Puschel Sorensen undergoing physical therapy using Cyberdyne's hybrid assistive limb, left, and with her grandson while still in a wheelchair.
Puschel Sorensen first noticed something was wrong when her fingertips began to tingle. Later that day, she grew weak and fell.
It picked up small electrical impulses on her skin's surface and turned them into full movement in her legs.
Her family rushed her to the doctor, where she received the devastating diagnosis of Guillain-Barré Syndrome -- a rare and rapidly progressing autoimmune disorder that attacks the myelin sheath covering nerves.
Sorensen, a once-spry grandmother in her late fifties, spent 54 days in intensive care in 2018. When she was finally transferred to a rehab facility near her home in Florida, she was still on a feeding tube and ventilator, and was paralyzed from the neck down. Progress with traditional physical therapy was slow.
Sorensen in the hospital after her diagnosis of Guillain-Barré syndrome.
And then everything changed. Sorensen began using a cutting-edge technology called an exoskeleton to relearn how to walk. In the vein of Iron Man's fictional power suit, it confers strength and mobility to the wearer that isn't possible otherwise. In Sorensen's case, her device, called HAL – for hybrid assistive limb -- picked up small electrical impulses on her skin's surface and turned them into full movement in her legs while she attempted to walk on a treadmill.
"It was very difficult, but super awesome," recalls Sorensen, of first using the device. "The robot was having to do all the work for me."
Amazingly, within a year, she was running. She's one of 38 patients who have used HAL to recover from accidents or medical catastrophes.
"How do you thank someone for giving them back the ability to walk, the ability to live your life again?" Sorensen asks effusively.
It's still early days for such exoskeleton devices, which number perhaps a few thousand worldwide, according to data from the handful of manufacturers who create them with any scale. But the devices' ability to dramatically rehabilitate patients like Sorensen highlights their potential to extract untold numbers of people from wheelchairs, and even to usher in a new paradigm for caregiving – one of the fastest growing segments of the U.S. economy.
"I've been a physical therapist for 16 years, and (these devices) help teach patients the right way to move in rehabilitation," says Robert McIver, director of clinical technology at the Brooks Cybernic Treatment Center, part of the Brooks Rehabilitation Hospital in Jacksonville, Fla, where Sorensen recovered.
Another patient there, a 17-year-old named George with a snowboarding injury that paralyzed his legs, was getting around with a walker within 20 sessions.
As patients progress in their recoveries, so does exoskeleton technology. Jack Peurach, CEO of Ekso, one of the leaders in the space, believes within a decade they could resemble an article of clothing (a "magic pair of pants" is his phrase). They also may become inexpensive and reliable enough to transition from a medical to a consumer device. McIver sees them eventually being used in the home on an ongoing basis as a personal assistive device, much like a walker or cane, to prevent falls in elderly people.
Such a transition "certainly could eventually lessen the need for caregivers," says Sharona Hoffman, a professor of law at Case Western University in Cleveland who has written extensively on aging and bioethics. "We have a real shortage of caregivers, so that would be a good thing."
Of course, having an aging and disabled population using exoskeletons in much the same way as an Apple Watch raises issues of its own.
Dr. Elizabeth Landsverk, a California-based geriatrician and founder of a company that performs house calls for elderly patients, believes the tech holds some promise in easing the burden on caregivers, who sometimes have to lift or move patients without assistance. But she also believes exoskeletons could become overhyped.
"I don't see robotics as completely replacing the caregiver," she says. And even if exoskeletons became akin to articles of clothing, she is skeptical of how convenient they could become.
"It's hard enough to get into support hose. Would an older person be able to get in and out of it on their own?" she asks, noting that a patient's cognitive levels could pose a huge barrier to donning such a device without assistance.
If personal exoskeletons did wildly succeed, Hoffman wonders whether they would leave the elderly more physically mobile yet also more socially isolated, since caregivers or even residing in an assisted living facility may no longer be required. Or, if they were priced in the hundreds or thousands of dollars, he worries that the cost would exacerbate social inequalities among the elderly and disabled.
"It's almost like a bad dream that [my illness] happened."
With any technology that confers superhuman ability, there's also the question of appropriate usage. Even the fictional Power Loader in the movie Alien required an operator's license. In the real world, such an approach would likely pay dividends.
"We would have to make sure physicians are well-trained in these devices, and patients have a way of getting training to operate them that is thorough and responsible," Hoffman says.
But despite some unresolved questions, it is a remarkable achievement to be able to give people back their lives thanks to new technology.
"It's almost like a bad dream that [my illness] happened," says Sorensen, who managed to walk in her daughter's wedding after her recovery. "Because now everything is pretty much back to normal and it's awesome."
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
