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#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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#432568 Tech Optimists See a Golden ...

Technology evangelists dream about a future where we’re all liberated from the more mundane aspects of our jobs by artificial intelligence. Other futurists go further, imagining AI will enable us to become superhuman, enhancing our intelligence, abandoning our mortal bodies, and uploading ourselves to the cloud.

Paradise is all very well, although your mileage may vary on whether these scenarios are realistic or desirable. The real question is, how do we get there?

Economist John Maynard Keynes notably argued in favor of active intervention when an economic crisis hits, rather than waiting for the markets to settle down to a more healthy equilibrium in the long run. His rebuttal to critics was, “In the long run, we are all dead.” After all, if it takes 50 years of upheaval and economic chaos for things to return to normality, there has been an immense amount of human suffering first.

Similar problems arise with the transition to a world where AI is intimately involved in our lives. In the long term, automation of labor might benefit the human species immensely. But in the short term, it has all kinds of potential pitfalls, especially in exacerbating inequality within societies where AI takes on a larger role. A new report from the Institute for Public Policy Research has deep concerns about the future of work.

Uneven Distribution
While the report doesn’t foresee the same gloom and doom of mass unemployment that other commentators have considered, the concern is that the gains in productivity and economic benefits from AI will be unevenly distributed. In the UK, jobs that account for £290 billion worth of wages in today’s economy could potentially be automated with current technology. But these are disproportionately jobs held by people who are already suffering from social inequality.

Low-wage jobs are five times more likely to be automated than high-wage jobs. A greater proportion of jobs held by women are likely to be automated. The solution that’s often suggested is that people should simply “retrain”; but if no funding or assistance is provided, this burden is too much to bear. You can’t expect people to seamlessly transition from driving taxis to writing self-driving car software without help. As we have already seen, inequality is exacerbated when jobs that don’t require advanced education (even if they require a great deal of technical skill) are the first to go.

No Room for Beginners
Optimists say algorithms won’t replace humans, but will instead liberate us from the dull parts of our jobs. Lawyers used to have to spend hours trawling through case law to find legal precedents; now AI can identify the most relevant documents for them. Doctors no longer need to look through endless scans and perform diagnostic tests; machines can do this, leaving the decision-making to humans. This boosts productivity and provides invaluable tools for workers.

But there are issues with this rosy picture. If humans need to do less work, the economic incentive is for the boss to reduce their hours. Some of these “dull, routine” parts of the job were traditionally how people getting into the field learned the ropes: paralegals used to look through case law, but AI may render them obsolete. Even in the field of journalism, there’s now software that will rewrite press releases for publication, traditionally something close to an entry-level task. If there are no entry-level jobs, or if entry-level now requires years of training, the result is to exacerbate inequality and reduce social mobility.

Automating Our Biases
The adoption of algorithms into employment has already had negative impacts on equality. Cathy O’Neil, mathematics PhD from Harvard, raises these concerns in her excellent book Weapons of Math Destruction. She notes that algorithms designed by humans often encode the biases of that society, whether they’re racial or based on gender and sexuality.

Google’s search engine advertises more executive-level jobs to users it thinks are male. AI programs predict that black offenders are more likely to re-offend than white offenders; they receive correspondingly longer sentences. It needn’t necessarily be that bias has been actively programmed; perhaps the algorithms just learn from historical data, but this means they will perpetuate historical inequalities.

Take candidate-screening software HireVue, used by many major corporations to assess new employees. It analyzes “verbal and non-verbal cues” of candidates, comparing them to employees that historically did well. Either way, according to Cathy O’Neil, they are “using people’s fear and trust of mathematics to prevent them from asking questions.” With no transparency or understanding of how the algorithm generates its results, and no consensus over who’s responsible for the results, discrimination can occur automatically, on a massive scale.

Combine this with other demographic trends. In rich countries, people are living longer. An increasing burden will be placed on a shrinking tax base to support that elderly population. A recent study said that due to the accumulation of wealth in older generations, millennials stand to inherit more than any previous generation, but it won’t happen until they’re in their 60s. Meanwhile, those with savings and capital will benefit as the economy shifts: the stock market and GDP will grow, but wages and equality will fall, a situation that favors people who are already wealthy.

Even in the most dramatic AI scenarios, inequality is exacerbated. If someone develops a general intelligence that’s near-human or super-human, and they manage to control and monopolize it, they instantly become immensely wealthy and powerful. If the glorious technological future that Silicon Valley enthusiasts dream about is only going to serve to make the growing gaps wider and strengthen existing unfair power structures, is it something worth striving for?

What Makes a Utopia?
We urgently need to redefine our notion of progress. Philosophers worry about an AI that is misaligned—the things it seeks to maximize are not the things we want maximized. At the same time, we measure the development of our countries by GDP, not the quality of life of workers or the equality of opportunity in the society. Growing wealth with increased inequality is not progress.

Some people will take the position that there are always winners and losers in society, and that any attempt to redress the inequalities of our society will stifle economic growth and leave everyone worse off. Some will see this as an argument for a new economic model, based around universal basic income. Any moves towards this will need to take care that it’s affordable, sustainable, and doesn’t lead towards an entrenched two-tier society.

Walter Schiedel’s book The Great Leveller is a huge survey of inequality across all of human history, from the 21st century to prehistoric cave-dwellers. He argues that only revolutions, wars, and other catastrophes have historically reduced inequality: a perfect example is the Black Death in Europe, which (by reducing the population and therefore the labor supply that was available) increased wages and reduced inequality. Meanwhile, our solution to the financial crisis of 2007-8 may have only made the problem worse.

But in a world of nuclear weapons, of biowarfare, of cyberwarfare—a world of unprecedented, complex, distributed threats—the consequences of these “safety valves” could be worse than ever before. Inequality increases the risk of global catastrophe, and global catastrophes could scupper any progress towards the techno-utopia that the utopians dream of. And a society with entrenched inequality is no utopia at all.

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#431907 The Future of Cancer Treatment Is ...

In an interview at Singularity University’s Exponential Medicine in San Diego, Richard Wender, chief cancer control officer at the American Cancer Society, discussed how technology has changed cancer care and treatment in recent years.
Just a few years ago, microscopes were the primary tool used in cancer diagnoses, but we’ve come a long way since.
“We still look at a microscope, we still look at what organ the cancer started in,” Wender said. “But increasingly we’re looking at the molecular signature. It’s not just the genomics, and it’s not just the genes. It’s also the cellular environment around that cancer. We’re now targeting our therapies to the mutations that are found in that particular cancer.”
Cancer treatments in the past have been largely reactionary, but they don’t need to be. Most cancer is genetic, which means that treatment can be preventative. This is one reason why newer cancer treatment techniques are searching for actionable targets in the specific gene before the cancer develops.

When asked how artificial intelligence and machine learning technologies are reshaping clinical trials, Wender acknowledged that how clinical trials have been run in the past won’t work moving forward.
“Our traditional ways of learning about cancer were by finding a particular cancer type and conducting a long clinical trial that took a number of years enrolling patients from around the country. That is not how we’re going to learn to treat individual patients in the future.”
Instead, Wender emphasized the need for gathering as much data as possible, and from as many individual patients as possible. This data should encompass clinical, pathological, and molecular data and should be gathered from a patient all the way through their final outcome. “Literally every person becomes a clinical trial of one,” Wender said.
For the best cancer treatment and diagnostics, Wender says the answer is to make the process collaborative by pulling in resources from organizations and companies that are both established and emerging.
It’s no surprise to hear that the best solutions come from pairing together uncommon partners to innovate.
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#431389 Tech Is Becoming Emotionally ...

Many people get frustrated with technology when it malfunctions or is counterintuitive. The last thing people might expect is for that same technology to pick up on their emotions and engage with them differently as a result.
All of that is now changing. Computers are increasingly able to figure out what we’re feeling—and it’s big business.
A recent report predicts that the global affective computing market will grow from $12.2 billion in 2016 to $53.98 billion by 2021. The report by research and consultancy firm MarketsandMarkets observed that enabling technologies have already been adopted in a wide range of industries and noted a rising demand for facial feature extraction software.
Affective computing is also referred to as emotion AI or artificial emotional intelligence. Although many people are still unfamiliar with the category, researchers in academia have already discovered a multitude of uses for it.
At the University of Tokyo, Professor Toshihiko Yamasaki decided to develop a machine learning system that evaluates the quality of TED Talk videos. Of course, a TED Talk is only considered to be good if it resonates with a human audience. On the surface, this would seem too qualitatively abstract for computer analysis. But Yamasaki wanted his system to watch videos of presentations and predict user impressions. Could a machine learning system accurately evaluate the emotional persuasiveness of a speaker?
Yamasaki and his colleagues came up with a method that analyzed correlations and “multimodal features including linguistic as well as acoustic features” in a dataset of 1,646 TED Talk videos. The experiment was successful. The method obtained “a statistically significant macro-average accuracy of 93.3 percent, outperforming several competitive baseline methods.”
A machine was able to predict whether or not a person would emotionally connect with other people. In their report, the authors noted that these findings could be used for recommendation purposes and also as feedback to the presenters, in order to improve the quality of their public presentation. However, the usefulness of affective computing goes far beyond the way people present content. It may also transform the way they learn it.
Researchers from North Carolina State University explored the connection between students’ affective states and their ability to learn. Their software was able to accurately predict the effectiveness of online tutoring sessions by analyzing the facial expressions of participating students. The software tracked fine-grained facial movements such as eyebrow raising, eyelid tightening, and mouth dimpling to determine engagement, frustration, and learning. The authors concluded that “analysis of facial expressions has great potential for educational data mining.”
This type of technology is increasingly being used within the private sector. Affectiva is a Boston-based company that makes emotion recognition software. When asked to comment on this emerging technology, Gabi Zijderveld, chief marketing officer at Affectiva, explained in an interview for this article, “Our software measures facial expressions of emotion. So basically all you need is our software running and then access to a camera so you can basically record a face and analyze it. We can do that in real time or we can do this by looking at a video and then analyzing data and sending it back to folks.”
The technology has particular relevance for the advertising industry.
Zijderveld said, “We have products that allow you to measure how consumers or viewers respond to digital content…you could have a number of people looking at an ad, you measure their emotional response so you aggregate the data and it gives you insight into how well your content is performing. And then you can adapt and adjust accordingly.”
Zijderveld explained that this is the first market where the company got traction. However, they have since packaged up their core technology in software development kits or SDKs. This allows other companies to integrate emotion detection into whatever they are building.
By licensing its technology to others, Affectiva is now rapidly expanding into a wide variety of markets, including gaming, education, robotics, and healthcare. The core technology is also used in human resources for the purposes of video recruitment. The software analyzes the emotional responses of interviewees, and that data is factored into hiring decisions.
Richard Yonck is founder and president of Intelligent Future Consulting and the author of a book about our relationship with technology. “One area I discuss in Heart of the Machine is the idea of an emotional economy that will arise as an ecosystem of emotionally aware businesses, systems, and services are developed. This will rapidly expand into a multi-billion-dollar industry, leading to an infrastructure that will be both emotionally responsive and potentially exploitive at personal, commercial, and political levels,” said Yonck, in an interview for this article.
According to Yonck, these emotionally-aware systems will “better anticipate needs, improve efficiency, and reduce stress and misunderstandings.”
Affectiva is uniquely positioned to profit from this “emotional economy.” The company has already created the world’s largest emotion database. “We’ve analyzed a little bit over 4.7 million faces in 75 countries,” said Zijderveld. “This is data first and foremost, it’s data gathered with consent. So everyone has opted in to have their faces analyzed.”
The vastness of that database is essential for deep learning approaches. The software would be inaccurate if the data was inadequate. According to Zijderveld, “If you don’t have massive amounts of data of people of all ages, genders, and ethnicities, then your algorithms are going to be pretty biased.”
This massive database has already revealed cultural insights into how people express emotion. Zijderveld explained, “Obviously everyone knows that women are more expressive than men. But our data confirms that, but not only that, it can also show that women smile longer. They tend to smile more often. There’s also regional differences.”
Yonck believes that affective computing will inspire unimaginable forms of innovation and that change will happen at a fast pace.
He explained, “As businesses, software, systems, and services develop, they’ll support and make possible all sorts of other emotionally aware technologies that couldn’t previously exist. This leads to a spiral of increasingly sophisticated products, just as happened in the early days of computing.”
Those who are curious about affective technology will soon be able to interact with it.
Hubble Connected unveiled the Hubble Hugo at multiple trade shows this year. Hugo is billed as “the world’s first smart camera,” with emotion AI video analytics powered by Affectiva. The product can identify individuals, figure out how they’re feeling, receive voice commands, video monitor your home, and act as a photographer and videographer of events. Media can then be transmitted to the cloud. The company’s website describes Hugo as “a fun pal to have in the house.”
Although he sees the potential for improved efficiencies and expanding markets, Richard Yonck cautions that AI technology is not without its pitfalls.
“It’s critical that we understand we are headed into very unknown territory as we develop these systems, creating problems unlike any we’ve faced before,” said Yonck. “We should put our focus on ensuring AI develops in a way that represents our human values and ideals.”
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#430640 RE2 Robotics Receives Air Force Funding ...

PITTSBURGH, PA – June 21, 2017 – RE2 Robotics announced today that the Company was selected by the Air Force to develop a drop-in robotic system to rapidly convert a variety of traditionally manned aircraft to robotically piloted, autonomous aircraft under the Small Business Innovation Research (SBIR) program. This robotic system, named “Common Aircraft Retrofit for Novel Autonomous Control” (CARNAC), will operate the aircraft similarly to a human pilot and will not require any modifications to the aircraft.
Automation and autonomy have broad value to the Department of Defense with the potential to enhance system performance of existing platforms, reduce costs, and enable new missions and capabilities, especially with reduced human exposure to dangerous or life-threatening situations. The CARNAC project leverages existing aviation assets and advances in vehicle automation technologies to develop a cutting-edge drop-in robotic flight system.
During the program, RE2 Robotics will demonstrate system architecture feasibility, humanoid-like robotic manipulation capabilities, vision-based flight-status recognition, and cognitive architecture-based decision making.
“Our team is excited to incorporate the Company’s robotic manipulation expertise with proven technologies in applique systems, vision processing algorithms, and decision making to create a customized application that will allow a wide variety of existing aircraft to be outfitted with a robotic pilot,” stated Jorgen Pedersen, president and CEO of RE2 Robotics. “By creating a drop-in robotic pilot, we have the ability to insert autonomy into and expand the capabilities of not only traditionally manned air vehicles, but ground and underwater vehicles as well. This application will open up a whole new market for our mobile robotic manipulator systems.”
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About RE2 RoboticsRE2 Robotics develops mobile robotic technologies that enable robot users to remotely interact with their world from a safe distance — whether on the ground, in the air, or underwater. RE2 creates interoperable robotic manipulator arms with human-like performance, intuitive human robot interfaces, and advanced autonomy software for mobile robotics. For more information, please visit www.resquared.com or call 412.681.6382.
Media Contact: RE2 Public Relations, pr@resquared.com, 412.681.6382.
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