Tech
Does AI really boost productivity at work? Research shows gains don’t come cheap or easy
Artificial intelligence (AI) is being touted as a way to boost lagging productivity growth.
The AI productivity push has some powerful multinational backers: the tech companies who make AI products and the consulting companies who sell AI-related services. It also has interest from governments.
Next week, the federal government will hold a roundtable on economic reform, where AI will be a key part of the agenda.
However, the evidence AI actually enhances productivity is far from clear.
To learn more about how AI is working and being procured in real organizations, we are interviewing senior bureaucrats in the Victorian Public Service. Our research is ongoing, but results from the first 12 participants are showing some shared key concerns.
Our interviewees are bureaucrats who buy, use and administer AI services. They told us increasing productivity through AI requires difficult, complex, and expensive organizational groundwork. The results are hard to measure, and AI use may create new risks and problems for workers.
Introducing AI can be slow and expensive
Public service workers told us introducing AI tools to existing workflows can be slow and expensive. Finding time and resources to research products and retrain staff presents a real challenge.
Not all organizations approach AI the same way. We found well-funded entities can afford to test different AI uses for “proofs of concept.” Smaller ones with fewer resources struggle with the costs of implementing and maintaining AI tools.
In the words of one participant: “It’s like driving a Ferrari on a smaller budget […] Sometimes those solutions aren’t fit for purpose for those smaller operations, but they’re bloody expensive to run, they’re hard to support.”
‘Data is the hard work’
Making an AI system useful may also involve a lot of groundwork.
Off-the-shelf AI tools such as Copilot and ChatGPT can make some relatively straightforward tasks easier and faster. Extracting information from large sets of documents or images is one example, and transcribing and summarizing meetings is another. (Though our findings suggest staff may feel uncomfortable with AI transcription, particularly in internal and confidential situations.)
But more complex use cases, such as call center chatbots or internal information retrieval tools, involve running an AI model over internal data describing business details and policies. Good results will depend on high-quality, well-structured data, and organizations may be liable for mistakes.
However, few organizations have invested enough in the quality of their data to make commercial AI products work as promised.
Without this foundational work, AI tools won’t perform as advertised. As one person told us, “data is the hard work.”
Privacy and cybersecurity risks are real
Using AI creates complex data flows between an organization and servers controlled by giant multinational tech companies. Large AI providers promise these data flows comply with laws about, for instance, keeping organizational and personal data in Australia and not using it to train their systems.
However, we found users were cautious about the reliability of these promises. There was also considerable concern about how products could introduce new AI functions without organizations knowing. Using those AI capabilities may create new data flows without the necessary risk assessments or compliance checking.
If organizations handle sensitive information or data that could create safety risks if leaked, vendors and products must be monitored to ensure they comply with existing rules. There are also risks if workers use publicly available AI tools such as ChatGPT, which don’t guarantee confidentiality for users.
How AI is really used
We found AI has increased productivity on “low-skill” tasks such as taking meeting notes and customer service, or work done by junior workers. Here AI can help smooth the outputs of workers who may have poor language skills or are learning new tasks.
But maintaining quality and accountability typically requires human oversight of AI outputs. The workers with less skill and experience, who would benefit most from AI tools, are also the least able to oversee and double-check AI output.
In areas where the stakes and risks are higher, the amount of human oversight necessary may undermine whatever productivity gains are made.
What’s more, we found when jobs become primarily about overseeing an AI system, workers may feel alienated and less satisfied with their experience of work.
We found AI is often used for questionable purposes, too. Workers may use AI to take shortcuts, without understanding the nuances of compliance within organizational guidelines.
Not only are there data security and privacy concerns, but using AI to review and extract information can introduce other ethical risks, such as magnifying existing human bias.
In our research, we saw how those risks prompted organizations to use more AI—for enhanced workplace surveillance and forms of workplace control. A recent Victorian government inquiry recognized that these methods may be harmful to workers.
Productivity is tricky to measure
There’s no easy way for an organization to measure changes in productivity due to AI. We found organizations often rely on feedback from a few skilled workers who are good at using AI, or on claims from vendors.
One interviewee told us:
“I’m going to use the word ‘research’ very loosely here, but Microsoft did its own research about the productivity gains organizations can achieve by using Copilot, and I was a little surprised by how high those numbers came back.”
Organizations may want AI to facilitate staff cuts or increase throughput.
But these measures don’t consider changes in the quality of products or services delivered to customers. They also don’t capture how the workplace experience changes for remaining workers, or the considerable costs that primarily go to multinational consultancies and tech firms.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Tech
The Weird, Twisting Tale of How China Spied on Alysa Liu and Her Dad
On November 16, 2021, Matthew Ziburis sat in his car in a residential neighborhood in the Bay Area stalking an “enemy,” as he put it. A veteran of both the US Army and Marine Corps, Ziburis had previously served in Iraq. But on this mission, he was working at the behest of China’s government. The targets that autumn day were American citizens: Arthur Liu and his teenage daughter, Alysa.
Arthur’s personal story was an exemplar of the American Dream. As a university student, he took part in the 1989 pro-democracy movement in China. After the crackdown at Tiananmen Square that year, he fled to the United States, settling in California. Arthur poured a small fortune and an equal amount of energy into molding Alysa into a figure skating phenom. As a national champion at age 13, she bantered along with Jimmy Fallon on The Tonight Show, and was at the time on track to represent America at the Winter Olympics the following year in Beijing.
Ziburis was surveilling the Liu home when he called Arthur, falsely claiming that he was a member of the US Olympic Committee who needed to discuss upcoming travel to Beijing, Arthur says. Ziburis was adamant that Arthur fax him copies of his and his daughter’s passports as part of a travel “preparedness check,” Liu tells WIRED. This struck Arthur as odd. In his many years dealing with sports bodies, he had never fielded such a request. Alysa’s agent did not respond to a request for comment.
Ziburis’ surveillance of Arthur and Alysa Liu that November day five years ago was just one episode in a bizarre saga that spanned from California to Beijing, touched New York City mayors and members of the US Congress, and has seen two people plead guilty and two more awaiting trial.
Unbeknownst to Ziburis, as he sat outside Aurthur and Alysa’s Northern California home, he too was being watched.
Ziburis had allegedly been dispatched to Northern California by Frank Liu, a self-styled fixer in the Chinese community from Long Island, New York, who was in turn receiving orders from a person in China named Qiang Sun. According to US authorities, Sun was working at the behest of the Chinese government. A concerned private investigator who once worked for Frank Liu had alerted the FBI to Frank’s escapades and was assisting authorities. Law enforcement was already on to Ziburis by the time he arrived. Anthony Ricco, Ziburis’ lawyer, did not respond to requests for comment.
Officers watched as Ziburis surveyed Arthur’s home and visited his law office. The heavy-set man sulking around Arthur’s office also caught the attention of a neighbor, who approached Ziburis and asked him if he needed help, Arthur says. Apparently concerned, the FBI called Arthur to warn him that Ziburis was heading to his home. By then, in part because of the harassment, Arthur and Alysa were boarding a plane to fly out of California. “It was like a movie,” Arthur says.
Alysa’s showing in Beijing in 2022 was disappointing. Burned out, she retired from the sport. Then in February, after returning to the ice after a two year hiatus, Alysa became the first US women’s figure skater to win Olympic gold since 2002—intentionally without her father by her side.
Despite her much-publicized complicated relationship with Arthur, Alysa’s success—punctuated by her signature pierced smile, racoon-tail dye job, and palpable joy for her sport—has reignited interest in the long-running case of transnational repression against her and her father. Human rights advocates and researchers have documented in recent years the lengths Beijing has taken to suppress critical voices, even those residing abroad or whose perceived transgressions date back decades.
Tech
There’s New Evidence for How Loneliness Affects Memory in Old Age
Neuroscientists know that there is a link between loneliness and cognitive decline in older adults, although it is still difficult to understand the exact magnitude of the link. A new longitudinal study provides evidence that a proportion of people who feel lonely end up having more memory impairment, though this doesn’t necessarily mean that their brains age faster.
The report, published in Aging & Mental Health, shows that older adults with higher levels of loneliness scored lower on tests of immediate and delayed recall. Even so, the rate at which their memory declined over six years was virtually identical to those who were not lonely.
“It suggests that loneliness may play a more prominent role in the initial state of memory than in its progressive decline,” said Luis Carlos Venegas-Sanabria of the School of Medicine and Health Sciences at Universidad del Rosario, who led the research. “The study underscores the importance of addressing loneliness as a significant factor in the context of cognitive performance in older adults.”
Six-Year Study of Thousands of Single People
The team analyzed data from the Survey of Health, Ageing and Retirement in Europe (SHARE), one of the most robust longitudinal databases for studying aging. For six years, the researchers followed 10,217 adults, aged 65 to 94, from 12 European countries. They assessed their level of loneliness and their performance on memory tests.
The results show that age was the most important determinant of memory level and speed of decline. From the age of 75 onwards, scores began to fall more rapidly. After 85 the decline became more pronounced. Depression and chronic diseases such as diabetes also reduced the initial score. Loneliness, while influencing the starting point, did not accelerate the slope of cognitive decline.
The study also found that physical activity was associated with better initial memory scores. People who engaged in moderate or vigorous physical activity at least once a month recalled more words on immediate and delayed recall tests. This effect did not change the speed of decline, but it did raise the baseline level, which functions as a kind of “cognitive buffer.”
Although the study does not explore the causes of the link between loneliness and cognition, previous research has proposed plausible mechanisms. Loneliness is often associated with less social interaction, a factor that influences cognitive performance. It is also associated with increased risk of depression, which does directly affect memory tests. In addition, lonely people tend to have more health problems, such as hypertension or diabetes, which also affect cognitive function.
By 2050, according to United Nations projections, one in six people in the world will be over the age of 65. Societies are entering a stage where old age will no longer be the exception but will become the norm. Dementia, as well as other neurodegenerative diseases that appear with age, will be a major challenge for health care institutions.
Tech
Managing traffic in space
Chances are, you’ve already used a satellite today. Satellites make it possible for us to stream our favorite shows, call and text a friend, check weather and navigation apps, and make an online purchase. Satellites also monitor the Earth’s climate, the extent of agricultural crops, wildlife habitats, and impacts from natural disasters.
As we’ve found more uses for them, satellites have exploded in number. Today, there are more than 10,000 satellites operating in low-Earth orbit. Another 5,000 decommissioned satellites drift through this region, along with over 100 million pieces of debris comprising everything from spent rocket stages to flecks of spacecraft paint.
For MIT’s Richard Linares, the rapid ballooning of satellites raises pressing questions: How can we safely manage traffic and growing congestion in space? And at what point will we reach orbital capacity, where adding more satellites is not sustainable, and may in fact compromise spacecraft and the services that we rely on?
“It is a judgement that society has to make, of what value do we derive from launching more satellites,” says Linares, who recently received tenure as an associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “One of the things we try to do is approach these questions of traffic management and orbital capacity as engineering problems.”
Linares leads the MIT Astrodynamics, Space Robotics, and Controls Lab (ARCLab), a research group that applies astrodynamics (the motion and trajectory of orbiting objects) to help track and manage the millions of objects in orbit around the Earth. The group also develops tools to predict how space traffic and debris will change as operators launch large satellite “mega-constellations” into space.
He is also exploring the effects of space weather on satellites, as well as how climate change on Earth may limit the number of satellites that can safely orbit in space. And, anticipating that satellites will have to be smarter and faster to navigate a more cluttered environment, Linares is looking into artificial intelligence to help satellites autonomously learn and reason to adapt to changing conditions and fix issues onboard.
“Our research is pretty diverse,” Linares says. “But overall, we want to enable all these economic opportunities that satellites give us. And we are figuring out engineering solutions to make that possible.”
Grounding practical problems
Linares was born and raised in Yonkers, New York. His parents both worked as school bus drivers to support their children, Linares being the youngest of six. He was an active kid and loved sports, playing football throughout high school.
“Sports was a way to stay focused and organized, and to develop a work ethic,” Linares says. “It taught me to work hard.”
When applying for colleges, rather than aim for Division I schools like some of his teammates, Linares looked for programs that were strong in science, specifically in aerospace. Growing up, he was fascinated with Carl Sagan’s “Cosmos” docuseries. And being close to Manhattan, he took regular trips to the Hayden Planetarium to take in the center’s immersive projections of space and the technologies used to explore it.
“My interest in science came from the universe and trying to understand our place within it,” Linares recalls.
Choosing to stay close to home, he applied to in-state schools with strong aeronautical engineering departments, and happily landed at the State University of New York at Buffalo (SUNY Buffalo), where he would ultimately earn his bachelor’s, master’s, and doctoral degrees, all in aerospace engineering.
As an undergraduate, Linares took on a research project in astrodynamics, looking to solve the problem of how to determine the relative orientation of satellites flying in formation.
“Formation flying was a big topic in the early 2000s,” Linares says. “I liked the flavor of the math involved, which allowed me to go a layer deeper toward a solution.”
He worked out the math to show that when three satellites fly together, they essentially form a triangle, the angles of which can be calculated to determine where each satellite is in relation to the other two at any moment in time. His work introduced a new controls approach to enable satellites to fly safely together. The research had direct applications for the U.S. Air Force, which helped to sponsor the work.
As he expanded the research into a master’s thesis, Linares also took opportunities to work directly with the Air Force on issues of satellite tracking and orientation. He served two internships with the U.S. Air Force Research Lab, one at Kirtland Air Force Base in Albuquerque, New Mexico, and the other in Maui, Hawaii.
“Being able to collaborate with the Air Force back then kind of grounded the research in practical problems,” Linares says.
For his PhD, he turned to another practical problem of “uncorrelated tracks.” At the time, the Air Force operated a network of telescopes to observe more than 20,000 objects in space, which they were working to label and record in a catalog to help them track the objects over time. But while detecting objects was relatively straightforward, the challenge came in correlating a detected object with what was already in the catalog. In other words, is what they were seeing something they had already seen?
Linares developed image analysis techniques to identify key characteristics of objects such as their shape and orientation, which helped the Air Force “fingerprint” satellites and pieces of space debris, and track their activity — and potential for collisions — over time.
After completing his PhD, Linares worked as a postdoc at Los Alamos National Laboratory and the U.S. Naval Observatory. During that time he expanded his aerospace work to other areas including space weather, using satellite measurements to model how Earth’s ionosphere — the upper layer of the atmosphere that is ionized by the sun’s radiation — affects satellite drag.
He then accepted a position as assistant professor of aerospace engineering at the University of Minnesota at Minneapolis. For the next three years, he continued his research in modeling space weather, tracking space objects and coordinating satellites to fly in swarms.
Making space
In 2018, Linares made the move to MIT.
“I had a lot of respect for the people and for the history of the work that was done here,” says Linares, who was especially inspired by the legendary Charles Stark “Doc” Draper, who developed the first inertial guidance systems in the 1940s that would enable the self-navigation of airplanes, submarines, satellites, and spacecraft for decades to come. “This was essentially my field, and I knew MIT was the best place to continue my career.”
As a junior faculty member in AeroAstro, Linares spent his first years focused on an emerging challenge: space sustainability. Around that time, the first satellite constellations were launching into low-Earth orbit with SpaceX’s Starlink, which aimed to provide global internet coverage via a huge network of several thousand coordinating satellites. The launching of so many satellites, into orbits that already held other active and nonactive satellites, along with millions of pieces of space debris, raised questions about how to safely manage the satellite traffic and how much traffic an orbit can sustain.
“At what level do we reach a tipping point, where we have too many satellites in certain orbital regimes?” Linares says. “It was kind of a known problem at the time, but there weren’t many solutions.”
Linares’ group applied an understanding of astrodynamics, and the physics of how objects move in space, to figure out the best way to pack satellites in orbital “shells,” or lanes that would most likely prevent collisions. They also developed a state-of-the-art model of orbital traffic, that was able to simulate the trajectories of more than 10 million individual objects in space. Previous models were much more limited in the number of objects they could accurately simulate. Linares’ open-source model, called the MIT Orbital Capacity Assessment Tool, or MoCAT, could account for the millions of pieces of space debris, in addition to the many intact satellites in orbit.
The tools that his group has developed are used today by satellite operators to plan and predict safe spacecraft trajectories. His team is continuing to work on problems of space traffic management and orbital capacity. They are also branching out into space robotics. The team is testing ways to teleoperate a humanoid robot, which could potentially help to build future infrastructure and carry out long-duration tasks in space.
Linares is also exploring artificial intelligence, including ways that a satellite can autonomously “learn” from its experience and safely adapt to uncertain environments.
“Imagine if each satellite had a virtual Doc Draper onboard that could do the de-bugging that we did from the ground during the Apollo missions,” Linares says. “That way, satellites would become instantaneously more robust. And it’s not taking the human out of the equation. It’s allowing the human to be amplified. I think that’s within reach.”
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