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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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#433758 DeepMind’s New Research Plan to Make ...

Making sure artificial intelligence does what we want and behaves in predictable ways will be crucial as the technology becomes increasingly ubiquitous. It’s an area frequently neglected in the race to develop products, but DeepMind has now outlined its research agenda to tackle the problem.

AI safety, as the field is known, has been gaining prominence in recent years. That’s probably at least partly down to the overzealous warnings of a coming AI apocalypse from well-meaning, but underqualified pundits like Elon Musk and Stephen Hawking. But it’s also recognition of the fact that AI technology is quickly pervading all aspects of our lives, making decisions on everything from what movies we watch to whether we get a mortgage.

That’s why DeepMind hired a bevy of researchers who specialize in foreseeing the unforeseen consequences of the way we built AI back in 2016. And now the team has spelled out the three key domains they think require research if we’re going to build autonomous machines that do what we want.

In a new blog designed to provide updates on the team’s work, they introduce the ideas of specification, robustness, and assurance, which they say will act as the cornerstones of their future research. Specification involves making sure AI systems do what their operator intends; robustness means a system can cope with changes to its environment and attempts to throw it off course; and assurance involves our ability to understand what systems are doing and how to control them.

A classic thought experiment designed to illustrate how we could lose control of an AI system can help illustrate the problem of specification. Philosopher Nick Bostrom’s posited a hypothetical machine charged with making as many paperclips as possible. Because the creators fail to add what they might assume are obvious additional goals like not harming people, the AI wipes out humanity so we can’t switch it off before turning all matter in the universe into paperclips.

Obviously the example is extreme, but it shows how a poorly-specified goal can lead to unexpected and disastrous outcomes. Properly codifying the desires of the designer is no easy feat, though; often there are not neat ways to encompass both the explicit and implicit goals in ways that are understandable to the machine and don’t leave room for ambiguities, meaning we often rely on incomplete approximations.

The researchers note recent research by OpenAI in which an AI was trained to play a boat-racing game called CoastRunners. The game rewards players for hitting targets laid out along the race route. The AI worked out that it could get a higher score by repeatedly knocking over regenerating targets rather than actually completing the course. The blog post includes a link to a spreadsheet detailing scores of such examples.

Another key concern for AI designers is making their creation robust to the unpredictability of the real world. Despite their superhuman abilities on certain tasks, most cutting-edge AI systems are remarkably brittle. They tend to be trained on highly-curated datasets and so can fail when faced with unfamiliar input. This can happen by accident or by design—researchers have come up with numerous ways to trick image recognition algorithms into misclassifying things, including thinking a 3D printed tortoise was actually a gun.

Building systems that can deal with every possible encounter may not be feasible, so a big part of making AIs more robust may be getting them to avoid risks and ensuring they can recover from errors, or that they have failsafes to ensure errors don’t lead to catastrophic failure.

And finally, we need to have ways to make sure we can tell whether an AI is performing the way we expect it to. A key part of assurance is being able to effectively monitor systems and interpret what they’re doing—if we’re basing medical treatments or sentencing decisions on the output of an AI, we’d like to see the reasoning. That’s a major outstanding problem for popular deep learning approaches, which are largely indecipherable black boxes.

The other half of assurance is the ability to intervene if a machine isn’t behaving the way we’d like. But designing a reliable off switch is tough, because most learning systems have a strong incentive to prevent anyone from interfering with their goals.

The authors don’t pretend to have all the answers, but they hope the framework they’ve come up with can help guide others working on AI safety. While it may be some time before AI is truly in a position to do us harm, hopefully early efforts like these will mean it’s built on a solid foundation that ensures it is aligned with our goals.

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#433754 This Robotic Warehouse Fills Orders in ...

Shopping is becoming less and less of a consumer experience—or, for many, less of a chore—as the list of things that can be bought online and delivered to our homes grows to include, well, almost anything you can think of. An Israeli startup is working to make shopping and deliveries even faster and cheaper—and they’re succeeding.

Last week, CommonSense Robotics announced the launch of its first autonomous micro-fulfillment center in Tel Aviv. The company claims the facility is the smallest of its type in the world at 6,000 square feet. For comparison’s sake—most fulfillment hubs that incorporate robotics are at least 120,000 square feet. Amazon’s upcoming facility in Bessemer, Alabama will be a massive 855,000 square feet.

The thing about a building whose square footage is in the hundred-thousands is, you can fit a lot of stuff inside it, but there aren’t many places you can fit the building itself, especially not in major urban areas. So most fulfillment centers are outside cities, which means more time and more money to get your Moroccan oil shampoo, or your vegetable garden starter kit, or your 100-pack of organic protein bars from that fulfillment center to your front door.

CommonSense Robotics built the Tel Aviv center in an area that was previously thought too small for warehouse infrastructure. “In order to fit our site into small, tight urban spaces, we’ve designed every single element of it to optimize for space efficiency,” said Avital Sterngold, VP of operations. Using a robotic sorting system that includes hundreds of robots, plus AI software that assigns them specific tasks, the facility can prepare orders in less than five minutes end-to-end.

It’s not all automated, though—there’s still some human labor in the mix. The robots fetch goods and bring them to a team of people, who then pack the individual orders.

CommonSense raised $20 million this year in a funding round led by Palo Alto-based Playground Global. The company hopes to expand its operations to the US and UK in 2019. Its business model is to charge retailers a fee for each order fulfilled, while maintaining ownership and operation of the fulfillment centers. The first retailers to jump on the bandwagon were Super-Pharm, a drugstore chain, and Rami Levy, a retail supermarket chain.

“Staying competitive in today’s market is anchored by delivering orders quickly and determining how to fulfill and deliver orders efficiently, which are always the most complex aspects of any ecommerce operation. With robotics, we will be able to fulfill and deliver orders in under one hour, all while saving costs on said fulfillment and delivery,” said Super-Pharm VP Yossi Cohen. “Before CommonSense Robotics, we offered our customers next-day home delivery. With this partnership, we are now able to offer our customers same-day delivery and will very soon be offering them one-hour delivery.”

Long live the instant gratification economy—and the increasingly sophisticated technology that’s enabling it.

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

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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#433728 AI Is Kicking Space Exploration into ...

Artificial intelligence in space exploration is gathering momentum. Over the coming years, new missions look likely to be turbo-charged by AI as we voyage to comets, moons, and planets and explore the possibilities of mining asteroids.

“AI is already a game-changer that has made scientific research and exploration much more efficient. We are not just talking about a doubling but about a multiple of ten,” Leopold Summerer, Head of the Advanced Concepts and Studies Office at ESA, said in an interview with Singularity Hub.

Examples Abound
The history of AI and space exploration is older than many probably think. It has already played a significant role in research into our planet, the solar system, and the universe. As computer systems and software have developed, so have AI’s potential use cases.

The Earth Observer 1 (EO-1) satellite is a good example. Since its launch in the early 2000s, its onboard AI systems helped optimize analysis of and response to natural occurrences, like floods and volcanic eruptions. In some cases, the AI was able to tell EO-1 to start capturing images before the ground crew were even aware that the occurrence had taken place.

Other satellite and astronomy examples abound. Sky Image Cataloging and Analysis Tool (SKICAT) has assisted with the classification of objects discovered during the second Palomar Sky Survey, classifying thousands more objects caught in low resolution than a human would be able to. Similar AI systems have helped astronomers to identify 56 new possible gravitational lenses that play a crucial role in connection with research into dark matter.

AI’s ability to trawl through vast amounts of data and find correlations will become increasingly important in relation to getting the most out of the available data. ESA’s ENVISAT produces around 400 terabytes of new data every year—but will be dwarfed by the Square Kilometre Array, which will produce around the same amount of data that is currently on the internet in a day.

AI Readying For Mars
AI is also being used for trajectory and payload optimization. Both are important preliminary steps to NASA’s next rover mission to Mars, the Mars 2020 Rover, which is, slightly ironically, set to land on the red planet in early 2021.

An AI known as AEGIS is already on the red planet onboard NASA’s current rovers. The system can handle autonomous targeting of cameras and choose what to investigate. However, the next generation of AIs will be able to control vehicles, autonomously assist with study selection, and dynamically schedule and perform scientific tasks.

Throughout his career, John Leif Jørgensen from DTU Space in Denmark has designed equipment and systems that have been on board about 100 satellites—and counting. He is part of the team behind the Mars 2020 Rover’s autonomous scientific instrument PIXL, which makes extensive use of AI. Its purpose is to investigate whether there have been lifeforms like stromatolites on Mars.

“PIXL’s microscope is situated on the rover’s arm and needs to be placed 14 millimetres from what we want it to study. That happens thanks to several cameras placed on the rover. It may sound simple, but the handover process and finding out exactly where to place the arm can be likened to identifying a building from the street from a picture taken from the roof. This is something that AI is eminently suited for,” he said in an interview with Singularity Hub.

AI also helps PIXL operate autonomously throughout the night and continuously adjust as the environment changes—the temperature changes between day and night can be more than 100 degrees Celsius, meaning that the ground beneath the rover, the cameras, the robotic arm, and the rock being studied all keep changing distance.

“AI is at the core of all of this work, and helps almost double productivity,” Jørgensen said.

First Mars, Then Moons
Mars is likely far from the final destination for AIs in space. Jupiter’s moons have long fascinated scientists. Especially Europa, which could house a subsurface ocean, buried beneath an approximately 10 km thick ice crust. It is one of the most likely candidates for finding life elsewhere in the solar system.

While that mission may be some time in the future, NASA is currently planning to launch the James Webb Space Telescope into an orbit of around 1.5 million kilometers from Earth in 2020. Part of the mission will involve AI-empowered autonomous systems overseeing the full deployment of the telescope’s 705-kilo mirror.

The distances between Earth and Europa, or Earth and the James Webb telescope, means a delay in communications. That, in turn, makes it imperative for the crafts to be able to make their own decisions. Examples from the Mars Rover project show that communication between a rover and Earth can take 20 minutes because of the vast distance. A Europa mission would see much longer communication times.

Both missions, to varying degrees, illustrate one of the most significant challenges currently facing the use of AI in space exploration. There tends to be a direct correlation between how well AI systems perform and how much data they have been fed. The more, the better, as it were. But we simply don’t have very much data to feed such a system about what it’s likely to encounter on a mission to a place like Europa.

Computing power presents a second challenge. A strenuous, time-consuming approval process and the risk of radiation mean that your computer at home would likely be more powerful than anything going into space in the near future. A 200 GHz processor, 256 megabytes of ram, and 2 gigabytes of memory sounds a lot more like a Nokia 3210 (the one you could use as an ice hockey puck without it noticing) than an iPhone X—but it’s actually the ‘brain’ that will be onboard the next rover.

Private Companies Taking Off
Private companies are helping to push those limitations. CB Insights charts 57 startups in the space-space, covering areas as diverse as natural resources, consumer tourism, R&D, satellites, spacecraft design and launch, and data analytics.

David Chew works as an engineer for the Japanese satellite company Axelspace. He explained how private companies are pushing the speed of exploration and lowering costs.

“Many private space companies are taking advantage of fall-back systems and finding ways of using parts and systems that traditional companies have thought of as non-space-grade. By implementing fall-backs, and using AI, it is possible to integrate and use parts that lower costs without adding risk of failure,” he said in an interview with Singularity Hub.

Terraforming Our Future Home
Further into the future, moonshots like terraforming Mars await. Without AI, these kinds of projects to adapt other planets to Earth-like conditions would be impossible.

Autonomous crafts are already terraforming here on Earth. BioCarbon Engineering uses drones to plant up to 100,000 trees in a single day. Drones first survey and map an area, then an algorithm decides the optimal locations for the trees before a second wave of drones carry out the actual planting.

As is often the case with exponential technologies, there is a great potential for synergies and convergence. For example with AI and robotics, or quantum computing and machine learning. Why not send an AI-driven robot to Mars and use it as a telepresence for scientists on Earth? It could be argued that we are already in the early stages of doing just that by using VR and AR systems that take data from the Mars rovers and create a virtual landscape scientists can walk around in and make decisions on what the rovers should explore next.

One of the biggest benefits of AI in space exploration may not have that much to do with its actual functions. Chew believes that within as little as ten years, we could see the first mining of asteroids in the Kuiper Belt with the help of AI.

“I think one of the things that AI does to space exploration is that it opens up a whole range of new possible industries and services that have a more immediate effect on the lives of people on Earth,” he said. “It becomes a relatable industry that has a real effect on people’s daily lives. In a way, space exploration becomes part of people’s mindset, and the border between our planet and the solar system becomes less important.”

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