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#433284 Tech Can Sustainably Feed Developing ...
In the next 30 years, virtually all net population growth will occur in urban regions of developing countries. At the same time, worldwide food production will become increasingly limited by the availability of land, water, and energy. These constraints will be further worsened by climate change and the expected addition of two billion people to today’s four billion now living in urban regions. Meanwhile, current urban food ecosystems in the developing world are inefficient and critically inadequate to meet the challenges of the future.
Combined, these trends could have catastrophic economic and political consequences. A new path forward for urban food ecosystems needs to be found. But what is that path?
New technologies, coupled with new business models and supportive government policies, can create more resilient urban food ecosystems in the coming decades. These tech-enabled systems can sustainably link rural, peri-urban (areas just outside cities), and urban producers and consumers, increase overall food production, and generate opportunities for new businesses and jobs (Figure 1).
Figure 1: The urban food value chain nodes from rural, peri-urban and urban producers
to servicing end customers in urban and peri-urban markets.
Here’s a glimpse of the changes technology may bring to the systems feeding cities in the future.
A technology-linked urban food ecosystem would create unprecedented opportunities for small farms to reach wider markets and progress from subsistence farming to commercially producing niche cash crops and animal protein, such as poultry, fish, pork, and insects.
Meanwhile, new opportunities within cities will appear with the creation of vertical farms and other controlled-environment agricultural systems as well as production of plant-based and 3D printed foods and cultured meat. Uberized facilitation of production and distribution of food will reduce bottlenecks and provide new business opportunities and jobs. Off-the-shelf precision agriculture technology will increasingly be the new norm, from smallholders to larger producers.
As part of Agricultural Revolution 4.0, all this will be integrated into the larger collaborative economy—connected by digital platforms, the cloud, and the Internet of Things and powered by artificial intelligence. It will more efficiently and effectively use resources and people to connect the nexus of food, water, energy, nutrition, and human health. It will also aid in the development of a circular economy that is designed to be restorative and regenerative, minimizing waste and maximizing recycling and reuse to build economic, natural, and social capital.
In short, technology will enable transformation of urban food ecosystems, from expanded production in cities to more efficient and inclusive distribution and closer connections with rural farmers. Here’s a closer look at seven tech-driven trends that will help feed tomorrow’s cities.
1. Worldwide Connectivity: Information, Learning, and Markets
Connectivity from simple cell phone SMS communication to internet-enabled smartphones and cloud services are providing platforms for the increasingly powerful technologies enabling development of a new agricultural revolution. Internet connections currently reach more than 4 billion people, about 55% of the global population. That number will grow fast in coming years.
These information and communications technologies connect food producers to consumers with just-in-time data, enhanced good agricultural practices, mobile money and credit, telecommunications, market information and merchandising, and greater transparency and traceability of goods and services throughout the value chain. Text messages on mobile devices have become the one-stop-shop for small farmers to place orders, gain technology information for best management practices, and access market information to increase profitability.
Hershey’s CocoaLink in Ghana, for example, uses text and voice messages with cocoa industry experts and small farm producers. Digital Green is a technology-enabled communication system in Asia and Africa to bring needed agricultural and management practices to small farmers in their own language by filming and recording successful farmers in their own communities. MFarm is a mobile app that connects Kenyan farmers with urban markets via text messaging.
2. Blockchain Technology: Greater Access to Basic Financial Services and Enhanced Food Safety
Gaining access to credit and executing financial transactions have been persistent constraints for small farm producers. Blockchain promises to help the unbanked access basic financial services.
The Gates Foundation has released an open source platform, Mojaloop, to allow software developers and banks and financial service providers to build secure digital payment platforms at scale. Mojaloop software uses more secure blockchain technology to enable urban food system players in the developing world to conduct business and trade. The free software reduces complexity and cost in building payment platforms to connect small farmers with customers, merchants, banks, and mobile money providers. Such digital financial services will allow small farm producers in the developing world to conduct business without a brick-and-mortar bank.
Blockchain is also important for traceability and transparency requirements to meet food regulatory and consumer requirement during the production, post-harvest, shipping, processing and distribution to consumers. Combining blockchain with RFID technologies also will enhance food safety.
3. Uberized Services: On-Demand Equipment, Storage, and More
Uberized services can advance development of the urban food ecosystem across the spectrum, from rural to peri-urban to urban food production and distribution. Whereas Uber and Airbnb enable sharing of rides and homes, the model can be extended in the developing world to include on-demand use of expensive equipment, such as farm machinery, or storage space.
This includes uberization of planting and harvesting equipment (Hello Tractor), transportation vehicles, refrigeration facilities for temporary storage of perishable product, and “cloud kitchens” (EasyAppetite in Nigeria, FoodCourt in Rwanda, and Swiggy and Zomto in India) that produce fresh meals to be delivered to urban customers, enabling young people with motorbikes and cell phones to become entrepreneurs or contractors delivering meals to urban customers.
Another uberized service is marketing and distributing “ugly food” or imperfect produce to reduce food waste. About a third of the world’s food goes to waste, often because of appearance; this is enough to feed two billion people. Such services supply consumers with cheaper, nutritious, tasty, healthy fruits and vegetables that would normally be discarded as culls due to imperfections in shape or size.
4. Technology for Producing Plant-Based Foods in Cities
We need to change diet choices through education and marketing and by developing tasty plant-based substitutes. This is not only critical for environmental sustainability, but also offers opportunities for new businesses and services. It turns out that current agricultural production systems for “red meat” have a far greater detrimental impact on the environment than automobiles.
There have been great advances in plant-based foods, like the Impossible Burger and Beyond Meat, that can satisfy the consumer’s experience and perception of meat. Rather than giving up the experience of eating red meat, technology is enabling marketable, attractive plant-based products that can potentially drastically reduce world per capita consumption of red meat.
5. Cellular Agriculture, Lab-Grown Meat, and 3D Printed Food
Lab-grown meat, literally meat grown from cultured cells, may radically change where and how protein and food is produced, including the cities where it is consumed. There is a wide range of innovative alternatives to traditional meats that can supplement the need for livestock, farms, and butchers. The history of innovation is about getting rid of the bottleneck in the system, and with meat, the bottleneck is the animal. Finless Foods is a new company trying to replicate fish fillets, for example, while Memphis meats is working on beef and poultry.
3D printing or additive manufacturing is a “general purpose technology” used for making, plastic toys, human tissues, aircraft parts, and buildings. 3D printing can also be used to convert alternative ingredients such as proteins from algae, beet leaves, or insects into tasty and healthy products that can be produced by small, inexpensive printers in home kitchens. The food can be customized for individual health needs as well as preferences. 3D printing can also contribute to the food ecosystem by making possible on-demand replacement parts—which are badly needed in the developing world for tractors, pumps, and other equipment. Catapult Design 3D prints tractor replacement parts as well as corn shellers, cart designs, prosthetic limbs, and rolling water barrels for the Indian market.
6. Alt Farming: Vertical Farms to Produce Food in Urban Centers
Urban food ecosystem production systems will rely not only on field-grown crops, but also on production of food within cities. There are a host of new, alternative production systems using “controlled environmental agriculture.” These include low-cost, protected poly hoop houses, greenhouses, roof-top and sack/container gardens, and vertical farming in buildings using artificial lighting. Vertical farms enable year-round production of selected crops, regardless of weather—which will be increasingly important in response to climate change—and without concern for deteriorating soil conditions that affect crop quality and productivity. AeroFarms claims 390 times more productivity per square foot than normal field production.
7. Biotechnology and Nanotechnology for Sustainable Intensification of Agriculture
CRISPR is a promising gene editing technology that can be used to enhance crop productivity while avoiding societal concerns about GMOs. CRISPR can accelerate traditional breeding and selection programs for developing new climate and disease-resistant, higher-yielding, nutritious crops and animals.
Plant-derived coating materials, developed with nanotechnology, can decrease waste, extend shelf-life and transportability of fruits and vegetables, and significantly reduce post-harvest crop loss in developing countries that lack adequate refrigeration. Nanotechnology is also used in polymers to coat seeds to increase their shelf-life and increase their germination success and production for niche, high-value crops.
Putting It All Together
The next generation “urban food industry” will be part of the larger collaborative economy that is connected by digital platforms, the cloud, and the Internet of Things. A tech-enabled urban food ecosystem integrated with new business models and smart agricultural policies offers the opportunity for sustainable intensification (doing more with less) of agriculture to feed a rapidly growing global urban population—while also creating viable economic opportunities for rural and peri-urban as well as urban producers and value-chain players.
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#432190 In the Future, There Will Be No Limit to ...
New planets found in distant corners of the galaxy. Climate models that may improve our understanding of sea level rise. The emergence of new antimalarial drugs. These scientific advances and discoveries have been in the news in recent months.
While representing wildly divergent disciplines, from astronomy to biotechnology, they all have one thing in common: Artificial intelligence played a key role in their scientific discovery.
One of the more recent and famous examples came out of NASA at the end of 2017. The US space agency had announced an eighth planet discovered in the Kepler-90 system. Scientists had trained a neural network—a computer with a “brain” modeled on the human mind—to re-examine data from Kepler, a space-borne telescope with a four-year mission to seek out new life and new civilizations. Or, more precisely, to find habitable planets where life might just exist.
The researchers trained the artificial neural network on a set of 15,000 previously vetted signals until it could identify true planets and false positives 96 percent of the time. It then went to work on weaker signals from nearly 700 star systems with known planets.
The machine detected Kepler 90i—a hot, rocky planet that orbits its sun about every two Earth weeks—through a nearly imperceptible change in brightness captured when a planet passes a star. It also found a sixth Earth-sized planet in the Kepler-80 system.
AI Handles Big Data
The application of AI to science is being driven by three great advances in technology, according to Ross King from the Manchester Institute of Biotechnology at the University of Manchester, leader of a team that developed an artificially intelligent “scientist” called Eve.
Those three advances include much faster computers, big datasets, and improved AI methods, King said. “These advances increasingly give AI superhuman reasoning abilities,” he told Singularity Hub by email.
AI systems can flawlessly remember vast numbers of facts and extract information effortlessly from millions of scientific papers, not to mention exhibit flawless logical reasoning and near-optimal probabilistic reasoning, King says.
AI systems also beat humans when it comes to dealing with huge, diverse amounts of data.
That’s partly what attracted a team of glaciologists to turn to machine learning to untangle the factors involved in how heat from Earth’s interior might influence the ice sheet that blankets Greenland.
Algorithms juggled 22 geologic variables—such as bedrock topography, crustal thickness, magnetic anomalies, rock types, and proximity to features like trenches, ridges, young rifts, and volcanoes—to predict geothermal heat flux under the ice sheet throughout Greenland.
The machine learning model, for example, predicts elevated heat flux upstream of Jakobshavn Glacier, the fastest-moving glacier in the world.
“The major advantage is that we can incorporate so many different types of data,” explains Leigh Stearns, associate professor of geology at Kansas University, whose research takes her to the polar regions to understand how and why Earth’s great ice sheets are changing, questions directly related to future sea level rise.
“All of the other models just rely on one parameter to determine heat flux, but the [machine learning] approach incorporates all of them,” Stearns told Singularity Hub in an email. “Interestingly, we found that there is not just one parameter…that determines the heat flux, but a combination of many factors.”
The research was published last month in Geophysical Research Letters.
Stearns says her team hopes to apply high-powered machine learning to characterize glacier behavior over both short and long-term timescales, thanks to the large amounts of data that she and others have collected over the last 20 years.
Emergence of Robot Scientists
While Stearns sees machine learning as another tool to augment her research, King believes artificial intelligence can play a much bigger role in scientific discoveries in the future.
“I am interested in developing AI systems that autonomously do science—robot scientists,” he said. Such systems, King explained, would automatically originate hypotheses to explain observations, devise experiments to test those hypotheses, physically run the experiments using laboratory robotics, and even interpret the results. The conclusions would then influence the next cycle of hypotheses and experiments.
His AI scientist Eve recently helped researchers discover that triclosan, an ingredient commonly found in toothpaste, could be used as an antimalarial drug against certain strains that have developed a resistance to other common drug therapies. The research was published in the journal Scientific Reports.
Automation using artificial intelligence for drug discovery has become a growing area of research, as the machines can work orders of magnitude faster than any human. AI is also being applied in related areas, such as synthetic biology for the rapid design and manufacture of microorganisms for industrial uses.
King argues that machines are better suited to unravel the complexities of biological systems, with even the most “simple” organisms are host to thousands of genes, proteins, and small molecules that interact in complicated ways.
“Robot scientists and semi-automated AI tools are essential for the future of biology, as there are simply not enough human biologists to do the necessary work,” he said.
Creating Shockwaves in Science
The use of machine learning, neural networks, and other AI methods can often get better results in a fraction of the time it would normally take to crunch data.
For instance, scientists at the National Center for Supercomputing Applications, located at the University of Illinois at Urbana-Champaign, have a deep learning system for the rapid detection and characterization of gravitational waves. Gravitational waves are disturbances in spacetime, emanating from big, high-energy cosmic events, such as the massive explosion of a star known as a supernova. The “Holy Grail” of this type of research is to detect gravitational waves from the Big Bang.
Dubbed Deep Filtering, the method allows real-time processing of data from LIGO, a gravitational wave observatory comprised of two enormous laser interferometers located thousands of miles apart in California and Louisiana. The research was published in Physics Letters B. You can watch a trippy visualization of the results below.
In a more down-to-earth example, scientists published a paper last month in Science Advances on the development of a neural network called ConvNetQuake to detect and locate minor earthquakes from ground motion measurements called seismograms.
ConvNetQuake uncovered 17 times more earthquakes than traditional methods. Scientists say the new method is particularly useful in monitoring small-scale seismic activity, which has become more frequent, possibly due to fracking activities that involve injecting wastewater deep underground. You can learn more about ConvNetQuake in this video:
King says he believes that in the long term there will be no limit to what AI can accomplish in science. He and his team, including Eve, are currently working on developing cancer therapies under a grant from DARPA.
“Robot scientists are getting smarter and smarter; human scientists are not,” he says. “Indeed, there is arguably a case that human scientists are less good. I don’t see any scientist alive today of the stature of a Newton or Einstein—despite the vast number of living scientists. The Physics Nobel [laureate] Frank Wilczek is on record as saying (10 years ago) that in 100 years’ time the best physicist will be a machine. I agree.”
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#431689 Robotic Materials Will Distribute ...
The classical view of a robot as a mechanical body with a central “brain” that controls its behavior could soon be on its way out. The authors of a recent article in Science Robotics argue that future robots will have intelligence distributed throughout their bodies.
The concept, and the emerging discipline behind it, are variously referred to as “material robotics” or “robotic materials” and are essentially a synthesis of ideas from robotics and materials science. Proponents say advances in both fields are making it possible to create composite materials capable of combining sensing, actuation, computation, and communication and operating independently of a central processing unit.
Much of the inspiration for the field comes from nature, with practitioners pointing to the adaptive camouflage of the cuttlefish’s skin, the ability of bird wings to morph in response to different maneuvers, or the banyan tree’s ability to grow roots above ground to support new branches.
Adaptive camouflage and morphing wings have clear applications in the defense and aerospace sector, but the authors say similar principles could be used to create everything from smart tires able to calculate the traction needed for specific surfaces to grippers that can tailor their force to the kind of object they are grasping.
“Material robotics represents an acknowledgment that materials can absorb some of the challenges of acting and reacting to an uncertain world,” the authors write. “Embedding distributed sensors and actuators directly into the material of the robot’s body engages computational capabilities and offloads the rigid information and computational requirements from the central processing system.”
The idea of making materials more adaptive is not new, and there are already a host of “smart materials” that can respond to stimuli like heat, mechanical stress, or magnetic fields by doing things like producing a voltage or changing shape. These properties can be carefully tuned to create materials capable of a wide variety of functions such as movement, self-repair, or sensing.
The authors say synthesizing these kinds of smart materials, alongside other advanced materials like biocompatible conductors or biodegradable elastomers, is foundational to material robotics. But the approach also involves integration of many different capabilities in the same material, careful mechanical design to make the most of mechanical capabilities, and closing the loop between sensing and control within the materials themselves.
While there are stand-alone applications for such materials in the near term, like smart fabrics or robotic grippers, the long-term promise of the field is to distribute decision-making in future advanced robots. As they are imbued with ever more senses and capabilities, these machines will be required to shuttle huge amounts of control and feedback data to and fro, placing a strain on both their communication and computation abilities.
Materials that can process sensor data at the source and either autonomously react to it or filter the most relevant information to be passed on to the central processing unit could significantly ease this bottleneck. In a press release related to an earlier study, Nikolaus Correll, an assistant professor of computer science at the University of Colorado Boulder who is also an author of the current paper, pointed out this is a tactic used by the human body.
“The human sensory system automatically filters out things like the feeling of clothing rubbing on the skin,” he said. “An artificial skin with possibly thousands of sensors could do the same thing, and only report to a central ‘brain’ if it touches something new.”
There are still considerable challenges to realizing this vision, though, the authors say, noting that so far the young field has only produced proof of concepts. The biggest challenge remains manufacturing robotic materials in a way that combines all these capabilities in a small enough package at an affordable cost.
Luckily, the authors note, the field can draw on convergent advances in both materials science, such as the development of new bulk materials with inherent multifunctionality, and robotics, such as the ever tighter integration of components.
And they predict that doing away with the prevailing dichotomy of “brain versus body” could lay the foundations for the emergence of “robots with brains in their bodies—the foundation of inexpensive and ubiquitous robots that will step into the real world.”
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