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#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.
Image Credit: Asgord / Shutterstock.com Continue reading
Posted in Human Robots
#431427 Why the Best Healthcare Hacks Are the ...
Technology has the potential to solve some of our most intractable healthcare problems. In fact, it’s already doing so, with inventions getting us closer to a medical Tricorder, and progress toward 3D printed organs, and AIs that can do point-of-care diagnosis.
No doubt these applications of cutting-edge tech will continue to push the needle on progress in medicine, diagnosis, and treatment. But what if some of the healthcare hacks we need most aren’t high-tech at all?
According to Dr. Darshak Sanghavi, this is exactly the case. In a talk at Singularity University’s Exponential Medicine last week, Sanghavi told the audience, “We often think in extremely complex ways, but I think a lot of the improvements in health at scale can be done in an analog way.”
Sanghavi is the chief medical officer and senior vice president of translation at OptumLabs, and was previously director of preventive and population health at the Center for Medicare and Medicaid Innovation, where he oversaw the development of large pilot programs aimed at improving healthcare costs and quality.
“How can we improve health at scale, not for only a small number of people, but for entire populations?” Sanghavi asked. With programs that benefit a small group of people, he explained, what tends to happen is that the average health of a population improves, but the disparities across the group worsen.
“My mantra became, ‘The denominator is everybody,’” he said. He shared details of some low-tech but crucial fixes he believes could vastly benefit the US healthcare system.
1. Regulatory Hacking
Healthcare regulations are ultimately what drive many aspects of patient care, for better or worse. Worse because the mind-boggling complexity of regulations (exhibit A: the Affordable Care Act is reportedly about 20,000 pages long) can make it hard for people to get the care they need at a cost they can afford, but better because, as Sanghavi explained, tweaking these regulations in the right way can result in across-the-board improvements in a given population’s health.
An adjustment to Medicare hospitalization rules makes for a relevant example. The code was updated to state that if people who left the hospital were re-admitted within 30 days, that hospital had to pay a penalty. The result was hospitals taking more care to ensure patients were released not only in good health, but also with a solid understanding of what they had to do to take care of themselves going forward. “Here, arguably the writing of a few lines of regulatory code resulted in a remarkable decrease in 30-day re-admissions, and the savings of several billion dollars,” Sanghavi said.
2. Long-Term Focus
It’s easy to focus on healthcare hacks that have immediate, visible results—but what about fixes whose benefits take years to manifest? How can we motivate hospitals, regulators, and doctors to take action when they know they won’t see changes anytime soon?
“I call this the reality TV problem,” Sanghavi said. “Reality shows don’t really care about who’s the most talented recording artist—they care about getting the most viewers. That is exactly how we think about health care.”
Sanghavi’s team wanted to address this problem for heart attacks. They found they could reliably determine someone’s 10-year risk of having a heart attack based on a simple risk profile. Rather than monitoring patients’ cholesterol, blood pressure, weight, and other individual factors, the team took the average 10-year risk across entire provider panels, then made providers responsible for controlling those populations.
“Every percentage point you lower that risk, by hook or by crook, you get some people to stop smoking, you get some people on cholesterol medication. It’s patient-centered decision-making, and the provider then makes money. This is the world’s first predictive analytic model, at scale, that’s actually being paid for at scale,” he said.
3. Aligned Incentives
If hospitals are held accountable for the health of the communities they’re based in, those hospitals need to have the right incentives to follow through. “Hospitals have to spend money on community benefit, but linking that benefit to a meaningful population health metric can catalyze significant improvements,” Sanghavi said.
Darshak Sanghavi speaking at Singularity University’s 2017 Exponential Medicine Summit in San Diego, CA.
He used smoking cessation as an example. His team designed a program where hospitals were given a score (determined by the Centers for Disease Control and Prevention) based on the smoking rate in the counties where they’re located, then given monetary incentives to improve their score. Improving their score, in turn, resulted in better health for their communities, which meant fewer patients to treat for smoking-related health problems.
4. Social Determinants of Health
Social determinants of health include factors like housing, income, family, and food security. The answer to getting people to pay attention to these factors at scale, and creating aligned incentives, Sanghavi said, is “Very simple. We just have to measure it to start with, and measure it universally.”
His team was behind a $157 million pilot program called Accountable Health Communities that went live this year. The program requires all Medicare and Medicaid beneficiaries get screened for various social determinants of health. With all that data being collected, analysts can pinpoint local trends, then target funds to address the underlying problem, whether it’s job training, drug use, or nutritional education. “You’re then free to invest the dollars where they’re needed…this is how we can improve health at scale, with very simple changes in the incentive structures that are created,” he said.
5. ‘Securitizing’ Public Health
Sanghavi’s final point tied back to his discussion of aligning incentives. As misguided as it may seem, the reality is that financial incentives can make a huge difference in healthcare outcomes, from both a patient and a provider perspective.
Sanghavi’s team did an experiment in which they created outcome benchmarks for three major health problems that exist across geographically diverse areas: smoking, adolescent pregnancy, and binge drinking. The team proposed measuring the baseline of these issues then creating what they called a social impact bond. If communities were able to lower their frequency of these conditions by a given percent within a stated period of time, they’d get paid for it.
“What that did was essentially say, ‘you have a buyer for this outcome if you can achieve it,’” Sanghavi said. “And you can try to get there in any way you like.” The program is currently in CMS clearance.
AI and Robots Not Required
Using robots to perform surgery and artificial intelligence to diagnose disease will undoubtedly benefit doctors and patients around the US and the world. But Sanghavi’s talk made it clear that our healthcare system needs much more than this, and that improving population health on a large scale is really a low-tech project—one involving more regulatory and financial innovation than technological innovation.
“The things that get measured are the things that get changed,” he said. “If we choose the right outcomes to predict long-term benefit, and we pay for those outcomes, that’s the way to make progress.”
Image Credit: Wonderful Nature / Shutterstock.com Continue reading
Posted in Human Robots
#431424 A ‘Google Maps’ for the Mouse Brain ...
Ask any neuroscientist to draw you a neuron, and it’ll probably look something like a star with two tails: one stubby with extensive tree-like branches, the other willowy, lengthy and dotted with spindly spikes.
While a decent abstraction, this cartoonish image hides the uncomfortable truth that scientists still don’t know much about what many neurons actually look like, not to mention the extent of their connections.
But without untangling the jumbled mess of neural wires that zigzag across the brain, scientists are stumped in trying to answer one of the most fundamental mysteries of the brain: how individual neuronal threads carry and assemble information, which forms the basis of our thoughts, memories, consciousness, and self.
What if there was a way to virtually trace and explore the brain’s serpentine fibers, much like the way Google Maps allows us to navigate the concrete tangles of our cities’ highways?
Thanks to an interdisciplinary team at Janelia Research Campus, we’re on our way. Meet MouseLight, the most extensive map of the mouse brain ever attempted. The ongoing project has an ambitious goal: reconstructing thousands—if not more—of the mouse’s 70 million neurons into a 3D map. (You can play with it here!)
With map in hand, neuroscientists around the world can begin to answer how neural circuits are organized in the brain, and how information flows from one neuron to another across brain regions and hemispheres.
The first release, presented Monday at the Society for Neuroscience Annual Conference in Washington, DC, contains information about the shape and sizes of 300 neurons.
And that’s just the beginning.
“MouseLight’s new dataset is the largest of its kind,” says Dr. Wyatt Korff, director of project teams. “It’s going to change the textbook view of neurons.”
http://mouselight.janelia.org/assets/carousel/ML-Movie.mp4
Brain Atlas
MouseLight is hardly the first rodent brain atlasing project.
The Mouse Brain Connectivity Atlas at the Allen Institute for Brain Science in Seattle tracks neuron activity across small circuits in an effort to trace a mouse’s connectome—a complete atlas of how the firing of one neuron links to the next.
MICrONS (Machine Intelligence from Cortical Networks), the $100 million government-funded “moonshot” hopes to distill brain computation into algorithms for more powerful artificial intelligence. Its first step? Brain mapping.
What makes MouseLight stand out is its scope and level of detail.
MICrONS, for example, is focused on dissecting a cubic millimeter of the mouse visual processing center. In contrast, MouseLight involves tracing individual neurons across the entire brain.
And while connectomics outlines the major connections between brain regions, the birds-eye view entirely misses the intricacies of each individual neuron. This is where MouseLight steps in.
Slice and Dice
With a width only a fraction of a human hair, neuron projections are hard to capture in their native state. Tug or squeeze the brain too hard, and the long, delicate branches distort or even shred into bits.
In fact, previous attempts at trying to reconstruct neurons at this level of detail topped out at just a dozen, stymied by technological hiccups and sky-high costs.
A few years ago, the MouseLight team set out to automate the entire process, with a few time-saving tweaks. Here’s how it works.
After injecting a mouse with a virus that causes a handful of neurons to produce a green-glowing protein, the team treated the brain with a sugar alcohol solution. This step “clears” the brain, transforming the beige-colored organ to translucent, making it easier for light to penetrate and boosting the signal-to-background noise ratio. The brain is then glued onto a small pedestal and ready for imaging.
Building upon an established method called “two-photon microscopy,” the team then tweaked several parameters to reduce imaging time from days (or weeks) down to a fraction of that. Endearingly known as “2P” by the experts, this type of laser microscope zaps the tissue with just enough photos to light up a single plane without damaging the tissue—sharper plane, better focus, crisper image.
After taking an image, the setup activates its vibrating razor and shaves off the imaged section of the brain—a waspy slice about 200 micrometers thick. The process is repeated until the whole brain is imaged.
This setup increased imaging speed by 16 to 48 times faster than conventional microscopy, writes team leader Dr. Jayaram Chandrashekar, who published a version of the method early last year in eLife.
The resulting images strikingly highlight every crook and cranny of a neuronal branch, popping out against a pitch-black background. But pretty pictures come at a hefty data cost: each image takes up a whopping 20 terabytes of data—roughly the storage space of 4,000 DVDs, or 10,000 hours of movies.
Stitching individual images back into 3D is an image-processing nightmare. The MouseLight team used a combination of computational power and human prowess to complete this final step.
The reconstructed images are handed off to a mighty team of seven trained neuron trackers. With the help of tracing algorithms developed in-house and a keen eye, each member can track roughly a neuron a day—significantly less time than the week or so previously needed.
A Numbers Game
Even with just 300 fully reconstructed neurons, MouseLight has already revealed new secrets of the brain.
While it’s widely accepted that axons, the neurons’ outgoing projection, can span the entire length of the brain, these extra-long connections were considered relatively rare. (In fact, one previously discovered “giant neuron” was thought to link to consciousness because of its expansive connections).
Images captured from two-photon microscopy show an axon and dendrites protruding from a neuron’s cell body (sphere in center). Image Credit: Janelia Research Center, MouseLight project team
MouseLight blows that theory out of the water.
The data clearly shows that “giant neurons” are far more common than previously thought. For example, four neurons normally associated with taste had wiry branches that stretched all the way into brain areas that control movement and process touch.
“We knew that different regions of the brain talked to each other, but seeing it in 3D is different,” says Dr. Eve Marder at Brandeis University.
“The results are so stunning because they give you a really clear view of how the whole brain is connected.”
With a tested and true system in place, the team is now aiming to add 700 neurons to their collection within a year.
But appearance is only part of the story.
We can’t tell everything about a person simply by how they look. Neurons are the same: scientists can only infer so much about a neuron’s function by looking at their shape and positions. The team also hopes to profile the gene expression patterns of each neuron, which could provide more hints to their roles in the brain.
MouseLight essentially dissects the neural infrastructure that allows information traffic to flow through the brain. These anatomical highways are just the foundation. Just like Google Maps, roads form only the critical first layer of the map. Street view, traffic information and other add-ons come later for a complete look at cities in flux.
The same will happen for understanding our ever-changing brain.
Image Credit: Janelia Research Campus, MouseLight project team Continue reading
Posted in Human Robots
#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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