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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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#433696 3 Big Ways Tech Is Disrupting Global ...

Disruptive business models are often powered by alternative financing. In Part 1 of this series, I discussed how mobile is redefining money and banking and shared some of the dramatic transformations in the global remittance infrastructure.

In this article, we’ll discuss:

Peer-to-peer lending
AI financial advisors and robo traders
Seamless Transactions

Let’s dive right back in…

Decentralized Lending = Democratized Access to Finances
Peer-to-peer (P2P) lending is an age-old practice, traditionally with high risk and extreme locality. Now, the P2P funding model is being digitized and delocalized, bringing lending online and across borders.

Zopa, the first official crowdlending platform, arrived in the United Kingdom in 2004. Since then, the consumer crowdlending platform has facilitated lending of over 3 billion euros ($3.5 billion USD) of loans.

Person-to-business crowdlending took off, again in the U.K., in 2005 with Funding Circle, now with over 5 billion euros (~5.8 billion USD) of capital loaned to small businesses around the world.

Crowdlending next took off in the US in 2006, with platforms like Prosper and Lending Club. The US crowdlending industry has boomed to $21 billion in loans, across 515,000 loans.

Let’s take a step back… to a time before banks, when lending took place between trusted neighbors in small villages across the globe. Lending started as peer-to-peer transactions.

As villages turned into towns, towns turned into cities, and cities turned into sprawling metropolises, neighborly trust and the ability to communicate across urban landscapes broke down. That’s where banks and other financial institutions came into play—to add trust back into the lending equation.

With crowdlending, we are evidently returning to this pre-centralized-banking model of loans, and moving away from cumbersome intermediaries (e.g. high fees, regulations, and extra complexity).

Fueled by the permeation of the internet, P2P lending took on a new form as ‘crowdlending’ in the early 2000s. Now, as blockchain and artificial intelligence arrive on the digital scene, P2P lending platforms are being overhauled with transparency, accountability, reliability, and immutability.

Artificial Intelligence Micro Lending & Credit Scores
We are beginning to augment our quantitative decision-making with neural networks processing borrowers’ financial data to determine their financial ‘fate’ (or, as some call it, your credit score). Companies like Smart Finance Group (backed by Kai Fu Lee and Sinovation Ventures) are using artificial intelligence to minimize default rates for tens of millions of microloans.

Smart Finance is fueled by users’ personal data, particularly smartphone data and usage behavior. Users are required to give Smart Finance access to their smartphone data, so that Smart Finance’s artificial intelligence engine can generate a credit score from the personal information.

The benefits of this AI-powered lending platform do not stop at increased loan payback rates; there’s a massive speed increase as well. Smart Finance loans are frequently approved in under eight seconds. As we’ve seen with other artificial intelligence disruptions, data is the new gold.

Digitizing access to P2P loans paves the way for billions of people currently without access to banking to leapfrog the centralized banking system, just as Africa bypassed landline phones and went straight to mobile. Leapfrogging centralized banking and the credit system is exactly what Smart Finance has done for hundreds of millions of people in China.

Blockchain-Backed Crowdlending
As artificial intelligence accesses even the most mundane mobile browsing data to assign credit scores, blockchain technologies, particularly immutable ledgers and smart contracts, are massive disruptors to the archaic banking system, building additional trust and transparency on top of current P2P lending models.

Immutable ledgers provide the necessary transparency for accurate credit and loan defaulting history. Smart contracts executed on these immutable ledgers bring the critical ability to digitally replace cumbersome, expensive third parties (like banks), allowing individual borrowers or businesses to directly connect with willing lenders.

Two of the leading blockchain platforms for P2P lending are ETHLend and SALT Lending.

ETHLend is an Ethereum-based decentralized application aiming to bring transparency and trust to P2P lending through Ethereum network smart contracts.

Secure Automated Lending Technology (SALT) allows cryptocurrency asset holders to use their digital assets as collateral for cash loans, without the need to liquidate their holdings, giving rise to a digital-asset-backed lending market.

While blockchain poses a threat to many of the large, centralized banking institutions, some are taking advantage of the new technology to optimize their internal lending, credit scoring, and collateral operations.

In March 2018, ING and Credit Suisse successfully exchanged 25 million euros using HQLA-X, a blockchain-based collateral lending platform.

HQLA-X runs on the R3 Corda blockchain, a platform designed specifically to help heritage financial and commerce institutions migrate away from their inefficient legacy financial infrastructure.

Blockchain and tokenization are going through their own fintech and regulation shakeup right now. In a future blog, I’ll discuss the various efforts to more readily assure smart contracts, and the disruptive business model of security tokens and the US Securities and Exchange Commission.

Parallels to the Global Abundance of Capital
The abundance of capital being created by the advent of P2P loans closely relates to the unprecedented global abundance of capital.

Initial coin offerings (ICOs) and crowdfunding are taking a strong stand in disrupting the $164 billion venture capital market. The total amount invested in ICOs has risen from $6.6 billion in 2017 to $7.15 billion USD in the first half of 2018. Crowdfunding helped projects raise more than $34 billion in 2017, with experts projecting that global crowdfunding investments will reach $300 billion by 2025.

In the last year alone, using ICOs, over a dozen projects have raised hundreds of millions of dollars in mere hours. Take Filecoin, for example, which raised $257 million  in only 30 days; its first $135 million was raised in the first hour. Similarly, the Dragon Coin project (which itself is revolutionizing remittance in high-stakes casinos around the world) raised $320 million in its 30-day public ICO.

Some Important Takeaways…

Technology-backed fundraising and financial services are disrupting the world’s largest financial institutions. Anyone, anywhere, at anytime will be able to access the capital they need to pursue their idea.

The speed at which we can go from “I’ve got an idea” to “I run a billion-dollar company” is moving faster than ever.

Following Ray Kurzweil’s Law of Accelerating Returns, the rapid decrease in time to access capital is intimately linked (and greatly dependent on) a financial infrastructure (technology, institutions, platforms, and policies) that can adapt and evolve just as rapidly.

This new abundance of capital requires financial decision-making with ever-higher market prediction precision. That’s exactly where artificial intelligence is already playing a massive role.

Artificial Intelligence, Robo Traders, and Financial Advisors
On May 6, 2010, the Dow Jones Industrial Average suddenly collapsed by 998.5 points (equal to 8 percent, or $1 trillion). The crash lasted over 35 minutes and is now known as the ‘Flash Crash’. While no one knows the specific reason for this 2010 stock market anomaly, experts widely agree that the Flash Crash had to do with algorithmic trading.

With the ability to have instant, trillion-dollar market impacts, algorithmic trading and artificial intelligence are undoubtedly ingrained in how financial markets operate.

In 2017, CNBC.com estimated that 90 percent of daily trading volume in stock trading is done by machine algorithms, and only 10 percent is carried out directly by humans.

Artificial intelligence and financial management algorithms are not only available to top Wall Street players.

Robo-advisor financial management apps, like Wealthfront and Betterment, are rapidly permeating the global market. Wealthfront currently has $9.5 billion in assets under management, and Betterment has $10 billion.

Artificial intelligent financial agents are already helping financial institutions protect your money and fight fraud. A prime application for machine learning is in detecting anomalies in your spending and transaction habits, and flagging potentially fraudulent transactions.

As artificial intelligence continues to exponentially increase in power and capabilities, increasingly powerful trading and financial management bots will come online, finding massive new and previously lost streams of wealth.

How else are artificial intelligence and automation transforming finance?

Disruptive Remittance and Seamless Transactions
When was the last time you paid in cash at a toll booth? How about for a taxi ride?

EZ-Pass, the electronic tolling company implemented extensively on the East Coast, has done wonders to reduce traffic congestion and increase traffic flow.

Driving down I-95 on the East Coast of the United States, drivers rarely notice their financial transaction with the state’s tolling agencies. The transactions are seamless.

The Uber app enables me to travel without my wallet. I can forget about payment on my trip, free up my mental bandwidth and time for higher-priority tasks. The entire process is digitized and, by extension, automated and integrated into Uber’s platform (Note: This incredible convenience many times causes me to accidentally walk out of taxi cabs without paying!).

In January 2018, we saw the success of the first cutting-edge, AI-powered Amazon Go store open in Seattle, Washington. The store marked a new era in remittance and transactions. Gone are the days of carrying credit cards and cash, and gone are the cash registers. And now, on the heals of these early ‘beta-tests’, Amazon is considering opening as many as 3,000 of these cashierless stores by 2023.

Amazon Go stores use AI algorithms that watch various video feeds (from advanced cameras) throughout the store to identify who picks up groceries, exactly what products they select, and how much to charge that person when they walk out of the store. It’s a grab and go experience.

Let’s extrapolate the notion of seamless, integrated payment systems from Amazon Go and Uber’s removal of post-ride payment to the rest of our day-to-day experience.

Imagine this near future:

As you near the front door of your home, your AI assistant summons a self-driving Uber that takes you to the Hyperloop station (after all, you work in L.A. but live in San Francisco).

At the station, you board your pod, without noticing that your ticket purchase was settled via a wireless payment checkpoint.

After work, you stop at the Amazon Go and pick up dinner. Your virtual AI assistant passes your Amazon account information to the store’s payment checkpoint, as the store’s cameras and sensors track you, your cart and charge you auto-magically.

At home, unbeknownst to you, your AI has already restocked your fridge and pantry with whatever items you failed to pick up at the Amazon Go.

Once we remove the actively transacting aspect of finance, what else becomes possible?

Top Conclusions
Extraordinary transformations are happening in the finance world. We’ve only scratched the surface of the fintech revolution. All of these transformative financial technologies require high-fidelity assurance, robust insurance, and a mechanism for storing value.

I’ll dive into each of these other facets of financial services in future articles.

For now, thanks to coming global communication networks being deployed on 5G, Alphabet’s LUNE, SpaceX’s Starlink and OneWeb, by 2024, nearly all 8 billion people on Earth will be online.

Once connected, these new minds, entrepreneurs, and customers need access to money and financial services to meaningfully participate in the world economy.

By connecting lenders and borrowers around the globe, decentralized lending drives down global interest rates, increases global financial market participation, and enables economic opportunity to the billions of people who are about to come online.

We’re living in the most abundant time in human history, and fintech is just getting started.

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#433689 The Rise of Dataism: A Threat to Freedom ...

What would happen if we made all of our data public—everything from wearables monitoring our biometrics, all the way to smartphones monitoring our location, our social media activity, and even our internet search history?

Would such insights into our lives simply provide companies and politicians with greater power to invade our privacy and manipulate us by using our psychological profiles against us?

A burgeoning new philosophy called dataism doesn’t think so.

In fact, this trending ideology believes that liberating the flow of data is the supreme value of the universe, and that it could be the key to unleashing the greatest scientific revolution in the history of humanity.

What Is Dataism?
First mentioned by David Brooks in his 2013 New York Times article “The Philosophy of Data,” dataism is an ethical system that has been most heavily explored and popularized by renowned historian, Yuval Noah Harari.

In his 2016 book Homo Deus, Harari described dataism as a new form of religion that celebrates the growing importance of big data.

Its core belief centers around the idea that the universe gives greater value and support to systems, individuals, and societies that contribute most heavily and efficiently to data processing. In an interview with Wired, Harari stated, “Humans were special and important because up until now they were the most sophisticated data processing system in the universe, but this is no longer the case.”

Now, big data and machine learning are proving themselves more sophisticated, and dataists believe we should hand over as much information and power to these algorithms as possible, allowing the free flow of data to unlock innovation and progress unlike anything we’ve ever seen before.

Pros: Progress and Personal Growth
When you let data run freely, it’s bound to be mixed and matched in new ways that inevitably spark progress. And as we enter the exponential future where every person is constantly connected and sharing their data, the potential for such collaborative epiphanies becomes even greater.

We can already see important increases in quality of life thanks to companies like Google. With Google Maps on your phone, your position is constantly updating on their servers. This information, combined with everyone else on the planet using a phone with Google Maps, allows your phone to inform you of traffic conditions. Based on the speed and location of nearby phones, Google can reroute you to less congested areas or help you avoid accidents. And since you trust that these algorithms have more data than you, you gladly hand over your power to them, following your GPS’s directions rather than your own.

We can do the same sort of thing with our bodies.

Imagine, for instance, a world where each person has biosensors in their bloodstreams—a not unlikely or distant possibility when considering diabetic people already wear insulin pumps that constantly monitor their blood sugar levels. And let’s assume this data was freely shared to the world.

Now imagine a virus like Zika or the Bird Flu breaks out. Thanks to this technology, the odd change in biodata coming from a particular region flags an artificial intelligence that feeds data to the CDC (Center for Disease Control and Prevention). Recognizing that a pandemic could be possible, AIs begin 3D printing vaccines on-demand, predicting the number of people who may be afflicted. When our personal AIs tell us the locations of the spreading epidemic and to take the vaccine it just delivered by drone to our homes, are we likely to follow its instructions? Almost certainly—and if so, it’s likely millions, if not billions, of lives will have been saved.

But to quickly create such vaccines, we’ll also need to liberate research.

Currently, universities and companies seeking to benefit humankind with medical solutions have to pay extensively to organize clinical trials and to find people who match their needs. But if all our biodata was freely aggregated, perhaps they could simply say “monitor all people living with cancer” to an AI, and thanks to the constant stream of data coming in from the world’s population, a machine learning program may easily be able to detect a pattern and create a cure.

As always in research, the more sample data you have, the higher the chance that such patterns will emerge. If data is flowing freely, then anyone in the world can suddenly decide they have a hunch they want to explore, and without having to spend months and months of time and money hunting down the data, they can simply test their hypothesis.

Whether garage tinkerers, at-home scientists, or PhD students—an abundance of free data allows for science to progress unhindered, each person able to operate without being slowed by lack of data. And any progress they make is immediately liberated, becoming free data shared with anyone else that may find a use for it.

Any individual with a curious passion would have the entire world’s data at their fingertips, empowering every one of us to become an expert in any subject that inspires us. Expertise we can then share back into the data stream—a positive feedback loop spearheading progress for the entirety of humanity’s knowledge.

Such exponential gains represent a dataism utopia.

Unfortunately, our current incentives and economy also show us the tragic failures of this model.

As Harari has pointed out, the rise of datism means that “humanism is now facing an existential challenge and the idea of ‘free will’ is under threat.”

Cons: Manipulation and Extortion
In 2017, The Economist declared that data was the most valuable resource on the planet—even more valuable than oil.

Perhaps this is because data is ‘priceless’: it represents understanding, and understanding represents control. And so, in the world of advertising and politics, having data on your consumers and voters gives you an incredible advantage.

This was evidenced by the Cambridge Analytica scandal, in which it’s believed that Donald Trump and the architects of Brexit leveraged users’ Facebook data to create psychological profiles that enabled them to manipulate the masses.

How powerful are these psychological models?

A team who built a model similar to that used by Cambridge Analytica said their model could understand someone as well as a coworker with access to only 10 Facebook likes. With 70 likes they could know them as well as a friend might, 150 likes to match their parents’ understanding, and at 300 likes they could even come to know someone better than their lovers. With more likes, they could even come to know someone better than that person knows themselves.

Proceeding With Caution
In a capitalist democracy, do we want businesses and politicians to know us better than we know ourselves?

In spite of the remarkable benefits that may result for our species by freely giving away our information, do we run the risk of that data being used to exploit and manipulate the masses towards a future without free will, where our daily lives are puppeteered by those who own our data?

It’s extremely possible.

And it’s for this reason that one of the most important conversations we’ll have as a species centers around data ownership: do we just give ownership of the data back to the users, allowing them to choose who to sell or freely give their data to? Or will that simply deter the entrepreneurial drive and cause all of the free services we use today, like Google Search and Facebook, to begin charging inaccessible prices? How much are we willing to pay for our freedom? And how much do we actually care?

If recent history has taught us anything, it’s that humans are willing to give up more privacy than they like to think. Fifteen years ago, it would have been crazy to suggest we’d all allow ourselves to be tracked by our cars, phones, and daily check-ins to our favorite neighborhood locations; but now most of us see it as a worthwhile trade for optimized commutes and dating. As we continue navigating that fine line between exploitation and innovation into a more technological future, what other trade-offs might we be willing to make?

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#433655 First-Ever Grad Program in Space Mining ...

Maybe they could call it the School of Space Rock: A new program being offered at the Colorado School of Mines (CSM) will educate post-graduate students on the nuts and bolts of extracting and using valuable materials such as rare metals and frozen water from space rocks like asteroids or the moon.

Officially called Space Resources, the graduate-level program is reputedly the first of its kind in the world to offer a course in the emerging field of space mining. Heading the program is Angel Abbud-Madrid, director of the Center for Space Resources at Mines, a well-known engineering school located in Golden, Colorado, where Molson Coors taps Rocky Mountain spring water for its earthly brews.

The first semester for the new discipline began last month. While Abbud-Madrid didn’t immediately respond to an interview request, Singularity Hub did talk to Chris Lewicki, president and CEO of Planetary Resources, a space mining company whose founders include Peter Diamandis, Singularity University co-founder.

A former NASA engineer who worked on multiple Mars missions, Lewicki says the Space Resources program at CSM, with its multidisciplinary focus on science, economics, and policy, will help students be light years ahead of their peers in the nascent field of space mining.

“I think it’s very significant that they’ve started this program,” he said. “Having students with that kind of background exposure just allows them to be productive on day one instead of having to kind of fill in a lot of things for them.”

Who would be attracted to apply for such a program? There are many professionals who could be served by a post-baccalaureate certificate, master’s degree, or even Ph.D. in Space Resources, according to Lewicki. Certainly aerospace engineers and planetary scientists would be among the faces in the classroom.

“I think it’s [also] people who have an interest in what I would call maybe space robotics,” he said. Lewicki is referring not only to the classic example of robotic arms like the Canadarm2, which lends a hand to astronauts aboard the International Space Station, but other types of autonomous platforms.

One example might be Planetary Resources’ own Arkyd-6, a small, autonomous satellite called a CubeSat launched earlier this year to test different technologies that might be used for deep-space exploration of resources. The proof-of-concept was as much a test for the technology—such as the first space-based use of a mid-wave infrared imager to detect water resources—as it was for being able to work in space on a shoestring budget.

“We really proved that doing one of these billion-dollar science missions to deep space can be done for a lot less if you have a very focused goal, and if you kind of cut a lot of corners and then put some commercial approaches into those things,” Lewicki said.

A Trillion-Dollar Industry
Why space mining? There are at least a trillion reasons.

Astrophysicist Neil deGrasse Tyson famously said that the first trillionaire will be the “person who exploits the natural resources on asteroids.” That’s because asteroids—rocky remnants from the formation of our solar system more than four billion years ago—harbor precious metals, ranging from platinum and gold to iron and nickel.

For instance, one future target of exploration by NASA—an asteroid dubbed 16 Psyche, orbiting the sun in the asteroid belt between Mars and Jupiter—is worth an estimated $10,000 quadrillion. It’s a number so mind-bogglingly big that it would crash the global economy, if someone ever figured out how to tow it back to Earth without literally crashing it into the planet.

Living Off the Land
Space mining isn’t just about getting rich. Many argue that humanity’s ability to extract resources in space, especially water that can be refined into rocket fuel, will be a key technology to extend our reach beyond near-Earth space.

The presence of frozen water around the frigid polar regions of the moon, for example, represents an invaluable source to power future deep-space missions. Splitting H20 into its component elements of hydrogen and oxygen would provide a nearly inexhaustible source of rocket fuel. Today, it costs $10,000 to put a pound of payload in Earth orbit, according to NASA.

Until more advanced rocket technology is developed, the moon looks to be the best bet for serving as the launching pad to Mars and beyond.

Moon Versus Asteroid
However, Lewicki notes that despite the moon’s proximity and our more intimate familiarity with its pockmarked surface, that doesn’t mean a lunar mission to extract resources is any easier than a multi-year journey to a fast-moving asteroid.

For one thing, fighting gravity to and from the moon is no easy feat, as the moon has a significantly stronger gravitational field than an asteroid. Another challenge is that the frozen water is located in permanently shadowed lunar craters, meaning space miners can’t rely on solar-powered equipment, but on some sort of external energy source.

And then there’s the fact that moon craters might just be the coldest places in the solar system. NASA’s Lunar Reconnaissance Orbiter found temperatures plummeted as low as 26 Kelvin, or more than minus 400 degrees Fahrenheit. In comparison, the coldest temperatures on Earth have been recorded near the South Pole in Antarctica—about minus 148 degrees F.

“We don’t operate machines in that kind of thermal environment,” Lewicki said of the extreme temperatures detected in the permanent dark regions of the moon. “Antarctica would be a balmy desert island compared to a lunar polar crater.”

Of course, no one knows quite what awaits us in the asteroid belt. Answers may soon be forthcoming. Last week, the Japan Aerospace Exploration Agency landed two small, hopping rovers on an asteroid called Ryugu. Meanwhile, NASA hopes to retrieve a sample from the near-Earth asteroid Bennu when its OSIRIS-REx mission makes contact at the end of this year.

No Bucks, No Buck Rogers
Visionaries like Elon Musk and Jeff Bezos talk about colonies on Mars, with millions of people living and working in space. The reality is that there’s probably a reason Buck Rogers was set in the 25th century: It’s going to take a lot of money and a lot of time to realize those sci-fi visions.

Or, as Lewicki put it: “No bucks, no Buck Rogers.”

The cost of operating in outer space can be prohibitive. Planetary Resources itself is grappling with raising additional funding, with reports this year about layoffs and even a possible auction of company assets.

Still, Lewicki is confident that despite economic and technical challenges, humanity will someday exceed even the boldest dreamers—skyscrapers on the moon, interplanetary trips to Mars—as judged against today’s engineering marvels.

“What we’re doing is going to be very hard, very painful, and almost certainly worth it,” he said. “Who would have thought that there would be a job for a space miner that you could go to school for, even just five or ten years ago. Things move quickly.”

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#433486 This AI Predicts Obesity ...

A research team at the University of Washington has trained an artificial intelligence system to spot obesity—all the way from space. The system used a convolutional neural network (CNN) to analyze 150,000 satellite images and look for correlations between the physical makeup of a neighborhood and the prevalence of obesity.

The team’s results, presented in JAMA Network Open, showed that features of a given neighborhood could explain close to two-thirds (64.8 percent) of the variance in obesity. Researchers found that analyzing satellite data could help increase understanding of the link between peoples’ environment and obesity prevalence. The next step would be to make corresponding structural changes in the way neighborhoods are built to encourage physical activity and better health.

Training AI to Spot Obesity
Convolutional neural networks (CNNs) are particularly adept at image analysis, object recognition, and identifying special hierarchies in large datasets.

Prior to analyzing 150,000 high-resolution satellite images of Bellevue, Seattle, Tacoma, Los Angeles, Memphis, and San Antonio, the researchers trained the CNN on 1.2 million images from the ImageNet database. The categorizations were correlated with obesity prevalence estimates for the six urban areas from census tracts gathered by the 500 Cities project.

The system was able to identify the presence of certain features that increased likelihood of obesity in a given area. Some of these features included tightly–packed houses, being close to roadways, and living in neighborhoods with a lack of greenery.

Visualization of features identified by the convolutional neural network (CNN) model. The images on the left column are satellite images taken from Google Static Maps API (application programming interface). Images in the middle and right columns are activation maps taken from the second convolutional layer of VGG-CNN-F network after forward pass of the respective satellite images through the network. From Google Static Maps API, DigitalGlobe, US Geological Survey (accessed July 2017). Credit: JAMA Network Open
Your Surroundings Are Key
In their discussion of the findings, the researchers stressed that there are limitations to the conclusions that can be drawn from the AI’s results. For example, socio-economic factors like income likely play a major role for obesity prevalence in a given geographic area.

However, the study concluded that the AI-powered analysis showed the prevalence of specific man-made features in neighborhoods consistently correlating with obesity prevalence and not necessarily correlating with socioeconomic status.

The system’s success rates varied between studied cities, with Memphis being the highest (73.3 percent) and Seattle being the lowest (55.8 percent).

AI Takes To the Sky
Around a third of the US population is categorized as obese. Obesity is linked to a number of health-related issues, and the AI-generated results could potentially help improve city planning and better target campaigns to limit obesity.

The study is one of the latest of a growing list that uses AI to analyze images and extrapolate insights.

A team at Stanford University has used a CNN to predict poverty via satellite imagery, assisting governments and NGOs to better target their efforts. A combination of the public Automatic Identification System for shipping, satellite imagery, and Google’s AI has proven able to identify illegal fishing activity. Researchers have even been able to use AI and Google Street View to predict what party a given city will vote for, based on what cars are parked on the streets.

In each case, the AI systems have been able to look at volumes of data about our world and surroundings that are beyond the capabilities of humans and extrapolate new insights. If one were to moralize about the good and bad sides of AI (new opportunities vs. potential job losses, for example) it could seem that it comes down to what we ask AI systems to look at—and what questions we ask of them.

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