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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.

Join Me
Abundance-Digital Online Community: I’ve created a digital community of bold, abundance-minded entrepreneurs called Abundance-Digital. Abundance-Digital is my ‘onramp’ for exponential entrepreneurs – those who want to get involved and play at a higher level. Click here to learn more.

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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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Posted in Human Robots

#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.

Join Me
Abundance Digital Online Community: I have created a Digital/Online community of bold, abundance-minded entrepreneurs called Abundance Digital. This is my ‘onramp’ for exponential entrepreneurs – those who want to get involved and play at a higher level. Click here to learn more.

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Posted in Human Robots

#433506 MIT’s New Robot Taught Itself to Pick ...

Back in 2016, somewhere in a Google-owned warehouse, more than a dozen robotic arms sat for hours quietly grasping objects of various shapes and sizes. For hours on end, they taught themselves how to pick up and hold the items appropriately—mimicking the way a baby gradually learns to use its hands.

Now, scientists from MIT have made a new breakthrough in machine learning: their new system can not only teach itself to see and identify objects, but also understand how best to manipulate them.

This means that, armed with the new machine learning routine referred to as “dense object nets (DON),” the robot would be capable of picking up an object that it’s never seen before, or in an unfamiliar orientation, without resorting to trial and error—exactly as a human would.

The deceptively simple ability to dexterously manipulate objects with our hands is a huge part of why humans are the dominant species on the planet. We take it for granted. Hardware innovations like the Shadow Dexterous Hand have enabled robots to softly grip and manipulate delicate objects for many years, but the software required to control these precision-engineered machines in a range of circumstances has proved harder to develop.

This was not for want of trying. The Amazon Robotics Challenge offers millions of dollars in prizes (and potentially far more in contracts, as their $775m acquisition of Kiva Systems shows) for the best dexterous robot able to pick and package items in their warehouses. The lucrative dream of a fully-automated delivery system is missing this crucial ability.

Meanwhile, the Robocup@home challenge—an offshoot of the popular Robocup tournament for soccer-playing robots—aims to make everyone’s dream of having a robot butler a reality. The competition involves teams drilling their robots through simple household tasks that require social interaction or object manipulation, like helping to carry the shopping, sorting items onto a shelf, or guiding tourists around a museum.

Yet all of these endeavors have proved difficult; the tasks often have to be simplified to enable the robot to complete them at all. New or unexpected elements, such as those encountered in real life, more often than not throw the system entirely. Programming the robot’s every move in explicit detail is not a scalable solution: this can work in the highly-controlled world of the assembly line, but not in everyday life.

Computer vision is improving all the time. Neural networks, including those you train every time you prove that you’re not a robot with CAPTCHA, are getting better at sorting objects into categories, and identifying them based on sparse or incomplete data, such as when they are occluded, or in different lighting.

But many of these systems require enormous amounts of input data, which is impractical, slow to generate, and often needs to be laboriously categorized by humans. There are entirely new jobs that require people to label, categorize, and sift large bodies of data ready for supervised machine learning. This can make machine learning undemocratic. If you’re Google, you can make thousands of unwitting volunteers label your images for you with CAPTCHA. If you’re IBM, you can hire people to manually label that data. If you’re an individual or startup trying something new, however, you will struggle to access the vast troves of labeled data available to the bigger players.

This is why new systems that can potentially train themselves over time or that allow robots to deal with situations they’ve never seen before without mountains of labelled data are a holy grail in artificial intelligence. The work done by MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is part of a new wave of “self-supervised” machine learning systems—little of the data used was labeled by humans.

The robot first inspects the new object from multiple angles, building up a 3D picture of the object with its own coordinate system. This then allows the robotic arm to identify a particular feature on the object—such as a handle, or the tongue of a shoe—from various different angles, based on its relative distance to other grid points.

This is the real innovation: the new means of representing objects to grasp as mapped-out 3D objects, with grid points and subsections of their own. Rather than using a computer vision algorithm to identify a door handle, and then activating a door handle grasping subroutine, the DON system treats all objects by making these spatial maps before classifying or manipulating them, enabling it to deal with a greater range of objects than in other approaches.

“Many approaches to manipulation can’t identify specific parts of an object across the many orientations that object may encounter,” said PhD student Lucas Manuelli, who wrote a new paper about the system with lead author and fellow student Pete Florence, alongside MIT professor Russ Tedrake. “For example, existing algorithms would be unable to grasp a mug by its handle, especially if the mug could be in multiple orientations, like upright, or on its side.”

Class-specific descriptors, which can be applied to the object features, can allow the robot arm to identify a mug, find the handle, and pick the mug up appropriately. Object-specific descriptors allow the robot arm to select a particular mug from a group of similar items. I’m already dreaming of a robot butler reliably picking my favourite mug when it serves me coffee in the morning.

Google’s robot arm-y was an attempt to develop a general grasping algorithm: one that could identify, categorize, and appropriately grip as many items as possible. This requires a great deal of training time and data, which is why Google parallelized their project by having 14 robot arms feed data into a single neural network brain: even then, the algorithm may fail with highly specific tasks. Specialist grasping algorithms might require less training if they’re limited to specific objects, but then your software is useless for general tasks.

As the roboticists noted, their system, with its ability to identify parts of an object rather than just a single object, is better suited to specific tasks, such as “grasp the racquet by the handle,” than Amazon Robotics Challenge robots, which identify whole objects by segmenting an image.

This work is small-scale at present. It has been tested with a few classes of objects, including shoes, hats, and mugs. Yet the use of these dense object nets as a way for robots to represent and manipulate new objects may well be another step towards the ultimate goal of generalized automation: a robot capable of performing every task a person can. If that point is reached, the question that will remain is how to cope with being obsolete.

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#433474 How to Feed Global Demand for ...

“You really can’t justify tuna in Chicago as a source of sustenance.” That’s according to Dr. Sylvia Earle, a National Geographic Society Explorer who was the first female chief scientist at NOAA. She came to the Good Food Institute’s Good Food Conference to deliver a call to action around global food security, agriculture, environmental protection, and the future of consumer choice.

It seems like all options should be on the table to feed an exploding population threatened by climate change. But Dr. Earle, who is faculty at Singularity University, drew a sharp distinction between seafood for sustenance versus seafood as a choice. “There is this widespread claim that we must take large numbers of wildlife from the sea in order to have food security.”

A few minutes later, Dr. Earle directly addressed those of us in the audience. “We know the value of a dead fish,” she said. That’s market price. “But what is the value of a live fish in the ocean?”

That’s when my mind blew open. What is the value—or put another way, the cost—of using the ocean as a major source of protein for humans? How do you put a number on that? Are we talking about dollars and cents, or about something far larger?

Dr. Liz Specht of the Good Food Institute drew the audience’s attention to a strange imbalance. Currently, about half of the yearly global catch of seafood comes from aquaculture. That means that the other half is wild caught. It’s hard to imagine half of your meat coming directly from the forests and the plains, isn’t it? And yet half of the world’s seafood comes from direct harvesting of the oceans, by way of massive overfishing, a terrible toll from bycatch, a widespread lack of regulation and enforcement, and even human rights violations such as slavery.

The search for solutions is on, from both within the fishing industry and from external agencies such as governments and philanthropists. Could there be another way?

Makers of plant-based seafood and clean seafood think they know how to feed the global demand for seafood without harming the ocean. These companies are part of a larger movement harnessing technology to reduce our reliance on wild and domesticated animals—and all the environmental, economic, and ethical issues that come with it.

Producers of plant-based seafood (20 or so currently) are working to capture the taste, texture, and nutrition of conventional seafood without the limitations of geography or the health of a local marine population. Like with plant-based meat, makers of plant-based seafood are harnessing food science and advances in chemistry, biology, and engineering to make great food. The industry’s strategy? Start with what the consumer wants, and then figure out how to achieve that great taste through technology.

So how does plant-based seafood taste? Pretty good, as it turns out. (The biggest benefit of a food-oriented conference is that your mouth is always full!)

I sampled “tuna” salad made from Good Catch Food’s fish-free tuna, which is sourced from legumes; the texture was nearly indistinguishable from that of flaked albacore tuna, and there was no lingering fishy taste to overpower my next bite. In a blind taste test, I probably wouldn’t have known that I was eating a plant-based seafood alternative. Next I reached for Ocean Hugger Food’s Ahimi, a tomato-based alternative to raw tuna. I adore Hawaiian poke, so I was pleasantly surprised when my Ahimi-based poke captured the bite of ahi tuna. It wasn’t quite as delightfully fatty as raw tuna, but with wild tuna populations struggling to recover from a 97% decline in numbers from 40 years ago, Ahimi is a giant stride in the right direction.

These plant-based alternatives aren’t the only game in town, however.

The clean meat industry, which has also been called “cultured meat” or “cellular agriculture,” isn’t seeking to lure consumers away from animal protein. Instead, cells are sampled from live animals and grown in bioreactors—meaning that no animal is slaughtered to produce real meat.

Clean seafood is poised to piggyback off platforms developed for clean meat; growing fish cells in the lab should rely on the same processes as growing meat cells. I know of four companies currently focusing on seafood (Finless Foods, Wild Type, BlueNalu, and Seafuture Sustainable Biotech), and a few more are likely to emerge from stealth mode soon.

Importantly, there’s likely not much difference between growing clean seafood from the top or the bottom of the food chain. Tuna, for example, are top predators that must grow for at least 10 years before they’re suitable as food. Each year, a tuna consumes thousands of pounds of other fish, shellfish, and plankton. That “long tail of groceries,” said Dr. Earle, “is a pretty expensive choice.” Excitingly, clean tuna would “level the trophic playing field,” as Dr. Specht pointed out.

All this is only the beginning of what might be possible.

Combining synthetic biology with clean meat and seafood means that future products could be personalized for individual taste preferences or health needs, by reprogramming the DNA of the cells in the lab. Industries such as bioremediation and biofuels likely have a lot to teach us about sourcing new ingredients and flavors from algae and marine plants. By harnessing rapid advances in automation, robotics, sensors, machine vision, and other big-data analytics, the manufacturing and supply chains for clean seafood could be remarkably safe and robust. Clean seafood would be just that: clean, without pathogens, parasites, or the plastic threatening to fill our oceans, meaning that you could enjoy it raw.

What about price? Dr. Mark Post, a pioneer in clean meat who is also faculty at Singularity University, estimated that 80% of clean-meat production costs come from the expensive medium in which cells are grown—and some ingredients in the medium are themselves sourced from animals, which misses the point of clean meat. Plus, to grow a whole cut of food, like a fish fillet, the cells need to be coaxed into a complex 3D structure with various cell types like muscle cells and fat cells. These two technical challenges must be solved before clean meat and seafood give consumers the experience they want, at the price they want.

In this respect clean seafood has an unusual edge. Most of what we know about growing animal cells in the lab comes from the research and biomedical industries (from tissue engineering, for example)—but growing cells to replace an organ has different constraints than growing cells for food. The link between clean seafood and biomedicine is less direct, empowering innovators to throw out dogma and find novel reagents, protocols, and equipment to grow seafood that captures the tastes, textures, smells, and overall experience of dining by the ocean.

Asked to predict when we’ll be seeing clean seafood in the grocery store, Lou Cooperhouse the CEO of BlueNalu, explained that the challenges aren’t only in the lab: marketing, sales, distribution, and communication with consumers are all critical. As Niya Gupta, the founder of Fork & Goode, said, “The question isn’t ‘can we do it’, but ‘can we sell it’?”

The good news is that the clean meat and seafood industry is highly collaborative; there are at least two dozen companies in the space, and they’re all talking to each other. “This is an ecosystem,” said Dr. Uma Valeti, the co-founder of Memphis Meats. “We’re not competing with each other.” It will likely be at least a decade before science, business, and regulation enable clean meat and seafood to routinely appear on restaurant menus, let alone market shelves.

Until then, think carefully about your food choices. Meditate on Dr. Earle’s question: “What is the real cost of that piece of halibut?” Or chew on this from Dr. Ricardo San Martin, of the Sutardja Center at the University of California, Berkeley: “Food is a system of meanings, not an object.” What are you saying when you choose your food, about your priorities and your values and how you want the future to look? Do you think about animal welfare? Most ethical regulations don’t extend to marine life, and if you don’t think that ocean creatures feel pain, consider the lobster.

Seafood is largely an acquired taste, since most of us don’t live near the water. Imagine a future in which children grow up loving the taste of delicious seafood but without hurting a living animal, the ocean, or the global environment.

Do more than imagine. As Dr. Earle urged us, “Convince the public at large that this is a really cool idea.”

Widely available
Medium availability
Emerging

Gardein
Ahimi (Ocean Hugger)
New Wave Foods

Sophie’s Kitchen
Cedar Lake
To-funa Fish

Quorn
SoFine Foods
Seamore

Vegetarian Plus
Akua
Good Catch

Heritage
Hungry Planet
Odontella

Loma Linda
Heritage Health Food
Terramino Foods

The Vegetarian Butcher
May Wah

VBites

Table based on Figure 5 of the report “An Ocean of Opportunity: Plant-based and clean seafood for sustainable oceans without sacrifice,” from The Good Food Institute.

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