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With the Thanksgiving holiday upon us, it’s a great time to reflect on the future of food. Over the last few years, we have seen a dramatic rise in exponential technologies transforming the food industry from seed to plate. Food is important in many ways—too little or too much of it can kill us, and it is often at the heart of family, culture, our daily routines, and our biggest celebrations. The agriculture and food industries are also two of the world’s biggest employers. Let’s take a look to see what is in store for the future.
Over the last few years, we have seen a number of new companies emerge in the robotic farming industry. This includes new types of farming equipment used in arable fields, as well as indoor robotic vertical farms. In November 2017, Hands Free Hectare became the first in the world to remotely grow an arable crop. They used autonomous tractors to sow and spray crops, small rovers to take soil samples, drones to monitor crop growth, and an unmanned combine harvester to collect the crops. Since then, they’ve also grown and harvested a field of winter wheat, and have been adding additional technologies and capabilities to their arsenal of robotic farming equipment.
Indoor vertical farming is also rapidly expanding. As Engadget reported in October 2018, a number of startups are now growing crops like leafy greens, tomatoes, flowers, and herbs. These farms can grow food in urban areas, reducing transport, water, and fertilizer costs, and often don’t need pesticides since they are indoors. IronOx, which is using robots to grow plants with navigation technology used by self-driving cars, can grow 30 times more food per acre of land using 90 percent less water than traditional farmers. Vertical farming company Plenty was recently funded by Softbank’s Vision Fund, Jeff Bezos, and others to build 300 vertical farms in China.
These startups are not only succeeding in wealthy countries. Hello Tractor, an “uberized” tractor, has worked with 250,000 smallholder farms in Africa, creating both food security and tech-infused agriculture jobs. The World Food Progam’s Innovation Accelerator (an impact partner of Singularity University) works with hundreds of startups aimed at creating zero hunger. One project is focused on supporting refugees in developing “food computers” in refugee camps—computerized devices that grow food while also adjusting to the conditions around them. As exponential trends drive down the costs of robotics, sensors, software, and energy, we should see robotic farming scaling around the world and becoming the main way farming takes place.
Exponential technologies are not only revolutionizing how we grow vegetables and grains, but also how we generate protein and meat. The new cultured meat industry is rapidly expanding, led by startups such as Memphis Meats, Mosa Meats, JUST Meat, Inc. and Finless Foods, and backed by heavyweight investors including DFJ, Bill Gates, Richard Branson, Cargill, and Tyson Foods.
Cultured meat is grown in a bioreactor using cells from an animal, a scaffold, and a culture. The process is humane and, potentially, scientists can make the meat healthier by adding vitamins, removing fat, or customizing it to an individual’s diet and health concerns. Another benefit is that cultured meats, if grown at scale, would dramatically reduce environmental destruction, pollution, and climate change caused by the livestock and fishing industries. Similar to vertical farms, cultured meat is produced using technology and can be grown anywhere, on-demand and in a decentralized way.
Similar to robotic farming equipment, bioreactors will also follow exponential trends, rapidly falling in cost. In fact, the first cultured meat hamburger (created by Singularity University faculty Member Mark Post of Mosa Meats in 2013) cost $350,000 dollars. In 2018, Fast Company reported the cost was now about $11 per burger, and the Israeli startup Future Meat Technologies predicted they will produce beef at about $2 per pound in 2020, which will be competitive with existing prices. For those who have turkey on their mind, one can read about New Harvest’s work (one of the leading think tanks and research centers for the cultured meat and cellular agriculture industry) in funding efforts to generate a nugget of cultured turkey meat.
One outstanding question is whether cultured meat is safe to eat and how it will interact with the overall food supply chain. In the US, regulators like the Food and Drug Administration (FDA) and the US Department of Agriculture (USDA) are working out their roles in this process, with the FDA overseeing the cellular process and the FDA overseeing production and labeling.
Tech companies are also making great headway in streamlining food processing. Norwegian company Tomra Foods was an early leader in using imaging recognition, sensors, artificial intelligence, and analytics to more efficiently sort food based on shape, composition of fat, protein, and moisture, and other food safety and quality indicators. Their technologies have improved food yield by 5-10 percent, which is significant given they own 25 percent of their market.
These advances are also not limited to large food companies. In 2016 Google reported how a small family farm in Japan built a world-class cucumber sorting device using their open-source machine learning tool TensorFlow. SU startup Impact Vision uses hyper-spectral imaging to analyze food quality, which increases revenues and reduces food waste and product recalls from contamination.
These examples point to a question many have on their mind: will we live in a future where a few large companies use advanced technologies to grow the majority of food on the planet, or will the falling costs of these technologies allow family farms, startups, and smaller players to take part in creating a decentralized system? Currently, the future could flow either way, but it is important for smaller companies to take advantage of the most cutting-edge technology in order to stay competitive.
Food Purchasing and Delivery
In the last year, we have also seen a number of new developments in technology improving access to food. Amazon Go is opening grocery stores in Seattle, San Francisco, and Chicago where customers use an app that allows them to pick up their products and pay without going through cashier lines. Sam’s Club is not far behind, with an app that also allows a customer to purchase goods in-store.
The market for food delivery is also growing. In 2017, Morgan Stanley estimated that the online food delivery market from restaurants could grow to $32 billion by 2021, from $12 billion in 2017. Companies like Zume are pioneering robot-powered pizza making and delivery. In addition to using robotics to create affordable high-end gourmet pizzas in their shop, they also have a pizza delivery truck that can assemble and cook pizzas while driving. Their system combines predictive analytics using past customer data to prepare pizzas for certain neighborhoods before the orders even come in. In early November 2018, the Wall Street Journal estimated that Zume is valued at up to $2.25 billion.
While each of these developments is promising on its own, it’s also important to note that since all these technologies are in some way digitized and connected to the internet, the various food tech players can collaborate. In theory, self-driving delivery restaurants could share data on what they are selling to their automated farm equipment, facilitating coordination of future crops. There is a tremendous opportunity to improve efficiency, lower costs, and create an abundance of healthy, sustainable food for all.
On the other hand, these technologies are also deeply disruptive. According to the Food and Agricultural Organization of the United Nations, in 2010 about one billion people, or a third of the world’s workforce, worked in the farming and agricultural industries. We need to ensure these farmers are linked to new job opportunities, as well as facilitate collaboration between existing farming companies and technologists so that the industries can continue to grow and lead rather than be displaced.
Just as importantly, each of us might think about how these changes in the food industry might impact our own ways of life and culture. Thanksgiving celebrates community and sharing of food during a time of scarcity. Technology will help create an abundance of food and less need for communities to depend on one another. What are the ways that you will create community, sharing, and culture in this new world?
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Shopping is becoming less and less of a consumer experience—or, for many, less of a chore—as the list of things that can be bought online and delivered to our homes grows to include, well, almost anything you can think of. An Israeli startup is working to make shopping and deliveries even faster and cheaper—and they’re succeeding.
Last week, CommonSense Robotics announced the launch of its first autonomous micro-fulfillment center in Tel Aviv. The company claims the facility is the smallest of its type in the world at 6,000 square feet. For comparison’s sake—most fulfillment hubs that incorporate robotics are at least 120,000 square feet. Amazon’s upcoming facility in Bessemer, Alabama will be a massive 855,000 square feet.
The thing about a building whose square footage is in the hundred-thousands is, you can fit a lot of stuff inside it, but there aren’t many places you can fit the building itself, especially not in major urban areas. So most fulfillment centers are outside cities, which means more time and more money to get your Moroccan oil shampoo, or your vegetable garden starter kit, or your 100-pack of organic protein bars from that fulfillment center to your front door.
CommonSense Robotics built the Tel Aviv center in an area that was previously thought too small for warehouse infrastructure. “In order to fit our site into small, tight urban spaces, we’ve designed every single element of it to optimize for space efficiency,” said Avital Sterngold, VP of operations. Using a robotic sorting system that includes hundreds of robots, plus AI software that assigns them specific tasks, the facility can prepare orders in less than five minutes end-to-end.
It’s not all automated, though—there’s still some human labor in the mix. The robots fetch goods and bring them to a team of people, who then pack the individual orders.
CommonSense raised $20 million this year in a funding round led by Palo Alto-based Playground Global. The company hopes to expand its operations to the US and UK in 2019. Its business model is to charge retailers a fee for each order fulfilled, while maintaining ownership and operation of the fulfillment centers. The first retailers to jump on the bandwagon were Super-Pharm, a drugstore chain, and Rami Levy, a retail supermarket chain.
“Staying competitive in today’s market is anchored by delivering orders quickly and determining how to fulfill and deliver orders efficiently, which are always the most complex aspects of any ecommerce operation. With robotics, we will be able to fulfill and deliver orders in under one hour, all while saving costs on said fulfillment and delivery,” said Super-Pharm VP Yossi Cohen. “Before CommonSense Robotics, we offered our customers next-day home delivery. With this partnership, we are now able to offer our customers same-day delivery and will very soon be offering them one-hour delivery.”
Long live the instant gratification economy—and the increasingly sophisticated technology that’s enabling it.
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Disruptive business models are often powered by alternative financing. In Part 1 of this series, I discussed how mobile is redefining money and banking and shared some of the dramatic transformations in the global remittance infrastructure.
In this article, we’ll discuss:
AI financial advisors and robo traders
Let’s dive right back in…
Decentralized Lending = Democratized Access to Finances
Peer-to-peer (P2P) lending is an age-old practice, traditionally with high risk and extreme locality. Now, the P2P funding model is being digitized and delocalized, bringing lending online and across borders.
Zopa, the first official crowdlending platform, arrived in the United Kingdom in 2004. Since then, the consumer crowdlending platform has facilitated lending of over 3 billion euros ($3.5 billion USD) of loans.
Person-to-business crowdlending took off, again in the U.K., in 2005 with Funding Circle, now with over 5 billion euros (~5.8 billion USD) of capital loaned to small businesses around the world.
Crowdlending next took off in the US in 2006, with platforms like Prosper and Lending Club. The US crowdlending industry has boomed to $21 billion in loans, across 515,000 loans.
Let’s take a step back… to a time before banks, when lending took place between trusted neighbors in small villages across the globe. Lending started as peer-to-peer transactions.
As villages turned into towns, towns turned into cities, and cities turned into sprawling metropolises, neighborly trust and the ability to communicate across urban landscapes broke down. That’s where banks and other financial institutions came into play—to add trust back into the lending equation.
With crowdlending, we are evidently returning to this pre-centralized-banking model of loans, and moving away from cumbersome intermediaries (e.g. high fees, regulations, and extra complexity).
Fueled by the permeation of the internet, P2P lending took on a new form as ‘crowdlending’ in the early 2000s. Now, as blockchain and artificial intelligence arrive on the digital scene, P2P lending platforms are being overhauled with transparency, accountability, reliability, and immutability.
Artificial Intelligence Micro Lending & Credit Scores
We are beginning to augment our quantitative decision-making with neural networks processing borrowers’ financial data to determine their financial ‘fate’ (or, as some call it, your credit score). Companies like Smart Finance Group (backed by Kai Fu Lee and Sinovation Ventures) are using artificial intelligence to minimize default rates for tens of millions of microloans.
Smart Finance is fueled by users’ personal data, particularly smartphone data and usage behavior. Users are required to give Smart Finance access to their smartphone data, so that Smart Finance’s artificial intelligence engine can generate a credit score from the personal information.
The benefits of this AI-powered lending platform do not stop at increased loan payback rates; there’s a massive speed increase as well. Smart Finance loans are frequently approved in under eight seconds. As we’ve seen with other artificial intelligence disruptions, data is the new gold.
Digitizing access to P2P loans paves the way for billions of people currently without access to banking to leapfrog the centralized banking system, just as Africa bypassed landline phones and went straight to mobile. Leapfrogging centralized banking and the credit system is exactly what Smart Finance has done for hundreds of millions of people in China.
As artificial intelligence accesses even the most mundane mobile browsing data to assign credit scores, blockchain technologies, particularly immutable ledgers and smart contracts, are massive disruptors to the archaic banking system, building additional trust and transparency on top of current P2P lending models.
Immutable ledgers provide the necessary transparency for accurate credit and loan defaulting history. Smart contracts executed on these immutable ledgers bring the critical ability to digitally replace cumbersome, expensive third parties (like banks), allowing individual borrowers or businesses to directly connect with willing lenders.
Two of the leading blockchain platforms for P2P lending are ETHLend and SALT Lending.
ETHLend is an Ethereum-based decentralized application aiming to bring transparency and trust to P2P lending through Ethereum network smart contracts.
Secure Automated Lending Technology (SALT) allows cryptocurrency asset holders to use their digital assets as collateral for cash loans, without the need to liquidate their holdings, giving rise to a digital-asset-backed lending market.
While blockchain poses a threat to many of the large, centralized banking institutions, some are taking advantage of the new technology to optimize their internal lending, credit scoring, and collateral operations.
In March 2018, ING and Credit Suisse successfully exchanged 25 million euros using HQLA-X, a blockchain-based collateral lending platform.
HQLA-X runs on the R3 Corda blockchain, a platform designed specifically to help heritage financial and commerce institutions migrate away from their inefficient legacy financial infrastructure.
Blockchain and tokenization are going through their own fintech and regulation shakeup right now. In a future blog, I’ll discuss the various efforts to more readily assure smart contracts, and the disruptive business model of security tokens and the US Securities and Exchange Commission.
Parallels to the Global Abundance of Capital
The abundance of capital being created by the advent of P2P loans closely relates to the unprecedented global abundance of capital.
Initial coin offerings (ICOs) and crowdfunding are taking a strong stand in disrupting the $164 billion venture capital market. The total amount invested in ICOs has risen from $6.6 billion in 2017 to $7.15 billion USD in the first half of 2018. Crowdfunding helped projects raise more than $34 billion in 2017, with experts projecting that global crowdfunding investments will reach $300 billion by 2025.
In the last year alone, using ICOs, over a dozen projects have raised hundreds of millions of dollars in mere hours. Take Filecoin, for example, which raised $257 million in only 30 days; its first $135 million was raised in the first hour. Similarly, the Dragon Coin project (which itself is revolutionizing remittance in high-stakes casinos around the world) raised $320 million in its 30-day public ICO.
Some Important Takeaways…
Technology-backed fundraising and financial services are disrupting the world’s largest financial institutions. Anyone, anywhere, at anytime will be able to access the capital they need to pursue their idea.
The speed at which we can go from “I’ve got an idea” to “I run a billion-dollar company” is moving faster than ever.
Following Ray Kurzweil’s Law of Accelerating Returns, the rapid decrease in time to access capital is intimately linked (and greatly dependent on) a financial infrastructure (technology, institutions, platforms, and policies) that can adapt and evolve just as rapidly.
This new abundance of capital requires financial decision-making with ever-higher market prediction precision. That’s exactly where artificial intelligence is already playing a massive role.
Artificial Intelligence, Robo Traders, and Financial Advisors
On May 6, 2010, the Dow Jones Industrial Average suddenly collapsed by 998.5 points (equal to 8 percent, or $1 trillion). The crash lasted over 35 minutes and is now known as the ‘Flash Crash’. While no one knows the specific reason for this 2010 stock market anomaly, experts widely agree that the Flash Crash had to do with algorithmic trading.
With the ability to have instant, trillion-dollar market impacts, algorithmic trading and artificial intelligence are undoubtedly ingrained in how financial markets operate.
In 2017, CNBC.com estimated that 90 percent of daily trading volume in stock trading is done by machine algorithms, and only 10 percent is carried out directly by humans.
Artificial intelligence and financial management algorithms are not only available to top Wall Street players.
Robo-advisor financial management apps, like Wealthfront and Betterment, are rapidly permeating the global market. Wealthfront currently has $9.5 billion in assets under management, and Betterment has $10 billion.
Artificial intelligent financial agents are already helping financial institutions protect your money and fight fraud. A prime application for machine learning is in detecting anomalies in your spending and transaction habits, and flagging potentially fraudulent transactions.
As artificial intelligence continues to exponentially increase in power and capabilities, increasingly powerful trading and financial management bots will come online, finding massive new and previously lost streams of wealth.
How else are artificial intelligence and automation transforming finance?
Disruptive Remittance and Seamless Transactions
When was the last time you paid in cash at a toll booth? How about for a taxi ride?
EZ-Pass, the electronic tolling company implemented extensively on the East Coast, has done wonders to reduce traffic congestion and increase traffic flow.
Driving down I-95 on the East Coast of the United States, drivers rarely notice their financial transaction with the state’s tolling agencies. The transactions are seamless.
The Uber app enables me to travel without my wallet. I can forget about payment on my trip, free up my mental bandwidth and time for higher-priority tasks. The entire process is digitized and, by extension, automated and integrated into Uber’s platform (Note: This incredible convenience many times causes me to accidentally walk out of taxi cabs without paying!).
In January 2018, we saw the success of the first cutting-edge, AI-powered Amazon Go store open in Seattle, Washington. The store marked a new era in remittance and transactions. Gone are the days of carrying credit cards and cash, and gone are the cash registers. And now, on the heals of these early ‘beta-tests’, Amazon is considering opening as many as 3,000 of these cashierless stores by 2023.
Amazon Go stores use AI algorithms that watch various video feeds (from advanced cameras) throughout the store to identify who picks up groceries, exactly what products they select, and how much to charge that person when they walk out of the store. It’s a grab and go experience.
Let’s extrapolate the notion of seamless, integrated payment systems from Amazon Go and Uber’s removal of post-ride payment to the rest of our day-to-day experience.
Imagine this near future:
As you near the front door of your home, your AI assistant summons a self-driving Uber that takes you to the Hyperloop station (after all, you work in L.A. but live in San Francisco).
At the station, you board your pod, without noticing that your ticket purchase was settled via a wireless payment checkpoint.
After work, you stop at the Amazon Go and pick up dinner. Your virtual AI assistant passes your Amazon account information to the store’s payment checkpoint, as the store’s cameras and sensors track you, your cart and charge you auto-magically.
At home, unbeknownst to you, your AI has already restocked your fridge and pantry with whatever items you failed to pick up at the Amazon Go.
Once we remove the actively transacting aspect of finance, what else becomes possible?
Extraordinary transformations are happening in the finance world. We’ve only scratched the surface of the fintech revolution. All of these transformative financial technologies require high-fidelity assurance, robust insurance, and a mechanism for storing value.
I’ll dive into each of these other facets of financial services in future articles.
For now, thanks to coming global communication networks being deployed on 5G, Alphabet’s LUNE, SpaceX’s Starlink and OneWeb, by 2024, nearly all 8 billion people on Earth will be online.
Once connected, these new minds, entrepreneurs, and customers need access to money and financial services to meaningfully participate in the world economy.
By connecting lenders and borrowers around the globe, decentralized lending drives down global interest rates, increases global financial market participation, and enables economic opportunity to the billions of people who are about to come online.
We’re living in the most abundant time in human history, and fintech is just getting started.
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A research team at the University of Washington has trained an artificial intelligence system to spot obesity—all the way from space. The system used a convolutional neural network (CNN) to analyze 150,000 satellite images and look for correlations between the physical makeup of a neighborhood and the prevalence of obesity.
The team’s results, presented in JAMA Network Open, showed that features of a given neighborhood could explain close to two-thirds (64.8 percent) of the variance in obesity. Researchers found that analyzing satellite data could help increase understanding of the link between peoples’ environment and obesity prevalence. The next step would be to make corresponding structural changes in the way neighborhoods are built to encourage physical activity and better health.
Training AI to Spot Obesity
Convolutional neural networks (CNNs) are particularly adept at image analysis, object recognition, and identifying special hierarchies in large datasets.
Prior to analyzing 150,000 high-resolution satellite images of Bellevue, Seattle, Tacoma, Los Angeles, Memphis, and San Antonio, the researchers trained the CNN on 1.2 million images from the ImageNet database. The categorizations were correlated with obesity prevalence estimates for the six urban areas from census tracts gathered by the 500 Cities project.
The system was able to identify the presence of certain features that increased likelihood of obesity in a given area. Some of these features included tightly–packed houses, being close to roadways, and living in neighborhoods with a lack of greenery.
Visualization of features identified by the convolutional neural network (CNN) model. The images on the left column are satellite images taken from Google Static Maps API (application programming interface). Images in the middle and right columns are activation maps taken from the second convolutional layer of VGG-CNN-F network after forward pass of the respective satellite images through the network. From Google Static Maps API, DigitalGlobe, US Geological Survey (accessed July 2017). Credit: JAMA Network Open
Your Surroundings Are Key
In their discussion of the findings, the researchers stressed that there are limitations to the conclusions that can be drawn from the AI’s results. For example, socio-economic factors like income likely play a major role for obesity prevalence in a given geographic area.
However, the study concluded that the AI-powered analysis showed the prevalence of specific man-made features in neighborhoods consistently correlating with obesity prevalence and not necessarily correlating with socioeconomic status.
The system’s success rates varied between studied cities, with Memphis being the highest (73.3 percent) and Seattle being the lowest (55.8 percent).
AI Takes To the Sky
Around a third of the US population is categorized as obese. Obesity is linked to a number of health-related issues, and the AI-generated results could potentially help improve city planning and better target campaigns to limit obesity.
The study is one of the latest of a growing list that uses AI to analyze images and extrapolate insights.
A team at Stanford University has used a CNN to predict poverty via satellite imagery, assisting governments and NGOs to better target their efforts. A combination of the public Automatic Identification System for shipping, satellite imagery, and Google’s AI has proven able to identify illegal fishing activity. Researchers have even been able to use AI and Google Street View to predict what party a given city will vote for, based on what cars are parked on the streets.
In each case, the AI systems have been able to look at volumes of data about our world and surroundings that are beyond the capabilities of humans and extrapolate new insights. If one were to moralize about the good and bad sides of AI (new opportunities vs. potential job losses, for example) it could seem that it comes down to what we ask AI systems to look at—and what questions we ask of them.
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