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When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.
The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”
Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.
The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.
The country’s ambition is reflected in the team’s parting words.
“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.
A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.
However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.
As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.
For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.
But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.
Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.
First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.
Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.
Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.
In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.
Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.
Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.
Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.
However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.
Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.
*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.
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A few years back, DeepMind’s Demis Hassabis famously prophesized that AI and neuroscience will positively feed into each other in a “virtuous circle.” If realized, this would fundamentally expand our insight into intelligence, both machine and human.
We’ve already seen some proofs of concept, at least in the brain-to-AI direction. For example, memory replay, a biological mechanism that fortifies our memories during sleep, also boosted AI learning when abstractly appropriated into deep learning models. Reinforcement learning, loosely based on our motivation circuits, is now behind some of AI’s most powerful tools.
Hassabis is about to be proven right again.
Last week, two studies independently tapped into the power of ANNs to solve a 70-year-old neuroscience mystery: how does our visual system perceive reality?
The first, published in Cell, used generative networks to evolve DeepDream-like images that hyper-activate complex visual neurons in monkeys. These machine artworks are pure nightmare fuel to the human eye; but together, they revealed a fundamental “visual hieroglyph” that may form a basic rule for how we piece together visual stimuli to process sight into perception.
In the second study, a team used a deep ANN model—one thought to mimic biological vision—to synthesize new patterns tailored to control certain networks of visual neurons in the monkey brain. When directly shown to monkeys, the team found that the machine-generated artworks could reliably activate predicted populations of neurons. Future improved ANN models could allow even better control, giving neuroscientists a powerful noninvasive tool to study the brain. The work was published in Science.
The individual results, though fascinating, aren’t necessarily the point. Rather, they illustrate how scientists are now striving to complete the virtuous circle: tapping AI to probe natural intelligence. Vision is only the beginning—the tools can potentially be expanded into other sensory domains. And the more we understand about natural brains, the better we can engineer artificial ones.
It’s a “great example of leveraging artificial intelligence to study organic intelligence,” commented Dr. Roman Sandler at Kernel.co on Twitter.
ANNs and biological vision have quite the history.
In the late 1950s, the legendary neuroscientist duo David Hubel and Torsten Wiesel became some of the first to use mathematical equations to understand how neurons in the brain work together.
In a series of experiments—many using cats—the team carefully dissected the structure and function of the visual cortex. Using myriads of images, they revealed that vision is processed in a hierarchy: neurons in “earlier” brain regions, those closer to the eyes, tend to activate when they “see” simple patterns such as lines. As we move deeper into the brain, from the early V1 to a nub located slightly behind our ears, the IT cortex, neurons increasingly respond to more complex or abstract patterns, including faces, animals, and objects. The discovery led some scientists to call certain IT neurons “Jennifer Aniston cells,” which fire in response to pictures of the actress regardless of lighting, angle, or haircut. That is, IT neurons somehow extract visual information into the “gist” of things.
That’s not trivial. The complex neural connections that lead to increasing abstraction of what we see into what we think we see—what we perceive—is a central question in machine vision: how can we teach machines to transform numbers encoding stimuli into dots, lines, and angles that eventually form “perceptions” and “gists”? The answer could transform self-driving cars, facial recognition, and other computer vision applications as they learn to better generalize.
Hubel and Wiesel’s Nobel-prize-winning studies heavily influenced the birth of ANNs and deep learning. Much of earlier ANN “feed-forward” model structures are based on our visual system; even today, the idea of increasing layers of abstraction—for perception or reasoning—guide computer scientists to build AI that can better generalize. The early romance between vision and deep learning is perhaps the bond that kicked off our current AI revolution.
It only seems fair that AI would feed back into vision neuroscience.
Hieroglyphs and Controllers
In the Cell study, a team led by Dr. Margaret Livingstone at Harvard Medical School tapped into generative networks to unravel IT neurons’ complex visual alphabet.
Scientists have long known that neurons in earlier visual regions (V1) tend to fire in response to “grating patches” oriented in certain ways. Using a limited set of these patches like letters, V1 neurons can “express a visual sentence” and represent any image, said Dr. Arash Afraz at the National Institute of Health, who was not involved in the study.
But how IT neurons operate remained a mystery. Here, the team used a combination of genetic algorithms and deep generative networks to “evolve” computer art for every studied neuron. In seven monkeys, the team implanted electrodes into various parts of the visual IT region so that they could monitor the activity of a single neuron.
The team showed each monkey an initial set of 40 images. They then picked the top 10 images that stimulated the highest neural activity, and married them to 30 new images to “evolve” the next generation of images. After 250 generations, the technique, XDREAM, generated a slew of images that mashed up contorted face-like shapes with lines, gratings, and abstract shapes.
This image shows the evolution of an optimum image for stimulating a visual neuron in a monkey. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“The evolved images look quite counter-intuitive,” explained Afraz. Some clearly show detailed structures that resemble natural images, while others show complex structures that can’t be characterized by our puny human brains.
This figure shows natural images (right) and images evolved by neurons in the inferotemporal cortex of a monkey (left). Image Credit: Ponce, Xiao, and Schade et al. – Cell.
“What started to emerge during each experiment were pictures that were reminiscent of shapes in the world but were not actual objects in the world,” said study author Carlos Ponce. “We were seeing something that was more like the language cells use with each other.”
This image was evolved by a neuron in the inferotemporal cortex of a monkey using AI. Image Credit: Ponce, Xiao, and Schade et al. – Cell.
Although IT neurons don’t seem to use a simple letter alphabet, it does rely on a vast array of characters like hieroglyphs or Chinese characters, “each loaded with more information,” said Afraz.
The adaptive nature of XDREAM turns it into a powerful tool to probe the inner workings of our brains—particularly for revealing discrepancies between biology and models.
The Science study, led by Dr. James DiCarlo at MIT, takes a similar approach. Using ANNs to generate new patterns and images, the team was able to selectively predict and independently control neuron populations in a high-level visual region called V4.
“So far, what has been done with these models is predicting what the neural responses would be to other stimuli that they have not seen before,” said study author Dr. Pouya Bashivan. “The main difference here is that we are going one step further and using the models to drive the neurons into desired states.”
It suggests that our current ANN models for visual computation “implicitly capture a great deal of visual knowledge” which we can’t really describe, but which the brain uses to turn vision information into perception, the authors said. By testing AI-generated images on biological vision, however, the team concluded that today’s ANNs have a degree of understanding and generalization. The results could potentially help engineer even more accurate ANN models of biological vision, which in turn could feed back into machine vision.
“One thing is clear already: Improved ANN models … have led to control of a high-level neural population that was previously out of reach,” the authors said. “The results presented here have likely only scratched the surface of what is possible with such implemented characterizations of the brain’s neural networks.”
To Afraz, the power of AI here is to find cracks in human perception—both our computational models of sensory processes, as well as our evolved biological software itself. AI can be used “as a perfect adversarial tool to discover design cracks” of IT, said Afraz, such as finding computer art that “fools” a neuron into thinking the object is something else.
“As artificial intelligence researchers develop models that work as well as the brain does—or even better—we will still need to understand which networks are more likely to behave safely and further human goals,” said Ponce. “More efficient AI can be grounded by knowledge of how the brain works.”
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As artificial intelligence systems take on more tasks and solve more problems, it’s hard to say which is rising faster: our interest in them or our fear of them. Futurist Ray Kurzweil famously predicted that “By 2029, computers will have emotional intelligence and be convincing as people.”
We don’t know how accurate this prediction will turn out to be. Even if it takes more than 10 years, though, is it really possible for machines to become conscious? If the machines Kurzweil describes say they’re conscious, does that mean they actually are?
Perhaps a more relevant question at this juncture is: what is consciousness, and how do we replicate it if we don’t understand it?
In a panel discussion at South By Southwest titled “How AI Will Design the Human Future,” experts from academia and industry discussed these questions and more.
Wait, What Is AI?
Most of AI’s recent feats—diagnosing illnesses, participating in debate, writing realistic text—involve machine learning, which uses statistics to find patterns in large datasets then uses those patterns to make predictions. However, “AI” has been used to refer to everything from basic software automation and algorithms to advanced machine learning and deep learning.
“The term ‘artificial intelligence’ is thrown around constantly and often incorrectly,” said Jennifer Strong, a reporter at the Wall Street Journal and host of the podcast “The Future of Everything.” Indeed, one study found that 40 percent of European companies that claim to be working on or using AI don’t actually use it at all.
Dr. Peter Stone, associate chair of computer science at UT Austin, was the study panel chair on the 2016 One Hundred Year Study on Artificial Intelligence (or AI100) report. Based out of Stanford University, AI100 is studying and anticipating how AI will impact our work, our cities, and our lives.
“One of the first things we had to do was define AI,” Stone said. They defined it as a collection of different technologies inspired by the human brain to be able to perceive their surrounding environment and figure out what actions to take given these inputs.
Modeling on the Unknown
Here’s the crazy thing about that definition (and about AI itself): we’re essentially trying to re-create the abilities of the human brain without having anything close to a thorough understanding of how the human brain works.
“We’re starting to pair our brains with computers, but brains don’t understand computers and computers don’t understand brains,” Stone said. Dr. Heather Berlin, cognitive neuroscientist and professor of psychiatry at the Icahn School of Medicine at Mount Sinai, agreed. “It’s still one of the greatest mysteries how this three-pound piece of matter can give us all our subjective experiences, thoughts, and emotions,” she said.
This isn’t to say we’re not making progress; there have been significant neuroscience breakthroughs in recent years. “This has been the stuff of science fiction for a long time, but now there’s active work being done in this area,” said Amir Husain, CEO and founder of Austin-based AI company Spark Cognition.
Advances in brain-machine interfaces show just how much more we understand the brain now than we did even a few years ago. Neural implants are being used to restore communication or movement capabilities in people who’ve been impaired by injury or illness. Scientists have been able to transfer signals from the brain to prosthetic limbs and stimulate specific circuits in the brain to treat conditions like Parkinson’s, PTSD, and depression.
But much of the brain’s inner workings remain a deep, dark mystery—one that will have to be further solved if we’re ever to get from narrow AI, which refers to systems that can perform specific tasks and is where the technology stands today, to artificial general intelligence, or systems that possess the same intelligence level and learning capabilities as humans.
The biggest question that arises here, and one that’s become a popular theme across stories and films, is if machines achieve human-level general intelligence, does that also mean they’d be conscious?
Wait, What Is Consciousness?
As valuable as the knowledge we’ve accumulated about the brain is, it seems like nothing more than a collection of disparate facts when we try to put it all together to understand consciousness.
“If you can replace one neuron with a silicon chip that can do the same function, then replace another neuron, and another—at what point are you still you?” Berlin asked. “These systems will be able to pass the Turing test, so we’re going to need another concept of how to measure consciousness.”
Is consciousness a measurable phenomenon, though? Rather than progressing by degrees or moving through some gray area, isn’t it pretty black and white—a being is either conscious or it isn’t?
This may be an outmoded way of thinking, according to Berlin. “It used to be that only philosophers could study consciousness, but now we can study it from a scientific perspective,” she said. “We can measure changes in neural pathways. It’s subjective, but depends on reportability.”
She described three levels of consciousness: pure subjective experience (“Look, the sky is blue”), awareness of one’s own subjective experience (“Oh, it’s me that’s seeing the blue sky”), and relating one subjective experience to another (“The blue sky reminds me of a blue ocean”).
“These subjective states exist all the way down the animal kingdom. As humans we have a sense of self that gives us another depth to that experience, but it’s not necessary for pure sensation,” Berlin said.
Husain took this definition a few steps farther. “It’s this self-awareness, this idea that I exist separate from everything else and that I can model myself,” he said. “Human brains have a wonderful simulator. They can propose a course of action virtually, in their minds, and see how things play out. The ability to include yourself as an actor means you’re running a computation on the idea of yourself.”
Most of the decisions we make involve envisioning different outcomes, thinking about how each outcome would affect us, and choosing which outcome we’d most prefer.
“Complex tasks you want to achieve in the world are tied to your ability to foresee the future, at least based on some mental model,” Husain said. “With that view, I as an AI practitioner don’t see a problem implementing that type of consciousness.”
Moving Forward Cautiously (But Not too Cautiously)
To be clear, we’re nowhere near machines achieving artificial general intelligence or consciousness, and whether a “conscious machine” is possible—not to mention necessary or desirable—is still very much up for debate.
As machine intelligence continues to advance, though, we’ll need to walk the line between progress and risk management carefully.
Improving the transparency and explainability of AI systems is one crucial goal AI developers and researchers are zeroing in on. Especially in applications that could mean the difference between life and death, AI shouldn’t advance without people being able to trace how it’s making decisions and reaching conclusions.
Medicine is a prime example. “There are already advances that could save lives, but they’re not being used because they’re not trusted by doctors and nurses,” said Stone. “We need to make sure there’s transparency.” Demanding too much transparency would also be a mistake, though, because it will hinder the development of systems that could at best save lives and at worst improve efficiency and free up doctors to have more face time with patients.
Similarly, self-driving cars have great potential to reduce deaths from traffic fatalities. But even though humans cause thousands of deadly crashes every day, we’re terrified by the idea of self-driving cars that are anything less than perfect. “If we only accept autonomous cars when there’s zero probability of an accident, then we will never accept them,” Stone said. “Yet we give 16-year-olds the chance to take a road test with no idea what’s going on in their brains.”
This brings us back to the fact that, in building tech modeled after the human brain—which has evolved over millions of years—we’re working towards an end whose means we don’t fully comprehend, be it something as basic as choosing when to brake or accelerate or something as complex as measuring consciousness.
“We shouldn’t charge ahead and do things just because we can,” Stone said. “The technology can be very powerful, which is exciting, but we have to consider its implications.”
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The key difference between science fiction and fantasy is that science fiction is entirely possible because of its grounding in scientific facts, while fantasy is not. This is where Black Mirror is both an entertaining and terrifying work of science fiction. Created by Charlie Brooker, the anthological series tells cautionary tales of emerging technology that could one day be an integral part of our everyday lives.
While watching the often alarming episodes, one can’t help but recognize the eerie similarities to some of the tech tools that are already abundant in our lives today. In fact, many previous Black Mirror predictions are already becoming reality.
The latest season of Black Mirror was arguably darker than ever. This time, Brooker seemed to focus on the ethical implications of one particular area: neurotechnology.
Warning: The remainder of this article may contain spoilers from Season 4 of Black Mirror.
Most of the storylines from season four revolve around neurotechnology and brain-machine interfaces. They are based in a world where people have the power to upload their consciousness onto machines, have fully immersive experiences in virtual reality, merge their minds with other minds, record others’ memories, and even track what others are thinking, feeling, and doing.
How can all this ever be possible? Well, these capabilities are already being developed by pioneers and researchers globally. Early last year, Elon Musk unveiled Neuralink, a company whose goal is to merge the human mind with AI through a neural lace. We’ve already connected two brains via the internet, allowing one brain to communicate with another. Various research teams have been able to develop mechanisms for “reading minds” or reconstructing memories of individuals via devices. The list goes on.
With many of the technologies we see in Black Mirror it’s not a question of if, but when. Futurist Ray Kurzweil has predicted that by the 2030s we will be able to upload our consciousness onto the cloud via nanobots that will “provide full-immersion virtual reality from within the nervous system, provide direct brain-to-brain communication over the internet, and otherwise greatly expand human intelligence.” While other experts continue to challenge Kurzweil on the exact year we’ll accomplish this feat, with the current exponential growth of our technological capabilities, we’re on track to get there eventually.
As always, technology is only half the conversation. Equally fascinating are the many ethical and moral questions this topic raises.
For instance, with the increasing convergence of artificial intelligence and virtual reality, we have to ask ourselves if our morality from the physical world transfers equally into the virtual world. The first episode of season four, USS Calister, tells the story of a VR pioneer, Robert Daley, who creates breakthrough AI and VR to satisfy his personal frustrations and sexual urges. He uses the DNA of his coworkers (and their children) to re-create them digitally in his virtual world, to which he escapes to torture them, while they continue to be indifferent in the “real” world.
Audiences are left asking themselves: should what happens in the digital world be considered any less “real” than the physical world? How do we know if the individuals in the virtual world (who are ultimately based on algorithms) have true feelings or sentiments? Have they been developed to exhibit characteristics associated with suffering, or can they really feel suffering? Fascinatingly, these questions point to the hard problem of consciousness—the question of if, why, and how a given physical process generates the specific experience it does—which remains a major mystery in neuroscience.
Towards the end of USS Calister, the hostages of Daley’s virtual world attempt to escape through suicide, by committing an act that will delete the code that allows them to exist. This raises yet another mind-boggling ethical question: if we “delete” code that signifies a digital being, should that be considered murder (or suicide, in this case)? Why shouldn’t it? When we murder someone we are, in essence, taking away their capacity to live and to be, without their consent. By unplugging a self-aware AI, wouldn’t we be violating its basic right to live in the same why? Does AI, as code, even have rights?
Brain implants can also have a radical impact on our self-identity and how we define the word “I”. In the episode Black Museum, instead of witnessing just one horror, we get a series of scares in little segments. One of those segments tells the story of a father who attempts to reincarnate the mother of his child by uploading her consciousness into his mind and allowing her to live in his head (essentially giving him multiple personality disorder). In this way, she can experience special moments with their son.
With “no privacy for him, and no agency for her” the good intention slowly goes very wrong. This story raises a critical question: should we be allowed to upload consciousness into limited bodies? Even more, if we are to upload our minds into “the cloud,” at what point do we lose our individuality to become one collective being?
These questions can form the basis of hours of debate, but we’re just getting started. There are no right or wrong answers with many of these moral dilemmas, but we need to start having such discussions.
The Downside of Dystopian Sci-Fi
Like last season’s San Junipero, one episode of the series, Hang the DJ, had an uplifting ending. Yet the overwhelming majority of the stories in Black Mirror continue to focus on the darkest side of human nature, feeding into the pre-existing paranoia of the general public. There is certainly some value in this; it’s important to be aware of the dangers of technology. After all, what better way to explore these dangers before they occur than through speculative fiction?
A big takeaway from every tale told in the series is that the greatest threat to humanity does not come from technology, but from ourselves. Technology itself is not inherently good or evil; it all comes down to how we choose to use it as a society. So for those of you who are techno-paranoid, beware, for it’s not the technology you should fear, but the humans who get their hands on it.
While we can paint negative visions for the future, though, it is also important to paint positive ones. The kind of visions we set for ourselves have the power to inspire and motivate generations. Many people are inherently pessimistic when thinking about the future, and that pessimism in turn can shape their contributions to humanity.
While utopia may not exist, the future of our species could and should be one of solving global challenges, abundance, prosperity, liberation, and cosmic transcendence. Now that would be a thrilling episode to watch.
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Does creativity make human intelligence special?
It may appear so at first glance. Though machines can calculate, analyze, and even perceive, creativity may seem far out of reach. Perhaps this is because we find it mysterious, even in ourselves. How can the output of a machine be anything more than that which is determined by its programmers?
Increasingly, however, artificial intelligence is moving into creativity’s hallowed domain, from art to industry. And though much is already possible, the future is sure to bring ever more creative machines.
What Is Machine Creativity?
Robotic art is just one example of machine creativity, a rapidly growing sub-field that sits somewhere between the study of artificial intelligence and human psychology.
The winning paintings from the 2017 Robot Art Competition are strikingly reminiscent of those showcased each spring at university exhibitions for graduating art students. Like the works produced by skilled artists, the compositions dreamed up by the competition’s robotic painters are aesthetically ambitious. One robot-made painting features a man’s bearded face gazing intently out from the canvas, his eyes locking with the viewer’s. Another abstract painting, “inspired” by data from EEG signals, visually depicts the human emotion of misery with jagged, gloomy stripes of black and purple.
More broadly, a creative machine is software (sometimes encased in a robotic body) that synthesizes inputs to generate new and valuable ideas, solutions to complex scientific problems, or original works of art. In a process similar to that followed by a human artist or scientist, a creative machine begins its work by framing a problem. Next, its software specifies the requirements the solution should have before generating “answers” in the form of original designs, patterns, or some other form of output.
Although the notion of machine creativity sounds a bit like science fiction, the basic concept is one that has been slowly developing for decades.
Nearly 50 years ago while a high school student, inventor and futurist Ray Kurzweil created software that could analyze the patterns in musical compositions and then compose new melodies in a similar style. Aaron, one of the world’s most famous painting robots, has been hard at work since the 1970s.
Industrial designers have used an automated, algorithm-driven process for decades to design computer chips (or machine parts) whose layout (or form) is optimized for a particular function or environment. Emily Howell, a computer program created by David Cope, writes original works in the style of classical composers, some of which have been performed by human orchestras to live audiences.
What’s different about today’s new and emerging generation of robotic artists, scientists, composers, authors, and product designers is their ubiquity and power.
“The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives.”
I’ve already mentioned the rapidly advancing fields of robotic art and music. In the realm of scientific research, so-called “robotic scientists” such as Eureqa and Adam and Eve develop new scientific hypotheses; their “insights” have contributed to breakthroughs that are cited by hundreds of academic research papers. In the medical industry, creative machines are hard at work creating chemical compounds for new pharmaceuticals. After it read over seven million words of 20th century English poetry, a neural network developed by researcher Jack Hopkins learned to write passable poetry in a number of different styles and meters.
The recent explosion of artificial creativity has been enabled by the rapid maturation of the same exponential technologies that have already re-drawn our daily lives, including faster processors, ubiquitous sensors and wireless networks, and better algorithms.
As they continue to improve, creative machines—like humans—will perform a broad range of creative activities, ranging from everyday problem solving (sometimes known as “Little C” creativity) to producing once-in-a-century masterpieces (“Big C” creativity). A creative machine’s outputs could range from a design for a cast for a marble sculpture to a schematic blueprint for a clever new gadget for opening bottles of wine.
In the coming decades, by automating the process of solving complex problems, creative machines will again transform our world. Creative machines will serve as a versatile source of on-demand talent.
In the battle to recruit a workforce that can solve complex problems, creative machines will put small businesses on equal footing with large corporations. Art and music lovers will enjoy fresh creative works that re-interpret the style of ancient disciplines. People with a health condition will benefit from individualized medical treatments, and low-income people will receive top-notch legal advice, to name but a few potentially beneficial applications.
How Can We Make Creative Machines, Unless We Understand Our Own Creativity?
One of the most intriguing—yet unsettling—aspects of watching robotic arms skillfully oil paint is that we humans still do not understand our own creative process. Over the centuries, several different civilizations have devised a variety of models to explain creativity.
The ancient Greeks believed that poets drew inspiration from a transcendent realm parallel to the material world where ideas could take root and flourish. In the Middle Ages, philosophers and poets attributed our peculiarly human ability to “make something of nothing” to an external source, namely divine inspiration. Modern academic study of human creativity has generated vast reams of scholarship, but despite the value of these insights, the human imagination remains a great mystery, second only to that of consciousness.
Today, the rise of machine creativity demonstrates (once again), that we do not have to fully understand a biological process in order to emulate it with advanced technology.
Past experience has shown that jet planes can fly higher and faster than birds by using the forward thrust of an engine rather than wings. Submarines propel themselves forward underwater without fins or a tail. Deep learning neural networks identify objects in randomly-selected photographs with super-human accuracy. Similarly, using a fairly straightforward software architecture, creative software (sometimes paired with a robotic body) can paint, write, hypothesize, or design with impressive originality, skill, and boldness.
At the heart of machine creativity is simple iteration. No matter what sort of output they produce, creative machines fall into one of three categories depending on their internal architecture.
Briefly, the first group consists of software programs that use traditional rule-based, or symbolic AI, the second group uses evolutionary algorithms, and the third group uses a variation of a form of machine learning called deep learning that has already revolutionized voice and facial recognition software.
1) Symbolic creative machines are the oldest artificial artists and musicians. In this approach—also known as “good old-fashioned AI (GOFAI) or symbolic AI—the human programmer plays a key role by writing a set of step-by-step instructions to guide the computer through a task. Despite the fact that symbolic AI is limited in its ability to adapt to environmental changes, it’s still possible for a robotic artist programmed this way to create an impressively wide variety of different outputs.
2) Evolutionary algorithms (EA) have been in use for several decades and remain powerful tools for design. In this approach, potential solutions “compete” in a software simulator in a Darwinian process reminiscent of biological evolution. The human programmer specifies a “fitness criterion” that will be used to score and rank the solutions generated by the software. The software then generates a “first generation” population of random solutions (which typically are pretty poor in quality), scores this first generation of solutions, and selects the top 50% (those random solutions deemed to be the best “fit”). The software then takes another pass and recombines the “winning” solutions to create the next generation and repeats this process for thousands (and sometimes millions) of generations.
3) Generative deep learning (DL) neural networks represent the newest software architecture of the three, since DL is data-dependent and resource-intensive. First, a human programmer “trains” a DL neural network to recognize a particular feature in a dataset, for example, an image of a dog in a stream of digital images. Next, the standard “feed forward” process is reversed and the DL neural network begins to generate the feature, for example, eventually producing new and sometimes original images of (or poetry about) dogs. Generative DL networks have tremendous and unexplored creative potential and are able to produce a broad range of original outputs, from paintings to music to poetry.
The Coming Explosion of Machine Creativity
In the near future as Moore’s Law continues its work, we will see sophisticated combinations of these three basic architectures. Since the 1950s, artificial intelligence has steadily mastered one human ability after another, and in the process of doing so, has reduced the cost of calculation, analysis, and most recently, perception. When creative software becomes as inexpensive and ubiquitous as analytical software is today, humans will no longer be the only intelligent beings capable of creative work.
This is why I have to bite my tongue when I hear the well-intended (but shortsighted) advice frequently dispensed to young people that they should pursue work that demands creativity to help them “AI-proof” their futures.
Instead, students should gain skills to harness the power of creative machines.
There are two skills in which humans excel that will enable us to remain useful in a world of ever-advancing artificial intelligence. One, the ability to frame and define a complex problem so that it can be handed off to a creative machine to solve. And two, the ability to communicate the value of both the framework and the proposed solution to the other humans involved.
What will happen to people when creative machines begin to capably tread on intellectual ground that was once considered the sole domain of the human mind, and before that, the product of divine inspiration? While machines engaging in Big C creativity—e.g., oil painting and composing new symphonies—tend to garner controversy and make the headlines, I suspect the real world-changing application of machine creativity will be in the realm of everyday problem solving, or Little C. The mainstream emergence of powerful problem-solving tools will help people create abundance where there was once scarcity.
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