Tag Archives: 2017
#433911 Thanksgiving Food for Thought: The Tech ...
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.
Robotic Farms
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.
Cultured Meat
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.
Food Processing
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.
Looking Ahead
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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#433884 Designer Babies, and Their Babies: How ...
As if stand-alone technologies weren’t advancing fast enough, we’re in age where we must study the intersection points of these technologies. How is what’s happening in robotics influenced by what’s happening in 3D printing? What could be made possible by applying the latest advances in quantum computing to nanotechnology?
Along these lines, one crucial tech intersection is that of artificial intelligence and genomics. Each field is seeing constant progress, but Jamie Metzl believes it’s their convergence that will really push us into uncharted territory, beyond even what we’ve imagined in science fiction. “There’s going to be this push and pull, this competition between the reality of our biology with its built-in limitations and the scope of our aspirations,” he said.
Metzl is a senior fellow at the Atlantic Council and author of the upcoming book Hacking Darwin: Genetic Engineering and the Future of Humanity. At Singularity University’s Exponential Medicine conference last week, he shared his insights on genomics and AI, and where their convergence could take us.
Life As We Know It
Metzl explained how genomics as a field evolved slowly—and then quickly. In 1953, James Watson and Francis Crick identified the double helix structure of DNA, and realized that the order of the base pairs held a treasure trove of genetic information. There was such a thing as a book of life, and we’d found it.
In 2003, when the Human Genome Project was completed (after 13 years and $2.7 billion), we learned the order of the genome’s 3 billion base pairs, and the location of specific genes on our chromosomes. Not only did a book of life exist, we figured out how to read it.
Jamie Metzl at Exponential Medicine
Fifteen years after that, it’s 2018 and precision gene editing in plants, animals, and humans is changing everything, and quickly pushing us into an entirely new frontier. Forget reading the book of life—we’re now learning how to write it.
“Readable, writable, and hackable, what’s clear is that human beings are recognizing that we are another form of information technology, and just like our IT has entered this exponential curve of discovery, we will have that with ourselves,” Metzl said. “And it’s intersecting with the AI revolution.”
Learning About Life Meets Machine Learning
In 2016, DeepMind’s AlphaGo program outsmarted the world’s top Go player. In 2017 AlphaGo Zero was created: unlike AlphaGo, AlphaGo Zero wasn’t trained using previous human games of Go, but was simply given the rules of Go—and in four days it defeated the AlphaGo program.
Our own biology is, of course, vastly more complex than the game of Go, and that, Metzl said, is our starting point. “The system of our own biology that we are trying to understand is massively, but very importantly not infinitely, complex,” he added.
Getting a standardized set of rules for our biology—and, eventually, maybe even outsmarting our biology—will require genomic data. Lots of it.
Multiple countries already starting to produce this data. The UK’s National Health Service recently announced a plan to sequence the genomes of five million Britons over the next five years. In the US the All of Us Research Program will sequence a million Americans. China is the most aggressive in sequencing its population, with a goal of sequencing half of all newborns by 2020.
“We’re going to get these massive pools of sequenced genomic data,” Metzl said. “The real gold will come from comparing people’s sequenced genomes to their electronic health records, and ultimately their life records.” Getting people comfortable with allowing open access to their data will be another matter; Metzl mentioned that Luna DNA and others have strategies to help people get comfortable with giving consent to their private information. But this is where China’s lack of privacy protection could end up being a significant advantage.
To compare genotypes and phenotypes at scale—first millions, then hundreds of millions, then eventually billions, Metzl said—we’re going to need AI and big data analytic tools, and algorithms far beyond what we have now. These tools will let us move from precision medicine to predictive medicine, knowing precisely when and where different diseases are going to occur and shutting them down before they start.
But, Metzl said, “As we unlock the genetics of ourselves, it’s not going to be about just healthcare. It’s ultimately going to be about who and what we are as humans. It’s going to be about identity.”
Designer Babies, and Their Babies
In Metzl’s mind, the most serious application of our genomic knowledge will be in embryo selection.
Currently, in-vitro fertilization (IVF) procedures can extract around 15 eggs, fertilize them, then do pre-implantation genetic testing; right now what’s knowable is single-gene mutation diseases and simple traits like hair color and eye color. As we get to the millions and then billions of people with sequences, we’ll have information about how these genetics work, and we’re going to be able to make much more informed choices,” Metzl said.
Imagine going to a fertility clinic in 2023. You give a skin graft or a blood sample, and using in-vitro gametogenesis (IVG)—infertility be damned—your skin or blood cells are induced to become eggs or sperm, which are then combined to create embryos. The dozens or hundreds of embryos created from artificial gametes each have a few cells extracted from them, and these cells are sequenced. The sequences will tell you the likelihood of specific traits and disease states were that embryo to be implanted and taken to full term. “With really anything that has a genetic foundation, we’ll be able to predict with increasing levels of accuracy how that potential child will be realized as a human being,” Metzl said.
This, he added, could lead to some wild and frightening possibilities: if you have 1,000 eggs and you pick one based on its optimal genetic sequence, you could then mate your embryo with somebody else who has done the same thing in a different genetic line. “Your five-day-old embryo and their five-day-old embryo could have a child using the same IVG process,” Metzl said. “Then that child could have a child with another five-day-old embryo from another genetic line, and you could go on and on down the line.”
Sounds insane, right? But wait, there’s more: as Jason Pontin reported earlier this year in Wired, “Gene-editing technologies such as Crispr-Cas9 would make it relatively easy to repair, add, or remove genes during the IVG process, eliminating diseases or conferring advantages that would ripple through a child’s genome. This all may sound like science fiction, but to those following the research, the combination of IVG and gene editing appears highly likely, if not inevitable.”
From Crazy to Commonplace?
It’s a slippery slope from gene editing and embryo-mating to a dystopian race to build the most perfect humans possible. If somebody’s investing so much time and energy in selecting their embryo, Metzl asked, how will they think about the mating choices of their children? IVG could quickly leave the realm of healthcare and enter that of evolution.
“We all need to be part of an inclusive, integrated, global dialogue on the future of our species,” Metzl said. “Healthcare professionals are essential nodes in this.” Not least among this dialogue should be the question of access to tech like IVG; are there steps we can take to keep it from becoming a tool for a wealthy minority, and thereby perpetuating inequality and further polarizing societies?
As Pontin points out, at its inception 40 years ago IVF also sparked fear, confusion, and resistance—and now it’s as normal and common as could be, with millions of healthy babies conceived using the technology.
The disruption that genomics, AI, and IVG will bring to reproduction could follow a similar story cycle—if we’re smart about it. As Metzl put it, “This must be regulated, because it is life.”
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#433828 Using Big Data to Give Patients Control ...
Big data, personalized medicine, artificial intelligence. String these three buzzphrases together, and what do you have?
A system that may revolutionize the future of healthcare, by bringing sophisticated health data directly to patients for them to ponder, digest, and act upon—and potentially stop diseases in their tracks.
At Singularity University’s Exponential Medicine conference in San Diego this week, Dr. Ran Balicer, director of the Clalit Research Institute in Israel, painted a futuristic picture of how big data can merge with personalized healthcare into an app-based system in which the patient is in control.
Dr. Ran Balicer at Exponential Medicine
Picture this: instead of going to a physician with your ailments, your doctor calls you with some bad news: “Within six hours, you’re going to have a heart attack. So why don’t you come into the clinic and we can fix that.” Crisis averted.
Following the treatment, you’re at home monitoring your biomarkers, lab test results, and other health information through an app with a clean, beautiful user interface. Within the app, you can observe how various health-influencing life habits—smoking, drinking, insufficient sleep—influence your chance of future cardiovascular disease risks by toggling their levels up or down.
There’s more: you can also set a health goal within the app—for example, stop smoking—which automatically informs your physician. The app will then suggest pharmaceuticals to help you ditch the nicotine and automatically sends the prescription to your local drug store. You’ll also immediately find a list of nearby support groups that can help you reach your health goal.
With this hefty dose of AI, you’re in charge of your health—in fact, probably more so than under current healthcare systems.
Sound fantastical? In fact, this type of preemptive care is already being provided in some countries, including Israel, at a massive scale, said Balicer. By mining datasets with deep learning and other powerful AI tools, we can predict the future—and put it into the hands of patients.
The Israeli Advantage
In order to apply big data approaches to medicine, you first need a giant database.
Israel is ahead of the game in this regard. With decades of electronic health records aggregated within a central warehouse, Israel offers a wealth of health-related data on the scale of millions of people and billions of data points. The data is incredibly multiplex, covering lab tests, drugs, hospital admissions, medical procedures, and more.
One of Balicer’s early successes was an algorithm that predicts diabetes, which allowed the team to notify physicians to target their care. Clalit has also been busy digging into data that predicts winter pneumonia, osteoporosis, and a long list of other preventable diseases.
So far, Balicer’s predictive health system has only been tested on a pilot group of patients, but he is expecting to roll out the platform to all patients in the database in the next few months.
Truly Personalized Medicine
To Balicer, whatever a machine can do better, it should be welcomed to do. AI diagnosticians have already enjoyed plenty of successes—but their collaboration remains mostly with physicians, at a point in time when the patient is already ill.
A particularly powerful use of AI in medicine is to bring insights and trends directly to the patient, such that they can take control over their own health and medical care.
For example, take the problem of tailored drug dosing. Current drug doses are based on average results conducted during clinical trials—the dosing is not tailored for any specific patient’s genetic and health makeup. But what if a doctor had already seen millions of other patients similar to your case, and could generate dosing recommendations more relevant to you based on that particular group of patients?
Such personalized recommendations are beyond the ability of any single human doctor. But with the help of AI, which can quickly process massive datasets to find similarities, doctors may soon be able to prescribe individually-tailored medications.
Tailored treatment doesn’t stop there. Another issue with pharmaceuticals and treatment regimes is that they often come with side effects: potentially health-threatening reactions that may, or may not, happen to you based on your biometrics.
Back in 2017, the New England Journal of Medicine launched the SPRINT Data Analysis Challenge, which urged physicians and data analysts to identify novel clinical findings using shared clinical trial data.
Working with Dr. Noa Dagan at the Clalit Research Institute, Balicer and team developed an algorithm that recommends whether or not a patient receives a particularly intensive treatment regime for hypertension.
Rather than simply looking at one outcome—normalized blood pressure—the algorithm takes into account an individual’s specific characteristics, laying out the treatment’s predicted benefits and harms for a particular patient.
“We built thousands of models for each patient to comprehensively understand the impact of the treatment for the individual; for example, a reduced risk for stroke and cardiovascular-related deaths could be accompanied by an increase in serious renal failure,” said Balicer. “This approach allows a truly personalized balance—allowing patients and their physicians to ultimately decide if the risks of the treatment are worth the benefits.”
This is already personalized medicine at its finest. But Balicer didn’t stop there.
We are not the sum of our biologics and medical stats, he said. A truly personalized approach needs to take a patient’s needs and goals and the sacrifices and tradeoffs they’re willing to make into account, rather than having the physician make decisions for them.
Balicer’s preventative system adds this layer of complexity by giving weights to different outcomes based on patients’ input of their own health goals. Rather than blindly following big data, the system holistically integrates the patient’s opinion to make recommendations.
Balicer’s system is just one example of how AI can truly transform personalized health care. The next big challenge is to work with physicians to further optimize these systems, in a way that doctors can easily integrate them into their workflow and embrace the technology.
“Health systems will not be replaced by algorithms, rest assured,” concluded Balicer, “but health systems that don’t use algorithms will be replaced by those that do.”
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