Tech-related injuries are becoming more common as many people depend on - and often develop addictions for - smart phones and computers.
In 2007, the first iPhone measured 3.5-inches diagonally, a measurement known as the display size. That’s been nearly doubled by the newest iPhone 13 Pro, which has a 6.7-inch display. Other phones, too, like the Google Pixel 6 and the Samsung Galaxy S22, have bigger screens than their predecessors. Physical therapists and orthopedic surgeons have had to come up with names for a variety of new conditions: selfie elbow, tech neck, texting thumb. Orthopedic surgeon Sonya Sloan says she sees selfie elbow in younger kids and in women more often than men. She hears complaints related to technology once or twice a day.
The addictive quality of smartphones and social media means that people spend more time on their devices, which exacerbates injuries. According to Statista, 68 percent of those surveyed spent over three hours a day on their phone, and almost half spent five to six hours a day. Another report showed that people dedicate a third of their day to checking their phones, while the Media Effects Research Laboratory at Pennsylvania State University has found that bigger screens, ideal for entertainment purposes, immerse their users more than smaller screens. Oversized screens also provide easier navigation and more space for those with bigger hands or trouble seeing.
But others with conditions like arthritis can benefit from smaller phones. In March of 2016, Apple released the iPhone SE with a display size of 4.7 inches—an inch smaller than the iPhone 7, released that September. Apple has since come out with two more versions of the diminutive iPhone SE, one in 2020 and another in 2022.
These devices are now an inextricable part of our lives. So where does the burden of responsibility lie? Is it with consumers to adjust body positioning, get ergonomic workstations, and change habits to abate tech-related pain? Or should tech companies be held accountable?
Kavin Senapathy, a freelance science journalist, has the Google Pixel 6. She was drawn to the phone because Google marketed the Pixel 6’s camera as better at capturing different skin tones. But this phone boasts one of the largest display sizes on the market: 6.4 inches.
Senapathy was diagnosed with carpal and cubital tunnel syndromes in 2017 and fibromyalgia in 2019. She has had to create a curated ergonomic workplace setup, otherwise her wrists and hands get weak and tingly, and she’s had to adjust how she holds her phone to prevent pain flares.
Recently, Senapathy underwent an electromyography, or an EMG, in which doctors insert electrodes into muscles to measure their electrical activity. The electrical response of the muscles tells doctors whether the nerve cells and muscles are successfully communicating. Depending on her results, steroid shots and even surgery might be required. Senapathy wants to stick with her Pixel 6, but the pain she’s experiencing may push her to buy a smaller phone. Unfortunately, options for these modestly sized phones are more limited.
These devices are now an inextricable part of our lives. So where does the burden of responsibility lie? Is it with consumers like Senapathy to adjust body positioning, get ergonomic workstations, and change habits to abate tech-related pain? Or should tech companies be held accountable for creating addictive devices that lead to musculoskeletal injury?
Kavin Senapathy, a freelance journalist, bought the Google Pixel 6 because of its high-quality camera, but she’s had to adjust how she holds the oversized phone to prevent pain flares.
Kavin Senapathy
A one-size-fits-all mentality for smartphones will continue to lead to injuries because every user has different wants and needs. S. Shyam Sundar, the founder of Penn State’s lab on media effects and a communications professor, says the needs for mobility and portability conflict with the desire for greater visibility. “The best thing a company can do is offer different sizes,” he says.
Joanna Bryson, an AI ethics expert and professor at The Hertie School of Governance in Berlin, Germany, echoed these sentiments. “A lot of the lack of choice we see comes from the fact that the markets have consolidated so much,” she says. “We want to make sure there’s sufficient diversity [of products].”
Consumers can still maintain some control despite the ubiquity of tech. Sloan, the orthopedic surgeon, has to pester her son to change his body positioning when using his tablet. Our heads get heavier as they bend forward: at rest, they weigh 12 pounds, but bent 60 degrees, they weigh 60. “I have to tell him, ‘Raise your head, son!’” she says. It’s important, Sloan explains, to consider that growth and development will affect ligaments and bones in the neck, potentially making kids even more vulnerable to injuries from misusing gadgets. She recommends that parents limit their kids’ tech time to alleviate strain. She also suggested that tech companies implement a timer to remind us to change our body positioning.
In 2017, Nan-Wei Gong, a former contractor for Google, founded Figur8, which uses wearable trackers to measure muscle function and joint movement. It’s like physical therapy with biofeedback. “Each unique injury has a different biomarker,” says Gong. “With Figur8, you are comparing yourself to yourself.” This allows an individual to self-monitor for wear and tear and strengthen an injury in a way that’s efficient and designed for their body. Gong noticed that the work-from-home model during the COVID-19 pandemic created a new set of ergonomic problems that resulted in injuries. Figur8 provides real-time data for these injuries because “behavioral change requires feedback.”
Gong worked on a project called Jacquard while at Google. Textile experts weave conductive thread into their fabric, and the result is a patch of the fabric—like the cuff of a Levi’s jacket—that responds to commands on your smartphone. One swipe can call your partner or check the weather. It was designed with cyclists in mind who can’t easily check their phones, and it’s part of a growing movement in the tech industry to deliver creative, hands-free design. Gong thinks that engineers at large corporations like Google have accessibility in mind; it’s part of what drives their decisions for new products.
Display sizes of iPhones have become larger over time.
Sourced from Screenrant https://screenrant.com/iphone-apple-release-chronological-order-smartphone/ and Apple Tech Specs: https://www.apple.com/iphone-se/specs/
Back in Germany, Joanna Bryson reminds us that products like smartphones should adhere to best practices. These rules may be especially important for phones and other products with AI that are addictive. Disclosure, accountability, and regulation are important for AI, she says. “The correct balance will keep changing. But we have responsibilities and obligations to each other.” She was on an AI Ethics Council at Google, but the committee was disbanded after only one week due to issues with one of their members.
Bryson was upset about the Council’s dissolution but has faith that other regulatory bodies will prevail. OECD.AI, and international nonprofit, has drafted policies to regulate AI, which countries can sign and implement. “As of July 2021, 46 governments have adhered to the AI principles,” their website reads.
Sundar, the media effects professor, also directs Penn State’s Center for Socially Responsible AI. He says that inclusivity is a crucial aspect of social responsibility and how devices using AI are designed. “We have to go beyond first designing technologies and then making them accessible,” he says. “Instead, we should be considering the issues potentially faced by all different kinds of users before even designing them.”
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.
