Tag Archives: emotional
#433620 Instilling the Best of Human Values in ...
Now that the era of artificial intelligence is unquestionably upon us, it behooves us to think and work harder to ensure that the AIs we create embody positive human values.
Science fiction is full of AIs that manifest the dark side of humanity, or are indifferent to humans altogether. Such possibilities cannot be ruled out, but nor is there any logical or empirical reason to consider them highly likely. I am among a large group of AI experts who see a strong potential for profoundly positive outcomes in the AI revolution currently underway.
We are facing a future with great uncertainty and tremendous promise, and the best we can do is to confront it with a combination of heart and mind, of common sense and rigorous science. In the realm of AI, what this means is, we need to do our best to guide the AI minds we are creating to embody the values we cherish: love, compassion, creativity, and respect.
The quest for beneficial AI has many dimensions, including its potential to reduce material scarcity and to help unlock the human capacity for love and compassion.
Reducing Scarcity
A large percentage of difficult issues in human society, many of which spill over into the AI domain, would be palliated significantly if material scarcity became less of a problem. Fortunately, AI has great potential to help here. AI is already increasing efficiency in nearly every industry.
In the next few decades, as nanotech and 3D printing continue to advance, AI-driven design will become a larger factor in the economy. Radical new tools like artificial enzymes built using Christian Schafmeister’s spiroligomer molecules, and designed using quantum physics-savvy AIs, will enable the creation of new materials and medicines.
For amazing advances like the intersection of AI and nanotech to lead toward broadly positive outcomes, however, the economic and political aspects of the AI industry may have to shift from the current status quo.
Currently, most AI development occurs under the aegis of military organizations or large corporations oriented heavily toward advertising and marketing. Put crudely, an awful lot of AI today is about “spying, brainwashing, or killing.” This is not really the ideal situation if we want our first true artificial general intelligences to be open-minded, warm-hearted, and beneficial.
Also, as the bulk of AI development now occurs in large for-profit organizations bound by law to pursue the maximization of shareholder value, we face a situation where AI tends to exacerbate global wealth inequality and class divisions. This has the potential to lead to various civilization-scale failure modes involving the intersection of geopolitics, AI, cyberterrorism, and so forth. Part of my motivation for founding the decentralized AI project SingularityNET was to create an alternative mode of dissemination and utilization of both narrow AI and AGI—one that operates in a self-organizing way, outside of the direct grip of conventional corporate and governmental structures.
In the end, though, I worry that radical material abundance and novel political and economic structures may fail to create a positive future, unless they are coupled with advances in consciousness and compassion. AGIs have the potential to be massively more ethical and compassionate than humans. But still, the odds of getting deeply beneficial AGIs seem higher if the humans creating them are fuller of compassion and positive consciousness—and can effectively pass these values on.
Transmitting Human Values
Brain-computer interfacing is another critical aspect of the quest for creating more positive AIs and more positive humans. As Elon Musk has put it, “If you can’t beat ’em, join’ em.” Joining is more fun than beating anyway. What better way to infuse AIs with human values than to connect them directly to human brains, and let them learn directly from the source (while providing humans with valuable enhancements)?
Millions of people recently heard Elon Musk discuss AI and BCI on the Joe Rogan podcast. Musk’s embrace of brain-computer interfacing is laudable, but he tends to dodge some of the tough issues—for instance, he does not emphasize the trade-off cyborgs will face between retaining human-ness and maximizing intelligence, joy, and creativity. To make this trade-off effectively, the AI portion of the cyborg will need to have a deep sense of human values.
Musk calls humanity the “biological boot loader” for AGI, but to me this colorful metaphor misses a key point—that we can seed the AGI we create with our values as an initial condition. This is one reason why it’s important that the first really powerful AGIs are created by decentralized networks, and not conventional corporate or military organizations. The decentralized software/hardware ecosystem, for all its quirks and flaws, has more potential to lead to human-computer cybernetic collective minds that are reasonable and benevolent.
Algorithmic Love
BCI is still in its infancy, but a more immediate way of connecting people with AIs to infuse both with greater love and compassion is to leverage humanoid robotics technology. Toward this end, I conceived a project called Loving AI, focused on using highly expressive humanoid robots like the Hanson robot Sophia to lead people through meditations and other exercises oriented toward unlocking the human potential for love and compassion. My goals here were to explore the potential of AI and robots to have a positive impact on human consciousness, and to use this application to study and improve the OpenCog and SingularityNET tools used to control Sophia in these interactions.
The Loving AI project has now run two small sets of human trials, both with exciting and positive results. These have been small—dozens rather than hundreds of people—but have definitively proven the point. Put a person in a quiet room with a humanoid robot that can look them in the eye, mirror their facial expressions, recognize some of their emotions, and lead them through simple meditation, listening, and consciousness-oriented exercises…and quite a lot of the time, the result is a more relaxed person who has entered into a shifted state of consciousness, at least for a period of time.
In a certain percentage of cases, the interaction with the robot consciousness guide triggered a dramatic change of consciousness in the human subject—a deep meditative trance state, for instance. In most cases, the result was not so extreme, but statistically the positive effect was quite significant across all cases. Furthermore, a similar effect was found using an avatar simulation of the robot’s face on a tablet screen (together with a webcam for facial expression mirroring and recognition), but not with a purely auditory interaction.
The Loving AI experiments are not only about AI; they are about human-robot and human-avatar interaction, with AI as one significant aspect. The facial interaction with the robot or avatar is pushing “biological buttons” that trigger emotional reactions and prime the mind for changes of consciousness. However, this sort of body-mind interaction is arguably critical to human values and what it means to be human; it’s an important thing for robots and AIs to “get.”
Halting or pausing the advance of AI is not a viable possibility at this stage. Despite the risks, the potential economic and political benefits involved are clear and massive. The convergence of narrow AI toward AGI is also a near inevitability, because there are so many important applications where greater generality of intelligence will lead to greater practical functionality. The challenge is to make the outcome of this great civilization-level adventure as positive as possible.
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Posted in Human Robots
#433386 What We Have to Gain From Making ...
The borders between the real world and the digital world keep crumbling, and the latter’s importance in both our personal and professional lives keeps growing. Some describe the melding of virtual and real worlds as part of the fourth industrial revolution. Said revolution’s full impact on us as individuals, our companies, communities, and societies is still unknown.
Greg Cross, chief business officer of New Zealand-based AI company Soul Machines, thinks one inescapable consequence of these crumbling borders is people spending more and more time interacting with technology. In a presentation at Singularity University’s Global Summit in San Francisco last month, Cross unveiled Soul Machines’ latest work and shared his views on the current state of human-like AI and where the technology may go in the near future.
Humanizing Technology Interaction
Cross started by introducing Rachel, one of Soul Machines’ “emotionally responsive digital humans.” The company has built 15 different digital humans of various sexes, groups, and ethnicities. Rachel, along with her “sisters” and “brothers,” has a virtual nervous system based on neural networks and biological models of different paths in the human brain. The system is controlled by virtual neurotransmitters and hormones akin to dopamine, serotonin, and oxytocin, which influence learning and behavior.
As a result, each digital human can have its own unique set of “feelings” and responses to interactions. People interact with them via visual and audio sensors, and the machines respond in real time.
“Over the last 20 or 30 years, the way we think about machines and the way we interact with machines has changed,” Cross said. “We’ve always had this view that they should actually be more human-like.”
The realism of the digital humans’ graphic representations comes thanks to the work of Soul Machines’ other co-founder, Dr. Mark Sager, who has won two Academy Awards for his work on some computer-generated movies, including James Cameron’s Avatar.
Cross pointed out, for example, that rather than being unrealistically flawless and clear, Rachel’s skin has blemishes and sun spots, just like real human skin would.
The Next Human-Machine Frontier
When people interact with each other face to face, emotional and intellectual engagement both heavily influence the interaction. What would it look like for machines to bring those same emotional and intellectual capacities to our interactions with them, and how would this type of interaction affect the way we use, relate to, and feel about AI?
Cross and his colleagues believe that humanizing artificial intelligence will make the technology more useful to humanity, and prompt people to use AI in more beneficial ways.
“What we think is a very important view as we move forward is that these machines can be more helpful to us. They can be more useful to us. They can be more interesting to us if they’re actually more like us,” Cross said.
It is an approach that seems to resonate with companies and organizations. For example, in the UK, where NatWest Bank is testing out Cora as a digital employee to help answer customer queries. In Germany, Daimler Financial Group plans to employ Sarah as something “similar to a personal concierge” for its customers. According to Cross, Daimler is looking at other ways it could deploy digital humans across the organization, from building digital service people, digital sales people, and maybe in the future, digital chauffeurs.
Soul Machines’ latest creation is Will, a digital teacher that can interact with children through a desktop, tablet, or mobile device and help them learn about renewable energy. Cross sees other social uses for digital humans, including potentially serving as doctors to rural communities.
Our Digital Friends—and Twins
Soul Machines is not alone in its quest to humanize technology. It is a direction many technology companies, including the likes of Amazon, also seem to be pursuing. Amazon is working on building a home robot that, according to Bloomberg, “could be a sort of mobile Alexa.”
Finding a more human form for technology seems like a particularly pervasive pursuit in Japan. Not just when it comes to its many, many robots, but also virtual assistants like Gatebox.
The Japanese approach was perhaps best summed up by famous android researcher Dr. Hiroshi Ishiguro, who I interviewed last year: “The human brain is set up to recognize and interact with humans. So, it makes sense to focus on developing the body for the AI mind, as well as the AI. I believe that the final goal for both Japanese and other companies and scientists is to create human-like interaction.”
During Cross’s presentation, Rob Nail, CEO and associate founder of Singularity University, joined him on the stage, extending an invitation to Rachel to be SU’s first fully digital faculty member. Rachel accepted, and though she’s the only digital faculty right now, she predicted this won’t be the case for long.
“In 10 years, all of you will have digital versions of yourself, just like me, to take on specific tasks and make your life a whole lot easier,” she said. “This is great news for me. I’ll have millions of digital friends.”
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Posted in Human Robots
#432487 Can We Make a Musical Turing Test?
As artificial intelligence advances, we’re encountering the same old questions. How much of what we consider to be fundamentally human can be reduced to an algorithm? Can we create something sufficiently advanced that people can no longer distinguish between the two? This, after all, is the idea behind the Turing Test, which has yet to be passed.
At first glance, you might think music is beyond the realm of algorithms. Birds can sing, and people can compose symphonies. Music is evocative; it makes us feel. Very often, our intense personal and emotional attachments to music are because it reminds us of our shared humanity. We are told that creative jobs are the least likely to be automated. Creativity seems fundamentally human.
But I think above all, we view it as reductionist sacrilege: to dissect beautiful things. “If you try to strangle a skylark / to cut it up, see how it works / you will stop its heart from beating / you will stop its mouth from singing.” A human musician wrote that; a machine might be able to string words together that are happy or sad; it might even be able to conjure up a decent metaphor from the depths of some neural network—but could it understand humanity enough to produce art that speaks to humans?
Then, of course, there’s the other side of the debate. Music, after all, has a deeply mathematical structure; you can train a machine to produce harmonics. “In the teachings of Pythagoras and his followers, music was inseparable from numbers, which were thought to be the key to the whole spiritual and physical universe,” according to Grout in A History of Western Music. You might argue that the process of musical composition cannot be reduced to a simple algorithm, yet musicians have often done so. Mozart, with his “Dice Music,” used the roll of a dice to decide how to order musical fragments; creativity through an 18th-century random number generator. Algorithmic music goes back a very long way, with the first papers on the subject from the 1960s.
Then there’s the techno-enthusiast side of the argument. iTunes has 26 million songs, easily more than a century of music. A human could never listen to and learn from them all, but a machine could. It could also memorize every note of Beethoven. Music can be converted into MIDI files, a nice chewable data format that allows even a character-by-character neural net you can run on your computer to generate music. (Seriously, even I could get this thing working.)
Indeed, generating music in the style of Bach has long been a test for AI, and you can see neural networks gradually learn to imitate classical composers while trying to avoid overfitting. When an algorithm overfits, it essentially starts copying the existing music, rather than being inspired by it but creating something similar: a tightrope the best human artists learn to walk. Creativity doesn’t spring from nowhere; even maverick musical geniuses have their influences.
Does a machine have to be truly ‘creative’ to produce something that someone would find valuable? To what extent would listeners’ attitudes change if they thought they were hearing a human vs. an AI composition? This all suggests a musical Turing Test. Of course, it already exists. In fact, it’s run out of Dartmouth, the school that hosted that first, seminal AI summer conference. This year, the contest is bigger than ever: alongside the PoetiX, LimeriX and LyriX competitions for poetry and lyrics, there’s a DigiKidLit competition for children’s literature (although you may have reservations about exposing your children to neural-net generated content… it can get a bit surreal).
There’s also a pair of musical competitions, including one for original compositions in different genres. Key genres and styles are represented by Charlie Parker for Jazz and the Bach chorales for classical music. There’s also a free composition, and a contest where a human and an AI try to improvise together—the AI must respond to a human spontaneously, in real time, and in a musically pleasing way. Quite a challenge! In all cases, if any of the generated work is indistinguishable from human performers, the neural net has passed the Turing Test.
Did they? Here’s part of 2017’s winning sonnet from Charese Smiley and Hiroko Bretz:
The large cabin was in total darkness.
Come marching up the eastern hill afar.
When is the clock on the stairs dangerous?
Everything seemed so near and yet so far.
Behind the wall silence alone replied.
Was, then, even the staircase occupied?
Generating the rhymes is easy enough, the sentence structure a little trickier, but what’s impressive about this sonnet is that it sticks to a single topic and appears to be a more coherent whole. I’d guess they used associated “lexical fields” of similar words to help generate something coherent. In a similar way, most of the more famous examples of AI-generated music still involve some amount of human control, even if it’s editorial; a human will build a song around an AI-generated riff, or select the most convincing Bach chorale from amidst many different samples.
We are seeing strides forward in the ability of AI to generate human voices and human likenesses. As the latter example shows, in the fake news era people have focused on the dangers of this tech– but might it also be possible to create a virtual performer, trained on a dataset of their original music? Did you ever want to hear another Beatles album, or jam with Miles Davis? Of course, these things are impossible—but could we create a similar experience that people would genuinely value? Even, to the untrained eye, something indistinguishable from the real thing?
And if it did measure up to the real thing, what would this mean? Jaron Lanier is a fascinating technology writer, a critic of strong AI, and a believer in the power of virtual reality to change the world and provide truly meaningful experiences. He’s also a composer and a musical aficionado. He pointed out in a recent interview that translation algorithms, by reducing the amount of work translators are commissioned to do, have, in some sense, profited from stolen expertise. They were trained on huge datasets purloined from human linguists and translators. If you can train an AI on someone’s creative output and it produces new music, who “owns” it?
Although companies that offer AI music tools are starting to proliferate, and some groups will argue that the musical Turing test has been passed already, AI-generated music is hardly racing to the top of the pop charts just yet. Even as the line between human-composed and AI-generated music starts to blur, there’s still a gulf between the average human and musical genius. In the next few years, we’ll see how far the current techniques can take us. It may be the case that there’s something in the skylark’s song that can’t be generated by machines. But maybe not, and then this song might need an extra verse.
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Posted in Human Robots
#432051 What Roboticists Are Learning From Early ...
You might not have heard of Hanson Robotics, but if you’re reading this, you’ve probably seen their work. They were the company behind Sophia, the lifelike humanoid avatar that’s made dozens of high-profile media appearances. Before that, they were the company behind that strange-looking robot that seemed a bit like Asimo with Albert Einstein’s head—or maybe you saw BINA48, who was interviewed for the New York Times in 2010 and featured in Jon Ronson’s books. For the sci-fi aficionados amongst you, they even made a replica of legendary author Philip K. Dick, best remembered for having books with titles like Do Androids Dream of Electric Sheep? turned into films with titles like Blade Runner.
Hanson Robotics, in other words, with their proprietary brand of life-like humanoid robots, have been playing the same game for a while. Sometimes it can be a frustrating game to watch. Anyone who gives the robot the slightest bit of thought will realize that this is essentially a chat-bot, with all the limitations this implies. Indeed, even in that New York Times interview with BINA48, author Amy Harmon describes it as a frustrating experience—with “rare (but invariably thrilling) moments of coherence.” This sensation will be familiar to anyone who’s conversed with a chatbot that has a few clever responses.
The glossy surface belies the lack of real intelligence underneath; it seems, at first glance, like a much more advanced machine than it is. Peeling back that surface layer—at least for a Hanson robot—means you’re peeling back Frubber. This proprietary substance—short for “Flesh Rubber,” which is slightly nightmarish—is surprisingly complicated. Up to thirty motors are required just to control the face; they manipulate liquid cells in order to make the skin soft, malleable, and capable of a range of different emotional expressions.
A quick combinatorial glance at the 30+ motors suggests that there are millions of possible combinations; researchers identify 62 that they consider “human-like” in Sophia, although not everyone agrees with this assessment. Arguably, the technical expertise that went into reconstructing the range of human facial expressions far exceeds the more simplistic chat engine the robots use, although it’s the second one that allows it to inflate the punters’ expectations with a few pre-programmed questions in an interview.
Hanson Robotics’ belief is that, ultimately, a lot of how humans will eventually relate to robots is going to depend on their faces and voices, as well as on what they’re saying. “The perception of identity is so intimately bound up with the perception of the human form,” says David Hanson, company founder.
Yet anyone attempting to design a robot that won’t terrify people has to contend with the uncanny valley—that strange blend of concern and revulsion people react with when things appear to be creepily human. Between cartoonish humanoids and genuine humans lies what has often been a no-go zone in robotic aesthetics.
The uncanny valley concept originated with roboticist Masahiro Mori, who argued that roboticists should avoid trying to replicate humans exactly. Since anything that wasn’t perfect, but merely very good, would elicit an eerie feeling in humans, shirking the challenge entirely was the only way to avoid the uncanny valley. It’s probably a task made more difficult by endless streams of articles about AI taking over the world that inexplicably conflate AI with killer humanoid Terminators—which aren’t particularly likely to exist (although maybe it’s best not to push robots around too much).
The idea behind this realm of psychological horror is fairly simple, cognitively speaking.
We know how to categorize things that are unambiguously human or non-human. This is true even if they’re designed to interact with us. Consider the popularity of Aibo, Jibo, or even some robots that don’t try to resemble humans. Something that resembles a human, but isn’t quite right, is bound to evoke a fear response in the same way slightly distorted music or slightly rearranged furniture in your home will. The creature simply doesn’t fit.
You may well reject the idea of the uncanny valley entirely. David Hanson, naturally, is not a fan. In the paper Upending the Uncanny Valley, he argues that great art forms have often resembled humans, but the ultimate goal for humanoid roboticists is probably to create robots we can relate to as something closer to humans than works of art.
Meanwhile, Hanson and other scientists produce competing experiments to either demonstrate that the uncanny valley is overhyped, or to confirm it exists and probe its edges.
The classic experiment involves gradually morphing a cartoon face into a human face, via some robotic-seeming intermediaries—yet it’s in movement that the real horror of the almost-human often lies. Hanson has argued that incorporating cartoonish features may help—and, sometimes, that the uncanny valley is a generational thing which will melt away when new generations grow used to the quirks of robots. Although Hanson might dispute the severity of this effect, it’s clearly what he’s trying to avoid with each new iteration.
Hiroshi Ishiguro is the latest of the roboticists to have dived headlong into the valley.
Building on the work of pioneers like Hanson, those who study human-robot interaction are pushing at the boundaries of robotics—but also of social science. It’s usually difficult to simulate what you don’t understand, and there’s still an awful lot we don’t understand about how we interpret the constant streams of non-verbal information that flow when you interact with people in the flesh.
Ishiguro took this imitation of human forms to extreme levels. Not only did he monitor and log the physical movements people made on videotapes, but some of his robots are based on replicas of people; the Repliee series began with a ‘replicant’ of his daughter. This involved making a rubber replica—a silicone cast—of her entire body. Future experiments were focused on creating Geminoid, a replica of Ishiguro himself.
As Ishiguro aged, he realized that it would be more effective to resemble his replica through cosmetic surgery rather than by continually creating new casts of his face, each with more lines than the last. “I decided not to get old anymore,” Ishiguro said.
We love to throw around abstract concepts and ideas: humans being replaced by machines, cared for by machines, getting intimate with machines, or even merging themselves with machines. You can take an idea like that, hold it in your hand, and examine it—dispassionately, if not without interest. But there’s a gulf between thinking about it and living in a world where human-robot interaction is not a field of academic research, but a day-to-day reality.
As the scientists studying human-robot interaction develop their robots, their replicas, and their experiments, they are making some of the first forays into that world. We might all be living there someday. Understanding ourselves—decrypting the origins of empathy and love—may be the greatest challenge to face. That is, if you want to avoid the valley.
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Posted in Human Robots