How AI will change astronomy, healthcare, and social justice

Although there is much guesswork as to the future of artificial intelligence, today’s AI systems continue to be a boon for science.

How AI will change astronomy, healthcare, and social justice
Roland Birke/Getty Images

Artificial intelligence has seen a resurgence of late in the cultural consciousness. Doomsayers worry AI will prove an existential threat to humanity, futurists wonder how we’ll integrate aware algorithms into our social fabric, and optimists see further develops as the path to unimaginable human prosperity.

But it is often unclear which “AI” is being discussed in any given debate or hot take. The umbrella term can signify artificial general intelligence, also called “strong AI,” essentially a program that could perform cognitive tasks similar to, or beyond those of, people. It is also completely theoretical and conclusions about it are, at best, guesswork.

The term can also apply to narrow artificial intelligence (a.k.a. weak AI). Unlike general AI, narrow AI focuses its processing and learning power on a highly-specialized task. It is widely used in hardware and software all around us and, on the whole, has been a boon for humanity. For every facial recognition system cracked by a mask, or AI chatterbox that turns pro-Hitler, there is an AI that saves lives, predicts failures, improves efficiency, and reduces environmental waste.

One area where AI is changing human lives for the better is science. Thanks to a combination of strict focus, computational speed, and self-improvement through deep learning, AI can more effectively complete time-consuming tasks, such as sifting through vast amounts of data to recognize patterns. This frees scientists up to put their time and energy toward creative and analytic pursuits or productively implementing their research toward human well-being.

Here are three scientific fields that AI is improving.

Looking toward the stars

Pixabay / Creative Commons

The universe is vast, and the data modern telescopes and satellites can collect are staggering. To help sift through these data, astronomers have turned to artificial intelligence, and the results have been enlightening.

One study used computer simulations of galaxy formations to trained a deep learning algorithm to identify these faraway star clusters. The algorithm learned to recognize three key phases of galaxy evolution, and when shown images taken on the Hubble Space Telescope for the CANDELS project, it was able to successfully identify real galaxies.

Another AI is helping us learn about our own galactic suburb. Trained on Lunar Reconnaissance Orbiter images of the moon, the algorithm analyzed a third of the lunar landscape and discovered 6,000 new lunar craters. And it isn’t just data sifting that has astronomers excited. One team of researchers is using generative adversarial networks — which have been used previously to create convincingly fake faces based on pictures of celebrities – to sharpen the resolution of blurry telescope images.

Currently, the AI has only been tested on low-resolution versions of high-quality originals, but the results have been promisingly accurate. One day the AI could use knowledge of the known universe to fill in the blanks of its farthest reaches to improve astronomical datasets.

An AI a day

Wikicommons, Creative Commons

Another field with huge amounts of data is medicine. A single patient’s medical history can encompass his or her genetics, family history, environmental impacts, and a lifetime’s worth of disease, operations, and prescriptions. It can be difficult to know the best approach for a single patient, let along a hospital’s worth.

To help, doctors are turning to algorithms to keep track of health records more accurately than current systems. One such advanced algorithm used predictive modeling to evaluate more than 216,000 adult patient hospitalizations to predict unexpected readmissions, long hospital stays, and in-hospital deaths. 

AI has also been employed to assist in addiction recovery. Researchers at the University of Southern California created an algorithm that sorted intervention program participants into groups based on social ties and prior history. The algorithm’s goal was to promote positive social connections and reduce “deviancy training” from peers, and it performed significantly better than control strategies for forming such groups.

Social justice algorithms

Pixabay, CC

Artificial intelligence is also working toward social change. Stanford researchers have proposed combining machine learning with satellite data to map poverty across the world. Traditionally, social scientists had to gather local-level information from a survey, a slow and difficult task for some of the more remote and impoverished regions in the world.

To gather information quicker, researchers taught an AI to search satellite images for the signs of prosperity and poverty (roads, nightlights, urban areas, farmlands, etc.) to determine an area’s wealth. Unlike traditional methods, relying on satellite images means the dataset can be updated frequently, and these maps can be used to better target outreach and resources.

Another team of Standford researchers has implemented AI to measure social change. Analyzing databases of books, newspapers, and other texts, the researchers measured linguistic changes across social shifts. Co-author and history professor Londa Schiebinger said she hopes the project will “allow humanities scholars to go after questions about the evolution of stereotypes and biases at a scale that has never been done before.” One of the algorithm’s findings was an increase in the positive portrayals of women in every decade since the 1960s.

 Of course, these three fields are a tiny sampling of the improvements AI has had on science. Materials scientists are using algorithms to find metallic glass faster, neuroscientists are better understanding mammalian brains through virtual nerve cells, paleontologists are combining AI and satellite images to locate fossils more easily, and conservationists are protecting biodiversity by using AI to combat the illegal wildlife trade through social media.

That about wraps it up. If artificial intelligence truly will rise up to destroy humanity, it looks like its strategy is death by kindness.


A landslide is imminent and so is its tsunami

An open letter predicts that a massive wall of rock is about to plunge into Barry Arm Fjord in Alaska.

Image source: Christian Zimmerman/USGS/Big Think
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  • A remote area visited by tourists and cruises, and home to fishing villages, is about to be visited by a devastating tsunami.
  • A wall of rock exposed by a receding glacier is about crash into the waters below.
  • Glaciers hold such areas together — and when they're gone, bad stuff can be left behind.

The Barry Glacier gives its name to Alaska's Barry Arm Fjord, and a new open letter forecasts trouble ahead.

Thanks to global warming, the glacier has been retreating, so far removing two-thirds of its support for a steep mile-long slope, or scarp, containing perhaps 500 million cubic meters of material. (Think the Hoover Dam times several hundred.) The slope has been moving slowly since 1957, but scientists say it's become an avalanche waiting to happen, maybe within the next year, and likely within 20. When it does come crashing down into the fjord, it could set in motion a frightening tsunami overwhelming the fjord's normally peaceful waters .

"It could happen anytime, but the risk just goes way up as this glacier recedes," says hydrologist Anna Liljedahl of Woods Hole, one of the signatories to the letter.

The Barry Arm Fjord

Camping on the fjord's Black Sand Beach

Image source: Matt Zimmerman

The Barry Arm Fjord is a stretch of water between the Harriman Fjord and the Port Wills Fjord, located at the northwest corner of the well-known Prince William Sound. It's a beautiful area, home to a few hundred people supporting the local fishing industry, and it's also a popular destination for tourists — its Black Sand Beach is one of Alaska's most scenic — and cruise ships.

Not Alaska’s first watery rodeo, but likely the biggest

Image source:

There have been at least two similar events in the state's recent history, though not on such a massive scale. On July 9, 1958, an earthquake nearby caused 40 million cubic yards of rock to suddenly slide 2,000 feet down into Lituya Bay, producing a tsunami whose peak waves reportedly reached 1,720 feet in height. By the time the wall of water reached the mouth of the bay, it was still 75 feet high. At Taan Fjord in 2015, a landslide caused a tsunami that crested at 600 feet. Both of these events thankfully occurred in sparsely populated areas, so few fatalities occurred.

The Barry Arm event will be larger than either of these by far.

"This is an enormous slope — the mass that could fail weighs over a billion tonnes," said geologist Dave Petley, speaking to Earther. "The internal structure of that rock mass, which will determine whether it collapses, is very complex. At the moment we don't know enough about it to be able to forecast its future behavior."

Outside of Alaska, on the west coast of Greenland, a landslide-produced tsunami towered 300 feet high, obliterating a fishing village in its path.

What the letter predicts for Barry Arm Fjord

Moving slowly at first...

Image source:

"The effects would be especially severe near where the landslide enters the water at the head of Barry Arm. Additionally, areas of shallow water, or low-lying land near the shore, would be in danger even further from the source. A minor failure may not produce significant impacts beyond the inner parts of the fiord, while a complete failure could be destructive throughout Barry Arm, Harriman Fiord, and parts of Port Wells. Our initial results show complex impacts further from the landslide than Barry Arm, with over 30 foot waves in some distant bays, including Whittier."

The discovery of the impeding landslide began with an observation by the sister of geologist Hig Higman of Ground Truth, an organization in Seldovia, Alaska. Artist Valisa Higman was vacationing in the area and sent her brother some photos of worrying fractures she noticed in the slope, taken while she was on a boat cruising the fjord.

Higman confirmed his sister's hunch via available satellite imagery and, digging deeper, found that between 2009 and 2015 the slope had moved 600 feet downhill, leaving a prominent scar.

Ohio State's Chunli Dai unearthed a connection between the movement and the receding of the Barry Glacier. Comparison of the Barry Arm slope with other similar areas, combined with computer modeling of the possible resulting tsunamis, led to the publication of the group's letter.

While the full group of signatories from 14 organizations and institutions has only been working on the situation for a month, the implications were immediately clear. The signers include experts from Ohio State University, the University of Southern California, and the Anchorage and Fairbanks campuses of the University of Alaska.

Once informed of the open letter's contents, the Alaska's Department of Natural Resources immediately released a warning that "an increasingly likely landslide could generate a wave with devastating effects on fishermen and recreationalists."

How do you prepare for something like this?

Image source:

The obvious question is what can be done to prepare for the landslide and tsunami? For one thing, there's more to understand about the upcoming event, and the researchers lay out their plan in the letter:

"To inform and refine hazard mitigation efforts, we would like to pursue several lines of investigation: Detect changes in the slope that might forewarn of a landslide, better understand what could trigger a landslide, and refine tsunami model projections. By mapping the landslide and nearby terrain, both above and below sea level, we can more accurately determine the basic physical dimensions of the landslide. This can be paired with GPS and seismic measurements made over time to see how the slope responds to changes in the glacier and to events like rainstorms and earthquakes. Field and satellite data can support near-real time hazard monitoring, while computer models of landslide and tsunami scenarios can help identify specific places that are most at risk."

In the letter, the authors reached out to those living in and visiting the area, asking, "What specific questions are most important to you?" and "What could be done to reduce the danger to people who want to visit or work in Barry Arm?" They also invited locals to let them know about any changes, including even small rock-falls and landslides.

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