Tag Archives: embedded
#434755 This Week’s Awesome Stories From ...
ARTIFICIAL INTELLIGENCE
DeepMind and Google: The Battle to Control Artificial Intelligence
Hal Hodson | 1843
“Hassabis thought DeepMind would be a hybrid: it would have the drive of a startup, the brains of the greatest universities, and the deep pockets of one of the world’s most valuable companies. Every element was in place to hasten the arrival of AGI and solve the causes of human misery.”
ROBOTICS
Robot Valets Are Now Parking Cars in One of France’s Busiest Airports
James Vincent | The Verge
“Stanley Robotics say its system uses space much more efficiently than humans, fitting 50 percent more cars into the same area. This is thanks in part to the robots’ precision driving, but also because the system keeps track of when customers will return. This means the robots can park cars three or four deep, but then dig out the right vehicle ready for its owner’s return.”
COMPUTING
Quantum Computing Should Supercharge This Machine-Learning Technique
Will Knight | MIT Technology Review
“Quantum computing and artificial intelligence are both hyped ridiculously. But it seems a combination of the two may indeed combine to open up new possibilities.”
BIOTECH
Scientists Reawaken Cells From a 28,000-Year-Old Mammoth
Becky Ferreira | Motherboard
“Yuka the woolly mammoth died a long time ago, but scientists gave her cells a short second life in mouse egg cells.”
ETHICS
CRISPR Experts Are Calling for a Global Moratorium on Heritable Gene Editing
Niall Firth | MIT Technology Review
“We still don’t know what the majority of our genes do, so the risks of unintended consequences or so-called off-target effects—good or bad—are huge. …Changes in a genome might have unforeseen outcomes in future generations as well. ‘Attempting to reshape the species on the basis of our current state of knowledge would be hubris,’ the letter reads.”
GENETICS
Unleash the Full Potential of the Human Genome Project
Paul Glimcher | The Hill
“So how do the risks embedded in our genes become the diseases, the so-called phenotypes, we seek to cure or prevent? …It is not just nature, but also nurture, which leads to disease. This is something that we have known for centuries, but which we seem to have conveniently forgotten in our rush to embrace the technology of genetics. In 1990 the only thing we could measure comprehensively was genetics, so we did it. But why did we stop there?”
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Posted in Human Robots
#434658 The Next Data-Driven Healthtech ...
Increasing your healthspan (i.e. making 100 years old the new 60) will depend to a large degree on artificial intelligence. And, as we saw in last week’s blog, healthcare AI systems are extremely data-hungry.
Fortunately, a slew of new sensors and data acquisition methods—including over 122 million wearables shipped in 2018—are bursting onto the scene to meet the massive demand for medical data.
From ubiquitous biosensors, to the mobile healthcare revolution, to the transformative power of the Health Nucleus, converging exponential technologies are fundamentally transforming our approach to healthcare.
In Part 4 of this blog series on Longevity & Vitality, I expand on how we’re acquiring the data to fuel today’s AI healthcare revolution.
In this blog, I’ll explore:
How the Health Nucleus is transforming “sick care” to healthcare
Sensors, wearables, and nanobots
The advent of mobile health
Let’s dive in.
Health Nucleus: Transforming ‘Sick Care’ to Healthcare
Much of today’s healthcare system is actually sick care. Most of us assume that we’re perfectly healthy, with nothing going on inside our bodies, until the day we travel to the hospital writhing in pain only to discover a serious or life-threatening condition.
Chances are that your ailment didn’t materialize that morning; rather, it’s been growing or developing for some time. You simply weren’t aware of it. At that point, once you’re diagnosed as “sick,” our medical system engages to take care of you.
What if, instead of this retrospective and reactive approach, you were constantly monitored, so that you could know the moment anything was out of whack?
Better yet, what if you more closely monitored those aspects of your body that your gene sequence predicted might cause you difficulty? Think: your heart, your kidneys, your breasts. Such a system becomes personalized, predictive, and possibly preventative.
This is the mission of the Health Nucleus platform built by Human Longevity, Inc. (HLI). While not continuous—that will come later, with the next generation of wearable and implantable sensors—the Health Nucleus was designed to ‘digitize’ you once per year to help you determine whether anything is going on inside your body that requires immediate attention.
The Health Nucleus visit provides you with the following tests during a half-day visit:
Whole genome sequencing (30x coverage)
Whole body (non-contrast) MRI
Brain magnetic resonance imaging/angiography (MRI/MRA)
CT (computed tomography) of the heart and lungs
Coronary artery calcium scoring
Electrocardiogram
Echocardiogram
Continuous cardiac monitoring
Clinical laboratory tests and metabolomics
In late 2018, HLI published the results of the first 1,190 clients through the Health Nucleus. The results were eye-opening—especially since these patients were all financially well-off, and already had access to the best doctors.
Following are the physiological and genomic findings in these clients who self-selected to undergo evaluation at HLI’s Health Nucleus.
Physiological Findings [TG]
Two percent had previously unknown tumors detected by MRI
2.5 percent had previously undetected aneurysms detected by MRI
Eight percent had cardiac arrhythmia found on cardiac rhythm monitoring, not previously known
Nine percent had moderate-severe coronary artery disease risk, not previously known
16 percent discovered previously unknown cardiac structure/function abnormalities
30 percent had elevated liver fat, not previously known
Genomic Findings [TG]
24 percent of clients uncovered a rare (unknown) genetic mutation found on WGS
63 percent of clients had a rare genetic mutation with a corresponding phenotypic finding
In summary, HLI’s published results found that 14.4 percent of clients had significant findings that are actionable, requiring immediate or near-term follow-up and intervention.
Long-term value findings were found in 40 percent of the clients we screened. Long-term clinical findings include discoveries that require medical attention or monitoring but are not immediately life-threatening.
The bottom line: most people truly don’t know their actual state of health. The ability to take a fully digital deep dive into your health status at least once per year will enable you to detect disease at stage zero or stage one, when it is most curable.
Sensors, Wearables, and Nanobots
Wearables, connected devices, and quantified self apps will allow us to continuously collect enormous amounts of useful health information.
Wearables like the Quanttus wristband and Vital Connect can transmit your electrocardiogram data, vital signs, posture, and stress levels anywhere on the planet.
In April 2017, we were proud to grant $2.5 million in prize money to the winning team in the Qualcomm Tricorder XPRIZE, Final Frontier Medical Devices.
Using a group of noninvasive sensors that collect data on vital signs, body chemistry, and biological functions, Final Frontier integrates this data in their powerful, AI-based DxtER diagnostic engine for rapid, high-precision assessments.
Their engine combines learnings from clinical emergency medicine and data analysis from actual patients.
Google is developing a full range of internal and external sensors (e.g. smart contact lenses) that can monitor the wearer’s vitals, ranging from blood sugar levels to blood chemistry.
In September 2018, Apple announced its Series 4 Apple Watch, including an FDA-approved mobile, on-the-fly ECG. Granted its first FDA approval, Apple appears to be moving deeper into the sensing healthcare market.
Further, Apple is reportedly now developing sensors that can non-invasively monitor blood sugar levels in real time for diabetic treatment. IoT-connected sensors are also entering the world of prescription drugs.
Last year, the FDA approved the first sensor-embedded pill, Abilify MyCite. This new class of digital pills can now communicate medication data to a user-controlled app, to which doctors may be granted access for remote monitoring.
Perhaps what is most impressive about the next generation of wearables and implantables is the density of sensors, processing, networking, and battery capability that we can now cheaply and compactly integrate.
Take the second-generation OURA ring, for example, which focuses on sleep measurement and management.
The OURA ring looks like a slightly thick wedding band, yet contains an impressive array of sensors and capabilities, including:
Two infrared LED
One infrared sensor
Three temperature sensors
One accelerometer
A six-axis gyro
A curved battery with a seven-day life
The memory, processing, and transmission capability required to connect with your smartphone
Disrupting Medical Imaging Hardware
In 2018, we saw lab breakthroughs that will drive the cost of an ultrasound sensor to below $100, in a packaging smaller than most bandages, powered by a smartphone. Dramatically disrupting ultrasound is just the beginning.
Nanobots and Nanonetworks
While wearables have long been able to track and transmit our steps, heart rate, and other health data, smart nanobots and ingestible sensors will soon be able to monitor countless new parameters and even help diagnose disease.
Some of the most exciting breakthroughs in smart nanotechnology from the past year include:
Researchers from the École Polytechnique Fédérale de Lausanne (EPFL) and the Swiss Federal Institute of Technology in Zurich (ETH Zurich) demonstrated artificial microrobots that can swim and navigate through different fluids, independent of additional sensors, electronics, or power transmission.
Researchers at the University of Chicago proposed specific arrangements of DNA-based molecular logic gates to capture the information contained in the temporal portion of our cells’ communication mechanisms. Accessing the otherwise-lost time-dependent information of these cellular signals is akin to knowing the tune of a song, rather than solely the lyrics.
MIT researchers built micron-scale robots able to sense, record, and store information about their environment. These tiny robots, about 100 micrometers in diameter (approximately the size of a human egg cell), can also carry out pre-programmed computational tasks.
Engineers at University of California, San Diego developed ultrasound-powered nanorobots that swim efficiently through your blood, removing harmful bacteria and the toxins they produce.
But it doesn’t stop there.
As nanosensor and nanonetworking capabilities develop, these tiny bots may soon communicate with each other, enabling the targeted delivery of drugs and autonomous corrective action.
Mobile Health
The OURA ring and the Series 4 Apple Watch are just the tip of the spear when it comes to our future of mobile health. This field, predicted to become a $102 billion market by 2022, puts an on-demand virtual doctor in your back pocket.
Step aside, WebMD.
In true exponential technology fashion, mobile device penetration has increased dramatically, while image recognition error rates and sensor costs have sharply declined.
As a result, AI-powered medical chatbots are flooding the market; diagnostic apps can identify anything from a rash to diabetic retinopathy; and with the advent of global connectivity, mHealth platforms enable real-time health data collection, transmission, and remote diagnosis by medical professionals.
Already available to residents across North London, Babylon Health offers immediate medical advice through AI-powered chatbots and video consultations with doctors via its app.
Babylon now aims to build up its AI for advanced diagnostics and even prescription. Others, like Woebot, take on mental health, using cognitive behavioral therapy in communications over Facebook messenger with patients suffering from depression.
In addition to phone apps and add-ons that test for fertility or autism, the now-FDA-approved Clarius L7 Linear Array Ultrasound Scanner can connect directly to iOS and Android devices and perform wireless ultrasounds at a moment’s notice.
Next, Healthy.io, an Israeli startup, uses your smartphone and computer vision to analyze traditional urine test strips—all you need to do is take a few photos.
With mHealth platforms like ClickMedix, which connects remotely-located patients to medical providers through real-time health data collection and transmission, what’s to stop us from delivering needed treatments through drone delivery or robotic telesurgery?
Welcome to the age of smartphone-as-a-medical-device.
Conclusion
With these DIY data collection and diagnostic tools, we save on transportation costs (time and money), and time bottlenecks.
No longer will you need to wait for your urine or blood results to go through the current information chain: samples will be sent to the lab, analyzed by a technician, results interpreted by your doctor, and only then relayed to you.
Just like the “sage-on-the-stage” issue with today’s education system, healthcare has a “doctor-on-the-dais” problem. Current medical procedures are too complicated and expensive for a layperson to perform and analyze on their own.
The coming abundance of healthcare data promises to transform how we approach healthcare, putting the power of exponential technologies in the patient’s hands and revolutionizing how we live.
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Posted in Human Robots
#434256 Singularity Hub’s Top Articles of the ...
2018 was a big year for science and technology. The first gene-edited babies were born, as were the first cloned monkeys. SpaceX successfully launched the Falcon Heavy, and NASA’s InSight lander placed a seismometer on Mars. Bitcoin’s value plummeted, as did the cost of renewable energy. The world’s biggest neuromorphic supercomputer was switched on, and quantum communication made significant progress.
As 2018 draws to a close and we start anticipating the developments that will happen in 2019, here’s a look back at our ten most-read articles of the year.
This 3D Printed House Goes Up in a Day for Under $10,000
Vanessa Bates Ramirez | 3/18/18
“ICON and New Story’s vision is one of 3D printed houses acting as a safe, affordable housing alternative for people in need. New Story has already built over 800 homes in Haiti, El Salvador, Bolivia, and Mexico, partnering with the communities they serve to hire local labor and purchase local materials rather than shipping everything in from abroad.”
Machines Teaching Each Other Could Be the Biggest Exponential Trend in AI
Aaron Frank | 1/21/18
“Data is the fuel of machine learning, but even for machines, some data is hard to get—it may be risky, slow, rare, or expensive. In those cases, machines can share experiences or create synthetic experiences for each other to augment or replace data. It turns out that this is not a minor effect, it actually is self-amplifying, and therefore exponential.”
Low-Cost Soft Robot Muscles Can Lift 200 Times Their Weight and Self-Heal
Edd Gent | 1/11/18
“Now researchers at the University of Colorado Boulder have built a series of low-cost artificial muscles—as little as 10 cents per device—using soft plastic pouches filled with electrically insulating liquids that contract with the force and speed of mammalian skeletal muscles when a voltage is applied to them.”
These Are the Most Exciting Industries and Jobs of the Future
Raya Bidshahri | 1/29/18
“Technological trends are giving rise to what many thought leaders refer to as the “imagination economy.” This is defined as “an economy where intuitive and creative thinking create economic value, after logical and rational thinking have been outsourced to other economies.” Unsurprisingly, humans continue to outdo machines when it comes to innovating and pushing intellectual, imaginative, and creative boundaries, making jobs involving these skills the hardest to automate.”
Inside a $1 Billion Real Estate Company Operating Entirely in VR
Aaron Frank | 4/8/18
“Incredibly, this growth is largely the result of eXp Realty’s use of an online virtual world similar to Second Life. That means every employee, contractor, and the thousands of agents who work at the company show up to work—team meetings, training seminars, onboarding sessions—all inside a virtual reality campus.To be clear, this is a traditional real estate brokerage helping people buy and sell physical homes—but they use a virtual world as their corporate offices.”
How Fast Is AI Progressing? Stanford’s New Report Card for Artificial Intelligence
Thomas Hornigold | 1/18/18
“Progress in AI over the next few years is far more likely to resemble a gradual rising tide—as more and more tasks can be turned into algorithms and accomplished by software—rather than the tsunami of a sudden intelligence explosion or general intelligence breakthrough. Perhaps measuring the ability of an AI system to learn and adapt to the work routines of humans in office-based tasks could be possible.”
When Will We Finally Achieve True Artificial Intelligence?
Thomas Hornigold | 1/1/18
“The issue with trying to predict the exact date of human-level AI is that we don’t know how far is left to go. This is unlike Moore’s Law. Moore’s Law, the doubling of processing power roughly every couple of years, makes a very concrete prediction about a very specific phenomenon. We understand roughly how to get there—improved engineering of silicon wafers—and we know we’re not at the fundamental limits of our current approach. You cannot say the same about artificial intelligence.”
IBM’s New Computer Is the Size of a Grain of Salt and Costs Less Than 10 Cents
Edd Gent | 3/26/18
“Costing less than 10 cents to manufacture, the company envisions the device being embedded into products as they move around the supply chain. The computer’s sensing, processing, and communicating capabilities mean it could effectively turn every item in the supply chain into an Internet of Things device, producing highly granular supply chain data that could streamline business operations.”
Why the Rise of Self-Driving Vehicles Will Actually Increase Car Ownership
Melba Kurman and Hod Lipson / 2/14/18
“When people predict the demise of car ownership, they are overlooking the reality that the new autonomous automotive industry is not going to be just a re-hash of today’s car industry with driverless vehicles. Instead, the automotive industry of the future will be selling what could be considered an entirely new product: a wide variety of intelligent, self-guiding transportation robots. When cars become a widely used type of transportation robot, they will be cheap, ubiquitous, and versatile.”
A Model for the Future of Education
Peter Diamandis | 9/12/18
“I imagine a relatively near-term future in which robotics and artificial intelligence will allow any of us, from ages 8 to 108, to easily and quickly find answers, create products, or accomplish tasks, all simply by expressing our desires. From ‘mind to manufactured in moments.’ In short, we’ll be able to do and create almost whatever we want. In this future, what attributes will be most critical for our children to learn to become successful in their adult lives? What’s most important for educating our children today?”
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Posted in Human Robots
#433872 Breaking Out of the Corporate Bubble ...
For big companies, success is a blessing and a curse. You don’t get big without doing something (or many things) very right. It might start with an invention or service the world didn’t know it needed. Your product takes off, and growth brings a whole new set of logistical challenges. Delivering consistent quality, hiring the right team, establishing a strong culture, tapping into new markets, satisfying shareholders. The list goes on.
Eventually, however, what made you successful also makes you resistant to change.
You’ve built a machine for one purpose, and it’s running smoothly, but what about retooling that machine to make something new? Not so easy. Leaders of big companies know there is no future for their organizations without change. And yet, they struggle to drive it.
In their new book, Leading Transformation: How to Take Charge of Your Company’s Future, Kyle Nel, Nathan Furr, and Thomas Ramsøy aim to deliver a roadmap for corporate transformation.
The book focuses on practical tools that have worked in big companies to break down behavioral and cognitive biases, envision radical futures, and run experiments. These include using science fiction and narrative to see ahead and adopting better measures of success for new endeavors.
A thread throughout is how to envision a new future and move into that future.
We’re limited by the bubbles in which we spend the most time—the corporate bubble, the startup bubble, the nonprofit bubble. The mutually beneficial convergence of complementary bubbles, then, can be a powerful tool for kickstarting transformation. The views and experiences of one partner can challenge the accepted wisdom of the other; resources can flow into newly co-created visions and projects; and connections can be made that wouldn’t otherwise exist.
The authors call such alliances uncommon partners. In the following excerpt from the book, Made In Space, a startup building 3D printers for space, helps Lowe’s explore an in-store 3D printing system, and Lowe’s helps Made In Space expand its vision and focus.
Uncommon Partners
In a dingy conference room at NASA, five prototypical nerds, smelling of Thai food, laid out the path to printing satellites in space and buildings on distant planets. At the end of their four-day marathon, they emerged with an artifact trail that began with early prototypes for the first 3D printer on the International Space Station and ended in the additive-manufacturing future—a future much bigger than 3D printing.
In the additive-manufacturing future, we will view everything as transient, or capable of being repurposed into new things. Rather than throwing away a soda bottle or a bent nail, we will simply reprocess these things into a new hinge for the fence we are building or a light switch plate for the tool shed. Indeed, we might not even go buy bricks for the tool shed, but instead might print them from impurities pulled from the air and the dirt beneath our feet. Such a process would both capture carbon in the air to make the bricks and avoid all the carbon involved in making and then transporting traditional bricks to your house.
If it all sounds a little too science fiction, think again. Lowe’s has already been honored as a Champion of Change by the US government for its prototype system to recycle plastic (e.g., plastic bags and bottles). The future may be closer than you have imagined. But to get there, Lowe’s didn’t work alone. It had to work with uncommon partners to create the future.
Uncommon partners are the types of organizations you might not normally work with, but which can greatly help you create radical new futures. Increasingly, as new technologies emerge and old industries converge, companies are finding that working independently to create all the necessary capabilities to enter new industries or create new technologies is costly, risky, and even counterproductive. Instead, organizations are finding that they need to collaborate with uncommon partners as an ecosystem to cocreate the future together. Nathan [Furr] and his colleague at INSEAD, Andrew Shipilov, call this arrangement an adaptive ecosystem strategy and described how companies such as Lowe’s, Samsung, Mastercard, and others are learning to work differently with partners and to work with different kinds of partners to more effectively discover new opportunities. For Lowe’s, an adaptive ecosystem strategy working with uncommon partners forms the foundation of capturing new opportunities and transforming the company. Despite its increased agility, Lowe’s can’t be (and shouldn’t become) an independent additive-manufacturing, robotics-using, exosuit-building, AR-promoting, fill-in-the-blank-what’s-next-ing company in addition to being a home improvement company. Instead, Lowe’s applies an adaptive ecosystem strategy to find the uncommon partners with which it can collaborate in new territory.
To apply the adaptive ecosystem strategy with uncommon partners, start by identifying the technical or operational components required for a particular focus area (e.g., exosuits) and then sort these components into three groups. First, there are the components that are emerging organically without any assistance from the orchestrator—the leader who tries to bring together the adaptive ecosystem. Second, there are the elements that might emerge, with encouragement and support. Third are the elements that won’t happen unless you do something about it. In an adaptive ecosystem strategy, you can create regular partnerships for the first two elements—those already emerging or that might emerge—if needed. But you have to create the elements in the final category (those that won’t emerge) either with an uncommon partner or by yourself.
For example, when Lowe’s wanted to explore the additive-manufacturing space, it began a search for an uncommon partner to provide the missing but needed capabilities. Unfortunately, initial discussions with major 3D printing companies proved disappointing. The major manufacturers kept trying to sell Lowe’s 3D printers. But the vision our group had created with science fiction was not for vendors to sell Lowe’s a printer, but for partners to help the company build a system—something that would allow customers to scan, manipulate, print, and eventually recycle additive-manufacturing objects. Every time we discussed 3D printing systems with these major companies, they responded that they could do it and then tried to sell printers. When Carin Watson, one of the leading lights at Singularity University, introduced us to Made In Space (a company being incubated in Singularity University’s futuristic accelerator), we discovered an uncommon partner that understood what it meant to cocreate a system.
Initially, Made In Space had been focused on simply getting 3D printing to work in space, where you can’t rely on gravity, you can’t send up a technician if the machine breaks, and you can’t release noxious fumes into cramped spacecraft quarters. But after the four days in the conference room going over the comic for additive manufacturing, Made In Space and Lowe’s emerged with a bigger vision. The company helped lay out an artifact trail that included not only the first printer on the International Space Station but also printing system services in Lowe’s stores.
Of course, the vision for an additive-manufacturing future didn’t end there. It also reshaped Made In Space’s trajectory, encouraging the startup, during those four days in a NASA conference room, to design a bolder future. Today, some of its bold projects include the Archinaut, a system that enables satellites to build themselves while in space, a direction that emerged partly from the science fiction narrative we created around additive manufacturing.
In summary, uncommon partners help you succeed by providing you with the capabilities you shouldn’t be building yourself, as well as with fresh insights. You also help uncommon partners succeed by creating new opportunities from which they can prosper.
Helping Uncommon Partners Prosper
Working most effectively with uncommon partners can require a shift from more familiar outsourcing or partnership relationships. When working with uncommon partners, you are trying to cocreate the future, which entails a great deal more uncertainty. Because you can’t specify outcomes precisely, agreements are typically less formal than in other types of relationships, and they operate under the provisions of shared vision and trust more than binding agreement clauses. Moreover, your goal isn’t to extract all the value from the relationship. Rather, you need to find a way to share the value.
Ideally, your uncommon partners should be transformed for the better by the work you do. For example, Lowe’s uncommon partner developing the robotics narrative was a small startup called Fellow Robots. Through their work with Lowe’s, Fellow Robots transformed from a small team focused on a narrow application of robotics (which was arguably the wrong problem) to a growing company developing a very different and valuable set of capabilities: putting cutting-edge technology on top of the old legacy systems embedded at the core of most companies. Working with Lowe’s allowed Fellow Robots to discover new opportunities, and today Fellow Robots works with retailers around the world, including BevMo! and Yamada. Ultimately, working with uncommon partners should be transformative for both of you, so focus more on creating a bigger pie than on how you are going to slice up a smaller pie.
The above excerpt appears in the new book Leading Transformation: How to Take Charge of Your Company’s Future by Kyle Nel, Nathan Furr, and Thomas Ramsøy, published by Harvard Business Review Press.
Image Credit: Here / Shutterstock.com
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Posted in Human Robots
#433770 Will Tech Make Insurance Obsolete in the ...
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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