhat crude effects, tricks like zebrafication are starting to fill in the Valley's lower basin. Consider the work from Kavita Bala's lab at Cornell, where deep learning can infuse one photo's style, such as a twinkly nighttime ambience, into a snapshot of a drab metropolis-and fool human reviewers into thinking the composite place is real. Inspired by the potential of artificial intelligence to discern aesthetic qualities, Bala cofounded a company called Grokstyle around this idea. Say you admired the throw pillows on a friend's couch or a magazine spread caught your eye. Feed Grokstyle's algorithm an image, and it will surface similar objects with that look.

"What I like about these technologies is they are democratizing design and style," Bala says. "I'm a technologist-I appreciate beauty and style but can't produce it worth a damn. So this work makes it available to me. And there's a joy in making it available to others, so people can play with beauty. Just because we are not gifted on this certain axis doesn't mean we have to live in a dreary land."

Dig Deeper with Our Longreads Newsletter

Sign up to get our best longform features, investigations, and thought-provoking essays, in your inbox every Sunday.

At Adobe, machine learning has been a part of the company's creative products for well over a decade, but only recently has AI become transformative. In October engineers working on Sensei, the company's set of AI technologies, showed off a prospective video editing tool called Adobe Cloak, which allows its user to seamlessly remove, say, a lamppost from a video clip-a task that would ordinarily be excruciating for an experienced human editor. Another experiment, called Project Puppetron, applies an artistic style to a video in real time. For example, it can take a live feed of a person and render him as a chatty bronze statue or a hand-drawn cartoon. "People can basically do a performance in front of a web cam or any camera and turn that into animation, in real time," says Jon Brandt, senior principal scientist and director of Adobe Research. (Sensei's experiments don't always turn into commercial products.)

Machine learning makes these projects possible because it can understand the parts of a face or the difference between foreground and background better than previous approaches in computer vision. Sensei's tools let artists work with concepts, rather than the raw material. "Photoshop is great at manipulating pixels, but what people are trying to do is manipulate the content that is represented by the pixels," Brandt explains.

That's a good thing. When artists no longer waste their time wrangling individual dots on a screen, their productivity increases, and perhaps also their ingenuity, says Brandt. "I am excited about the possibility of new art forms emerging, which I expect will be coming."

But it's not hard to see how this creative explosion could all go very wrong. For Yuanshun Yao, a University of Chicago graduate student, it was a fake video that set him on his recent project probing some of the dangers of machine learning. He had hit play on a recent clip of an AI-generated, very real-looking Barack Obama giving a speech, and got to thinking: Could he do a similar thing with text?

More Predictions for 2018

A text composition needs to be nearly perfect to deceive most readers, so he started with a forgiving target, fake online reviews for platforms like Yelp or Amazon. A review can be just a few sentences long, and readers don't expect high-quality writing. So he and his colleagues designed a neural network that spat out Yelp-style blurbs of about five sentences each. Out came a bank of reviews that declared such things as, "Our favorite spot for sure!" and "I went with my brother and we had the vegetarian pasta and it was delicious." He asked humans to then guess whether they were real or fake, and sure enough, the humans were often fooled.

With fake reviews costing around $10 to $50 each from micro-task marketplaces, Yao figured it was just a matter of time before a motivated engineer tried to automate the process, driving down the price and kicking off a plague of false reviews. (He also explored using neural nets to defend a platform against fake content, with some success.) "As far as we know there are not any such systems, yet," Yao says. "But maybe in five or ten years, we will be surrounded by AI-generated stuff." His next target? Generating convincing news articles.

Progress on videos may move faster. Hany Farid, an expert at detecting fake photos and videos and a professor at Dartmouth, worries about how fast viral content spreads, and how slow the verification process is. Farid imagines a near future in which a convincing fake video of President Trump ordering the total nuclear annihilation of North Korea goes viral and incites panic, like a recast War of the Worlds for the AI era. "I try not to make hysterical predictions, but I don't think this is far-fetched," he says. "This is in the realm of what's possible today."

Fake Trump speeches are already circulating on the internet, a product of Lyrebird, the voice synthesis startup-though in the audio clips the company has shared with the public, Trump keeps his finger off the button, limiting himself to praising Lyrebird. Jose Sotelo, the company's cofounder and CEO, argues that the technology is inevitable, so he and his colleagues might as well be the ones to do it, with ethical guidelines in place. He believes that the best defense, for now, is raising awareness of what machine learning is capable of. "If you were to see a picture of me on the moon, you would think it's probably some image editing software," Sotelo says. "But if you hear convincing audio of your best friend saying bad things about you, you might get worried. It's a really new technology and a really challenging problem."

Likely nothing can stop the coming wave of AI-generated content-if we even wanted to. At its worst, scammers and political operatives will deploy machine learning algorithms to generate untold volumes of misinformation. Because social networks selectively transmit the most attention-grabbing content, these systems' output will evolve to be maximally likeable, clickable, and shareable.

But at its best, AI-generated content is likely to heal our social fabric in as many ways as it may rend it. Sotelo of Lyrebird dreams of how his company's technology could restore speech to people who have lost their voice to diseases such as ALS or cancer. That horse-to-zebra video out of Berkeley? It was a side effect of work to improve how we train self-driving cars. Often, driving software is trained in virtual environments first, but a world like Grand Theft Auto only roughly resembles reality. The zebrafication algorithm was designed to shrink the distance between the virtual environment and the real world, ultimately making self-driving cars safer.

These are the two edges of the AI sword. As it improves, it mimics human actions more and more closely. Eventually, it has no choice but to become all too human: capable of good and evil in equal measure.

# data[].attributes.attachment_link.title
AI Is Killing the Uncanny Valley and Our Grasp on Reality | Backchannel

# data[].attributes.attachment_link.description
There's a revolution afoot, and you will know it by the stripes. Earlier this year, a group of Berkeley researchers released a pair of videos. In one, a horse trots behind a chain link fence. In the second video, the horse is suddenly sporting a zebra's black-and-white pattern.

# data[].attributes.headline
The Guardian on finding human like robots uncanny

# data[].attributes.subject
The Guardian on finding human like robots uncanny

# data[].attributes.attachment_link.readable_text
The "uncanny valley" is a characteristic dip in emotional response that happens when we encounter an entity that is almost, but not quite, human. It was first hypothesised in 1970 by the Japanese roboticist Masahiro Mori, who identified that as robots became more human-like, people would find them to be more acceptable and appealing than their mechanical counterparts. But this only held true to up a point. When they were close to, but not quite, human, people developed a sense of unease and discomfort. If human-likeness increased beyond this point, and they became very close to human, the emotional response returned to being positive. It is this distinctive dip in the relationship between human-likeness and emotional response that is called the uncanny valley.
Artificial intelligence: 'Homo sapiens will be split into a handful of gods and the rest of us'
Anything with a highly human-like appearance can be subject to the uncanny valley effect, but the most common examples are androids, computer game characters and life-like dolls.

However, not all near-human robots are eerie, and the perception of eeriness varies from person to person. So what evidence exists for the effect and what properties of near-humans might make us feel so uncomfortable?

Dolls and other human-like agents

Since 1970, the uncanny valley effect has been explored from many perspectives, from the practical interest of roboticists to theoretical approaches from philosophers and experimental studies conducted by psychologists. Research started in earnest from 2005, when Karl MacDorman and Takashi Minato translated Mori's original paper into English. Interest in the area has since expanded quickly: so far, 510 academic papers reference the effect in 2015, compared to just 35 in 2004.

Although the effect is easy to describe, and feels intuitively "right" when looking at some examples of human-like agents such as reborn dolls or game characters, it is notoriously difficult to research such a circular and subjective concept. Academics are even engaged in an active debate as to whether the uncanny valley exists at all - Jari Kätsyri and colleagues recently reviewed the evidence and drew the conclusion that the effect remains elusive.

Either way, the debate has not yet been won - although perhaps the most compelling evidence for the existence of the uncanny valley has just been published. Maya Mathur and David Reichling studied 80 real-world robots and found a clear valley effect in how much people liked and were willing to trust them.

Their data showed the classic rise-dip-rise that Mori originally predicted. This was found with the original robots and also with CGI images built to systematically vary in human likeness. The challenge now is to explain just why this might be happening. I have been researching in this area since 2006, and my own review of the literature found at least seven explanations - but at the moment there are three theories that seem particularly promising.

Where the uncanny boundary lies

First, the uncanny valley might occur at the boundary where something moves from one category to another, in this case, between non-human and human. Christine Looser and Thalia Wheatley looked at mannequin faces that were morphed into human faces and found a valley at the point where the inanimate face started to look alive.

Second, the presence of a valley may hinge on whether we're able to believe that near-human entities possess a mind, as we do. A study by Kurt Gray and Daniel Wegner found that robots were only unnerving when people thought that they had the ability to sense and experience things, and robots that did not seem to posses a mind were not frightening.

A compelling area for research is that it occurs because of mismatches between the robot's appearance and/or behaviour

A final compelling area for future research is that the uncanny valley occurs because of mismatches between aspects of the robot's appearance and/or behaviour. Angela Tinwell's work has looked at many types of mismatch, including speech synchronisation, speech speed and facial expressions. In one 2013 study, near-human agents that reacted to a startling noise by showing surprise in the lower part of their face (not the upper part) were found to be particularly eerie. The study suggested that this may even be reminiscent of the pattern of expressive behaviour exhibited by humans with psychopathic traits.

My latest research has built on Tinwell's findings and looked at responses to faces with different emotional expressions shown in the eyes and the rest of the face. I found that the eeriest combinations were those where happy faces were paired with fearful or angry eyes, possibly suggesting the agent was trying to suppress an unpleasant emotion.

Android science progresses rapidly, and is creating increasingly realistic robots. The uncanny valley would not exist if a robot were indistinguishable from a human, because there would no longer be a relative dip in emotional response.

One theory, that of an uncanny wall, suggests that it will always be possible to tell artificial from human because as robots become more realistic, we will also become more sensitive and will always be able to tell that something is not right.

However, we're certainly not there yet, and we could eventually find that the uncanny valley effect was an artefact of this particular period in the history of artificial humans, when representations were clearly distinguishable from human. For some, not being able to distinguish between robots and humans, for example, may throw up problems beyond being creepy. Speaking as a researcher of the uncanny valley, I plan to enjoy its sense of subtle eeriness while it lasts.

* This article was originally published on the Conversation, part of the Guardian Comment Network
Topics

# data[].attributes.attachment_link.title
Uncanny valley: why we find human-like robots and dolls so creepy | Stephanie Lay

# data[].attributes.attachment_link.description
The "uncanny valley" is a characteristic dip in emotional response that happens when we encounter an entity that is almost, but not quite, human. It was first hypothesised in 1970 by the Japanese roboticist Masahiro Mori, who identified that as robots became more human-like, people would find them to be more acceptable and appealing than their mechanical counterparts.

# data[].attributes.attachment_link.description
ARS ELECTRONICA HOME DELIVERY Wir kommen zu Ihnen! FUTURE THINKING SCHOOL by Ars Electronica CENTER Museum der Zukunft, Schule der Zukunft PRIX Weltweiter Wettbewerb für CyberArts FESTIVAL für Kunst, Technologie und Gesellschaft FUTURELAB Labor und Atelier für Zukunftsentw&

# data[].attributes.attachment_link.readable_text
Artificial intelligences are an interesting test case for how readers construct fictional characters in SF in general. William Gibson's and Pat Cadigan's post-cyberpunk novels offer a different emphasis with regard to the attribution of gender characteristics because of the way they employ male and female characters in figural narrative situations to focalize the emergence of the new virtual life forms.

Journal Information

The Yearbook is partly intended to provide an additional outlet for articles dealing with the literature and language of English-speaking countries submitted to The Modern Language Review, including some of the more ambitious or more specialized papers. A substantial proportion of each volume consists of specially commissioned articles on a broad topic or theme.

Publisher Information

The Modern Humanities Research Association (MHRA) is an international organization with members in all parts of the world. The Association's purpose is to encourage and promote advanced study and research in the field of the modern humanities. It is concerned to break down the barriers between scholars working in different disciplines and to maintain the unity of humanistic scholarship in the face of increasing specialization.

# data[].attributes.attachment_link.title
(En)gendering Artificial Intelligence in Cyberspace on JSTOR

# data[].attributes.attachment_link.description
Artificial intelligences are an interesting test case for how readers construct fictional characters in SF in general.

# data[].attributes.body
Additional Reading

# data[].attributes.attachment_link.readable_text
ICCV 2019, Seoul, Korea

In 2019, for the Second Workshop on Computer Vision for Fashion, Art and Design, at the International Conference on Computer Vision (ICCV) in Seoul, South Korea, a call was put out for artworks dealing with computer vision technologies.

Computer vision techniques have long been connected to the arts. From the study of visual art with computer vision techniques, to the development of new vision techniques based on insights from the arts community, to artwork generated algorithmically, there is a rich history of exchange between the fields.

In recent years the success of a new crop of generative models is creating excitement and change in vision and graphics, and these new approaches to creation have led to a rich community of artistic exploration as well, much of which is happening in online visual culture. Many artists have incorporated computer vision techniques into their artistic practice to explore novel ways of representing the human form, experiment with datasets of their own drawings and critique the machine's ability to understand the world.

Further, the outsized influence of computer vision techniques in socially important areas such as healthcare, ubiquitous surveillance, autonomous vehicles and robotics, and warfare makes a social and artistic engagement with the field vital.

Here we present works from 30+ artists from around the world, selected from an open call by the organizers. Some of these works were selected for their technical novelty, some for their aesthetic merit, some for their conceptual contribution.

Organizers

The 2019 ICCV Computer Vision Art Gallery was organized by Luba Elliott and Xavier Snelgrove.

The gallery was part of the Second Workshop on Computer Vision for Fashion, Art and Design, which was organized by Negar Rostamzadeh, Hui Wu, Ping Luo, Julia Lasserre, Xavier Snelgrove, David Vazquez, Thomas Boquet, Yuying Ge, Wayne Zhang, Leonidas Lefakis, Ruimao Zhang, Wei Zhang, and Reza Shirvany

# data[].attributes.attachment_link.description
In 2019, for the Second Workshop on Computer Vision for Fashion, Art and Design, at the International Conference on Computer Vision (ICCV) in Seoul, South Korea, a call was put out for artworks dealing with computer vision technologies. Computer vision techniques have long been connected to the arts.