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#433884 Designer Babies, and Their Babies: How ...

As if stand-alone technologies weren’t advancing fast enough, we’re in age where we must study the intersection points of these technologies. How is what’s happening in robotics influenced by what’s happening in 3D printing? What could be made possible by applying the latest advances in quantum computing to nanotechnology?

Along these lines, one crucial tech intersection is that of artificial intelligence and genomics. Each field is seeing constant progress, but Jamie Metzl believes it’s their convergence that will really push us into uncharted territory, beyond even what we’ve imagined in science fiction. “There’s going to be this push and pull, this competition between the reality of our biology with its built-in limitations and the scope of our aspirations,” he said.

Metzl is a senior fellow at the Atlantic Council and author of the upcoming book Hacking Darwin: Genetic Engineering and the Future of Humanity. At Singularity University’s Exponential Medicine conference last week, he shared his insights on genomics and AI, and where their convergence could take us.

Life As We Know It
Metzl explained how genomics as a field evolved slowly—and then quickly. In 1953, James Watson and Francis Crick identified the double helix structure of DNA, and realized that the order of the base pairs held a treasure trove of genetic information. There was such a thing as a book of life, and we’d found it.

In 2003, when the Human Genome Project was completed (after 13 years and $2.7 billion), we learned the order of the genome’s 3 billion base pairs, and the location of specific genes on our chromosomes. Not only did a book of life exist, we figured out how to read it.

Jamie Metzl at Exponential Medicine
Fifteen years after that, it’s 2018 and precision gene editing in plants, animals, and humans is changing everything, and quickly pushing us into an entirely new frontier. Forget reading the book of life—we’re now learning how to write it.

“Readable, writable, and hackable, what’s clear is that human beings are recognizing that we are another form of information technology, and just like our IT has entered this exponential curve of discovery, we will have that with ourselves,” Metzl said. “And it’s intersecting with the AI revolution.”

Learning About Life Meets Machine Learning
In 2016, DeepMind’s AlphaGo program outsmarted the world’s top Go player. In 2017 AlphaGo Zero was created: unlike AlphaGo, AlphaGo Zero wasn’t trained using previous human games of Go, but was simply given the rules of Go—and in four days it defeated the AlphaGo program.

Our own biology is, of course, vastly more complex than the game of Go, and that, Metzl said, is our starting point. “The system of our own biology that we are trying to understand is massively, but very importantly not infinitely, complex,” he added.

Getting a standardized set of rules for our biology—and, eventually, maybe even outsmarting our biology—will require genomic data. Lots of it.

Multiple countries already starting to produce this data. The UK’s National Health Service recently announced a plan to sequence the genomes of five million Britons over the next five years. In the US the All of Us Research Program will sequence a million Americans. China is the most aggressive in sequencing its population, with a goal of sequencing half of all newborns by 2020.

“We’re going to get these massive pools of sequenced genomic data,” Metzl said. “The real gold will come from comparing people’s sequenced genomes to their electronic health records, and ultimately their life records.” Getting people comfortable with allowing open access to their data will be another matter; Metzl mentioned that Luna DNA and others have strategies to help people get comfortable with giving consent to their private information. But this is where China’s lack of privacy protection could end up being a significant advantage.

To compare genotypes and phenotypes at scale—first millions, then hundreds of millions, then eventually billions, Metzl said—we’re going to need AI and big data analytic tools, and algorithms far beyond what we have now. These tools will let us move from precision medicine to predictive medicine, knowing precisely when and where different diseases are going to occur and shutting them down before they start.

But, Metzl said, “As we unlock the genetics of ourselves, it’s not going to be about just healthcare. It’s ultimately going to be about who and what we are as humans. It’s going to be about identity.”

Designer Babies, and Their Babies
In Metzl’s mind, the most serious application of our genomic knowledge will be in embryo selection.

Currently, in-vitro fertilization (IVF) procedures can extract around 15 eggs, fertilize them, then do pre-implantation genetic testing; right now what’s knowable is single-gene mutation diseases and simple traits like hair color and eye color. As we get to the millions and then billions of people with sequences, we’ll have information about how these genetics work, and we’re going to be able to make much more informed choices,” Metzl said.

Imagine going to a fertility clinic in 2023. You give a skin graft or a blood sample, and using in-vitro gametogenesis (IVG)—infertility be damned—your skin or blood cells are induced to become eggs or sperm, which are then combined to create embryos. The dozens or hundreds of embryos created from artificial gametes each have a few cells extracted from them, and these cells are sequenced. The sequences will tell you the likelihood of specific traits and disease states were that embryo to be implanted and taken to full term. “With really anything that has a genetic foundation, we’ll be able to predict with increasing levels of accuracy how that potential child will be realized as a human being,” Metzl said.

This, he added, could lead to some wild and frightening possibilities: if you have 1,000 eggs and you pick one based on its optimal genetic sequence, you could then mate your embryo with somebody else who has done the same thing in a different genetic line. “Your five-day-old embryo and their five-day-old embryo could have a child using the same IVG process,” Metzl said. “Then that child could have a child with another five-day-old embryo from another genetic line, and you could go on and on down the line.”

Sounds insane, right? But wait, there’s more: as Jason Pontin reported earlier this year in Wired, “Gene-editing technologies such as Crispr-Cas9 would make it relatively easy to repair, add, or remove genes during the IVG process, eliminating diseases or conferring advantages that would ripple through a child’s genome. This all may sound like science fiction, but to those following the research, the combination of IVG and gene editing appears highly likely, if not inevitable.”

From Crazy to Commonplace?
It’s a slippery slope from gene editing and embryo-mating to a dystopian race to build the most perfect humans possible. If somebody’s investing so much time and energy in selecting their embryo, Metzl asked, how will they think about the mating choices of their children? IVG could quickly leave the realm of healthcare and enter that of evolution.

“We all need to be part of an inclusive, integrated, global dialogue on the future of our species,” Metzl said. “Healthcare professionals are essential nodes in this.” Not least among this dialogue should be the question of access to tech like IVG; are there steps we can take to keep it from becoming a tool for a wealthy minority, and thereby perpetuating inequality and further polarizing societies?

As Pontin points out, at its inception 40 years ago IVF also sparked fear, confusion, and resistance—and now it’s as normal and common as could be, with millions of healthy babies conceived using the technology.

The disruption that genomics, AI, and IVG will bring to reproduction could follow a similar story cycle—if we’re smart about it. As Metzl put it, “This must be regulated, because it is life.”

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#433872 Breaking Out of the Corporate Bubble ...

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Such exponential gains represent a dataism utopia.

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

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

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

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

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

How powerful are these psychological models?

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

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

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

It’s extremely possible.

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

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

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#433668 A Decade of Commercial Space ...

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

This article is republished from The Conversation under a Creative Commons license. Read the original article. Continue reading

Posted in Human Robots

#433620 Instilling the Best of Human Values in ...

Now that the era of artificial intelligence is unquestionably upon us, it behooves us to think and work harder to ensure that the AIs we create embody positive human values.

Science fiction is full of AIs that manifest the dark side of humanity, or are indifferent to humans altogether. Such possibilities cannot be ruled out, but nor is there any logical or empirical reason to consider them highly likely. I am among a large group of AI experts who see a strong potential for profoundly positive outcomes in the AI revolution currently underway.

We are facing a future with great uncertainty and tremendous promise, and the best we can do is to confront it with a combination of heart and mind, of common sense and rigorous science. In the realm of AI, what this means is, we need to do our best to guide the AI minds we are creating to embody the values we cherish: love, compassion, creativity, and respect.

The quest for beneficial AI has many dimensions, including its potential to reduce material scarcity and to help unlock the human capacity for love and compassion.

Reducing Scarcity
A large percentage of difficult issues in human society, many of which spill over into the AI domain, would be palliated significantly if material scarcity became less of a problem. Fortunately, AI has great potential to help here. AI is already increasing efficiency in nearly every industry.

In the next few decades, as nanotech and 3D printing continue to advance, AI-driven design will become a larger factor in the economy. Radical new tools like artificial enzymes built using Christian Schafmeister’s spiroligomer molecules, and designed using quantum physics-savvy AIs, will enable the creation of new materials and medicines.

For amazing advances like the intersection of AI and nanotech to lead toward broadly positive outcomes, however, the economic and political aspects of the AI industry may have to shift from the current status quo.

Currently, most AI development occurs under the aegis of military organizations or large corporations oriented heavily toward advertising and marketing. Put crudely, an awful lot of AI today is about “spying, brainwashing, or killing.” This is not really the ideal situation if we want our first true artificial general intelligences to be open-minded, warm-hearted, and beneficial.

Also, as the bulk of AI development now occurs in large for-profit organizations bound by law to pursue the maximization of shareholder value, we face a situation where AI tends to exacerbate global wealth inequality and class divisions. This has the potential to lead to various civilization-scale failure modes involving the intersection of geopolitics, AI, cyberterrorism, and so forth. Part of my motivation for founding the decentralized AI project SingularityNET was to create an alternative mode of dissemination and utilization of both narrow AI and AGI—one that operates in a self-organizing way, outside of the direct grip of conventional corporate and governmental structures.

In the end, though, I worry that radical material abundance and novel political and economic structures may fail to create a positive future, unless they are coupled with advances in consciousness and compassion. AGIs have the potential to be massively more ethical and compassionate than humans. But still, the odds of getting deeply beneficial AGIs seem higher if the humans creating them are fuller of compassion and positive consciousness—and can effectively pass these values on.

Transmitting Human Values
Brain-computer interfacing is another critical aspect of the quest for creating more positive AIs and more positive humans. As Elon Musk has put it, “If you can’t beat ’em, join’ em.” Joining is more fun than beating anyway. What better way to infuse AIs with human values than to connect them directly to human brains, and let them learn directly from the source (while providing humans with valuable enhancements)?

Millions of people recently heard Elon Musk discuss AI and BCI on the Joe Rogan podcast. Musk’s embrace of brain-computer interfacing is laudable, but he tends to dodge some of the tough issues—for instance, he does not emphasize the trade-off cyborgs will face between retaining human-ness and maximizing intelligence, joy, and creativity. To make this trade-off effectively, the AI portion of the cyborg will need to have a deep sense of human values.

Musk calls humanity the “biological boot loader” for AGI, but to me this colorful metaphor misses a key point—that we can seed the AGI we create with our values as an initial condition. This is one reason why it’s important that the first really powerful AGIs are created by decentralized networks, and not conventional corporate or military organizations. The decentralized software/hardware ecosystem, for all its quirks and flaws, has more potential to lead to human-computer cybernetic collective minds that are reasonable and benevolent.

Algorithmic Love
BCI is still in its infancy, but a more immediate way of connecting people with AIs to infuse both with greater love and compassion is to leverage humanoid robotics technology. Toward this end, I conceived a project called Loving AI, focused on using highly expressive humanoid robots like the Hanson robot Sophia to lead people through meditations and other exercises oriented toward unlocking the human potential for love and compassion. My goals here were to explore the potential of AI and robots to have a positive impact on human consciousness, and to use this application to study and improve the OpenCog and SingularityNET tools used to control Sophia in these interactions.

The Loving AI project has now run two small sets of human trials, both with exciting and positive results. These have been small—dozens rather than hundreds of people—but have definitively proven the point. Put a person in a quiet room with a humanoid robot that can look them in the eye, mirror their facial expressions, recognize some of their emotions, and lead them through simple meditation, listening, and consciousness-oriented exercises…and quite a lot of the time, the result is a more relaxed person who has entered into a shifted state of consciousness, at least for a period of time.

In a certain percentage of cases, the interaction with the robot consciousness guide triggered a dramatic change of consciousness in the human subject—a deep meditative trance state, for instance. In most cases, the result was not so extreme, but statistically the positive effect was quite significant across all cases. Furthermore, a similar effect was found using an avatar simulation of the robot’s face on a tablet screen (together with a webcam for facial expression mirroring and recognition), but not with a purely auditory interaction.

The Loving AI experiments are not only about AI; they are about human-robot and human-avatar interaction, with AI as one significant aspect. The facial interaction with the robot or avatar is pushing “biological buttons” that trigger emotional reactions and prime the mind for changes of consciousness. However, this sort of body-mind interaction is arguably critical to human values and what it means to be human; it’s an important thing for robots and AIs to “get.”

Halting or pausing the advance of AI is not a viable possibility at this stage. Despite the risks, the potential economic and political benefits involved are clear and massive. The convergence of narrow AI toward AGI is also a near inevitability, because there are so many important applications where greater generality of intelligence will lead to greater practical functionality. The challenge is to make the outcome of this great civilization-level adventure as positive as possible.

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