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#433728 AI Is Kicking Space Exploration into ...

Artificial intelligence in space exploration is gathering momentum. Over the coming years, new missions look likely to be turbo-charged by AI as we voyage to comets, moons, and planets and explore the possibilities of mining asteroids.

“AI is already a game-changer that has made scientific research and exploration much more efficient. We are not just talking about a doubling but about a multiple of ten,” Leopold Summerer, Head of the Advanced Concepts and Studies Office at ESA, said in an interview with Singularity Hub.

Examples Abound
The history of AI and space exploration is older than many probably think. It has already played a significant role in research into our planet, the solar system, and the universe. As computer systems and software have developed, so have AI’s potential use cases.

The Earth Observer 1 (EO-1) satellite is a good example. Since its launch in the early 2000s, its onboard AI systems helped optimize analysis of and response to natural occurrences, like floods and volcanic eruptions. In some cases, the AI was able to tell EO-1 to start capturing images before the ground crew were even aware that the occurrence had taken place.

Other satellite and astronomy examples abound. Sky Image Cataloging and Analysis Tool (SKICAT) has assisted with the classification of objects discovered during the second Palomar Sky Survey, classifying thousands more objects caught in low resolution than a human would be able to. Similar AI systems have helped astronomers to identify 56 new possible gravitational lenses that play a crucial role in connection with research into dark matter.

AI’s ability to trawl through vast amounts of data and find correlations will become increasingly important in relation to getting the most out of the available data. ESA’s ENVISAT produces around 400 terabytes of new data every year—but will be dwarfed by the Square Kilometre Array, which will produce around the same amount of data that is currently on the internet in a day.

AI Readying For Mars
AI is also being used for trajectory and payload optimization. Both are important preliminary steps to NASA’s next rover mission to Mars, the Mars 2020 Rover, which is, slightly ironically, set to land on the red planet in early 2021.

An AI known as AEGIS is already on the red planet onboard NASA’s current rovers. The system can handle autonomous targeting of cameras and choose what to investigate. However, the next generation of AIs will be able to control vehicles, autonomously assist with study selection, and dynamically schedule and perform scientific tasks.

Throughout his career, John Leif Jørgensen from DTU Space in Denmark has designed equipment and systems that have been on board about 100 satellites—and counting. He is part of the team behind the Mars 2020 Rover’s autonomous scientific instrument PIXL, which makes extensive use of AI. Its purpose is to investigate whether there have been lifeforms like stromatolites on Mars.

“PIXL’s microscope is situated on the rover’s arm and needs to be placed 14 millimetres from what we want it to study. That happens thanks to several cameras placed on the rover. It may sound simple, but the handover process and finding out exactly where to place the arm can be likened to identifying a building from the street from a picture taken from the roof. This is something that AI is eminently suited for,” he said in an interview with Singularity Hub.

AI also helps PIXL operate autonomously throughout the night and continuously adjust as the environment changes—the temperature changes between day and night can be more than 100 degrees Celsius, meaning that the ground beneath the rover, the cameras, the robotic arm, and the rock being studied all keep changing distance.

“AI is at the core of all of this work, and helps almost double productivity,” Jørgensen said.

First Mars, Then Moons
Mars is likely far from the final destination for AIs in space. Jupiter’s moons have long fascinated scientists. Especially Europa, which could house a subsurface ocean, buried beneath an approximately 10 km thick ice crust. It is one of the most likely candidates for finding life elsewhere in the solar system.

While that mission may be some time in the future, NASA is currently planning to launch the James Webb Space Telescope into an orbit of around 1.5 million kilometers from Earth in 2020. Part of the mission will involve AI-empowered autonomous systems overseeing the full deployment of the telescope’s 705-kilo mirror.

The distances between Earth and Europa, or Earth and the James Webb telescope, means a delay in communications. That, in turn, makes it imperative for the crafts to be able to make their own decisions. Examples from the Mars Rover project show that communication between a rover and Earth can take 20 minutes because of the vast distance. A Europa mission would see much longer communication times.

Both missions, to varying degrees, illustrate one of the most significant challenges currently facing the use of AI in space exploration. There tends to be a direct correlation between how well AI systems perform and how much data they have been fed. The more, the better, as it were. But we simply don’t have very much data to feed such a system about what it’s likely to encounter on a mission to a place like Europa.

Computing power presents a second challenge. A strenuous, time-consuming approval process and the risk of radiation mean that your computer at home would likely be more powerful than anything going into space in the near future. A 200 GHz processor, 256 megabytes of ram, and 2 gigabytes of memory sounds a lot more like a Nokia 3210 (the one you could use as an ice hockey puck without it noticing) than an iPhone X—but it’s actually the ‘brain’ that will be onboard the next rover.

Private Companies Taking Off
Private companies are helping to push those limitations. CB Insights charts 57 startups in the space-space, covering areas as diverse as natural resources, consumer tourism, R&D, satellites, spacecraft design and launch, and data analytics.

David Chew works as an engineer for the Japanese satellite company Axelspace. He explained how private companies are pushing the speed of exploration and lowering costs.

“Many private space companies are taking advantage of fall-back systems and finding ways of using parts and systems that traditional companies have thought of as non-space-grade. By implementing fall-backs, and using AI, it is possible to integrate and use parts that lower costs without adding risk of failure,” he said in an interview with Singularity Hub.

Terraforming Our Future Home
Further into the future, moonshots like terraforming Mars await. Without AI, these kinds of projects to adapt other planets to Earth-like conditions would be impossible.

Autonomous crafts are already terraforming here on Earth. BioCarbon Engineering uses drones to plant up to 100,000 trees in a single day. Drones first survey and map an area, then an algorithm decides the optimal locations for the trees before a second wave of drones carry out the actual planting.

As is often the case with exponential technologies, there is a great potential for synergies and convergence. For example with AI and robotics, or quantum computing and machine learning. Why not send an AI-driven robot to Mars and use it as a telepresence for scientists on Earth? It could be argued that we are already in the early stages of doing just that by using VR and AR systems that take data from the Mars rovers and create a virtual landscape scientists can walk around in and make decisions on what the rovers should explore next.

One of the biggest benefits of AI in space exploration may not have that much to do with its actual functions. Chew believes that within as little as ten years, we could see the first mining of asteroids in the Kuiper Belt with the help of AI.

“I think one of the things that AI does to space exploration is that it opens up a whole range of new possible industries and services that have a more immediate effect on the lives of people on Earth,” he said. “It becomes a relatable industry that has a real effect on people’s daily lives. In a way, space exploration becomes part of people’s mindset, and the border between our planet and the solar system becomes less important.”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Such exponential gains represent a dataism utopia.

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

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

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

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

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

How powerful are these psychological models?

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

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

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

It’s extremely possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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#432487 Can We Make a Musical Turing Test?

As artificial intelligence advances, we’re encountering the same old questions. How much of what we consider to be fundamentally human can be reduced to an algorithm? Can we create something sufficiently advanced that people can no longer distinguish between the two? This, after all, is the idea behind the Turing Test, which has yet to be passed.

At first glance, you might think music is beyond the realm of algorithms. Birds can sing, and people can compose symphonies. Music is evocative; it makes us feel. Very often, our intense personal and emotional attachments to music are because it reminds us of our shared humanity. We are told that creative jobs are the least likely to be automated. Creativity seems fundamentally human.

But I think above all, we view it as reductionist sacrilege: to dissect beautiful things. “If you try to strangle a skylark / to cut it up, see how it works / you will stop its heart from beating / you will stop its mouth from singing.” A human musician wrote that; a machine might be able to string words together that are happy or sad; it might even be able to conjure up a decent metaphor from the depths of some neural network—but could it understand humanity enough to produce art that speaks to humans?

Then, of course, there’s the other side of the debate. Music, after all, has a deeply mathematical structure; you can train a machine to produce harmonics. “In the teachings of Pythagoras and his followers, music was inseparable from numbers, which were thought to be the key to the whole spiritual and physical universe,” according to Grout in A History of Western Music. You might argue that the process of musical composition cannot be reduced to a simple algorithm, yet musicians have often done so. Mozart, with his “Dice Music,” used the roll of a dice to decide how to order musical fragments; creativity through an 18th-century random number generator. Algorithmic music goes back a very long way, with the first papers on the subject from the 1960s.

Then there’s the techno-enthusiast side of the argument. iTunes has 26 million songs, easily more than a century of music. A human could never listen to and learn from them all, but a machine could. It could also memorize every note of Beethoven. Music can be converted into MIDI files, a nice chewable data format that allows even a character-by-character neural net you can run on your computer to generate music. (Seriously, even I could get this thing working.)

Indeed, generating music in the style of Bach has long been a test for AI, and you can see neural networks gradually learn to imitate classical composers while trying to avoid overfitting. When an algorithm overfits, it essentially starts copying the existing music, rather than being inspired by it but creating something similar: a tightrope the best human artists learn to walk. Creativity doesn’t spring from nowhere; even maverick musical geniuses have their influences.

Does a machine have to be truly ‘creative’ to produce something that someone would find valuable? To what extent would listeners’ attitudes change if they thought they were hearing a human vs. an AI composition? This all suggests a musical Turing Test. Of course, it already exists. In fact, it’s run out of Dartmouth, the school that hosted that first, seminal AI summer conference. This year, the contest is bigger than ever: alongside the PoetiX, LimeriX and LyriX competitions for poetry and lyrics, there’s a DigiKidLit competition for children’s literature (although you may have reservations about exposing your children to neural-net generated content… it can get a bit surreal).

There’s also a pair of musical competitions, including one for original compositions in different genres. Key genres and styles are represented by Charlie Parker for Jazz and the Bach chorales for classical music. There’s also a free composition, and a contest where a human and an AI try to improvise together—the AI must respond to a human spontaneously, in real time, and in a musically pleasing way. Quite a challenge! In all cases, if any of the generated work is indistinguishable from human performers, the neural net has passed the Turing Test.

Did they? Here’s part of 2017’s winning sonnet from Charese Smiley and Hiroko Bretz:

The large cabin was in total darkness.
Come marching up the eastern hill afar.
When is the clock on the stairs dangerous?
Everything seemed so near and yet so far.
Behind the wall silence alone replied.
Was, then, even the staircase occupied?
Generating the rhymes is easy enough, the sentence structure a little trickier, but what’s impressive about this sonnet is that it sticks to a single topic and appears to be a more coherent whole. I’d guess they used associated “lexical fields” of similar words to help generate something coherent. In a similar way, most of the more famous examples of AI-generated music still involve some amount of human control, even if it’s editorial; a human will build a song around an AI-generated riff, or select the most convincing Bach chorale from amidst many different samples.

We are seeing strides forward in the ability of AI to generate human voices and human likenesses. As the latter example shows, in the fake news era people have focused on the dangers of this tech– but might it also be possible to create a virtual performer, trained on a dataset of their original music? Did you ever want to hear another Beatles album, or jam with Miles Davis? Of course, these things are impossible—but could we create a similar experience that people would genuinely value? Even, to the untrained eye, something indistinguishable from the real thing?

And if it did measure up to the real thing, what would this mean? Jaron Lanier is a fascinating technology writer, a critic of strong AI, and a believer in the power of virtual reality to change the world and provide truly meaningful experiences. He’s also a composer and a musical aficionado. He pointed out in a recent interview that translation algorithms, by reducing the amount of work translators are commissioned to do, have, in some sense, profited from stolen expertise. They were trained on huge datasets purloined from human linguists and translators. If you can train an AI on someone’s creative output and it produces new music, who “owns” it?

Although companies that offer AI music tools are starting to proliferate, and some groups will argue that the musical Turing test has been passed already, AI-generated music is hardly racing to the top of the pop charts just yet. Even as the line between human-composed and AI-generated music starts to blur, there’s still a gulf between the average human and musical genius. In the next few years, we’ll see how far the current techniques can take us. It may be the case that there’s something in the skylark’s song that can’t be generated by machines. But maybe not, and then this song might need an extra verse.

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#432433 Just a Few of the Amazing Things AI Is ...

In an interview at Singularity University’s Exponential Medicine in San Diego, Neil Jacobstein shared some groundbreaking developments in artificial intelligence for healthcare.

Jacobstein is Singularity University’s faculty chair in AI and robotics, a distinguished visiting scholar at Stanford University’s MediaX Program, and has served as an AI technical consultant on research and development projects for organizations like DARPA, Deloitte, NASA, Boeing, and many more.

According to Jacobstein, 2017 was an exciting year for AI, not only due to how the technology matured, but also thanks to new applications and successes in several health domains.

Among the examples cited in his interview, Jacobstein referenced a 2017 breakthrough at Stanford University where an AI system was used for skin cancer identification. To train the system, the team showed a convolutional neural network images of 129,000 skin lesions. The system was able to differentiate between images displaying malignant melanomas and benign skin lesions. When tested against 21 board–certified dermatologists, the system made comparable diagnostic calls.

Pattern recognition and image detection are just two examples of successful uses of AI in healthcare and medicine—the list goes on.

“We’re seeing AI and machine learning systems performing at narrow tasks remarkably well, and getting breakthrough results both in AI for problem-solving and AI with medicine,” Jacobstein said.

He continued, “We are not seeing super-human terminator systems. But we are seeing more members of the AI community paying attention to managing the downside risk of AI responsibly.”

Watch the full interview to learn more examples of how AI is advancing in healthcare and medicine and elsewhere and what Jacobstein thinks is coming next.

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