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#432893 These 4 Tech Trends Are Driving Us ...
From a first-principles perspective, the task of feeding eight billion people boils down to converting energy from the sun into chemical energy in our bodies.
Traditionally, solar energy is converted by photosynthesis into carbohydrates in plants (i.e., biomass), which are either eaten by the vegans amongst us, or fed to animals, for those with a carnivorous preference.
Today, the process of feeding humanity is extremely inefficient.
If we could radically reinvent what we eat, and how we create that food, what might you imagine that “future of food” would look like?
In this post we’ll cover:
Vertical farms
CRISPR engineered foods
The alt-protein revolution
Farmer 3.0
Let’s dive in.
Vertical Farming
Where we grow our food…
The average American meal travels over 1,500 miles from farm to table. Wine from France, beef from Texas, potatoes from Idaho.
Imagine instead growing all of your food in a 50-story tall vertical farm in downtown LA or off-shore on the Great Lakes where the travel distance is no longer 1,500 miles but 50 miles.
Delocalized farming will minimize travel costs at the same time that it maximizes freshness.
Perhaps more importantly, vertical farming also allows tomorrow’s farmer the ability to control the exact conditions of her plants year round.
Rather than allowing the vagaries of the weather and soil conditions to dictate crop quality and yield, we can now perfectly control the growing cycle.
LED lighting provides the crops with the maximum amount of light, at the perfect frequency, 24 hours a day, 7 days a week.
At the same time, sensors and robots provide the root system the exact pH and micronutrients required, while fine-tuning the temperature of the farm.
Such precision farming can generate yields that are 200% to 400% above normal.
Next let’s explore how we can precision-engineer the genetic properties of the plant itself.
CRISPR and Genetically Engineered Foods
What food do we grow?
A fundamental shift is occurring in our relationship with agriculture. We are going from evolution by natural selection (Darwinism) to evolution by human direction.
CRISPR (the cutting edge gene editing tool) is providing a pathway for plant breeding that is more predictable, faster and less expensive than traditional breeding methods.
Rather than our crops being subject to nature’s random, environmental whim, CRISPR unlocks our capability to modify our crops to match the available environment.
Further, using CRISPR we will be able to optimize the nutrient density of our crops, enhancing their value and volume.
CRISPR may also hold the key to eliminating common allergens from crops. As we identify the allergen gene in peanuts, for instance, we can use CRISPR to silence that gene, making the crops we raise safer for and more accessible to a rapidly growing population.
Yet another application is our ability to make plants resistant to infection or more resistant to drought or cold.
Helping to accelerate the impact of CRISPR, the USDA recently announced that genetically engineered crops will not be regulated—providing an opening for entrepreneurs to capitalize on the opportunities for optimization CRISPR enables.
CRISPR applications in agriculture are an opportunity to help a billion people and become a billionaire in the process.
Protecting crops against volatile environments, combating crop diseases and increasing nutrient values, CRISPR is a promising tool to help feed the world’s rising population.
The Alt-Protein/Lab-Grown Meat Revolution
Something like a third of the Earth’s arable land is used for raising livestock—a massive amount of land—and global demand for meat is predicted to double in the coming decade.
Today, we must grow an entire cow—all bones, skin, and internals included—to produce a steak.
Imagine if we could instead start with a single muscle stem cell and only grow the steak, without needing the rest of the cow? Think of it as cellular agriculture.
Imagine returning millions, perhaps billions, of acres of grazing land back to the wilderness? This is the promise of lab-grown meats.
Lab-grown meat can also be engineered (using technology like CRISPR) to be packed with nutrients and be the healthiest, most delicious protein possible.
We’re watching this technology develop in real time. Several startups across the globe are already working to bring artificial meats to the food industry.
JUST, Inc. (previously Hampton Creek) run by my friend Josh Tetrick, has been on a mission to build a food system where everyone can get and afford delicious, nutritious food. They started by exploring 300,000+ species of plants all around the world to see how they can make food better and now are investing heavily in stem-cell-grown meats.
Backed by Richard Branson and Bill Gates, Memphis Meats is working on ways to produce real meat from animal cells, rather than whole animals. So far, they have produced beef, chicken, and duck using cultured cells from living animals.
As with vertical farming, transitioning production of our majority protein source to a carefully cultivated environment allows for agriculture to optimize inputs (water, soil, energy, land footprint), nutrients and, importantly, taste.
Farmer 3.0
Vertical farming and cellular agriculture are reinventing how we think about our food supply chain and what food we produce.
The next question to answer is who will be producing the food?
Let’s look back at how farming evolved through history.
Farmers 0.0 (Neolithic Revolution, around 9000 BCE): The hunter-gatherer to agriculture transition gains momentum, and humans cultivated the ability to domesticate plants for food production.
Farmers 1.0 (until around the 19th century): Farmers spent all day in the field performing backbreaking labor, and agriculture accounted for most jobs.
Farmers 2.0 (mid-20th century, Green Revolution): From the invention of the first farm tractor in 1812 through today, transformative mechanical biochemical technologies (fertilizer) boosted yields and made the job of farming easier, driving the US farm job rate down to less than two percent today.
Farmers 3.0: In the near future, farmers will leverage exponential technologies (e.g., AI, networks, sensors, robotics, drones), CRISPR and genetic engineering, and new business models to solve the world’s greatest food challenges and efficiently feed the eight-billion-plus people on Earth.
An important driver of the Farmer 3.0 evolution is the delocalization of agriculture driven by vertical and urban farms. Vertical farms and urban agriculture are empowering a new breed of agriculture entrepreneurs.
Let’s take a look at an innovative incubator in Brooklyn, New York called Square Roots.
Ten farm-in-a-shipping-containers in a Brooklyn parking lot represent the first Square Roots campus. Each 8-foot x 8.5-foot x 20-foot shipping container contains an equivalent of 2 acres of produce and can yield more than 50 pounds of produce each week.
For 13 months, one cohort of next-generation food entrepreneurs takes part in a curriculum with foundations in farming, business, community and leadership.
The urban farming incubator raised a $5.4 million seed funding round in August 2017.
Training a new breed of entrepreneurs to apply exponential technology to growing food is essential to the future of farming.
One of our massive transformative purposes at the Abundance Group is to empower entrepreneurs to generate extraordinary wealth while creating a world of abundance. Vertical farms and cellular agriculture are key elements enabling the next generation of food and agriculture entrepreneurs.
Conclusion
Technology is driving food abundance.
We’re already seeing food become demonetized, as the graph below shows.
From 1960 to 2014, the percent of income spent on food in the U.S. fell from 19 percent to under 10 percent of total disposable income—a dramatic decrease over the 40 percent of household income spent on food in 1900.
The dropping percent of per-capita disposable income spent on food. Source: USDA, Economic Research Service, Food Expenditure Series
Ultimately, technology has enabled a massive variety of food at a significantly reduced cost and with fewer resources used for production.
We’re increasingly going to optimize and fortify the food supply chain to achieve more reliable, predictable, and nutritious ways to obtain basic sustenance.
And that means a world with abundant, nutritious, and inexpensive food for every man, woman, and child.
What an extraordinary time to be alive.
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#431828 This Self-Driving AI Is Learning to ...
I don’t have to open the doors of AImotive’s white 2015 Prius to see that it’s not your average car. This particular Prius has been christened El Capitan, the name written below the rear doors, and two small cameras are mounted on top of the car. Bundles of wire snake out from them, as well as from the two additional cameras on the car’s hood and trunk.
Inside is where things really get interesting, though. The trunk holds a computer the size of a microwave, and a large monitor covers the passenger glove compartment and dashboard. The center console has three switches labeled “Allowed,” “Error,” and “Active.”
Budapest-based AImotive is working to provide scalable self-driving technology alongside big players like Waymo and Uber in the autonomous vehicle world. On a highway test ride with CEO Laszlo Kishonti near the company’s office in Mountain View, California, I got a glimpse of just how complex that world is.
Camera-Based Feedback System
AImotive’s approach to autonomous driving is a little different from that of some of the best-known systems. For starters, they’re using cameras, not lidar, as primary sensors. “The traffic system is visual and the cost of cameras is low,” Kishonti said. “A lidar can recognize when there are people near the car, but a camera can differentiate between, say, an elderly person and a child. Lidar’s resolution isn’t high enough to recognize the subtle differences of urban driving.”
Image Credit: AImotive
The company’s aiDrive software uses data from the camera sensors to feed information to its algorithms for hierarchical decision-making, grouped under four concurrent activities: recognition, location, motion, and control.
Kishonti pointed out that lidar has already gotten more cost-efficient, and will only continue to do so.
“Ten years ago, lidar was best because there wasn’t enough processing power to do all the calculations by AI. But the cost of running AI is decreasing,” he said. “In our approach, computer vision and AI processing are key, and for safety, we’ll have fallback sensors like radar or lidar.”
aiDrive currently runs on Nvidia chips, which Kishonti noted were originally designed for graphics, and are not terribly efficient given how power-hungry they are. “We’re planning to substitute lower-cost, lower-energy chips in the next six months,” he said.
Testing in Virtual Reality
Waymo recently announced its fleet has now driven four million miles autonomously. That’s a lot of miles, and hard to compete with. But AImotive isn’t trying to compete, at least not by logging more real-life test miles. Instead, the company is doing 90 percent of its testing in virtual reality. “This is what truly differentiates us from competitors,” Kishonti said.
He outlined the three main benefits of VR testing: it can simulate scenarios too dangerous for the real world (such as hitting something), too costly (not every company has Waymo’s funds to run hundreds of cars on real roads), or too time-consuming (like waiting for rain, snow, or other weather conditions to occur naturally and repeatedly).
“Real-world traffic testing is very skewed towards the boring miles,” he said. “What we want to do is test all the cases that are hard to solve.”
On a screen that looked not unlike multiple games of Mario Kart, he showed me the simulator. Cartoon cars cruised down winding streets, outfitted with all the real-world surroundings: people, trees, signs, other cars. As I watched, a furry kangaroo suddenly hopped across one screen. “Volvo had an issue in Australia,” Kishonti explained. “A kangaroo’s movement is different than other animals since it hops instead of running.” Talk about cases that are hard to solve.
AImotive is currently testing around 1,000 simulated scenarios every night, with a steadily-rising curve of successful tests. These scenarios are broken down into features, and the car’s behavior around those features fed into a neural network. As the algorithms learn more features, the level of complexity the vehicles can handle goes up.
On the Road
After Kishonti and his colleagues filled me in on the details of their product, it was time to test it out. A safety driver sat in the driver’s seat, a computer operator in the passenger seat, and Kishonti and I in back. The driver maintained full control of the car until we merged onto the highway. Then he flicked the “Allowed” switch, his copilot pressed the “Active” switch, and he took his hands off the wheel.
What happened next, you ask?
A few things. El Capitan was going exactly the speed limit—65 miles per hour—which meant all the other cars were passing us. When a car merged in front of us or cut us off, El Cap braked accordingly (if a little abruptly). The monitor displayed the feed from each of the car’s cameras, plus multiple data fields and a simulation where a blue line marked the center of the lane, measured by the cameras tracking the lane markings on either side.
I noticed El Cap wobbling out of our lane a bit, but it wasn’t until two things happened in a row that I felt a little nervous: first we went under a bridge, then a truck pulled up next to us, both bridge and truck casting a complete shadow over our car. At that point El Cap lost it, and we swerved haphazardly to the right, narrowly missing the truck’s rear wheels. The safety driver grabbed the steering wheel and took back control of the car.
What happened, Kishonti explained, was that the shadows made it hard for the car’s cameras to see the lane markings. This was a new scenario the algorithm hadn’t previously encountered. If we’d only gone under a bridge or only been next to the truck for a second, El Cap may not have had so much trouble, but the two events happening in a row really threw the car for a loop—almost literally.
“This is a new scenario we’ll add to our testing,” Kishonti said. He added that another way for the algorithm to handle this type of scenario, rather than basing its speed and positioning on the lane markings, is to mimic nearby cars. “The human eye would see that other cars are still moving at the same speed, even if it can’t see details of the road,” he said.
After another brief—and thankfully uneventful—hands-off cruise down the highway, the safety driver took over, exited the highway, and drove us back to the office.
Driving into the Future
I climbed out of the car feeling amazed not only that self-driving cars are possible, but that driving is possible at all. I squint when driving into a tunnel, swerve to avoid hitting a stray squirrel, and brake gradually at stop signs—all without consciously thinking to do so. On top of learning to steer, brake, and accelerate, self-driving software has to incorporate our brains’ and bodies’ unconscious (but crucial) reactions, like our pupils dilating to let in more light so we can see in a tunnel.
Despite all the progress of machine learning, artificial intelligence, and computing power, I have a wholly renewed appreciation for the thing that’s been in charge of driving up till now: the human brain.
Kishonti seemed to feel similarly. “I don’t think autonomous vehicles in the near future will be better than the best drivers,” he said. “But they’ll be better than the average driver. What we want to achieve is safe, good-quality driving for everyone, with scalability.”
AImotive is currently working with American tech firms and with car and truck manufacturers in Europe, China, and Japan.
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#431808 Should you get your child an AI doll ...
The technological revolution has hit the doll aisle this holiday season in the form of artificial intelligence dolls. The dolls blend a physical toy with either a mobile device and app, or technological sensors, to simulate signs of intelligence. Continue reading