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#433872 Breaking Out of the Corporate Bubble ...

Posted on November 12, 2018 by Android

For big companies, success is a blessing and a curse. You don’t get big without doing something (or many things) very right. It might start with an invention or service the world didn’t know it needed. Your product takes off, and growth brings a whole new set of logistical challenges. Delivering consistent quality, hiring the right team, establishing a strong culture, tapping into new markets, satisfying shareholders. The list goes on.

Eventually, however, what made you successful also makes you resistant to change.

You’ve built a machine for one purpose, and it’s running smoothly, but what about retooling that machine to make something new? Not so easy. Leaders of big companies know there is no future for their organizations without change. And yet, they struggle to drive it.

In their new book, Leading Transformation: How to Take Charge of Your Company’s Future, Kyle Nel, Nathan Furr, and Thomas Ramsøy aim to deliver a roadmap for corporate transformation.

The book focuses on practical tools that have worked in big companies to break down behavioral and cognitive biases, envision radical futures, and run experiments. These include using science fiction and narrative to see ahead and adopting better measures of success for new endeavors.

A thread throughout is how to envision a new future and move into that future.

We’re limited by the bubbles in which we spend the most time—the corporate bubble, the startup bubble, the nonprofit bubble. The mutually beneficial convergence of complementary bubbles, then, can be a powerful tool for kickstarting transformation. The views and experiences of one partner can challenge the accepted wisdom of the other; resources can flow into newly co-created visions and projects; and connections can be made that wouldn’t otherwise exist.

The authors call such alliances uncommon partners. In the following excerpt from the book, Made In Space, a startup building 3D printers for space, helps Lowe’s explore an in-store 3D printing system, and Lowe’s helps Made In Space expand its vision and focus.

Uncommon Partners
In a dingy conference room at NASA, five prototypical nerds, smelling of Thai food, laid out the path to printing satellites in space and buildings on distant planets. At the end of their four-day marathon, they emerged with an artifact trail that began with early prototypes for the first 3D printer on the International Space Station and ended in the additive-manufacturing future—a future much bigger than 3D printing.

In the additive-manufacturing future, we will view everything as transient, or capable of being repurposed into new things. Rather than throwing away a soda bottle or a bent nail, we will simply reprocess these things into a new hinge for the fence we are building or a light switch plate for the tool shed. Indeed, we might not even go buy bricks for the tool shed, but instead might print them from impurities pulled from the air and the dirt beneath our feet. Such a process would both capture carbon in the air to make the bricks and avoid all the carbon involved in making and then transporting traditional bricks to your house.

If it all sounds a little too science fiction, think again. Lowe’s has already been honored as a Champion of Change by the US government for its prototype system to recycle plastic (e.g., plastic bags and bottles). The future may be closer than you have imagined. But to get there, Lowe’s didn’t work alone. It had to work with uncommon partners to create the future.

Uncommon partners are the types of organizations you might not normally work with, but which can greatly help you create radical new futures. Increasingly, as new technologies emerge and old industries converge, companies are finding that working independently to create all the necessary capabilities to enter new industries or create new technologies is costly, risky, and even counterproductive. Instead, organizations are finding that they need to collaborate with uncommon partners as an ecosystem to cocreate the future together. Nathan [Furr] and his colleague at INSEAD, Andrew Shipilov, call this arrangement an adaptive ecosystem strategy and described how companies such as Lowe’s, Samsung, Mastercard, and others are learning to work differently with partners and to work with different kinds of partners to more effectively discover new opportunities. For Lowe’s, an adaptive ecosystem strategy working with uncommon partners forms the foundation of capturing new opportunities and transforming the company. Despite its increased agility, Lowe’s can’t be (and shouldn’t become) an independent additive-manufacturing, robotics-using, exosuit-building, AR-promoting, fill-in-the-blank-what’s-next-ing company in addition to being a home improvement company. Instead, Lowe’s applies an adaptive ecosystem strategy to find the uncommon partners with which it can collaborate in new territory.

To apply the adaptive ecosystem strategy with uncommon partners, start by identifying the technical or operational components required for a particular focus area (e.g., exosuits) and then sort these components into three groups. First, there are the components that are emerging organically without any assistance from the orchestrator—the leader who tries to bring together the adaptive ecosystem. Second, there are the elements that might emerge, with encouragement and support. Third are the elements that won’t happen unless you do something about it. In an adaptive ecosystem strategy, you can create regular partnerships for the first two elements—those already emerging or that might emerge—if needed. But you have to create the elements in the final category (those that won’t emerge) either with an uncommon partner or by yourself.

For example, when Lowe’s wanted to explore the additive-manufacturing space, it began a search for an uncommon partner to provide the missing but needed capabilities. Unfortunately, initial discussions with major 3D printing companies proved disappointing. The major manufacturers kept trying to sell Lowe’s 3D printers. But the vision our group had created with science fiction was not for vendors to sell Lowe’s a printer, but for partners to help the company build a system—something that would allow customers to scan, manipulate, print, and eventually recycle additive-manufacturing objects. Every time we discussed 3D printing systems with these major companies, they responded that they could do it and then tried to sell printers. When Carin Watson, one of the leading lights at Singularity University, introduced us to Made In Space (a company being incubated in Singularity University’s futuristic accelerator), we discovered an uncommon partner that understood what it meant to cocreate a system.

Initially, Made In Space had been focused on simply getting 3D printing to work in space, where you can’t rely on gravity, you can’t send up a technician if the machine breaks, and you can’t release noxious fumes into cramped spacecraft quarters. But after the four days in the conference room going over the comic for additive manufacturing, Made In Space and Lowe’s emerged with a bigger vision. The company helped lay out an artifact trail that included not only the first printer on the International Space Station but also printing system services in Lowe’s stores.

Of course, the vision for an additive-manufacturing future didn’t end there. It also reshaped Made In Space’s trajectory, encouraging the startup, during those four days in a NASA conference room, to design a bolder future. Today, some of its bold projects include the Archinaut, a system that enables satellites to build themselves while in space, a direction that emerged partly from the science fiction narrative we created around additive manufacturing.

In summary, uncommon partners help you succeed by providing you with the capabilities you shouldn’t be building yourself, as well as with fresh insights. You also help uncommon partners succeed by creating new opportunities from which they can prosper.

Helping Uncommon Partners Prosper
Working most effectively with uncommon partners can require a shift from more familiar outsourcing or partnership relationships. When working with uncommon partners, you are trying to cocreate the future, which entails a great deal more uncertainty. Because you can’t specify outcomes precisely, agreements are typically less formal than in other types of relationships, and they operate under the provisions of shared vision and trust more than binding agreement clauses. Moreover, your goal isn’t to extract all the value from the relationship. Rather, you need to find a way to share the value.

Ideally, your uncommon partners should be transformed for the better by the work you do. For example, Lowe’s uncommon partner developing the robotics narrative was a small startup called Fellow Robots. Through their work with Lowe’s, Fellow Robots transformed from a small team focused on a narrow application of robotics (which was arguably the wrong problem) to a growing company developing a very different and valuable set of capabilities: putting cutting-edge technology on top of the old legacy systems embedded at the core of most companies. Working with Lowe’s allowed Fellow Robots to discover new opportunities, and today Fellow Robots works with retailers around the world, including BevMo! and Yamada. Ultimately, working with uncommon partners should be transformative for both of you, so focus more on creating a bigger pie than on how you are going to slice up a smaller pie.

The above excerpt appears in the new book Leading Transformation: How to Take Charge of Your Company’s Future by Kyle Nel, Nathan Furr, and Thomas Ramsøy, published by Harvard Business Review Press.

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Posted in Human Robots

#433799 The First Novel Written by AI Is ...

Posted on October 25, 2018 by Android

Last year, a novelist went on a road trip across the USA. The trip was an attempt to emulate Jack Kerouac—to go out on the road and find something essential to write about in the experience. There is, however, a key difference between this writer and anyone else talking your ear off in the bar. This writer is just a microphone, a GPS, and a camera hooked up to a laptop and a whole bunch of linear algebra.

People who are optimistic that artificial intelligence and machine learning won’t put us all out of a job say that human ingenuity and creativity will be difficult to imitate. The classic argument is that, just as machines freed us from repetitive manual tasks, machine learning will free us from repetitive intellectual tasks.

This leaves us free to spend more time on the rewarding aspects of our work, pursuing creative hobbies, spending time with loved ones, and generally being human.

In this worldview, creative works like a great novel or symphony, and the emotions they evoke, cannot be reduced to lines of code. Humans retain a dimension of superiority over algorithms.

But is creativity a fundamentally human phenomenon? Or can it be learned by machines?

And if they learn to understand us better than we understand ourselves, could the great AI novel—tailored, of course, to your own predispositions in fiction—be the best you’ll ever read?

Maybe Not a Beach Read
This is the futurist’s view, of course. The reality, as the jury-rigged contraption in Ross Goodwin’s Cadillac for that road trip can attest, is some way off.

“This is very much an imperfect document, a rapid prototyping project. The output isn’t perfect. I don’t think it’s a human novel, or anywhere near it,” Goodwin said of the novel that his machine created. 1 The Road is currently marketed as the first novel written by AI.

Once the neural network has been trained, it can generate any length of text that the author desires, either at random or working from a specific seed word or phrase. Goodwin used the sights and sounds of the road trip to provide these seeds: the novel is written one sentence at a time, based on images, locations, dialogue from the microphone, and even the computer’s own internal clock.

The results are… mixed.

The novel begins suitably enough, quoting the time: “It was nine seventeen in the morning, and the house was heavy.” Descriptions of locations begin according to the Foursquare dataset fed into the algorithm, but rapidly veer off into the weeds, becoming surreal. While experimentation in literature is a wonderful thing, repeatedly quoting longitude and latitude coordinates verbatim is unlikely to win anyone the Booker Prize.

Data In, Art Out?
Neural networks as creative agents have some advantages. They excel at being trained on large datasets, identifying the patterns in those datasets, and producing output that follows those same rules. Music inspired by or written by AI has become a growing subgenre—there’s even a pop album by human-machine collaborators called the Songularity.

A neural network can “listen to” all of Bach and Mozart in hours, and train itself on the works of Shakespeare to produce passable pseudo-Bard. The idea of artificial creativity has become so widespread that there’s even a meme format about forcibly training neural network ‘bots’ on human writing samples, with hilarious consequences—although the best joke was undoubtedly human in origin.

The AI that roamed from New York to New Orleans was an LSTM (long short-term memory) neural net. By default, information contained in individual neurons is preserved, and only small parts can be “forgotten” or “learned” in an individual timestep, rather than neurons being entirely overwritten.

The LSTM architecture performs better than previous recurrent neural networks at tasks such as handwriting and speech recognition. The neural net—and its programmer—looked further in search of literary influences, ingesting 60 million words (360 MB) of raw literature according to Goodwin’s recipe: one third poetry, one third science fiction, and one third “bleak” literature.

In this way, Goodwin has some creative control over the project; the source material influences the machine’s vocabulary and sentence structuring, and hence the tone of the piece.

The Thoughts Beneath the Words
The problem with artificially intelligent novelists is the same problem with conversational artificial intelligence that computer scientists have been trying to solve from Turing’s day. The machines can understand and reproduce complex patterns increasingly better than humans can, but they have no understanding of what these patterns mean.

Goodwin’s neural network spits out sentences one letter at a time, on a tiny printer hooked up to the laptop. Statistical associations such as those tracked by neural nets can form words from letters, and sentences from words, but they know nothing of character or plot.

When talking to a chatbot, the code has no real understanding of what’s been said before, and there is no dataset large enough to train it through all of the billions of possible conversations.

Unless restricted to a predetermined set of options, it loses the thread of the conversation after a reply or two. In a similar way, the creative neural nets have no real grasp of what they’re writing, and no way to produce anything with any overarching coherence or narrative.

Goodwin’s experiment is an attempt to add some coherent backbone to the AI “novel” by repeatedly grounding it with stimuli from the cameras or microphones—the thematic links and narrative provided by the American landscape the neural network drives through.

Goodwin feels that this approach (the car itself moving through the landscape, as if a character) borrows some continuity and coherence from the journey itself. “Coherent prose is the holy grail of natural-language generation—feeling that I had somehow solved a small part of the problem was exhilarating. And I do think it makes a point about language in time that’s unexpected and interesting.”

AI Is Still No Kerouac
A coherent tone and semantic “style” might be enough to produce some vaguely-convincing teenage poetry, as Google did, and experimental fiction that uses neural networks can have intriguing results. But wading through the surreal AI prose of this era, searching for some meaning or motif beyond novelty value, can be a frustrating experience.

Maybe machines can learn the complexities of the human heart and brain, or how to write evocative or entertaining prose. But they’re a long way off, and somehow “more layers!” or a bigger corpus of data doesn’t feel like enough to bridge that gulf.

Real attempts by machines to write fiction have so far been broadly incoherent, but with flashes of poetry—dreamlike, hallucinatory ramblings.

Neural networks might not be capable of writing intricately-plotted works with charm and wit, like Dickens or Dostoevsky, but there’s still an eeriness to trying to decipher the surreal, Finnegans’ Wake mish-mash.

You might see, in the odd line, the flickering ghost of something like consciousness, a deeper understanding. Or you might just see fragments of meaning thrown into a neural network blender, full of hype and fury, obeying rules in an occasionally striking way, but ultimately signifying nothing. In that sense, at least, the RNN’s grappling with metaphor feels like a metaphor for the hype surrounding the latest AI summer as a whole.

Or, as the human author of On The Road put it: “You guys are going somewhere or just going?”

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Posted in Human Robots

#433770 Will Tech Make Insurance Obsolete in the ...

Posted on October 18, 2018 by Android

We profit from it, we fear it, and we find it impossibly hard to quantify: risk.

While not the sexiest of industries, insurance can be a life-saving protector, pooling everyone’s premiums to safeguard against some of our greatest, most unexpected losses.

One of the most profitable in the world, the insurance industry exceeded $1.2 trillion in annual revenue since 2011 in the US alone.

But risk is becoming predictable. And insurance is getting disrupted fast.

By 2025, we’ll be living in a trillion-sensor economy. And as we enter a world where everything is measured all the time, we’ll start to transition from protecting against damages to preventing them in the first place.

But what happens to health insurance when Big Brother is always watching? Do rates go up when you sneak a cigarette? Do they go down when you eat your vegetables?

And what happens to auto insurance when most cars are autonomous? Or life insurance when the human lifespan doubles?

For that matter, what happens to insurance brokers when blockchain makes them irrelevant?

In this article, I’ll be discussing four key transformations:

Sensors and AI replacing your traditional broker
Blockchain
The ecosystem approach
IoT and insurance connectivity

Let’s dive in.

AI and the Trillion-Sensor Economy
As sensors continue to proliferate across every context—from smart infrastructure to millions of connected home devices to medicine—smart environments will allow us to ask any question, anytime, anywhere.

And as I often explain, once your AI has access to this treasure trove of ubiquitous sensor data in real time, it will be the quality of your questions that make or break your business.

But perhaps the most exciting insurance application of AI’s convergence with sensors is in healthcare. Tremendous advances in genetic screening are empowering us with predictive knowledge about our long-term health risks.

Leading the charge in genome sequencing, Illumina predicts that in a matter of years, decoding the full human genome will drop to $100, taking merely one hour to complete. Other companies are racing to get you sequences faster and cheaper.

Adopting an ecosystem approach, incumbent insurers and insurtech firms will soon be able to collaborate to provide risk-minimizing services in the health sector. Using sensor data and AI-driven personalized recommendations, insurance partnerships could keep consumers healthy, dramatically reducing the cost of healthcare.

Some fear that information asymmetry will allow consumers to learn of their health risks and leave insurers in the dark. However, both parties could benefit if insurers become part of the screening process.

A remarkable example of this is Gilad Meiri’s company, Neura AI. Aiming to predict health patterns, Neura has developed machine learning algorithms that analyze data from all of a user’s connected devices (sometimes from up to 54 apps!).

Neura predicts a user’s behavior and draws staggering insights about consumers’ health risks. Meiri soon began selling his personal risk assessment tool to insurers, who could then help insured customers mitigate long-term health risks.

But artificial intelligence will impact far more than just health insurance.

In October of 2016, a claim was submitted to Lemonade, the world’s first peer-to-peer insurance company. Rather than being processed by a human, every step in this claim resolution chain—from initial triage through fraud mitigation through final payment—was handled by an AI.

This transaction marks the first time an AI has processed an insurance claim. And it won’t be the last. A traditional human-processed claim takes 40 days to pay out. In Lemonade’s case, payment was transferred within three seconds.

However, Lemonade’s achievement only marks a starting point. Over the course of the next decade, nearly every facet of the insurance industry will undergo a similarly massive transformation.

New business models like peer-to-peer insurance are replacing traditional brokerage relationships, while AI and blockchain pairings significantly reduce the layers of bureaucracy required (with each layer getting a cut) for traditional insurance.

Consider Juniper, a startup that scrapes social media to build your risk assessment, subsequently asking you 12 questions via an iPhone app. Geared with advanced analytics, the platform can generate a million-dollar life insurance policy, approved in less than five minutes.

But what’s keeping all your data from unwanted hands?

Blockchain Building Trust
Current distrust in centralized financial services has led to staggering rates of underinsurance. Add to this fear of poor data and privacy protection, particularly in the wake of 2017’s widespread cybercriminal hacks.

Enabling secure storage and transfer of personal data, blockchain holds remarkable promise against the fraudulent activity that often plagues insurance firms.

The centralized model of insurance companies and other organizations is becoming redundant. Developing blockchain-based solutions for capital markets, Symbiont develops smart contracts to execute payments with little to no human involvement.

But distributed ledger technology (DLT) is enabling far more than just smart contracts.

Also targeting insurance is Tradle, leveraging blockchain for its proclaimed goal of “building a trust provisioning network.” Built around “know-your-customer” (KYC) data, Tradle aims to verify KYC data so that it can be securely forwarded to other firms without any further verification.

By requiring a certain number of parties to reuse pre-verified data, the platform makes your data much less vulnerable to hacking and allows you to keep it on a personal device. Only its verification—let’s say of a transaction or medical exam—is registered in the blockchain.

As insurance data grow increasingly decentralized, key insurance players will experience more and more pressure to adopt an ecosystem approach.

The Ecosystem Approach
Just as exponential technologies converge to provide new services, exponential businesses must combine the strengths of different sectors to expand traditional product lines.

By partnering with platform-based insurtech firms, forward-thinking insurers will no longer serve only as reactive policy-providers, but provide risk-mitigating services as well.

Especially as digital technologies demonetize security services—think autonomous vehicles—insurers must create new value chains and span more product categories.

For instance, France’s multinational AXA recently partnered with Alibaba and Ant Financial Services to sell a varied range of insurance products on Alibaba’s global e-commerce platform at the click of a button.

Building another ecosystem, Alibaba has also collaborated with Ping An Insurance and Tencent to create ZhongAn Online Property and Casualty Insurance—China’s first internet-only insurer, offering over 300 products. Now with a multibillion-dollar valuation, Zhong An has generated about half its business from selling shipping return insurance to Alibaba consumers.

But it doesn’t stop there. Insurers that participate in digital ecosystems can now sell risk-mitigating services that prevent damage before it occurs.

Imagine a corporate manufacturer whose sensors collect data on environmental factors affecting crop yield in an agricultural community. With the backing of investors and advanced risk analytics, such a manufacturer could sell crop insurance to farmers. By implementing an automated, AI-driven UI, they could automatically make payments when sensors detect weather damage to crops.

Now let’s apply this concept to your house, your car, your health insurance.

What’s stopping insurers from partnering with third-party IoT platforms to predict fires, collisions, chronic heart disease—and then empowering the consumer with preventive services?

This brings us to the powerful field of IoT.

Internet of Things and Insurance Connectivity
Leap ahead a few years. With a centralized hub like Echo, your smart home protects itself with a network of sensors. While gone, you’ve left on a gas burner and your internet-connected stove notifies you via a home app.

Better yet, home sensors monitoring heat and humidity levels run this data through an AI, which then remotely controls heating, humidity levels, and other connected devices based on historical data patterns and fire risk factors.

Several firms are already working toward this reality.

AXA plans to one day cooperate with a centralized home hub whereby remote monitoring will collect data for future analysis and detect abnormalities.

With remote monitoring and app-centralized control for users, MonAXA is aimed at customizing insurance bundles. These would reflect exact security features embedded in smart homes.

Wouldn’t you prefer not to have to rely on insurance after a burglary? With digital ecosystems, insurers may soon prevent break-ins from the start.

By gathering sensor data from third parties on neighborhood conditions, historical theft data, suspicious activity and other risk factors, an insurtech firm might automatically put your smart home on high alert, activating alarms and specialized locks in advance of an attack.

Insurance policy premiums are predicted to vastly reduce with lessened likelihood of insured losses. But insurers moving into preventive insurtech will likely turn a profit from other areas of their business. PricewaterhouseCoopers predicts that the connected home market will reach $149 billion USD by 2020.

Let’s look at car insurance.

Car insurance premiums are currently calculated according to the driver and traits of the car. But as more autonomous vehicles take to the roads, not only does liability shift to manufacturers and software engineers, but the risk of collision falls dramatically.

But let’s take this a step further.

In a future of autonomous cars, you will no longer own your car, instead subscribing to Transport as a Service (TaaS) and giving up the purchase of automotive insurance altogether.

This paradigm shift has already begun with Waymo, which automatically provides passengers with insurance every time they step into a Waymo vehicle.

And with the rise of smart traffic systems, sensor-embedded roads, and skyrocketing autonomous vehicle technology, the risks involved in transit only continue to plummet.

Final Thoughts
Insurtech firms are hitting the market fast. IoT, autonomous vehicles and genetic screening are rapidly making us invulnerable to risk. And AI-driven services are quickly pushing conventional insurers out of the market.

By 2024, roll-out of 5G on the ground, as well as OneWeb and Starlink in orbit are bringing 4.2 billion new consumers to the web—most of whom will need insurance. Yet, because of the changes afoot in the industry, none of them will buy policies from a human broker.

While today’s largest insurance companies continue to ignore this fact at their peril (and this segment of the market), thousands of entrepreneurs see it more clearly: as one of the largest opportunities ahead.

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Posted in Human Robots

#433728 AI Is Kicking Space Exploration into ...

Posted on October 7, 2018 by Android

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

#433668 A Decade of Commercial Space ...

Posted on September 28, 2018 by Android

In many industries, a decade is barely enough time to cause dramatic change unless something disruptive comes along—a new technology, business model, or service design. The space industry has recently been enjoying all three.

But 10 years ago, none of those innovations were guaranteed. In fact, on Sept. 28, 2008, an entire company watched and hoped as their flagship product attempted a final launch after three failures. With cash running low, this was the last shot. Over 21,000 kilograms of kerosene and liquid oxygen ignited and powered two booster stages off the launchpad.

This first official picture of the Soviet satellite Sputnik I was issued in Moscow Oct. 9, 1957. The satellite measured 1 foot, 11 inches and weighed 184 pounds. The Space Age began as the Soviet Union launched Sputnik, the first man-made satellite, into orbit, on Oct. 4, 1957.AP Photo/TASS
When that Falcon 1 rocket successfully reached orbit and the company secured a subsequent contract with NASA, SpaceX had survived its ‘startup dip’. That milestone, the first privately developed liquid-fueled rocket to reach orbit, ignited a new space industry that is changing our world, on this planet and beyond. What has happened in the intervening years, and what does it mean going forward?

While scientists are busy developing new technologies that address the countless technical problems of space, there is another segment of researchers, including myself, studying the business angle and the operations issues facing this new industry. In a recent paper, my colleague Christopher Tang and I investigate the questions firms need to answer in order to create a sustainable space industry and make it possible for humans to establish extraterrestrial bases, mine asteroids and extend space travel—all while governments play an increasingly smaller role in funding space enterprises. We believe these business solutions may hold the less-glamorous key to unlocking the galaxy.

The New Global Space Industry
When the Soviet Union launched their Sputnik program, putting a satellite in orbit in 1957, they kicked off a race to space fueled by international competition and Cold War fears. The Soviet Union and the United States played the primary roles, stringing together a series of “firsts” for the record books. The first chapter of the space race culminated with Neil Armstrong and Buzz Aldrin’s historic Apollo 11 moon landing which required massive public investment, on the order of US$25.4 billion, almost $200 billion in today’s dollars.

Competition characterized this early portion of space history. Eventually, that evolved into collaboration, with the International Space Station being a stellar example, as governments worked toward shared goals. Now, we’ve entered a new phase—openness—with private, commercial companies leading the way.

The industry for spacecraft and satellite launches is becoming more commercialized, due, in part, to shrinking government budgets. According to a report from the investment firm Space Angels, a record 120 venture capital firms invested over $3.9 billion in private space enterprises last year. The space industry is also becoming global, no longer dominated by the Cold War rivals, the United States and USSR.

In 2018 to date, there have been 72 orbital launches, an average of two per week, from launch pads in China, Russia, India, Japan, French Guinea, New Zealand, and the US.

The uptick in orbital launches of actual rockets as well as spacecraft launches, which includes satellites and probes launched from space, coincides with this openness over the past decade.

More governments, firms and even amateurs engage in various spacecraft launches than ever before. With more entities involved, innovation has flourished. As Roberson notes in Digital Trends, “Private, commercial spaceflight. Even lunar exploration, mining, and colonization—it’s suddenly all on the table, making the race for space today more vital than it has felt in years.”

Worldwide launches into space. Orbital launches include manned and unmanned spaceships launched into orbital flight from Earth. Spacecraft launches include all vehicles such as spaceships, satellites and probes launched from Earth or space. Wooten, J. and C. Tang (2018) Operations in space, Decision Sciences; Space Launch Report (Kyle 2017); Spacecraft Encyclopedia (Lafleur 2017), CC BY-ND

One can see this vitality plainly in the news. On Sept. 21, Japan announced that two of its unmanned rovers, dubbed Minerva-II-1, had landed on a small, distant asteroid. For perspective, the scale of this landing is similar to hitting a 6-centimeter target from 20,000 kilometers away. And earlier this year, people around the world watched in awe as SpaceX’s Falcon Heavy rocket successfully launched and, more impressively, returned its two boosters to a landing pad in a synchronized ballet of epic proportions.

Challenges and Opportunities
Amidst the growth of capital, firms, and knowledge, both researchers and practitioners must figure out how entities should manage their daily operations, organize their supply chain, and develop sustainable operations in space. This is complicated by the hurdles space poses: distance, gravity, inhospitable environments, and information scarcity.

One of the greatest challenges involves actually getting the things people want in space, into space. Manufacturing everything on Earth and then launching it with rockets is expensive and restrictive. A company called Made In Space is taking a different approach by maintaining an additive manufacturing facility on the International Space Station and 3D printing right in space. Tools, spare parts, and medical devices for the crew can all be created on demand. The benefits include more flexibility and better inventory management on the space station. In addition, certain products can be produced better in space than on Earth, such as pure optical fiber.

How should companies determine the value of manufacturing in space? Where should capacity be built and how should it be scaled up? The figure below breaks up the origin and destination of goods between Earth and space and arranges products into quadrants. Humans have mastered the lower left quadrant, made on Earth—for use on Earth. Moving clockwise from there, each quadrant introduces new challenges, for which we have less and less expertise.

A framework of Earth-space operations. Wooten, J. and C. Tang (2018) Operations in Space, Decision Sciences, CC BY-ND
I first became interested in this particular problem as I listened to a panel of robotics experts discuss building a colony on Mars (in our third quadrant). You can’t build the structures on Earth and easily send them to Mars, so you must manufacture there. But putting human builders in that extreme environment is equally problematic. Essentially, an entirely new mode of production using robots and automation in an advance envoy may be required.

Resources in Space
You might wonder where one gets the materials for manufacturing in space, but there is actually an abundance of resources: Metals for manufacturing can be found within asteroids, water for rocket fuel is frozen as ice on planets and moons, and rare elements like helium-3 for energy are embedded in the crust of the moon. If we brought that particular isotope back to Earth, we could eliminate our dependence on fossil fuels.

As demonstrated by the recent Minerva-II-1 asteroid landing, people are acquiring the technical know-how to locate and navigate to these materials. But extraction and transport are open questions.

How do these cases change the economics in the space industry? Already, companies like Planetary Resources, Moon Express, Deep Space Industries, and Asterank are organizing to address these opportunities. And scholars are beginning to outline how to navigate questions of property rights, exploitation and partnerships.

Threats From Space Junk
A computer-generated image of objects in Earth orbit that are currently being tracked. Approximately 95 percent of the objects in this illustration are orbital debris – not functional satellites. The dots represent the current location of each item. The orbital debris dots are scaled according to the image size of the graphic to optimize their visibility and are not scaled to Earth. NASA
The movie “Gravity” opens with a Russian satellite exploding, which sets off a chain reaction of destruction thanks to debris hitting a space shuttle, the Hubble telescope, and part of the International Space Station. The sequence, while not perfectly plausible as written, is a very real phenomenon. In fact, in 2013, a Russian satellite disintegrated when it was hit with fragments from a Chinese satellite that exploded in 2007. Known as the Kessler effect, the danger from the 500,000-plus pieces of space debris has already gotten some attention in public policy circles. How should one prevent, reduce or mitigate this risk? Quantifying the environmental impact of the space industry and addressing sustainable operations is still to come.

NASA scientist Mark Matney is seen through a fist-sized hole in a 3-inch thick piece of aluminum at Johnson Space Center’s orbital debris program lab. The hole was created by a thumb-size piece of material hitting the metal at very high speed simulating possible damage from space junk. AP Photo/Pat Sullivan
What’s Next?
It’s true that space is becoming just another place to do business. There are companies that will handle the logistics of getting your destined-for-space module on board a rocket; there are companies that will fly those rockets to the International Space Station; and there are others that can make a replacement part once there.

What comes next? In one sense, it’s anybody’s guess, but all signs point to this new industry forging ahead. A new breakthrough could alter the speed, but the course seems set: exploring farther away from home, whether that’s the moon, asteroids, or Mars. It’s hard to believe that 10 years ago, SpaceX launches were yet to be successful. Today, a vibrant private sector consists of scores of companies working on everything from commercial spacecraft and rocket propulsion to space mining and food production. The next step is working to solidify the business practices and mature the industry.

Standing in a large hall at the University of Pittsburgh as part of the White House Frontiers Conference, I see the future. Wrapped around my head are state-of-the-art virtual reality goggles. I’m looking at the surface of Mars. Every detail is immediate and crisp. This is not just a video game or an aimless exercise. The scientific community has poured resources into such efforts because exploration is preceded by information. And who knows, maybe 10 years from now, someone will be standing on the actual surface of Mars.

Image Credit: SpaceX

Joel Wooten, Assistant Professor of Management Science, University of South Carolina

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