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#432262 How We Can ‘Robot-Proof’ Education ...
Like millions of other individuals in the workforce, you’re probably wondering if you will one day be replaced by a machine. If you’re a student, you’re probably wondering if your chosen profession will even exist by the time you’ve graduated. From driving to legal research, there isn’t much that technology hasn’t already automated (or begun to automate). Many of us will need to adapt to this disruption in the workforce.
But it’s not enough for students and workers to adapt, become lifelong learners, and re-skill themselves. We also need to see innovation and initiative at an institutional and governmental level. According to research by The Economist, almost half of all jobs could be automated by computers within the next two decades, and no government in the world is prepared for it.
While many see the current trend in automation as a terrifying threat, others see it as an opportunity. In Robot-Proof: Higher Education in the Age of Artificial Intelligence, Northeastern University president Joseph Aoun proposes educating students in a way that will allow them to do the things that machines can’t. He calls for a new paradigm that teaches young minds “to invent, to create, and to discover”—filling the relevant needs of our world that robots simply can’t fill. Aoun proposes a much-needed novel framework that will allow us to “robot-proof” education.
Literacies and Core Cognitive Capacities of the Future
Aoun lays a framework for a new discipline, humanics, which discusses the important capacities and literacies for emerging education systems. At its core, the framework emphasizes our uniquely human abilities and strengths.
The three key literacies include data literacy (being able to manage and analyze big data), technological literacy (being able to understand exponential technologies and conduct computational thinking), and human literacy (being able to communicate and evaluate social, ethical, and existential impact).
Beyond the literacies, at the heart of Aoun’s framework are four cognitive capacities that are crucial to develop in our students if they are to be resistant to automation: critical thinking, systems thinking, entrepreneurship, and cultural agility.
“These capacities are mindsets rather than bodies of knowledge—mental architecture rather than mental furniture,” he writes. “Going forward, people will still need to know specific bodies of knowledge to be effective in the workplace, but that alone will not be enough when intelligent machines are doing much of the heavy lifting of information. To succeed, tomorrow’s employees will have to demonstrate a higher order of thought.”
Like many other experts in education, Joseph Aoun emphasizes the importance of critical thinking. This is important not just when it comes to taking a skeptical approach to information, but also being able to logically break down a claim or problem into multiple layers of analysis. We spend so much time teaching students how to answer questions that we often neglect to teach them how to ask questions. Asking questions—and asking good ones—is a foundation of critical thinking. Before you can solve a problem, you must be able to critically analyze and question what is causing it. This is why critical thinking and problem solving are coupled together.
The second capacity, systems thinking, involves being able to think holistically about a problem. The most creative problem-solvers and thinkers are able to take a multidisciplinary perspective and connect the dots between many different fields. According to Aoun, it “involves seeing across areas that machines might be able to comprehend individually but that they cannot analyze in an integrated way, as a whole.” It represents the absolute opposite of how most traditional curricula is structured with emphasis on isolated subjects and content knowledge.
Among the most difficult-to-automate tasks or professions is entrepreneurship.
In fact, some have gone so far as to claim that in the future, everyone will be an entrepreneur. Yet traditionally, initiative has been something students show in spite of or in addition to their schoolwork. For most students, developing a sense of initiative and entrepreneurial skills has often been part of their extracurricular activities. It needs to be at the core of our curricula, not a supplement to it. At its core, teaching entrepreneurship is about teaching our youth to solve complex problems with resilience, to become global leaders, and to solve grand challenges facing our species.
Finally, with an increasingly globalized world, there is a need for more workers with cultural agility, the ability to build amongst different cultural contexts and norms.
One of the major trends today is the rise of the contingent workforce. We are seeing an increasing percentage of full-time employees working on the cloud. Multinational corporations have teams of employees collaborating at different offices across the planet. Collaboration across online networks requires a skillset of its own. As education expert Tony Wagner points out, within these digital contexts, leadership is no longer about commanding with top-down authority, but rather about leading by influence.
An Emphasis on Creativity
The framework also puts an emphasis on experiential or project-based learning, wherein the heart of the student experience is not lectures or exams but solving real-life problems and learning by doing, creating, and executing. Unsurprisingly, humans continue to outdo machines when it comes to innovating and pushing intellectual, imaginative, and creative boundaries, making jobs involving these skills the hardest to automate.
In fact, technological trends are giving rise to what many thought leaders refer to as the imagination economy. This is defined as “an economy where intuitive and creative thinking create economic value, after logical and rational thinking have been outsourced to other economies.” Consequently, we need to develop our students’ creative abilities to ensure their success against machines.
In its simplest form, creativity represents the ability to imagine radical ideas and then go about executing them in reality.
In many ways, we are already living in our creative imaginations. Consider this: every invention or human construct—whether it be the spaceship, an architectural wonder, or a device like an iPhone—once existed as a mere idea, imagined in someone’s mind. The world we have designed and built around us is an extension of our imaginations and is only possible because of our creativity. Creativity has played a powerful role in human progress—now imagine what the outcomes would be if we tapped into every young mind’s creative potential.
The Need for a Radical Overhaul
What is clear from the recommendations of Aoun and many other leading thinkers in this space is that an effective 21st-century education system is radically different from the traditional systems we currently have in place. There is a dramatic contrast between these future-oriented frameworks and the way we’ve structured our traditional, industrial-era and cookie-cutter-style education systems.
It’s time for a change, and incremental changes or subtle improvements are no longer enough. What we need to see are more moonshots and disruption in the education sector. In a world of exponential growth and accelerating change, it is never too soon for a much-needed dramatic overhaul.
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Posted in Human Robots
#431872 AI Uses Titan Supercomputer to Create ...
You don’t have to dig too deeply into the archive of dystopian science fiction to uncover the horror that intelligent machines might unleash. The Matrix and The Terminator are probably the most well-known examples of self-replicating, intelligent machines attempting to enslave or destroy humanity in the process of building a brave new digital world.
The prospect of artificially intelligent machines creating other artificially intelligent machines took a big step forward in 2017. However, we’re far from the runaway technological singularity futurists are predicting by mid-century or earlier, let alone murderous cyborgs or AI avatar assassins.
The first big boost this year came from Google. The tech giant announced it was developing automated machine learning (AutoML), writing algorithms that can do some of the heavy lifting by identifying the right neural networks for a specific job. Now researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL), using the most powerful supercomputer in the US, have developed an AI system that can generate neural networks as good if not better than any developed by a human in less than a day.
It can take months for the brainiest, best-paid data scientists to develop deep learning software, which sends data through a complex web of mathematical algorithms. The system is modeled after the human brain and known as an artificial neural network. Even Google’s AutoML took weeks to design a superior image recognition system, one of the more standard operations for AI systems today.
Computing Power
Of course, Google Brain project engineers only had access to 800 graphic processing units (GPUs), a type of computer hardware that works especially well for deep learning. Nvidia, which pioneered the development of GPUs, is considered the gold standard in today’s AI hardware architecture. Titan, the supercomputer at ORNL, boasts more than 18,000 GPUs.
The ORNL research team’s algorithm, called MENNDL for Multinode Evolutionary Neural Networks for Deep Learning, isn’t designed to create AI systems that cull cute cat photos from the internet. Instead, MENNDL is a tool for testing and training thousands of potential neural networks to work on unique science problems.
That requires a different approach from the Google and Facebook AI platforms of the world, notes Steven Young, a postdoctoral research associate at ORNL who is on the team that designed MENNDL.
“We’ve discovered that those [neural networks] are very often not the optimal network for a lot of our problems, because our data, while it can be thought of as images, is different,” he explains to Singularity Hub. “These images, and the problems, have very different characteristics from object detection.”
AI for Science
One application of the technology involved a particle physics experiment at the Fermi National Accelerator Laboratory. Fermilab researchers are interested in understanding neutrinos, high-energy subatomic particles that rarely interact with normal matter but could be a key to understanding the early formation of the universe. One Fermilab experiment involves taking a sort of “snapshot” of neutrino interactions.
The team wanted the help of an AI system that could analyze and classify Fermilab’s detector data. MENNDL evaluated 500,000 neural networks in 24 hours. Its final solution proved superior to custom models developed by human scientists.
In another case involving a collaboration with St. Jude Children’s Research Hospital in Memphis, MENNDL improved the error rate of a human-designed algorithm for identifying mitochondria inside 3D electron microscopy images of brain tissue by 30 percent.
“We are able to do better than humans in a fraction of the time at designing networks for these sort of very different datasets that we’re interested in,” Young says.
What makes MENNDL particularly adept is its ability to define the best or most optimal hyperparameters—the key variables—to tackle a particular dataset.
“You don’t always need a big, huge deep network. Sometimes you just need a small network with the right hyperparameters,” Young says.
A Virtual Data Scientist
That’s not dissimilar to the approach of a company called H20.ai, a startup out of Silicon Valley that uses open source machine learning platforms to “democratize” AI. It applies machine learning to create business solutions for Fortune 500 companies, including some of the world’s biggest banks and healthcare companies.
“Our software is more [about] pattern detection, let’s say anti-money laundering or fraud detection or which customer is most likely to churn,” Dr. Arno Candel, chief technology officer at H2O.ai, tells Singularity Hub. “And that kind of insight-generating software is what we call AI here.”
The company’s latest product, Driverless AI, promises to deliver the data scientist equivalent of a chessmaster to its customers (the company claims several such grandmasters in its employ and advisory board). In other words, the system can analyze a raw dataset and, like MENNDL, automatically identify what features should be included in the computer model to make the most of the data based on the best “chess moves” of its grandmasters.
“So we’re using those algorithms, but we’re giving them the human insights from those data scientists, and we automate their thinking,” he explains. “So we created a virtual data scientist that is relentless at trying these ideas.”
Inside the Black Box
Not unlike how the human brain reaches a conclusion, it’s not always possible to understand how a machine, despite being designed by humans, reaches its own solutions. The lack of transparency is often referred to as the AI “black box.” Experts like Young say we can learn something about the evolutionary process of machine learning by generating millions of neural networks and seeing what works well and what doesn’t.
“You’re never going to be able to completely explain what happened, but maybe we can better explain it than we currently can today,” Young says.
Transparency is built into the “thought process” of each particular model generated by Driverless AI, according to Candel.
The computer even explains itself to the user in plain English at each decision point. There is also real-time feedback that allows users to prioritize features, or parameters, to see how the changes improve the accuracy of the model. For example, the system may include data from people in the same zip code as it creates a model to describe customer turnover.
“That’s one of the advantages of our automatic feature engineering: it’s basically mimicking human thinking,” Candel says. “It’s not just neural nets that magically come up with some kind of number, but we’re trying to make it statistically significant.”
Moving Forward
Much digital ink has been spilled over the dearth of skilled data scientists, so automating certain design aspects for developing artificial neural networks makes sense. Experts agree that automation alone won’t solve that particular problem. However, it will free computer scientists to tackle more difficult issues, such as parsing the inherent biases that exist within the data used by machine learning today.
“I think the world has an opportunity to focus more on the meaning of things and not on the laborious tasks of just fitting a model and finding the best features to make that model,” Candel notes. “By automating, we are pushing the burden back for the data scientists to actually do something more meaningful, which is think about the problem and see how you can address it differently to make an even bigger impact.”
The team at ORNL expects it can also make bigger impacts beginning next year when the lab’s next supercomputer, Summit, comes online. While Summit will boast only 4,600 nodes, it will sport the latest and greatest GPU technology from Nvidia and CPUs from IBM. That means it will deliver more than five times the computational performance of Titan, the world’s fifth-most powerful supercomputer today.
“We’ll be able to look at much larger problems on Summit than we were able to with Titan and hopefully get to a solution much faster,” Young says.
It’s all in a day’s work.
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Posted in Human Robots
#431653 9 Robot Animals Built From Nature’s ...
Millions of years of evolution have allowed animals to develop some elegant and highly efficient solutions to problems like locomotion, flight, and dexterity. As Boston Dynamics unveils its latest mechanical animals, here’s a rundown of nine recent robots that borrow from nature and why.
SpotMini – Boston Dynamics
Starting with BigDog in 2005, the US company has built a whole stable of four-legged robots in recent years. Their first product was designed to be a robotic packhorse for soldiers that borrowed the quadrupedal locomotion of animals to travel over terrain too rough for conventional vehicles.
The US Army ultimately rejected the robot for being too noisy, according to the Guardian, but since then the company has scaled down its design, first to the Spot, then a first edition of the SpotMini that came out last year.
The latter came with a robotic arm where its head should be and was touted as a domestic helper, but a sleeker second edition without the arm was released earlier this month. There’s little detail on what the new robot is designed for, but the more polished design suggests a more consumer-focused purpose.
OctopusGripper – Festo
Festo has released a long line of animal-inspired machines over the years, from a mechanical kangaroo to robotic butterflies. Its latest creation isn’t a full animal—instead it’s a gripper based on an octopus tentacle that can be attached to the end of a robotic arm.
The pneumatically-powered device is made of soft silicone and features two rows of suction cups on its inner edge. By applying compressed air the tentacle can wrap around a wide variety of differently shaped objects, just like its natural counterpart, and a vacuum can be applied to the larger suction cups to grip the object securely. Because it’s soft, it holds promise for robots required to operate safely in collaboration with humans.
CRAM – University of California, Berkeley
Cockroaches are renowned for their hardiness and ability to disappear down cracks that seem far too small for them. Researchers at UC Berkeley decided these capabilities could be useful for search and rescue missions and so set about experimenting on the insects to find out their secrets.
They found the bugs can squeeze into gaps a fifth of their normal standing height by splaying their legs out to the side without significantly slowing themselves down. So they built a palm-sized robot with a jointed plastic shell that could do the same to squeeze into crevices half its normal height.
Snake Robot – Carnegie Mellon University
Search and rescue missions are a common theme for animal-inspired robots, but the snake robot built by CMU researchers is one of the first to be tested in a real disaster.
A team of roboticists from the university helped Mexican Red Cross workers search collapsed buildings for survivors after the 7.1-magnitude earthquake that struck Mexico City in September. The snake design provides a small diameter and the ability to move in almost any direction, which makes the robot ideal for accessing tight spaces, though the team was unable to locate any survivors.
The snake currently features a camera on the front, but researchers told IEEE Spectrum that the experience helped them realize they should also add a microphone to listen for people trapped under the rubble.
Bio-Hybrid Stingray – Harvard University
Taking more than just inspiration from the animal kingdom, a group from Harvard built a robotic stingray out of silicone and rat heart muscle cells.
The robot uses the same synchronized undulations along the edge of its fins to propel itself as a ray does. But while a ray has two sets of muscles to pull the fins up and down, the new device has only one that pulls them down, with a springy gold skeleton that pulls them back up again. The cells are also genetically modified to be activated by flashes of light.
The project’s leader eventually hopes to engineer a human heart, and both his stingray and an earlier jellyfish bio-robot are primarily aimed at better understanding how that organ works.
Bat Bot – Caltech
Most recent advances in drone technology have come from quadcopters, but Caltech engineers think rigid devices with rapidly spinning propellers are probably not ideal for use in close quarters with humans.
That’s why they turned to soft-winged bats for inspiration. That’s no easy feat, though, considering bats use more than 40 joints with each flap of their wings, so the team had to optimize down to nine joints to avoid it becoming too bulky. The simplified bat can’t ascend yet, but its onboard computer and sensors let it autonomously carry out glides, turns, and dives.
Salto – UC Berkeley
While even the most advanced robots tend to plod around, tree-dwelling animals have the ability to spring from branch to branch to clear obstacles and climb quickly. This could prove invaluable for search and rescue robots by allowing them to quickly traverse disordered rubble.
UC Berkeley engineers turned to the Senegal bush baby for inspiration after determining it scored highest in “vertical jumping agility”—a combination of how high and how frequently an animal can jump. They recreated its ability to get into a super-low crouch that stores energy in its tendons to create a robot that could carry out parkour-style double jumps off walls to quickly gain height.
Pleurobot – École Polytechnique Fédérale de Lausanne
Normally robots are masters of air, land, or sea, but the robotic salamander built by researchers at EPFL can both walk and swim.
Its designers used X-ray videos to carefully study how the amphibians move before using this to build a true-to-life robotic version using 3D printed bones, motorized joints, and a synthetic nervous system made up of electronic circuitry.
The robot’s low center of mass and segmented legs make it great at navigating rough terrain without losing balance, and the ability to swim gives added versatility. They also hope it will help paleontologists gain a better understanding of the movements of the first tetrapods to transition from water to land, which salamanders are the best living analog of.
Eelume – Eelume
A snakelike body isn’t only useful on land—eels are living proof it’s an efficient way to travel underwater, too. Norwegian robotics company Eelume has borrowed these principles to build a robot capable of sub-sea inspection, maintenance, and repair.
The modular design allows operators to put together their own favored configuration of joints and payloads such as sensors and tools. And while an early version of the robot used the same method of locomotion as an eel, the latest version undergoing sea trials has added a variety of thrusters for greater speeds and more maneuverability.
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Posted in Human Robots
#431599 8 Ways AI Will Transform Our Cities by ...
How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound.
The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, technical fellow and a managing director at Microsoft Research.
Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city. Here’s how they think it will affect eight key domains of city life in the next fifteen years.
1. Transportation
The speed of the transition to AI-guided transport may catch the public by surprise. Self-driving vehicles will be widely adopted by 2020, and it won’t just be cars — driverless delivery trucks, autonomous delivery drones, and personal robots will also be commonplace.
Uber-style “cars as a service” are likely to replace car ownership, which may displace public transport or see it transition towards similar on-demand approaches. Commutes will become a time to relax or work productively, encouraging people to live further from home, which could combine with reduced need for parking to drastically change the face of modern cities.
Mountains of data from increasing numbers of sensors will allow administrators to model individuals’ movements, preferences, and goals, which could have major impact on the design city infrastructure.
Humans won’t be out of the loop, though. Algorithms that allow machines to learn from human input and coordinate with them will be crucial to ensuring autonomous transport operates smoothly. Getting this right will be key as this will be the public’s first experience with physically embodied AI systems and will strongly influence public perception.
2. Home and Service Robots
Robots that do things like deliver packages and clean offices will become much more common in the next 15 years. Mobile chipmakers are already squeezing the power of last century’s supercomputers into systems-on-a-chip, drastically boosting robots’ on-board computing capacity.
Cloud-connected robots will be able to share data to accelerate learning. Low-cost 3D sensors like Microsoft’s Kinect will speed the development of perceptual technology, while advances in speech comprehension will enhance robots’ interactions with humans. Robot arms in research labs today are likely to evolve into consumer devices around 2025.
But the cost and complexity of reliable hardware and the difficulty of implementing perceptual algorithms in the real world mean general-purpose robots are still some way off. Robots are likely to remain constrained to narrow commercial applications for the foreseeable future.
3. Healthcare
AI’s impact on healthcare in the next 15 years will depend more on regulation than technology. The most transformative possibilities of AI in healthcare require access to data, but the FDA has failed to find solutions to the difficult problem of balancing privacy and access to data. Implementation of electronic health records has also been poor.
If these hurdles can be cleared, AI could automate the legwork of diagnostics by mining patient records and the scientific literature. This kind of digital assistant could allow doctors to focus on the human dimensions of care while using their intuition and experience to guide the process.
At the population level, data from patient records, wearables, mobile apps, and personal genome sequencing will make personalized medicine a reality. While fully automated radiology is unlikely, access to huge datasets of medical imaging will enable training of machine learning algorithms that can “triage” or check scans, reducing the workload of doctors.
Intelligent walkers, wheelchairs, and exoskeletons will help keep the elderly active while smart home technology will be able to support and monitor them to keep them independent. Robots may begin to enter hospitals carrying out simple tasks like delivering goods to the right room or doing sutures once the needle is correctly placed, but these tasks will only be semi-automated and will require collaboration between humans and robots.
4. Education
The line between the classroom and individual learning will be blurred by 2030. Massive open online courses (MOOCs) will interact with intelligent tutors and other AI technologies to allow personalized education at scale. Computer-based learning won’t replace the classroom, but online tools will help students learn at their own pace using techniques that work for them.
AI-enabled education systems will learn individuals’ preferences, but by aggregating this data they’ll also accelerate education research and the development of new tools. Online teaching will increasingly widen educational access, making learning lifelong, enabling people to retrain, and increasing access to top-quality education in developing countries.
Sophisticated virtual reality will allow students to immerse themselves in historical and fictional worlds or explore environments and scientific objects difficult to engage with in the real world. Digital reading devices will become much smarter too, linking to supplementary information and translating between languages.
5. Low-Resource Communities
In contrast to the dystopian visions of sci-fi, by 2030 AI will help improve life for the poorest members of society. Predictive analytics will let government agencies better allocate limited resources by helping them forecast environmental hazards or building code violations. AI planning could help distribute excess food from restaurants to food banks and shelters before it spoils.
Investment in these areas is under-funded though, so how quickly these capabilities will appear is uncertain. There are fears valueless machine learning could inadvertently discriminate by correlating things with race or gender, or surrogate factors like zip codes. But AI programs are easier to hold accountable than humans, so they’re more likely to help weed out discrimination.
6. Public Safety and Security
By 2030 cities are likely to rely heavily on AI technologies to detect and predict crime. Automatic processing of CCTV and drone footage will make it possible to rapidly spot anomalous behavior. This will not only allow law enforcement to react quickly but also forecast when and where crimes will be committed. Fears that bias and error could lead to people being unduly targeted are justified, but well-thought-out systems could actually counteract human bias and highlight police malpractice.
Techniques like speech and gait analysis could help interrogators and security guards detect suspicious behavior. Contrary to concerns about overly pervasive law enforcement, AI is likely to make policing more targeted and therefore less overbearing.
7. Employment and Workplace
The effects of AI will be felt most profoundly in the workplace. By 2030 AI will be encroaching on skilled professionals like lawyers, financial advisers, and radiologists. As it becomes capable of taking on more roles, organizations will be able to scale rapidly with relatively small workforces.
AI is more likely to replace tasks rather than jobs in the near term, and it will also create new jobs and markets, even if it’s hard to imagine what those will be right now. While it may reduce incomes and job prospects, increasing automation will also lower the cost of goods and services, effectively making everyone richer.
These structural shifts in the economy will require political rather than purely economic responses to ensure these riches are shared. In the short run, this may include resources being pumped into education and re-training, but longer term may require a far more comprehensive social safety net or radical approaches like a guaranteed basic income.
8. Entertainment
Entertainment in 2030 will be interactive, personalized, and immeasurably more engaging than today. Breakthroughs in sensors and hardware will see virtual reality, haptics and companion robots increasingly enter the home. Users will be able to interact with entertainment systems conversationally, and they will show emotion, empathy, and the ability to adapt to environmental cues like the time of day.
Social networks already allow personalized entertainment channels, but the reams of data being collected on usage patterns and preferences will allow media providers to personalize entertainment to unprecedented levels. There are concerns this could endow media conglomerates with unprecedented control over people’s online experiences and the ideas to which they are exposed.
But advances in AI will also make creating your own entertainment far easier and more engaging, whether by helping to compose music or choreograph dances using an avatar. Democratizing the production of high-quality entertainment makes it nearly impossible to predict how highly fluid human tastes for entertainment will develop.
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#431343 How Technology Is Driving Us Toward Peak ...
At some point in the future—and in some ways we are already seeing this—the amount of physical stuff moving around the world will peak and begin to decline. By “stuff,” I am referring to liquid fuels, coal, containers on ships, food, raw materials, products, etc.
New technologies are moving us toward “production-at-the-point-of-consumption” of energy, food, and products with reduced reliance on a global supply chain.
The trade of physical stuff has been central to globalization as we’ve known it. So, this declining movement of stuff may signal we are approaching “peak globalization.”
To be clear, even as the movement of stuff may slow, if not decline, the movement of people, information, data, and ideas around the world is growing exponentially and is likely to continue doing so for the foreseeable future.
Peak globalization may provide a pathway to preserving the best of globalization and global interconnectedness, enhancing economic and environmental sustainability, and empowering individuals and communities to strengthen democracy.
At the same time, some of the most troublesome aspects of globalization may be eased, including massive financial transfers to energy producers and loss of jobs to manufacturing platforms like China. This shift could bring relief to the “losers” of globalization and ease populist, nationalist political pressures that are roiling the developed countries.
That is quite a claim, I realize. But let me explain the vision.
New Technologies and Businesses: Digital, Democratized, Decentralized
The key factors moving us toward peak globalization and making it economically viable are new technologies and innovative businesses and business models allowing for “production-at-the-point-of-consumption” of energy, food, and products.
Exponential technologies are enabling these trends by sharply reducing the “cost of entry” for creating businesses. Driven by Moore’s Law, powerful technologies have become available to almost anyone, anywhere.
Beginning with the microchip, which has had a 100-billion-fold improvement in 40 years—10,000 times faster and 10 million times cheaper—the marginal cost of producing almost everything that can be digitized has fallen toward zero.
A hard copy of a book, for example, will always entail the cost of materials, printing, shipping, etc., even if the marginal cost falls as more copies are produced. But the marginal cost of a second digital copy, such as an e-book, streaming video, or song, is nearly zero as it is simply a digital file sent over the Internet, the world’s largest copy machine.* Books are one product, but there are literally hundreds of thousands of dollars in once-physical, separate products jammed into our devices at little to no cost.
A smartphone alone provides half the human population access to artificial intelligence—from SIRI, search, and translation to cloud computing—geolocation, free global video calls, digital photography and free uploads to social network sites, free access to global knowledge, a million apps for a huge variety of purposes, and many other capabilities that were unavailable to most people only a few years ago.
As powerful as dematerialization and demonetization are for private individuals, they’re having a stronger effect on businesses. A small team can access expensive, advanced tools that before were only available to the biggest organizations. Foundational digital platforms, such as the internet and GPS, and the platforms built on top of them by the likes of Google, Apple, Amazon, and others provide the connectivity and services democratizing business tools and driving the next generation of new startups.
“As these trends gain steam in coming decades, they’ll bleed into and fundamentally transform global supply chains.”
An AI startup, for example, doesn’t need its own server farm to train its software and provide service to customers. The team can rent computing power from Amazon Web Services. This platform model enables small teams to do big things on the cheap. And it isn’t just in software. Similar trends are happening in hardware too. Makers can 3D print or mill industrial grade prototypes of physical stuff in a garage or local maker space and send or sell designs to anyone with a laptop and 3D printer via online platforms.
These are early examples of trends that are likely to gain steam in coming decades, and as they do, they’ll bleed into and fundamentally transform global supply chains.
The old model is a series of large, connected bits of centralized infrastructure. It makes sense to mine, farm, or manufacture in bulk when the conditions, resources, machines, and expertise to do so exist in particular places and are specialized and expensive. The new model, however, enables smaller-scale production that is local and decentralized.
To see this more clearly, let’s take a look at the technological trends at work in the three biggest contributors to the global trade of physical stuff—products, energy, and food.
Products
3D printing (additive manufacturing) allows for distributed manufacturing near the point of consumption, eliminating or reducing supply chains and factory production lines.
This is possible because product designs are no longer made manifest in assembly line parts like molds or specialized mechanical tools. Rather, designs are digital and can be called up at will to guide printers. Every time a 3D printer prints, it can print a different item, so no assembly line needs to be set up for every different product. 3D printers can also print an entire finished product in one piece or reduce the number of parts of larger products, such as engines. This further lessens the need for assembly.
Because each item can be customized and printed on demand, there is no cost benefit from scaling production. No inventories. No shipping items across oceans. No carbon emissions transporting not only the final product but also all the parts in that product shipped from suppliers to manufacturer. Moreover, 3D printing builds items layer by layer with almost no waste, unlike “subtractive manufacturing” in which an item is carved out of a piece of metal, and much or even most of the material can be waste.
Finally, 3D printing is also highly scalable, from inexpensive 3D printers (several hundred dollars) for home and school use to increasingly capable and expensive printers for industrial production. There are also 3D printers being developed for printing buildings, including houses and office buildings, and other infrastructure.
The technology for finished products is only now getting underway, and there are still challenges to overcome, such as speed, quality, and range of materials. But as methods and materials advance, it will likely creep into more manufactured goods.
Ultimately, 3D printing will be a general purpose technology that involves many different types of printers and materials—such as plastics, metals, and even human cells—to produce a huge range of items, from human tissue and potentially human organs to household items and a range of industrial items for planes, trains, and automobiles.
Energy
Renewable energy production is located at or relatively near the source of consumption.
Although electricity generated by solar, wind, geothermal, and other renewable sources can of course be transmitted over longer distances, it is mostly generated and consumed locally or regionally. It is not transported around the world in tankers, ships, and pipelines like petroleum, coal, and natural gas.
Moreover, the fuel itself is free—forever. There is no global price on sun or wind. The people relying on solar and wind power need not worry about price volatility and potential disruption of fuel supplies as a result of political, market, or natural causes.
Renewables have their problems, of course, including intermittency and storage, and currently they work best if complementary to other sources, especially natural gas power plants that, unlike coal plants, can be turned on or off and modulated like a gas stove, and are half the carbon emissions of coal.
Within the next decades or so, it is likely the intermittency and storage problems will be solved or greatly mitigated. In addition, unlike coal and natural gas power plants, solar is scalable, from solar panels on individual homes or even cars and other devices, to large-scale solar farms. Solar can be connected with microgrids and even allow for autonomous electricity generation by homes, commercial buildings, and communities.
It may be several decades before fossil fuel power plants can be phased out, but the development cost of renewables has been falling exponentially and, in places, is beginning to compete with coal and gas. Solar especially is expected to continue to increase in efficiency and decline in cost.
Given these trends in cost and efficiency, renewables should become obviously cheaper over time—if the fuel is free for solar and has to be continually purchased for coal and gas, at some point the former is cheaper than the latter. Renewables are already cheaper if externalities such as carbon emissions and environmental degradation involved in obtaining and transporting the fuel are included.
Food
Food can be increasingly produced near the point of consumption with vertical farms and eventually with printed food and even printed or cultured meat.
These sources bring production of food very near the consumer, so transportation costs, which can be a significant portion of the cost of food to consumers, are greatly reduced. The use of land and water are reduced by 95% or more, and energy use is cut by nearly 50%. In addition, fertilizers and pesticides are not required and crops can be grown 365 days a year whatever the weather and in more climates and latitudes than is possible today.
While it may not be practical to grow grains, corn, and other such crops in vertical farms, many vegetables and fruits can flourish in such facilities. In addition, cultured or printed meat is being developed—the big challenge is scaling up and reducing cost—that is based on cells from real animals without slaughtering the animals themselves.
There are currently some 70 billion animals being raised for food around the world [PDF] and livestock alone counts for about 15% of global emissions. Moreover, livestock places huge demands on land, water, and energy. Like vertical farms, cultured or printed meat could be produced with no more land use than a brewery and with far less water and energy.
A More Democratic Economy Goes Bottom Up
This is a very brief introduction to the technologies that can bring “production-at-the-point-of-consumption” of products, energy, and food to cities and regions.
What does this future look like? Here’s a simplified example.
Imagine a universal manufacturing facility with hundreds of 3D printers printing tens of thousands of different products on demand for the local community—rather than assembly lines in China making tens of thousands of the same product that have to be shipped all over the world since no local market can absorb all of the same product.
Nearby, a vertical farm and cultured meat facility produce much of tomorrow night’s dinner. These facilities would be powered by local or regional wind and solar. Depending on need and quality, some infrastructure and machinery, like solar panels and 3D printers, would live in these facilities and some in homes and businesses.
The facilities could be owned by a large global corporation—but still locally produce goods—or they could be franchised or even owned and operated independently by the local population. Upkeep and management at each would provide jobs for communities nearby. Eventually, not only would global trade of parts and products diminish, but even required supplies of raw materials and feed stock would decline since there would be less waste in production, and many materials would be recycled once acquired.
“Peak globalization could be a viable pathway to an economic foundation that puts people first while building a more economically and environmentally sustainable future.”
This model suggests a shift toward a “bottom up” economy that is more democratic, locally controlled, and likely to generate more local jobs.
The global trends in democratization of technology make the vision technologically plausible. Much of this technology already exists and is improving and scaling while exponentially decreasing in cost to become available to almost anyone, anywhere.
This includes not only access to key technologies, but also to education through digital platforms available globally. Online courses are available for free, ranging from advanced physics, math, and engineering to skills training in 3D printing, solar installations, and building vertical farms. Social media platforms can enable local and global collaboration and sharing of knowledge and best practices.
These new communities of producers can be the foundation for new forms of democratic governance as they recognize and “capitalize” on the reality that control of the means of production can translate to political power. More jobs and local control could weaken populist, anti-globalization political forces as people recognize they could benefit from the positive aspects of globalization and international cooperation and connectedness while diminishing the impact of globalization’s downsides.
There are powerful vested interests that stand to lose in such a global structural shift. But this vision builds on trends that are already underway and are gaining momentum. Peak globalization could be a viable pathway to an economic foundation that puts people first while building a more economically and environmentally sustainable future.
This article was originally posted on Open Democracy (CC BY-NC 4.0). The version above was edited with the author for length and includes additions. Read the original article on Open Democracy.
* See Jeremy Rifkin, The Zero Marginal Cost Society, (New York: Palgrave Macmillan, 2014), Part II, pp. 69-154.
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