Tag Archives: virtual
#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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#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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#433386 What We Have to Gain From Making ...
The borders between the real world and the digital world keep crumbling, and the latter’s importance in both our personal and professional lives keeps growing. Some describe the melding of virtual and real worlds as part of the fourth industrial revolution. Said revolution’s full impact on us as individuals, our companies, communities, and societies is still unknown.
Greg Cross, chief business officer of New Zealand-based AI company Soul Machines, thinks one inescapable consequence of these crumbling borders is people spending more and more time interacting with technology. In a presentation at Singularity University’s Global Summit in San Francisco last month, Cross unveiled Soul Machines’ latest work and shared his views on the current state of human-like AI and where the technology may go in the near future.
Humanizing Technology Interaction
Cross started by introducing Rachel, one of Soul Machines’ “emotionally responsive digital humans.” The company has built 15 different digital humans of various sexes, groups, and ethnicities. Rachel, along with her “sisters” and “brothers,” has a virtual nervous system based on neural networks and biological models of different paths in the human brain. The system is controlled by virtual neurotransmitters and hormones akin to dopamine, serotonin, and oxytocin, which influence learning and behavior.
As a result, each digital human can have its own unique set of “feelings” and responses to interactions. People interact with them via visual and audio sensors, and the machines respond in real time.
“Over the last 20 or 30 years, the way we think about machines and the way we interact with machines has changed,” Cross said. “We’ve always had this view that they should actually be more human-like.”
The realism of the digital humans’ graphic representations comes thanks to the work of Soul Machines’ other co-founder, Dr. Mark Sager, who has won two Academy Awards for his work on some computer-generated movies, including James Cameron’s Avatar.
Cross pointed out, for example, that rather than being unrealistically flawless and clear, Rachel’s skin has blemishes and sun spots, just like real human skin would.
The Next Human-Machine Frontier
When people interact with each other face to face, emotional and intellectual engagement both heavily influence the interaction. What would it look like for machines to bring those same emotional and intellectual capacities to our interactions with them, and how would this type of interaction affect the way we use, relate to, and feel about AI?
Cross and his colleagues believe that humanizing artificial intelligence will make the technology more useful to humanity, and prompt people to use AI in more beneficial ways.
“What we think is a very important view as we move forward is that these machines can be more helpful to us. They can be more useful to us. They can be more interesting to us if they’re actually more like us,” Cross said.
It is an approach that seems to resonate with companies and organizations. For example, in the UK, where NatWest Bank is testing out Cora as a digital employee to help answer customer queries. In Germany, Daimler Financial Group plans to employ Sarah as something “similar to a personal concierge” for its customers. According to Cross, Daimler is looking at other ways it could deploy digital humans across the organization, from building digital service people, digital sales people, and maybe in the future, digital chauffeurs.
Soul Machines’ latest creation is Will, a digital teacher that can interact with children through a desktop, tablet, or mobile device and help them learn about renewable energy. Cross sees other social uses for digital humans, including potentially serving as doctors to rural communities.
Our Digital Friends—and Twins
Soul Machines is not alone in its quest to humanize technology. It is a direction many technology companies, including the likes of Amazon, also seem to be pursuing. Amazon is working on building a home robot that, according to Bloomberg, “could be a sort of mobile Alexa.”
Finding a more human form for technology seems like a particularly pervasive pursuit in Japan. Not just when it comes to its many, many robots, but also virtual assistants like Gatebox.
The Japanese approach was perhaps best summed up by famous android researcher Dr. Hiroshi Ishiguro, who I interviewed last year: “The human brain is set up to recognize and interact with humans. So, it makes sense to focus on developing the body for the AI mind, as well as the AI. I believe that the final goal for both Japanese and other companies and scientists is to create human-like interaction.”
During Cross’s presentation, Rob Nail, CEO and associate founder of Singularity University, joined him on the stage, extending an invitation to Rachel to be SU’s first fully digital faculty member. Rachel accepted, and though she’s the only digital faculty right now, she predicted this won’t be the case for long.
“In 10 years, all of you will have digital versions of yourself, just like me, to take on specific tasks and make your life a whole lot easier,” she said. “This is great news for me. I’ll have millions of digital friends.”
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