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#433911 Thanksgiving Food for Thought: The Tech ...

Posted on November 22, 2018 by Android

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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Posted in Human Robots

#433884 Designer Babies, and Their Babies: How ...

Posted on November 14, 2018 by Android

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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Posted in Human Robots

#433785 DeepMind’s Eerie Reimagination of the ...

Posted on October 21, 2018 by Android

If a recent project using Google’s DeepMind were a recipe, you would take a pair of AI systems, images of animals, and a whole lot of computing power. Mix it all together, and you’d get a series of imagined animals dreamed up by one of the AIs. A look through the research paper about the project—or this open Google Folder of images it produced—will likely lead you to agree that the results are a mix of impressive and downright eerie.

But the eerie factor doesn’t mean the project shouldn’t be considered a success and a step forward for future uses of AI.

From GAN To BigGAN
The team behind the project consists of Andrew Brock, a PhD student at Edinburgh Center for Robotics, and DeepMind intern and researcher Jeff Donahue and Karen Simonyan.

They used a so-called Generative Adversarial Network (GAN) to generate the images. In a GAN, two AI systems collaborate in a game-like manner. One AI produces images of an object or creature. The human equivalent would be drawing pictures of, for example, a dog—without necessarily knowing what a dog exactly looks like. Those images are then shown to the second AI, which has already been fed images of dogs. The second AI then tells the first one how far off its efforts were. The first one uses this information to improve its images. The two go back and forth in an iterative process, and the goal is for the first AI to become so good at creating images of dogs that the second can’t tell the difference between its creations and actual pictures of dogs.

The team was able to draw on Google’s vast vaults of computational power to create images of a quality and life-like nature that were beyond almost anything seen before. In part, this was achieved by feeding the GAN with more images than is usually the case. According to IFLScience, the standard is to feed about 64 images per subject into the GAN. In this case, the research team fed about 2,000 images per subject into the system, leading to it being nicknamed BigGAN.

Their results showed that feeding the system with more images and using masses of raw computer power markedly increased the GAN’s precision and ability to create life-like renditions of the subjects it was trained to reproduce.

“The main thing these models need is not algorithmic improvements, but computational ones. […] When you increase model capacity and you increase the number of images you show at every step, you get this twofold combined effect,” Andrew Brock told Fast Company.

The Power Drain
The team used 512 of Google’s AI-focused Tensor Processing Units (TPU) to generate 512-pixel images. Each experiment took between 24 and 48 hours to run.

That kind of computing power needs a lot of electricity. As artist and Innovator-In-Residence at the Library of Congress Jer Thorp tongue-in-cheek put it on Twitter: “The good news is that AI can now give you a more believable image of a plate of spaghetti. The bad news is that it used roughly enough energy to power Cleveland for the afternoon.”

Thorp added that a back-of-the-envelope calculation showed that the computations to produce the images would require about 27,000 square feet of solar panels to have adequate power.

BigGAN’s images have been hailed by researchers, with Oriol Vinyals, research scientist at DeepMind, rhetorically asking if these were the ‘Best GAN samples yet?’

However, they are still not perfect. The number of legs on a given creature is one example of where the BigGAN seemed to struggle. The system was good at recognizing that something like a spider has a lot of legs, but seemed unable to settle on how many ‘a lot’ was supposed to be. The same applied to dogs, especially if the images were supposed to show said dogs in motion.

Those eerie images are contrasted by other renditions that show such lifelike qualities that a human mind has a hard time identifying them as fake. Spaniels with lolling tongues, ocean scenery, and butterflies were all rendered with what looks like perfection. The same goes for an image of a hamburger that was good enough to make me stop writing because I suddenly needed lunch.

The Future Use Cases
GAN networks were first introduced in 2014, and given their relative youth, researchers and companies are still busy trying out possible use cases.

One possible use is image correction—making pixillated images clearer. Not only does this help your future holiday snaps, but it could be applied in industries such as space exploration. A team from the University of Michigan and the Max Planck Institute have developed a method for GAN networks to create images from text descriptions. At Berkeley, a research group has used GAN to create an interface that lets users change the shape, size, and design of objects, including a handbag.

For anyone who has seen a film like Wag the Dog or read 1984, the possibilities are also starkly alarming. GANs could, in other words, make fake news look more real than ever before.

For now, it seems that while not all GANs require the computational and electrical power of the BigGAN, there is still some way to reach these potential use cases. However, if there’s one lesson from Moore’s Law and exponential technology, it is that today’s technical roadblock quickly becomes tomorrow’s minor issue as technology progresses.

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Posted in Human Robots

#433770 Will Tech Make Insurance Obsolete in the ...

Posted on October 18, 2018 by Android

We profit from it, we fear it, and we find it impossibly hard to quantify: risk.

While not the sexiest of industries, insurance can be a life-saving protector, pooling everyone’s premiums to safeguard against some of our greatest, most unexpected losses.

One of the most profitable in the world, the insurance industry exceeded $1.2 trillion in annual revenue since 2011 in the US alone.

But risk is becoming predictable. And insurance is getting disrupted fast.

By 2025, we’ll be living in a trillion-sensor economy. And as we enter a world where everything is measured all the time, we’ll start to transition from protecting against damages to preventing them in the first place.

But what happens to health insurance when Big Brother is always watching? Do rates go up when you sneak a cigarette? Do they go down when you eat your vegetables?

And what happens to auto insurance when most cars are autonomous? Or life insurance when the human lifespan doubles?

For that matter, what happens to insurance brokers when blockchain makes them irrelevant?

In this article, I’ll be discussing four key transformations:

Sensors and AI replacing your traditional broker
Blockchain
The ecosystem approach
IoT and insurance connectivity

Let’s dive in.

AI and the Trillion-Sensor Economy
As sensors continue to proliferate across every context—from smart infrastructure to millions of connected home devices to medicine—smart environments will allow us to ask any question, anytime, anywhere.

And as I often explain, once your AI has access to this treasure trove of ubiquitous sensor data in real time, it will be the quality of your questions that make or break your business.

But perhaps the most exciting insurance application of AI’s convergence with sensors is in healthcare. Tremendous advances in genetic screening are empowering us with predictive knowledge about our long-term health risks.

Leading the charge in genome sequencing, Illumina predicts that in a matter of years, decoding the full human genome will drop to $100, taking merely one hour to complete. Other companies are racing to get you sequences faster and cheaper.

Adopting an ecosystem approach, incumbent insurers and insurtech firms will soon be able to collaborate to provide risk-minimizing services in the health sector. Using sensor data and AI-driven personalized recommendations, insurance partnerships could keep consumers healthy, dramatically reducing the cost of healthcare.

Some fear that information asymmetry will allow consumers to learn of their health risks and leave insurers in the dark. However, both parties could benefit if insurers become part of the screening process.

A remarkable example of this is Gilad Meiri’s company, Neura AI. Aiming to predict health patterns, Neura has developed machine learning algorithms that analyze data from all of a user’s connected devices (sometimes from up to 54 apps!).

Neura predicts a user’s behavior and draws staggering insights about consumers’ health risks. Meiri soon began selling his personal risk assessment tool to insurers, who could then help insured customers mitigate long-term health risks.

But artificial intelligence will impact far more than just health insurance.

In October of 2016, a claim was submitted to Lemonade, the world’s first peer-to-peer insurance company. Rather than being processed by a human, every step in this claim resolution chain—from initial triage through fraud mitigation through final payment—was handled by an AI.

This transaction marks the first time an AI has processed an insurance claim. And it won’t be the last. A traditional human-processed claim takes 40 days to pay out. In Lemonade’s case, payment was transferred within three seconds.

However, Lemonade’s achievement only marks a starting point. Over the course of the next decade, nearly every facet of the insurance industry will undergo a similarly massive transformation.

New business models like peer-to-peer insurance are replacing traditional brokerage relationships, while AI and blockchain pairings significantly reduce the layers of bureaucracy required (with each layer getting a cut) for traditional insurance.

Consider Juniper, a startup that scrapes social media to build your risk assessment, subsequently asking you 12 questions via an iPhone app. Geared with advanced analytics, the platform can generate a million-dollar life insurance policy, approved in less than five minutes.

But what’s keeping all your data from unwanted hands?

Blockchain Building Trust
Current distrust in centralized financial services has led to staggering rates of underinsurance. Add to this fear of poor data and privacy protection, particularly in the wake of 2017’s widespread cybercriminal hacks.

Enabling secure storage and transfer of personal data, blockchain holds remarkable promise against the fraudulent activity that often plagues insurance firms.

The centralized model of insurance companies and other organizations is becoming redundant. Developing blockchain-based solutions for capital markets, Symbiont develops smart contracts to execute payments with little to no human involvement.

But distributed ledger technology (DLT) is enabling far more than just smart contracts.

Also targeting insurance is Tradle, leveraging blockchain for its proclaimed goal of “building a trust provisioning network.” Built around “know-your-customer” (KYC) data, Tradle aims to verify KYC data so that it can be securely forwarded to other firms without any further verification.

By requiring a certain number of parties to reuse pre-verified data, the platform makes your data much less vulnerable to hacking and allows you to keep it on a personal device. Only its verification—let’s say of a transaction or medical exam—is registered in the blockchain.

As insurance data grow increasingly decentralized, key insurance players will experience more and more pressure to adopt an ecosystem approach.

The Ecosystem Approach
Just as exponential technologies converge to provide new services, exponential businesses must combine the strengths of different sectors to expand traditional product lines.

By partnering with platform-based insurtech firms, forward-thinking insurers will no longer serve only as reactive policy-providers, but provide risk-mitigating services as well.

Especially as digital technologies demonetize security services—think autonomous vehicles—insurers must create new value chains and span more product categories.

For instance, France’s multinational AXA recently partnered with Alibaba and Ant Financial Services to sell a varied range of insurance products on Alibaba’s global e-commerce platform at the click of a button.

Building another ecosystem, Alibaba has also collaborated with Ping An Insurance and Tencent to create ZhongAn Online Property and Casualty Insurance—China’s first internet-only insurer, offering over 300 products. Now with a multibillion-dollar valuation, Zhong An has generated about half its business from selling shipping return insurance to Alibaba consumers.

But it doesn’t stop there. Insurers that participate in digital ecosystems can now sell risk-mitigating services that prevent damage before it occurs.

Imagine a corporate manufacturer whose sensors collect data on environmental factors affecting crop yield in an agricultural community. With the backing of investors and advanced risk analytics, such a manufacturer could sell crop insurance to farmers. By implementing an automated, AI-driven UI, they could automatically make payments when sensors detect weather damage to crops.

Now let’s apply this concept to your house, your car, your health insurance.

What’s stopping insurers from partnering with third-party IoT platforms to predict fires, collisions, chronic heart disease—and then empowering the consumer with preventive services?

This brings us to the powerful field of IoT.

Internet of Things and Insurance Connectivity
Leap ahead a few years. With a centralized hub like Echo, your smart home protects itself with a network of sensors. While gone, you’ve left on a gas burner and your internet-connected stove notifies you via a home app.

Better yet, home sensors monitoring heat and humidity levels run this data through an AI, which then remotely controls heating, humidity levels, and other connected devices based on historical data patterns and fire risk factors.

Several firms are already working toward this reality.

AXA plans to one day cooperate with a centralized home hub whereby remote monitoring will collect data for future analysis and detect abnormalities.

With remote monitoring and app-centralized control for users, MonAXA is aimed at customizing insurance bundles. These would reflect exact security features embedded in smart homes.

Wouldn’t you prefer not to have to rely on insurance after a burglary? With digital ecosystems, insurers may soon prevent break-ins from the start.

By gathering sensor data from third parties on neighborhood conditions, historical theft data, suspicious activity and other risk factors, an insurtech firm might automatically put your smart home on high alert, activating alarms and specialized locks in advance of an attack.

Insurance policy premiums are predicted to vastly reduce with lessened likelihood of insured losses. But insurers moving into preventive insurtech will likely turn a profit from other areas of their business. PricewaterhouseCoopers predicts that the connected home market will reach $149 billion USD by 2020.

Let’s look at car insurance.

Car insurance premiums are currently calculated according to the driver and traits of the car. But as more autonomous vehicles take to the roads, not only does liability shift to manufacturers and software engineers, but the risk of collision falls dramatically.

But let’s take this a step further.

In a future of autonomous cars, you will no longer own your car, instead subscribing to Transport as a Service (TaaS) and giving up the purchase of automotive insurance altogether.

This paradigm shift has already begun with Waymo, which automatically provides passengers with insurance every time they step into a Waymo vehicle.

And with the rise of smart traffic systems, sensor-embedded roads, and skyrocketing autonomous vehicle technology, the risks involved in transit only continue to plummet.

Final Thoughts
Insurtech firms are hitting the market fast. IoT, autonomous vehicles and genetic screening are rapidly making us invulnerable to risk. And AI-driven services are quickly pushing conventional insurers out of the market.

By 2024, roll-out of 5G on the ground, as well as OneWeb and Starlink in orbit are bringing 4.2 billion new consumers to the web—most of whom will need insurance. Yet, because of the changes afoot in the industry, none of them will buy policies from a human broker.

While today’s largest insurance companies continue to ignore this fact at their peril (and this segment of the market), thousands of entrepreneurs see it more clearly: as one of the largest opportunities ahead.

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Posted in Human Robots

#433748 Could Tech Make Government As We Know It ...

Posted on October 12, 2018 by Android

Governments are one of the last strongholds of an undigitized, linear sector of humanity, and they are falling behind fast. Apart from their struggle to keep up with private sector digitization, federal governments are in a crisis of trust.

At almost a 60-year low, only 18 percent of Americans reported that they could trust their government “always” or “most of the time” in a recent Pew survey. And the US is not alone. The Edelman Trust Barometer revealed last year that 41 percent of the world population distrust their nations’ governments.

In many cases, the private sector—particularly tech—is driving greater progress in regulation-targeted issues like climate change than state leaders. And as decentralized systems, digital disruption, and private sector leadership take the world by storm, traditional forms of government are beginning to fear irrelevance. However, the fight for exponential governance is not a lost battle.

Early visionaries like Estonia and the UAE are leading the way in digital governance, empowered by a host of converging technologies.

In this article, we will cover three key trends:

Digital governance divorced from land
AI-driven service delivery and regulation
Blockchain-enforced transparency

Let’s dive in.

Governments Going Digital
States and their governments have forever been tied to physical territories, and public services are often delivered through brick-and-mortar institutions. Yet public sector infrastructure and services will soon be hosted on servers, detached from land and physical form.

Enter e-Estonia. Perhaps the least expected on a list of innovative nations, this former Soviet Republic-turned digital society is ushering in an age of technological statecraft.

Hosting every digitizable government function on the cloud, Estonia could run its government almost entirely on a server. Starting in the 1990s, Estonia’s government has covered the nation with ultra-high-speed data connectivity, laying down tremendous amounts of fiber optic cable. By 2007, citizens could vote from their living rooms.

With digitized law, Estonia signs policies into effect using cryptographically secure digital signatures, and every stage of the legislative process is available to citizens online.

Citizens’ healthcare registry is run on the blockchain, allowing patients to own and access their own health data from anywhere in the world—X-rays, digital prescriptions, medical case notes—all the while tracking who has access.

Today, most banks have closed their offices, as 99 percent of banking transactions occur online (with 67 percent of citizens regularly using cryptographically secured e-IDs). And by 2020, e-tax will be entirely automated with Estonia’s new e-Tax and Customs Board portal, allowing companies and tax authority to exchange data automatically. And i-Voting, civil courts, land registries, banking, taxes, and countless e-facilities allow citizens to access almost any government service with an electronic ID and personal PIN online.

But perhaps Estonia’s most revolutionary breakthrough is its recently introduced e-residency. With over 30,000 e-residents, Estonia issues electronic IDs to global residents anywhere in the world. While e-residency doesn’t grant territorial rights, over 5,000 e-residents have already established companies within Estonia’s jurisdiction.

After registering companies online, entrepreneurs pay automated taxes—calculated in minutes and transmitted to the Estonian government with unprecedented ease.

The implications of e-residency and digital governance are huge. As with any software, open-source code for digital governance could be copied perfectly at almost zero cost, lowering the barrier to entry for any group or movement seeking statehood.

We may soon see the rise of competitive governing ecosystems, each testing new infrastructure and public e-services to compete with mainstream governments for taxpaying citizens.

And what better to accelerate digital governance than AI?

Legal Compliance Through AI
Just last year, the UAE became the first nation to appoint a State Minister for AI (actually a friend of mine, H.E. Omar Al Olama), aiming to digitize government services and halve annual costs. Among multiple sector initiatives, the UAE hopes to deploy robotic cops by 2030.

Meanwhile, the U.K. now has a Select Committee on Artificial Intelligence, and just last month, world leaders convened at the World Government Summit to discuss guidelines for AI’s global regulation.

As AI infuses government services, emerging applications have caught my eye:

Smart Borders and Checkpoints

With biometrics and facial recognition, traditional checkpoints will soon be a thing of the past. Cubic Transportation Systems—the company behind London’s ticketless public transit—is currently developing facial recognition for automated transport barriers. Digital security company Gemalto predicts that biometric systems will soon cross-reference individual faces with passport databases at security checkpoints, and China has already begun to test this at scale. While the Alibaba Ant Financial affiliate’s “Smile to Pay” feature allows users to authenticate digital payments with their faces, nationally overseen facial recognition technologies allow passengers to board planes, employees to enter office spaces, and students to access university halls. With biometric-geared surveillance at national borders, supply chains and international travelers could be tracked automatically, and granted or denied access according to biometrics and cross-referenced databases.

Policing and Security

Leveraging predictive analytics, China is also working to integrate security footage into a national surveillance and data-sharing system. By merging citizen data in its “Police Cloud”—including everything from criminal and medical records, transaction data, travel records and social media—it may soon be able to spot suspects and predict crime in advance. But China is not alone. During London’s Notting Hill Carnival this year, the Metropolitan Police used facial recognition cross-referenced with crime data to pre-identify and track likely offenders.

Smart Courts

AI may soon be reaching legal trials as well. UCL computer scientists have developed software capable of predicting courtroom outcomes based on data patterns with unprecedented accuracy. Assessing risk of flight, the National Bureau of Economic Research now uses an algorithm leveraging data from hundreds of thousands of NYC cases to recommend whether defendants should be granted bail. But while AI allows for streamlined governance, the public sector’s power to misuse our data is a valid concern and issues with bias as a result of historical data still remain. As tons of new information is generated about our every move, how do we keep governments accountable?

Enter the blockchain.

Transparent Governance and Accountability
Without doubt, alongside AI, government’s greatest disruptor is the newly-minted blockchain. Relying on a decentralized web of nodes, blockchain can securely verify transactions, signatures, and other information. This makes it essentially impossible for hackers, companies, officials, or even governments to falsify information on the blockchain.

As you’d expect, many government elites are therefore slow to adopt the technology, fearing enforced accountability. But blockchain’s benefits to government may be too great to ignore.

First, blockchain will be a boon for regulatory compliance.

As transactions on a blockchain are irreversible and transparent, uploaded sensor data can’t be corrupted. This means middlemen have no way of falsifying information to shirk regulation, and governments eliminate the need to enforce charges after the fact.

Apply this to carbon pricing, for instance, and emission sensors could fluidly log carbon credits onto a carbon credit blockchain, such as that developed by Ecosphere+. As carbon values are added to the price of everyday products or to corporations’ automated taxes, compliance and transparency would soon be digitally embedded.

Blockchain could also bolster government efforts in cybersecurity. As supercities and nation-states build IoT-connected traffic systems, surveillance networks, and sensor-tracked supply chain management, blockchain is critical in protecting connected devices from cyberattack.

But blockchain will inevitably hold governments accountable as well. By automating and tracking high-risk transactions, blockchain may soon eliminate fraud in cash transfers, public contracts and aid funds. Already, the UN World Food Program has piloted blockchain to manage cash-based transfers and aid flows to Syrian refugees in Jordan.

Blockchain-enabled “smart contracts” could automate exchange of real assets according to publicly visible, pre-programmed conditions, disrupting the $9.5 trillion market of public-sector contracts and public investment projects.

Eliminating leakages and increasing transparency, a distributed ledger has the potential to save trillions.

Future Implications
It is truly difficult to experiment with new forms of government. It’s not like there are new countries waiting to be discovered where we can begin fresh. And with entrenched bureaucracies and dominant industrial players, changing an existing nation’s form of government is extremely difficult and usually only happens during times of crisis or outright revolution.

Perhaps we will develop and explore new forms of government in the virtual world (to be explored during a future blog), or perhaps Sea Steading will allow us to physically build new island nations. And ultimately, as we move off the earth to Mars and space colonies, we will have yet another chance to start fresh.

But, without question, 90 percent or more of today’s political processes herald back to a day before technology, and it shows in terms of speed and efficiency.

Ultimately, there will be a shift to digital governments enabled with blockchain’s transparency, and we will redefine the relationship between citizens and the public sector.

One day I hope i-voting will allow anyone anywhere to participate in policy, and cloud-based governments will start to compete in e-services. As four billion new minds come online over the next several years, people may soon have the opportunity to choose their preferred government and citizenship digitally, independent of birthplace.

In 50 years, what will our governments look like? Will we have an interplanetary order, or a multitude of publicly-run ecosystems? Will cyber-ocracies rule our physical worlds with machine intelligence, or will blockchains allow for hive mind-like democracy?

The possibilities are endless, and only we can shape them.

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