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Many of us intuitively think about intelligence as an individual trait. As a society, we have a tendency to praise individual game-changers for accomplishments that would not be possible without their teams, often tens of thousands of people that work behind the scenes to make extraordinary things happen.
Matt Ridley, best-selling author of multiple books, including The Rational Optimist: How Prosperity Evolves, challenges this view. He argues that human achievement and intelligence are entirely “networking phenomena.” In other words, intelligence is collective and emergent as opposed to individual.
When asked what scientific concept would improve everybody’s cognitive toolkit, Ridley highlights collective intelligence: “It is by putting brains together through the division of labor— through trade and specialization—that human society stumbled upon a way to raise the living standards, carrying capacity, technological virtuosity, and knowledge base of the species.”
Ridley has spent a lifetime exploring human prosperity and the factors that contribute to it. In a conversation with Singularity Hub, he redefined how we perceive intelligence and human progress.
Raya Bidshahri: The common perspective seems to be that competition is what drives innovation and, consequently, human progress. Why do you think collaboration trumps competition when it comes to human progress?
Matt Ridley: There is a tendency to think that competition is an animal instinct that is natural and collaboration is a human instinct we have to learn. I think there is no evidence for that. Both are deeply rooted in us as a species. The evidence from evolutionary biology tells us that collaboration is just as important as competition. Yet, at the end, the Darwinian perspective is quite correct: it’s usually cooperation for the purpose of competition, wherein a given group tries to achieve something more effectively than another group. But the point is that the capacity to co-operate is very deep in our psyche.
RB: You write that “human achievement is entirely a networking phenomenon,” and we need to stop thinking about intelligence as an individual trait, and that instead we should look at what you refer to as collective intelligence. Why is that?
MR: The best way to think about it is that IQ doesn’t matter, because a hundred stupid people who are talking to each other will accomplish more than a hundred intelligent people who aren’t. It’s absolutely vital to see that everything from the manufacturing of a pencil to the manufacturing of a nuclear power station can’t be done by an individual human brain. You can’t possibly hold in your head all the knowledge you need to do these things. For the last 200,000 years we’ve been exchanging and specializing, which enables us to achieve much greater intelligence than we can as individuals.
RB: We often think of achievement and intelligence on individual terms. Why do you think it’s so counter-intuitive for us to think about collective intelligence?
MR: People are surprisingly myopic to the extent they understand the nature of intelligence. I think it goes back to a pre-human tendency to think in terms of individual stories and actors. For example, we love to read about the famous inventor or scientist who invented or discovered something. We never tell these stories as network stories. We tell them as individual hero stories.
“It’s absolutely vital to see that everything from the manufacturing of a pencil to the manufacturing of a nuclear power station can’t be done by an individual human brain.”
This idea of a brilliant hero who saves the world in the face of every obstacle seems to speak to tribal hunter-gatherer societies, where the alpha male leads and wins. But it doesn’t resonate with how human beings have structured modern society in the last 100,000 years or so. We modern-day humans haven’t internalized a way of thinking that incorporates this definition of distributed and collective intelligence.
RB: One of the books you’re best known for is The Rational Optimist. What does it mean to be a rational optimist?
MR: My optimism is rational because it’s not based on a feeling, it’s based on evidence. If you look at the data on human living standards over the last 200 years and compare it with the way that most people actually perceive our progress during that time, you’ll see an extraordinary gap. On the whole, people seem to think that things are getting worse, but things are actually getting better.
We’ve seen the most astonishing improvements in human living standards: we’ve brought the number of people living in extreme poverty to 9 percent from about 70 percent when I was born. The human lifespan is expanding by five hours a day, child mortality has gone down by two thirds in half a century, and much more. These feats dwarf the things that are going wrong. Yet most people are quite pessimistic about the future despite the things we’ve achieved in the past.
RB: Where does this idea of collective intelligence fit in rational optimism?
MR: Underlying the idea of rational optimism was understanding what prosperity is, and why it happens to us and not to rabbits or rocks. Why are we the only species in the world that has concepts like a GDP, growth rate, or living standard? My answer is that it comes back to this phenomena of collective intelligence. The reason for a rise in living standards is innovation, and the cause of that innovation is our ability to collaborate.
The grand theme of human history is exchange of ideas, collaborating through specialization and the division of labor. Throughout history, it’s in places where there is a lot of open exchange and trade where you get a lot of innovation. And indeed, there are some extraordinary episodes in human history when societies get cut off from exchange and their innovation slows down and they start moving backwards. One example of this is Tasmania, which was isolated and lost a lot of the technologies it started off with.
RB: Lots of people like to point out that just because the world has been getting better doesn’t guarantee it will continue to do so. How do you respond to that line of argumentation?
MR: There is a quote by Thomas Babington Macaulay from 1830, where he was fed up with the pessimists of the time saying things will only get worse. He says, “On what principle is it that with nothing but improvement behind us, we are to expect nothing but deterioration before us?” And this was back in the 1830s, where in Britain and a few other parts of the world, we were only seeing the beginning of the rise of living standards. It’s perverse to argue that because things were getting better in the past, now they are about to get worse.
“I think it’s worth remembering that good news tends to be gradual, and bad news tends to be sudden. Hence, the good stuff is rarely going to make the news.”
Another thing to point out is that people have always said this. Every generation thought they were at the peak looking downhill. If you think about the opportunities technology is about to give us, whether it’s through blockchain, gene editing, or artificial intelligence, there is every reason to believe that 2017 is going to look like a time of absolute misery compared to what our children and grandchildren are going to experience.
RB: There seems to be a fair amount of mayhem in today’s world, and lots of valid problems to pay attention to in the news. What would you say to empower our readers that we will push through it and continue to grow and improve as a species?
MR: I think it’s worth remembering that good news tends to be gradual, and bad news tends to be sudden. Hence, the good stuff is rarely going to make the news. It’s happening in an inexorable way, as a result of ordinary people exchanging, specializing, collaborating, and innovating, and it’s surprisingly hard to stop it.
Even if you look back to the 1940s, at the end of a world war, there was still a lot of innovation happening. In some ways it feels like we are going through a bad period now. I do worry a lot about the anti-enlightenment values that I see spreading in various parts of the world. But then I remind myself that people are working on innovative projects in the background, and these things are going to come through and push us forward.
Image Credit: Sahacha Nilkumhang / Shutterstock.com
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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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The brain has long inspired the design of computers and their software. Now Intel has become the latest tech company to decide that mimicking the brain’s hardware could be the next stage in the evolution of computing.
On Monday the company unveiled an experimental “neuromorphic” chip called Loihi. Neuromorphic chips are microprocessors whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses.
While neural networks—the in vogue approach to artificial intelligence and machine learning—are also inspired by the brain and use layers of virtual neurons, they are still implemented on conventional silicon hardware such as CPUs and GPUs.
The main benefit of mimicking the architecture of the brain on a physical chip, say neuromorphic computing’s proponents, is energy efficiency—the human brain runs on roughly 20 watts. The “neurons” in neuromorphic chips carry out the role of both processor and memory which removes the need to shuttle data back and forth between separate units, which is how traditional chips work. Each neuron also only needs to be powered while it’s firing.
At present, most machine learning is done in data centers due to the massive energy and computing requirements. Creating chips that capture some of nature’s efficiency could allow AI to be run directly on devices like smartphones, cars, and robots.
This is exactly the kind of application Michael Mayberry, managing director of Intel’s research arm, touts in a blog post announcing Loihi. He talks about CCTV cameras that can run image recognition to identify missing persons or traffic lights that can track traffic flow to optimize timing and keep vehicles moving.
There’s still a long way to go before that happens though. According to Wired, so far Intel has only been working with prototypes, and the first full-size version of the chip won’t be built until November.
Once complete, it will feature 130,000 neurons and 130 million synaptic connections split between 128 computing cores. The device will be 1,000 times more energy-efficient than standard approaches, according to Mayberry, but more impressive are claims the chip will be capable of continuous learning.
Intel’s newly launched self-learning neuromorphic chip.
Normally deep learning works by training a neural network on giant datasets to create a model that can then be applied to new data. The Loihi chip will combine training and inference on the same chip, which will allow it to learn on the fly, constantly updating its models and adapting to changing circumstances without having to be deliberately re-trained.
A select group of universities and research institutions will be the first to get their hands on the new chip in the first half of 2018, but Mayberry said it could be years before it’s commercially available. Whether commercialization happens at all may largely depend on whether early adopters can get the hardware to solve any practically useful problems.
So far neuromorphic computing has struggled to gain traction outside the research community. IBM released a neuromorphic chip called TrueNorth in 2014, but the device has yet to showcase any commercially useful applications.
Lee Gomes summarizes the hurdles facing neuromorphic computing excellently in IEEE Spectrum. One is that deep learning can run on very simple, low-precision hardware that can be optimized to use very little power, which suggests complicated new architectures may struggle to find purchase.
It’s also not easy to transfer deep learning approaches developed on conventional chips over to neuromorphic hardware, and even Intel Labs chief scientist Narayan Srinivasa admitted to Forbes Loihi wouldn’t work well with some deep learning models.
Finally, there’s considerable competition in the quest to develop new computer architectures specialized for machine learning. GPU vendors Nvidia and AMD have pivoted to take advantage of this newfound market and companies like Google and Microsoft are developing their own in-house solutions.
Intel, for its part, isn’t putting all its eggs in one basket. Last year it bought two companies building chips for specialized machine learning—Movidius and Nervana—and this was followed up with the $15 billion purchase of self-driving car chip- and camera-maker Mobileye.
And while the jury is still out on neuromorphic computing, it makes sense for a company eager to position itself as the AI chipmaker of the future to have its fingers in as many pies as possible. There are a growing number of voices suggesting that despite its undoubted power, deep learning alone will not allow us to imbue machines with the kind of adaptable, general intelligence humans possess.
What new approaches will get us there are hard to predict, but it’s entirely possible they will only work on hardware that closely mimics the one device we already know is capable of supporting this kind of intelligence—the human brain.
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Each week we scour the web for great articles and fascinating advances across our core topics, from AI to biotech and the brain. But robots have a special place in our hearts. This week, we took a look back at 2017 so far and unearthed a few favorite robots for your reading and viewing pleasure.
Tarzan the Swinging Robot Could Be the Future of FarmingMariella Moon | Engadget“Tarzan will be able to swing over crops using its 3D-printed claws and parallel guy-wires stretched over fields. It will then take measurements and pictures of each plant with its built-in camera while suspended…While it may take some time to achieve that goal, the researchers plan to start testing the robot soon.”
Grasping Robots Compete to Rule Amazon’s Warehouses Tom Simonite | Wired“Robots able to help with so-called picking tasks would boost Amazon’s efficiency—and make it much less reliant on human workers. It’s why the company has invited a motley crew of mechanical arms, grippers, suction cups—and their human handlers—to Nagoya, Japan, this week to show off their manipulation skills.”
Robots Learn to Speak Body LanguageAlyssa Pagano | IEEE Spectrum“One notable feature of the OpenPose system is that it can track not only a person’s head, torso, and limbs but also individual fingers. To do that, the researchers used CMU’s Panoptic Studio, a dome lined with 500 cameras, where they captured body poses at a variety of angles and then used those images to build a data set.”
I Watched Two Robots Chat Together on Stage at a Tech EventJon Russell | TechCrunch“The robots in question are Sophia and Han, and they belong to Hanson Robotics, a Hong Kong-based company that is developing and deploying artificial intelligence in humanoids. The duo took to the stage at Rise in Hong Kong with Hanson Robotics’ Chief Scientist Ben Goertzel directing the banter. The conversation, which was partially scripted, wasn’t as slick as the human-to-human panels at the show, but it was certainly a sight to behold for the packed audience.”
How This Japanese Robotics Master Is Building Better, More Human AndroidsHarry McCracken | Fast Company“On the tech side, making a robot look and behave like a person involves everything from electronics to the silicone Ishiguro’s team uses to simulate skin. ‘We have a technology to precisely control pneumatic actuators,’ he says, noting, as an example of what they need to re-create, that ‘the human shoulder has four degrees of freedom.’”
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