2026-07-20 21:00:01
In the chill of a London spring night, under overcast skies, iconic Trafalgar Square opens around me. Admiral Nelson rises on his pedestal, the National Gallery rests behind, the church of St Martin-in-the-Fields sits nearby. From the 13th century, the site served as the Royal Mews for hawks and then horses. By 1844, it was a public space at the heart of one of the biggest cities in the world.
Despite the square’s presence through that grand sweep of history, it’s not why I’m here. My interest is far more specific: I want to find out what happens to spaces like this when artificial light, specifically from light-emitting diodes (LEDs), intrudes. My companion tonight is Simon Thorp, a local lighting designer, who crouches in the shadows near Nelson’s spire, light meter in hand. “Two lux,” he reports, “and it’s very comfortable here.” Two lux is 10 to 20 times the illuminance of a full moon. We can see each other clearly, and a nearby sign assures us that closed-circuit television (CCTV) is in operation for safety’s sake.
Trafalgar Square captures the relationship between lighting and darkness that exists in almost every city, suburb, small town, and village around the world. The lighting here is a mishmash of old technologies and new, of shadow and glare, the ornamental gas lamps fronting the National Gallery all but washed out by the LEDs inside modern versions of traditional “brass and glass” fixtures a few meters away—21st-century technology housed in 19th-century designs.
The ugly truth of artificial lighting today is that in parts of London, as in many cities around the world, lighting levels are excessive, with unshielded illumination blasting in all directions. And the irony is that too much light invites danger: It creates shadows, impedes our vision, and gives the illusion, without the reality, of safety. Thorp notes that modern CCTV cameras are “pretty great” even at low-light levels, while harsh light makes it hard for both human eyes and digital sensors to see.
“The more bad light we add, the more bad light we think we need,” says Thorp. “We can’t see because of the light we’ve added. And it makes areas that were perfectly okay seem darker.”
Among the costs of this excess, the most alarming may be its toll on human health (and that of other animals) by disrupting circadian rhythms, impeding the production of melatonin, and contributing to sleep disorders that are tied to every major modern disease. New research shows that increased exposure to blue light from LEDs is having “substantial biological impacts” such as suppression of the sleep hormone melatonin and an increased risk for obesity, certain cancers, and type 2 diabetes.
I have come to London and Paris—which led the way in the expansion of public street lighting in the 19th century—because they embody both the current enormity of the problem as well as a future certain to be lit by trillions of chips: controllable, tunable, and energy-efficient LEDs.
The standard justification for nighttime illumination is public safety. Lighting experts’ term for the phenomenon is “artificial light at night.” While people won’t often admit it, the desire for light at night seems to stem from a primal fear of the dark. Darkness is where the bad guys hide. And if dark is bad and light is good, then more light can only be better.
This assumption has guided our use of nighttime light for hundreds of years. And yet, high-lumen output doesn’t necessarily correlate to a reduction in crime, research has found. In other words, if we relied on the data as much as we do our primal anxieties and paused those anxieties long enough to learn how light and darkness interact, our nights would almost certainly be lighted differently—especially now that we have the extraordinary technology that is the light-emitting diode.
The first visible red light-emitting diode was invented by Nick Holonyak in 1962, but LED lighting technology took several decades to develop, before exploding in recent years. Just a decade ago, LED streetlamps were rare. By 2019, more than half of U.S. streetlights were LEDs, and that number is predicted to top 90 percent by 2030. Similar uptake has occurred around the world, even in developing countries, where inexpensive Chinese-made LED fixtures are increasingly common.
This rapid global migration to LEDs represents a shift in the fundamental physics of how we illuminate our world. From oil lamps and candles to gas lamps, early examples of artificial light at night relied on a burning wick, an incredibly inefficient way to create light. An incandescent bulb is effectively a heater that happens to produce light as a by-product, so it squanders nearly all of its energy as heat.
By contrast, LEDs use semiconductors to convert electricity into light. Through this process of electroluminescence, LEDs use up to 90 percent less energy than incandescent bulbs do, which has enabled municipalities to realize an immediate energy savings of 50 percent or more. This fact alone has fueled the technology’s worldwide adoption.
But LEDs aren’t just more efficient and less expensive. The use of solid-state technology gives the lights an extraordinary life-span, often measured in decades rather than years. This significantly lowers the maintenance costs, as city workers spend far fewer hours replacing broken or burned-out lights. Even more striking, by manipulating the properties of the semiconductor material, engineers can dictate the precise color and intensity of the output, something that gives LEDs incredible versatility. For a lighting designer like Thorp, LEDs offer countless possibilities.
Wandering from Trafalgar Square along the edge of St. James’s Park, Thorp and I find ourselves near Westminster Bridge, one of nine city bridges that in 2021 were part of the Illuminated River project, meant to make the Thames more beautiful at night. Each bridge now features a new LED lighting scheme that moves and changes color and intensity to create a coordinated work of art. But Thorp is frustrated that the project did nothing to correct the often glary lighting on the riverbanks. “Why don’t you pay the money to correct all of this bad lighting instead of adding new lighting?” he says. LED technology, he points out, has the potential to fix that problem.
In fact, this may be the most meaningful potential of LEDs: the ability for a community to control when, where, at what levels, and in which colors its lights shine. A public space like Trafalgar Square could be lit more brightly during rush hour, then dimmed as the night progresses, the lights not only connected to one another but to the surrounding streetlights and commercial lights. Anywhere in the world, LED streetlights could be programmed to rise and fall in brightness depending on the time of night or time of year. They could even be turned off during bird migrations, to reduce the number of birds that are disoriented by the lights and ultimately killed in collisions with reflective and illuminated windows.
Up to now, LED public lighting has largely not been part of any comprehensive plan to curtail and control nighttime illumination. Most LED installations have simply replaced older, inefficient “dumb” electric lighting with newer “dumb” LEDs and thus made light pollution worse. The main reason? Because LED lighting is cheaper, we tend to use more of it—a literally shining example of the Jevons paradox. Even as awareness of light pollution grows, we aren’t yet taking advantage of LED technology’s full potential.
The good news is that we could start tonight. At DarkSky International, the world’s foremost organization fighting light pollution, CEO and executive director Ruskin Hartley tells me the organization has five principles for responsible outdoor lighting: It should be useful, targeted, low level, controlled, and warm-colored. “They’re enabled because of the capabilities of LEDs,” Hartley says.
The technology to control LEDs will be part of the solution. In the olden days of the analog era, a streetlight was either on or off. To change a lighting schedule, you had to physically rewire a circuit. Today, the Digital Addressable Lighting Interface (DALI) protocol turns each luminaire into part of a network, with its own digital address and a driver that reports to a central server. With DALI, the lighting is managed through software rather than physical switches.
Unfortunately, most LED streetlights have been deployed without this technology because it costs more. But Paul Drosihn, general manager of the DALI Alliance, says that using such controls makes the LEDs much easier to maintain. Before the new digital protocol, it took an average of nearly three visits to identify, diagnose, and repair a defective luminaire. “Now it’s one,” Drosihn says. “Saving the cost of sending two guys on a cherry picker to replace those [lights] is immeasurable. What I just described to you is pretty much all the utilities need to know.”
What’s more, DALI allows the tuning of the lights’ spectrum so that they become warmer and less disruptive as the night progresses. Digitally connected, full-spectrum luminaires allow cities to transform the nocturnal experience—saving money, increasing health and safety, and creating a warmer and more appealing atmosphere at night.
This digital intelligence is equally transformative for the “bleed lighting” that spills from building interiors, Drosihn says. “You don’t think of night lighting as coming from inside buildings, but it does,” he says. “Particularly in the States, you drive through any major city and all the lights are on, on every floor of every high-rise, even if no one is home.”
Already, the use of digital controls for interior lighting has become commonplace in some European cities, Drosihn says. By integrating DALI with occupancy sensors and building-management systems that monitor HVAC, electrical systems, and security networks, a skyscraper can become a dynamic participant in the urban environment—dropping a floor’s interior lights to zero the moment the last person leaves. As a result, electronic controls combined with LEDs can act like a dimmer switch for a city’s entire skyline.
With all the possibilities from LED technology, why are our nights too often lit with harsh and clinical light, casting glare and creating shadows, disrupting human and ecological health, erasing the stars from our skies? The answer starts with the color of LEDs. At first glance, an LED streetlight looks like a collection of small white bulbs, but it’s not. To produce a light we perceive as white, most manufacturers coat a blue semiconductor core with a yellow phosphor material that absorbs a portion of that high-energy blue light. The problem is that this “white” light is still heavily blue, which is exactly the color no species has evolved to expect at night.
And because blue light is the second most energetic part of the visible spectrum (violet is the most), it doesn’t just illuminate our streets and invade our homes. Blue light also scatters in the atmosphere more easily than any other color, which helps to create the hazy, illuminated fog known as sky glow over every city of any size.
“Cooler” colored LEDs in the 4,000- to 6,500-kelvin range offer the most lumens at the lowest cost, so most early adopters installed these blue-rich white lights. The good news is that LED technology has continued to advance, and a growing number of communities are choosing warmer-colored streetlights that have less blue. (Phoenix, for example, converted 100,000 streetlamps to 2,700 K LEDs in 2020.) And, of course, light pollution isn’t just a result of LEDs. Older lighting technology also adds to the glare—bright white metal-halide lights, especially—and cities are loath to replace something that isn’t yet broken.
But our main failure isn’t a technical one. It’s that we have yet to revise our thinking about lighting at night. We use LED technology just as we did the old sources of light. As a result, we have largely offset the gains that were promised in terms of reducing energy consumption and carbon emissions by making light pollution worse, and have so far let an incredible opportunity go unrealized.
Across the Channel in Paris, the failure to realize the potential of LEDs feels even more palpable. Unlike London, which suffered heavily from German bombs, the lovely 19th-century Paris that Baron Haussmann created largely escaped destruction in World War II. To nearly 50 million annual tourists, the beautiful uniformity of the architecture is instantly recognizable. But the City of Light’s nocturnal atmosphere is also part of the draw, and extensive attention has been given to relighting its buildings and monuments. When I wander into the Cour Carrée in the Louvre, for example, I’m stunned by rows of amber LEDs that together create a warm glow along the palace facades. When I see the Eiffel Tower, first from a distance walking along the Seine and then up close, I find myself staring as I would at a campfire, the structure’s metalwork amber-lit with more than 336 high-pressure sodium bulbs.
Still, the city’s night lighting is far from perfect. With millions of residents, thousands of stores and restaurants, and 300,000 streetlights, the city overall is among the world’s brightest. Even at the base of the Tower, bright white LED lamps illuminate the African émigrés selling cheap berets and Tour de France trinkets. And the city has been replacing its old sodium streetlights with new LEDs, swapping the warm yellow tones for which the city has long been known for bright white lamps no one wants to look at.
Nonetheless, the potential is here. In 2019, France introduced a nationwide law to reduce levels of light pollution, setting rules about both public lighting (preventing light from being projected above the horizontal) and private lighting such as stores, which are required to turn off their exterior and shop window lights after 1 a.m. In addition, an increasing number of French communities dim or turn off municipal lights after midnight to save energy and reduce carbon emissions. Although light pollution worldwide continues to increase by nearly 10 percent per year, France has managed to reduce its overall level. Chloé Beaudet, a researcher at Université Paris-Saclay, documented local light-reduction measures and found people generally agreed with the notion of dimming or turning off the lights, mainly for energy savings and ecological concerns.
“What I find is that people living in urban areas, they accept this kind of policy,” she tells me. “They’re like, okay, I don’t really use public space at night as a pedestrian, so what’s the point of having lights on?” For her, a key takeaway is that one lighting level does not fit all areas. “I think there is really a need for policy that is differentiated according to the neighborhood.”
In another positive development, the country has been minimizing artificial light to create ecological corridors designed to protect nocturnal species such as birds, bats, and insects. These corridors are connected and dark, mitigating the disruption to the 30 percent of vertebrates and more than 60 percent of invertebrates that are nocturnal. Even for city dwellers, this trame noire (“dark infrastructure”) helps to raise awareness of why controlling light pollution is important for life on Earth. Nationwide laws to control light pollution, the ability to light different parts of a city differently, dark corridors to protect biodiversity—these are exactly the kind of changes made possible with LEDs.
That’s not all. Almost until 1920, astronomers at the Paris Observatory were still gazing at the Milky Way. That’s impossible nowadays, but it could happen again. Despite the bright white LED streetlights now lining so many Paris streets, networks of LEDs using controls could lower lighting levels enough each night, so that the Milky Way could once again be visible over the French capital. And in the process, Paris could become the City of Light in ways that would set an example for other parts of the world.
“I think we should aspire to have cities that see the stars,” Simon Thorp says when I mention this view of Paris. “You just need everything to be coordinated.”
Nearing the end of our London walk, having turned from the river and back up toward the Strand, Thorp brings me down narrow Carting Lane behind the Savoy Hotel, to where a gas fixture tops a thick lamppost, an original from 1870. A small plaque reads, “The last remaining sewer gas destructor lamp in the city of Westminster.” Thorp explains that the thick pole hides a tube that allowed methane from the sewers to get burned off at the mantle. “An early example of renewable energy,” he jokes.
The fire-orange flame is pleasing to the eye. But even here, on a narrow lane with no vehicle traffic, in a touristy area of the city, the flame is overwhelmed by a nearby, unshielded LED security light. Thorp shakes his head. “It’s stunning that someone could put in a light like that and think, ‘Great, nice job.’”
Here is the crux of contemporary artificial lighting at night. We know how to light well, and LEDs give us the ability to do so. But while our technology is 21st century, too often our thinking about light and darkness, safety and security, is stuck in the past. We could be doing so much more with this technology than we are. We could relight our nights in ways that would not only reduce energy and maintenance costs but also bring a slew of benefits, including healthier nights for humans, safer skies for nocturnal creatures, and a restoration of the stars.
In 2026, the tale of these two cities and their artificial light at night is that of a brilliant technology that we’ve engineered but haven’t yet learned to master. In short, we have yet to change the way we think about artificial light at night and to use it more thoughtfully and carefully—as we might, as one hopes we will.
2026-07-18 02:00:01
Like many engineers, Sarah Downs says she knew she wanted to pursue a STEM career from a young age. As a teenager, she discovered robotics through her Tulsa, Okla., middle school’s First Lego League team, and she fell in love with the field, she says. Downs participated in the international robotics program from 2014 to 2016.
Watching PBS specials on NASA Mars rovers Spirit and Opportunity, and seeing the live broadcast of the Curiosity rover launch in 2011, inspired the teen to dream of a career working with NASA.
MEMBER GRADE
Graduate student member
UNIVERSITY
Texas A&M University in College Station
MAJOR
Electric engineering
This year the IEEE graduate student member achieved that dream. For her final project as a master’s degree candidate in electrical engineering at the University of Tulsa, she worked on an algorithm in collaboration with NASA and the U.S. Air Force.
The algorithm she developed enables a robot assembling satellites in space to insert an antenna into the correct spot, addressing robotics’s classic peg-in-hole problem of inserting an object into its corresponding hole.
Now a Ph.D. student in electrical engineering at Texas A&M University in College Station, Downs is continuing her research on satellite assembly and manipulation “but on a much larger scale,” she says.
Downs grew up in the Tulsa area. Her father, who died from a heart attack in 2015 when she was 13, was a safety advisor in the oil and gas industry. Her mother stayed home to take care of her brother, who has autism. After her father died, her mother went back to college to earn a bachelor’s degree in business so she could support the family.
“We didn’t have much income, and my mom was always worried about money,” Downs says. “That made me more aware of having a successful career, in a monetary sense.”
From then on, whenever she considered her future career, having a decent salary to support the family was high on her list.
By pursuing a career in robotics, she says, she can follow her passion while obtaining financial security.
In high school, Downs joined the First robotics club, where she found herself drawn to the electrical components used in the machines she and her classmates built.
During her final two years of high school, she participated in an extension program at Tulsa Tech, a training school. She spent half her day in high school classes and the other half taking engineering courses at the vocational school.
After graduating in 2020, she accepted scholarships to attend the University of Tulsa. She began her freshman year at UTulsa not knowing whether she wanted to major in electrical or mechanical engineering, she says, adding that her love of working with small systems helped her choose EE.
For her senior year capstone project, she and two of her classmates designed a lunar lander exhibit for the Tulsa Air and Space Museum. They created an interactive game that simulates missions on lunar and martian surfaces. Four celestial bodies—the moon, Venus, Mars, and Titan—are listed across three computer monitors. Using a game controller, museum visitors can explore the virtual surface of each one. The exhibit is still on display.
Downs earned her bachelor’s degree in electrical engineering in 2024 and continued her education at the university’s EE master’s degree program.
When Downs began her graduate studies, she was supposed to be part of a NASA robotics project for two years. But when a delay in government funding postponed the project’s start, she instead spent her first year in the school’s Institute for Robotics and Autonomy, then newly launched. Its main focus is developing robots to assist people who have mobility challenges.
Inspired by her grandmother, who was wheelchair-bound due to severe arthritis, Downs developed a robotic arm that helps older people and wheelchair users live independently. The arm was able to identify and place objects in the appropriate locations inside the home, such as unloading certain groceries from a shopping bag and placing them on a shelf or in separate containers.
Before the start of her sophomore year in 2025, the NASA project finally secured government funding. She developed a robot that achieves the peg-in-hole task without using any vision systems. Typically, cameras help guide robots’ satellite-assembly work. But in the harsh, remote environment of outer space, cameras might malfunction or encounter delays.
“Don’t stop asking questions. Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.”
Rather than using cameras, Downs’s robotic arm deploys a force-based insertion process to sense position and orientation of objects in the arm’s environment. The robot loosely grips an antenna and, with a torque sensor on its gripper, “feels” the force feedback of where the satellite and antenna are in relation to each other. The robot then guides the antenna assembly into a target opening on its satellite and maintains the position during adhesion.
Adding to the complexity, the robot performs its task in zero gravity.
“Without gravity, you now have to consider the arm’s reaction torques on the satellite to avoid flinging it into space,” Downs says. Any motion from the arm during the insertion process, especially from increased forces, could cause the satellite to continue movement in that direction.
To combat that, Downs is performing calculations for the project to direct targeted reverse thrusts and counter the force of the robot’s motions.
Her graduate project captures the simple yet complex nature of robotics that she finds fascinating, she says.
“I think robots are both more and also less complicated than people think,” she says. “Really, all you need to start programming a robot is its Denavit-Hartenberg parameters, and you can do a lot with that,” she says, referencing the four values used to describe the position and orientation of a robotic arm and manipulators. Even with different grippers and degrees of freedom, “fundamentally, all robot manipulators start there,” she says.
“But,” she adds, “we’re still learning so much about how robots interact with their environment. Even something simple to us, like manipulating a pen, is still incredibly complex for robots.”
Downs is completing her doctoral thesis in the Robotic Space Simulator project at Texas A&M’s Robotics and Automation Design (RAD) Lab, which specializes in developing machines that can survive in extreme environments. It collaborates with NASA.
Her thesis advisor is Robert Ambrose, a NASA veteran who launched the RAD Lab in 2022. The IEEE member is set to serve as associate director of the school’s Space Institute, due to open this year in Houston. The research facility is being built next to the Johnson Space Center.
After earning her Ph.D., Downs says, she hopes to one day work for NASA, developing rovers that collect samples from Mars or robotic arms that perform tasks on space stations.
To learn more about robots, check out IEEE Spectrum’s guide.
Downs joined IEEE in 2020 as a freshman at UTulsa to get more involved in electrical engineering events on campus. At the time, the COVID-19 pandemic kept clubs and organizations from meeting in person.
She was active in her school’s IEEE student branch and was elected as its 2022–2024 president. Under her leadership, the branch went from having a few events to hosting one every two weeks.
They included lunch-and-learn sessions and dinners that connected students with professional engineers and the university’s alumni. Downs also organized hands-on workshops on soldering, 3D printing, CAD modeling, and résumé-building.
Her efforts helped increase the branch’s executive board membership from roughly five students to 25 in 2023. The same year, her soldering workshop attracted about 80 students.
She says she enjoyed working with IEEE, especially “engaging with alumni and learning from engineers.”
IEEE is a great resource for networking opportunities, she says, noting that “during the COVID-19 pandemic, engineering students stayed in their bubbles.” IEEE events helped the students make connections that could serve them well, she says.
“Networking is very important, especially in today’s tough job market,” she says. “It’s a lot about who you know and how people observe your work ethic.”
Downs, who now serves as an IEEE graduate advisor for UTulsa’s student branch, says she has seen firsthand how the school’s student branch network has benefited its student members.
“A lot of them have found jobs” because of IEEE, she says.
As the IEEE graduate advisor for UTulsa’s student branch, Downs noticed that many engineering undergraduates finish college without any hands-on experience, whether it be a project or an internship.
“Their résumés are very sparse, and they have no proof of their technical skills,” she says. She herself completed a facilities engineering internship at Tulsa International Airport’s American Airlines maintenance facility after her sophomore year of college. And she was an electrical engineering intern at Flight Safety International outside Tulsa after her junior year and after she graduated. The company designs, builds, and maintains its own flight simulators.
Her advice to undergraduates is to hone and demonstrate both their hard and soft skills by working on research projects or even personal passion projects.
“A Raspberry Pi doesn’t cost that much, and you can start working with that immediately,” she says. Students also can take part in engineering interest groups and professional organizations at their school, she adds.
“Put yourself out there and join a research team,” she says. “It’s a great way to show people that you’re a good person to work with and you’d do a good job in the field.”
She adds that it’s also a fine way to keep learning—which is what drew her to a field that has developed only within the past century.
“We’re still constantly learning about robots,” she says.
“Don’t stop asking questions,” she advises students. “Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.”
2026-07-16 20:00:01
In the eastern Indian Ocean, south of Java in the vast sea stretching toward Australia, a fishing vessel slightly alters its course while operating near the boundary of its authorized fishing ground. Nothing appears unusual on deck. Nets remain in the water. Engines maintain a steady speed. To the crew, it is an ordinary day at sea.
Yet hundreds of kilometers above, satellites continuously record the vessel’s position. At Indonesia’s Marine and Fisheries Resources Surveillance Station, in Cilacap, where I work, a monitoring platform receives the signal and automatically compares it against fishing permits, designated fishing grounds, vessel characteristics, and historical movement patterns. Within minutes, the system identifies a potential violation. Before any patrol vessel leaves port, before any inspector boards a vessel, and before any warning is issued, we have begun enforcement.
This transformation reflects a profound shift in maritime governance. The ocean has historically been opaque to regulators. States could only enforce laws where patrol vessels happened to be present. Today, however, integrated systems combining data from vessel monitoring systems (VMS), satellite remote sensing, geospatial analytics, and increasingly sophisticated data-processing tools are making marine activity visible at an unprecedented scale. Global Fishing Watch alone tracks hundreds of thousands of vessels worldwide, generating a near real-time picture of fishing activity across the world’s oceans.
Indonesia has emerged as one of the most ambitious examples of this transition. As the world’s largest archipelagic state, managing more than 6 million square kilometers of maritime space, Indonesia faces a challenge familiar to many coastal nations: There are never enough patrol vessels. Digital surveillance is a practical necessity that makes my job possible, even as it creates new challenges.
The international legal framework governing the oceans was designed in an era when maritime enforcement depended almost entirely on physical presence. The United Nations Convention on the Law of the Sea (UNCLOS), adopted in 1982, assumes that states exercise authority through patrols, inspections, vessel boardings, and direct observation.
For countries with extensive coastlines and limited enforcement resources, this model has always faced practical constraints. Indonesia’s Fisheries Management Areas (WPP-NRI) span waters ranging from the Indian Ocean to the Pacific and from the Strait of Malacca to the maritime boundaries adjacent to Australia and Papua New Guinea. Monitoring such a vast domain solely through patrol operations is both expensive and operationally impossible.
Beginning in the late 2010s, Indonesia accelerated the integration of satellite-based monitoring into fisheries enforcement. Vessel monitoring systems became a cornerstone of this strategy. By early 2026, a total of 9,394 Indonesian fishing vessels were actively transmitting through the national VMS, representing an increase of 2,880 vessels during the 2021–2025 period. As part of Indonesia’s broader maritime surveillance architecture, VMS data are complemented by satellite remote sensing and other monitoring tools to help identify suspicious activities involving vessels operating without active transponders or outside the national VMS network.
Indonesian fisheries officials plan fishery patrols using data from tracking devices, satellites, and their understanding of the patterns of illegal fishing.Indonesian Ministry of Marine Affairs and Fisheries
The implications extend far beyond vessel tracking. Continuous digital monitoring enables authorities to reconstruct vessel movements, identify suspicious behavioral patterns, detect unauthorized fishing activity, and verify compliance with licensing conditions. Rather than waiting to discover violations during patrol operations, regulators can increasingly prioritize inspections based on data-derived risk assessments.
Maritime governance is shifting from reactive enforcement toward predictive oversight.
The expansion of surveillance infrastructure has already generated measurable enforcement outcomes.
The Ministry of Marine and Fisheries Affairs Indonesia imposed 2,550 administrative sanctions during 2025, many involving violations detected through the vessel monitoring system, including fishing outside authorized fishing grounds and deliberate deactivation of monitoring transmitters.
This statistic is significant because many of these violations would have been extremely difficult to detect under traditional patrol-based enforcement. A vessel that briefly crosses into a prohibited fishing zone may never encounter an enforcement vessel. Likewise, a captain who temporarily disables a transmitter may escape detection if oversight depends solely on physical inspections.
Digital monitoring fundamentally changes this equation. Every vessel movement creates a data trail. Authorities can reconstruct routes, identify anomalous behavior, and compare activities against permit conditions long after the event itself has occurred.
The first quarter of 2026 demonstrates the scale of this surveillance capability. During just three months, Indonesia’s fisheries monitoring system tracked 14,571 fishing vessels, 182 fishing gear units, and 208 registered home ports while identifying 491 suspected violations across the country’s fisheries management areas. These violations included unauthorized fishing grounds, illegal high-seas operations, transshipment-related offenses, port-base discrepancies, licensing irregularities, and indications of poaching.
Such numbers reveal a fundamental transformation. Enforcement is no longer limited by the number of patrol vessels available at sea. Instead, surveillance capacity increasingly depends on the ability to collect, process, and interpret big data.
Yet greater visibility does not eliminate illegal fishing. But it does change how poachers operate.
Indonesia’s expanding digital surveillance network, and a 2023 requirement that even small vessels use VMS when 12 nautical miles offshore, appears to have improved compliance among licensed fishing vessels. However, as enforcement capabilities become more sophisticated, some actors engaged in illegal fishing have also become more adept at exploiting technological and operational gaps.
Deliberately disabling VMS transmitters remains one of the most common enforcement concerns. While temporary signal losses, whether intentional or caused by technical failures—can complicate the reconstruction of vessel movements, they do not necessarily prevent authorities from detecting potentially illegal activity. Indonesia increasingly combines VMS with satellite-based observations, other maritime surveillance systems, intelligence-led analysis, and reports from community-based surveillance groups (Pokmaswas) to corroborate suspicious behavior and direct patrol resources where they are most needed. This layered approach—integrating digital technologies with local knowledge from coastal communities—helps reduce opportunities for illegal, unreported, and unregulated (IUU) fishing even when a single monitoring system is compromised.
A compromised surveillance network could potentially disrupt enforcement operations just as effectively as a vessel evading patrol detection.
As digital surveillance expands, one lesson from Indonesia’s experience is that stronger monitoring does not eliminate illegal fishing—it changes how illegal operators behave. Improved compliance across much of the fishing fleet has been accompanied by increasingly sophisticated attempts by a smaller group of offenders to avoid detection. This reflects a broader reality of technology-enabled enforcement: As monitoring capabilities evolve, so do the strategies used to circumvent them.
The result is a technological arms race. Every improvement in surveillance capability encourages new methods of avoidance, whether through disabling tracking devices, manipulating vessel identities, or exploiting gaps between different monitoring systems. Enforcement agencies must therefore continuously refine their analytical methods, integrate multiple sources of maritime information, and adapt their operational strategies to keep pace with evolving behavior at sea. Effective digital fisheries governance is not defined by a single technology but by the ability to combine data, human expertise, and operational intelligence into a resilient and adaptive enforcement system.
The future of fisheries enforcement may ultimately depend less on detecting vessels and more on ensuring confidence in the digital systems that generate enforcement decisions.
As surveillance networks become increasingly integrated, questions surrounding cybersecurity, algorithmic accountability, and data integrity become more important. What happens if vessel tracking data are manipulated? How should authorities verify automated risk assessments? What safeguards exist when enforcement actions increasingly originate from algorithmic analysis rather than direct human observation?
These questions are no longer theoretical.
Modern fisheries governance increasingly depends on interconnected networks of satellites, communication systems, databases, cloud infrastructure, and analytical platforms. While these technologies dramatically improve visibility, they also create new vulnerabilities. A compromised surveillance network could potentially disrupt enforcement operations just as effectively as a vessel evading patrol detection.
For Indonesia, this means that investment in digital surveillance must be accompanied by investment in digital resilience. The effectiveness of a monitoring system ultimately depends not only on the volume of data collected but also on the credibility, security, and reliability of the information produced.
Indonesia’s experience illustrates a broader global transformation in maritime governance. The ocean is becoming increasingly transparent to regulators. Activities that once occurred beyond the reach of enforcement agencies can now be observed, analyzed, and investigated through interconnected digital systems.
The benefits are substantial. Expanded VMS adoption, improved monitoring coverage, and thousands of administrative enforcement actions demonstrate that digital surveillance can significantly enhance fisheries governance. Yet the transition also introduces new challenges involving data quality, cybersecurity, algorithmic accountability, and adaptive criminal behavior.
The central question facing maritime regulators is how governments can ensure that increasingly powerful monitoring systems remain transparent, secure, and accountable while preserving public trust and legal legitimacy. The most important lesson may be that digital surveillance does not replace traditional enforcement. It changes where enforcement begins. For generations, maritime law enforcement started when a patrol vessel encountered a suspected violator. Today, it often starts when an algorithm detects a pattern.
That shift may prove as significant for ocean governance as the invention of radar was for maritime navigation.
2026-07-16 03:52:41
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Software engineers have some of the shortest tenures of any white-collar profession. The average software engineer stays at a company for roughly two years, about half as long as workers in most other knowledge professions. The layoffs of the past few years have certainly highlighted this instability, but it was already there.
This isn’t an essay about a broken job market though. Rather, it’s about how to turn that instability to your advantage, which is something I’ve spent the last decade doing on purpose.
I switched careers into software in my 30s. I had a stable job at a community college, complete with a union and a pension. It was about as secure as a career gets, and I learned to program on the side.
Then I did something nearly everyone in my life considered reckless: I quit, leaving the secure job to become a junior developer at 31. My own mother was skeptical. I took the riskier job anyway, for two reasons: It was the work I actually wanted, and I could see potential.
My first development job was at a grocery retailer. Good people and a company I liked. But I kept meeting engineers earning twice my salary for the same work. In the San Francisco Bay Area, surrounded by some of the best engineering talent in the world, I realized my skills were stagnating.
So I left for a small startup. I learned more in nine months than I had in the previous two years, and my salary doubled.
Over the years I’ve come to treat career risk as something to manage deliberately. It falls into two categories.
The first type of risk involves the job itself: Bet on yourself by striving for better roles and opportunities.
Job-hopping for money alone isn’t wrong, especially early on. But the returns shrink after the first few hops, and the stress of chasing a slightly bigger paycheck every year will wear you down.
There’s another career risk with rewards that compound: Seeking positions to work alongside the strongest engineers.
You might struggle to keep up. You might even get laid off. But the skills you absorb working alongside people better than you are the ones that create durable stability. You build marketable expertise, you see how different organizations actually operate, and every project becomes another tool you carry to the next opportunity. Working next to stronger engineers is a proven way to increase your own expertise.
If that feels too big, try volunteering for a project you have no idea how to do. The risk is that you fail in front of people. The reward is a new skill and a resume line that opens the next door.
Compare that with the “safe” path.
You stay at one company, assuming loyalty will be rewarded. It usually isn’t. And when you finally leave, by choice or not, you may find the skills you built are worth little on the open market. You might be the in-house expert in an aging tech stack while employers are hiring for more cutting edge technologies. Suddenly you’re competing against people with half your experience.
You could be taking on a risk you didn’t notice.
The second form is risking your time, which means betting on trends.
Some trends are non-negotiable. If you’re a software engineer, then cloud services, ReactJS, and AI are mainstream enough that ignoring them actively damages your career. A backend engineer who refuses to learn cloud architecture is volunteering for obsolescence.
The real gamble is with the smaller trends: the niche tools you stumble onto and find quietly interesting, with no idea whether they’ll matter.
About two and a half years ago, I learned about retrieval-augmented generation (RAG). Almost no one in my circle was talking about vector databases, a central piece of RAG. Today RAG is close to mainstream, and for once, I had the early-adopter advantage.
Most of these bets don’t pay off. But when one turns into a major trend, you’re already on the ground floor. Right now I’m making the same bet on voice AI. It isn’t mainstream. It may never be. But if it becomes the next thing, I’m already there, building a foundation.
Counter-intuitively, job-hopping and betting on trends gave me the thing I was after the whole time: stability. I’ve rarely struggled to find work, because every risky move stacked skills the market actually wanted.
If you feel stable and comfortable right now, enjoy it. But ask yourself whether you’re still learning. Because if you’re not, the comfortable choice and the dangerous one may have converged.
The goal isn’t to avoid the open market forever. It’s to make sure that when you land on it, you’re not at its mercy.
By Brian Jenney
P.S. Don’t forget to submit questions about your career or an upcoming decision that you want advice about here!
—Brian
Until recently, human mathematicians have been central to creating new proofs, even when the work relies on massive computational resources. AI is now challenging that status quo. Writer Benjamin Skuse surveys the ongoing debate in the field about the role of AI, and the existential questions mathematicians have about their own careers. If AI mathematicians surpass human knowledge, could these researchers become “priests to oracles”?
A new partnership between UCLA and five major semiconductor companies is the latest program aiming to bridge the gap between industry and academia. The US $125 million university-industry hub is meant to strengthen collaboration and speed up the R&D process to help meet AI’s fast-paced hardware demands.
True mentorship is far more than friendly advice. This key leadership skill requires advocacy and honest feedback via lasting relationships, and it can strongly benefit both mentor and mentee. Parul Jain, a product management leader at Deloitte, shares what she learned from serving as a mentor—something she didn’t have for much of her own early career.
2026-07-16 02:00:02
As of 21 June 2026, a Level 1 Expulsion has been imposed on IEEE Member Dr. Fei-Yue Wang, former editor-in-chief of the IEEE Transactions on Intelligent Vehicles. In accordance with IEEE Bylaw I-110.5(D)(i), Dr. Wang is no longer a member of IEEE, and is permanently banned from any type of membership in any IEEE organizational unit or participation in any IEEE activity. The Board of Directors also determined this notice to IEEE membership should be made.
2026-07-15 23:35:53
ELIZA is remembered as the world’s first AI star, a kindly therapist in chatbot form that gently probed users’ worries. Even its creator, Joseph Weizenbaum, was surprised by the warm reception given to his experiment in human-machine interaction. For some, it heralded an age of automated psychotherapy, while others believed the program demonstrated sentience, a fallacy soon known as the “ELIZA effect.” Based on published descriptions, ELIZA has been implemented on many different computers, but only recently has the actual source code been unearthed from MIT’s archives.
In Inventing ELIZA: How the First Chatbot Shaped the Future of AI, just published by MIT Press, a squad of researchers analyze the code and reveal a complex program capable of much more than faking psychiatry. In fact, it could assume several different personas. The authors have also created a faithful emulation of the therapist persona that you can try yourself after reading the book excerpt below.
When it debuted in the mid-1960s, the ELIZA software program transformed the way people thought about interacting with computers. As the first chatbot, ELIZA demonstrated how a calculation machine might engage in conversation, ushering in a host of social and technical questions that still resonate today. Now we don’t think twice about interacting with a machine in real time, conversing over text, or even speaking into the air to ask about the weather. In many ways, ELIZA shaped not only the way we think about interacting with computers but also how we think about them. It began to give a reality to the science fiction stories of how we expect computers to work.
This article is adapted from the new book “Inventing ELIZA: How the First Chatbot Shaped the Future of AI“ (MIT Press, 2026).
Although ELIZA was far from a faultless conversation partner, it astonished its users. The recent discovery and archaeology of the original ELIZA source code represents a significant intervention in the history of computing. By examining the actual implementation of ELIZA rather than relying on later reconstructions and reimplementations, we challenge taken-for-granted assumptions about this key software artifact.
For example, the source code reveals that ELIZA was not merely a simple pattern-matching chatbot but can be better understood as a sophisticated platform designed for multiple “personas,” or scripts, with a complex set of capabilities, including script editing and contextual memory. The script that most people conflate with the program ELIZA was actually called Doctor, which performed the role of a psychotherapist. Yet, like a modern chatbot prompted to behave with different personalities, ELIZA could take on many roles.
“This code and script…reveal underlying assumptions about language, therapy, and human-computer interaction that continue to influence modern AI development.”
This unearthed material transforms our understanding of early AI development by demonstrating that Joseph Weizenbaum’s technical innovations were far more advanced than previously documented. Moreover, the discrepancies between his published descriptions and the actual implementation help to show the gap between theoretical computational models and their material instantiations in computer source code, a tension that continues to shape digital culture today.
Although many technical innovations have emerged in the decades since ELIZA, examining the ELIZA/Doctor code offers a rare glimpse into one of the earliest formalized attempts to model human conversation. What makes ELIZA particularly fascinating is not only its historical significance but also what it reveals about Weizenbaum’s views on both computing and human interaction. This code and script do not merely showcase programming techniques of the 1960s; they reveal underlying assumptions about language, therapy, and human-computer interaction that continue to influence modern AI development. By examining this code, we can start to uncover the sophisticated linguistic and programming techniques that allowed a rudimentary pattern-matching system to create a convincing simulation of understanding. But before we can read the lines of code, let us offer an overview of the system.
The architectural distinction between ELIZA and Doctor represents an important design decision in AI history. Think of ELIZA as a system for interaction and Doctor as one set of rules that Weizenbaum devised, among others. This separation, manifested in ELIZA’s system-script dichotomy, presaged numerous contemporary software patterns, from configuration-as-data to plug-in architectures and domain-specific languages.
Based on published journal articles, ELIZA was re-created on many platforms, such as the IBM PC. However, the actual source code sat untouched in the MIT archives for many years. VCF Museum at InfoAge
Without question, the historical context of 1960s computing fundamentally shaped ELIZA’s architecture as well. Decisions in computing that reflect material constraints create path dependencies and eventually become programming cultural norms. These constraints manifested in ELIZA’s single-pass processing, tape-based storage and stack-oriented implementation. Yet within these limitations, Weizenbaum crafted an elegant solution. These technical features, though invisible to the users, are crucial to creating the illusion of understanding that made ELIZA so compelling.
Weizenbaum explained many of ELIZA’s technical features in the 10-page paper published in the January 1966 edition of the journal Communications of the Association for Computing Machinery (CACM). But he chose to omit some essential details.
In that paper Weizenbaum published ELIZA’s best known dialogue, which begins,
Men are all alike.
IN WHAT WAY
They’re always bugging us about something or other.
CAN YOU THINK OF A SPECIFIC EXAMPLE
Well, my boyfriend made me come here.
This dialogue marked ELIZA’s public debut in 1966 as one of the examples produced by the Doctor script. By finding the source code for ELIZA and examining how it performs the Doctor script, we now better understand these two separate parts of a system and can explore the many other personas of ELIZA. In just some of the other scripts known to date, ELIZA was programmed to discuss math, poetry, color, paradoxes, synchronization, relativity, France, and elevators.
These scripts work like templates. They are structured data that direct the ELIZA system to “play” a particular task or role. By comparing archival and published ELIZA dialogues from interactions with a variety of scripts, including Doctor, we can understand more about bot personas and how they function, paying close attention to how a bot evokes social dynamics between system and interactor.
Ultimately, studying the dialogues and scripts demonstrates the crucial role that collaboration plays in these exchanges, as bot and user cocreate the sense of their interaction. To understand the full range of ELIZA’s capabilities and conversational possibilities, let’s take a look at the variety of scripts that were created for the ELIZA system.
What distinguishes each ELIZA script is both its subject matter and the linguistic and stylistic choices used to deliver that content. These choices are not neutral; they can be said to construct a particular persona with characteristics that emerge through the script’s language patterns, vocabulary, and conversational approach. In short, it matters not just what you say but how you say it too.
“The aim was less to create a functional automated therapist and more to find a suitably constrained role to match the limitations of the programming environment.”
For example, with the Doctor script Weizenbaum deliberately echoed the style of a Rogerian “talk” therapist. He chose this persona because the psychiatric mode is one of the few types of conversations in which one person can “assume the pose of knowing almost nothing of the real world. If, for example, one were to tell a psychiatrist ‘I went for a long boat ride’ and he responded, ‘Tell me about boats,’ one would not assume that he knew nothing about boats but that he had some purpose in so directing the subsequent conversation.”
The first users of ELIZA interacted with it via teletype terminals.VCF Museum at InfoAge
Thus, the most famous persona created for ELIZA was a technical convenience. As human-computer interaction expert Lucy Suchman explains, “The Doctor program exploited the maxim that shared premises can remain unspoken: that the less we say in conversation, the more what is said is assumed to be self-evident.” In creating the original ELIZA effect, less was more.
The aim was less to create a functional automated therapist and more to find a suitably constrained role to match the limitations of the programming environment. Then Weizenbaum composed the script to match the role by choosing specific words that evoked rhetorical tone and characterization, for example, LET’S DISCUSS FURTHER WHY YOU … WHAT DOES THAT SUGGEST TO YOU. In Doctor, the machine side of the conversation needs to appear like a good listener who cares about what the user has mentioned before, so it often includes the user’s text in its replies and keeps its responses open-ended. Because a real doctor would be inquisitive, the script contains lots ofWHAT andWHY questions. In other scripts and dialogues, the script keywords and assigned responses reveal the design decisions that help create different specific personas. This variation becomes increasingly apparent as we look at the wider range of ELIZA scripts.
Let’s start with a script called Neweng, short for New England. By the mid-1950s, scientists were already taking up Turing’s proposition for conversing with a computer as a tangible idea. In 1959, a “conversation machine” made by a small team at the University of Toronto Computation Centre could make small talk about the weather if preprompted with current data. Its script included a ranked series of time words, weather words, and emotion words, but the system had “a rather narrow outlook,” limited vocabulary, and no knowledge of syntax. According to its creators, “in spite of this crudity, the results in passable conversation are surprisingly good, which in a way is a commentary on the shallowness of ordinary conversation about the weather.”
This fragment of ELIZA’s source code shows how scripted responses were encoded, in this case to make ELIZA sound like a Rogerian therapist. MIT Libraries, Distinctive Collections
One of ELIZA’s test scripts found in the archive had a similar function. Neweng does not soothe, therapize, or instruct, but only makes small talk. It chats as if at a boring dinner party.
WHAT NEW ENGLAND STATE DO YOU LIKE BEST OF ALL
i love rhode island
I ENJOY THE BEACHES ON LONG ISLAND SOUND
WHAT OTHER NEW ENGLAND STATE DO YOU LIKE
i like massachusetts
THAT IS MY HOME STATE
WHAT OTHER NEW ENGLAND STATE DO YOU LIKE
you must like massachusetts too
WHAT OTHER NEW ENGLAND STATE DO YOU LIKE
i enjoy skiing in new hampshire
PLEASANTLY COOL IN THE SUMMER UP THERE
WHAT OTHER NEW ENGLAND STATE DO YOU LIKE
i like missouri
MISSOURI IS NOT A NEW ENGLAND STATE
WHAT OTHER NEW ENGLAND STATE DO YOU LIKE
i like cape cod
THAT’S A BEAUTIFUL PART OF MASSACHUSETTS
What can we learn from such a basic conversation in which it seems like nothing interesting is shared? Despite the bland chat, a persona emerges from the bot script in phrases like PLEASANTLY COOL and UP THERE and in locations like THE BEACHES ON LONG ISLAND SOUND. From such comments, we can begin to imagine a persona who has not only a certain vocabulary but also certain hobbies and vacation spots. Perhaps implicit assumptions of race and class emerge as well.
A chatty persona might take on many forms, depending on where and when the conversation occurs. This one seems reserved, perhaps fitting its setting in 1960s New England. The system reminds the user that Missouri is not a New England state, but what if this conversation took place in Missouri, Texas, or Mexico? The machine persona would sound different in its cadence, tone, and references. What would we come to understand about a chat persona from Fire Island, from Brooklyn, from Berlin? What would they sound like, and what topics would they discuss?
These differences in subject matter do matter. They imply personas with entirely different backgrounds and experience, giving users wholly different interactions and affective relations. In this way, the Neweng script demonstrates how even simple algorithms making contextual responses about geography could generate a convincing sense of personhood and place. Whereas Neweng could be said to have created a casual, conversational persona focused on light social exchange, other scripts pushed ELIZA into more structured and educational roles. These scripts demonstrate how the system could be adapted not just for friendly chatter but for teaching.
Edwin Taylor, at MIT’s Education Research Center, developed alternate scripts for ELIZA, testing its ability to act as a teacher.MIT Libraries, Distinctive Collections
Meet ELIZA the tutor, quite unlike ELIZA the therapist or the chatty neighbor. Intrvw, Canvec, FVP1, and Arithm are a set of ELIZA scripts created as teaching tools used in experiments by Edwin F. Taylor at MIT’s Education Research Center. These scripts run on later versions of ELIZA that incorporated an important technical innovation called conditional keyword matching.
Unlike the original ELIZA, which simply looked for keywords and generated responses based on their presence, these updated versions could track what had been discussed previously and branch into different conversational paths based on specific user answers. This development allowed ELIZA to simulate a kind of Socratic method, where a tutor guides learning through carefully sequenced questions that respond to student answers rather than simply presenting information.
These scripts construct the tutor persona through many subtle linguistic gestures that create characterization and rhetorical tone. This tone differs from that of Doctor, which asks open-ended questions and comes across as gentle and nonscientific. In the tutoring scripts, large blocks of informative text from the bot tend to dominate the conversation, and the tone is often more dry and unemotional in these explanations. The dialogues indicate structured scripts that include guidance to lead the student through narrow, Socratic learning paths.
In particular, the teaching scripts feature praise and critique. The dialogues for Intrvw, Canvec, and FVP1 are peppered with EXCELLENT, VERY GOOD, RIGHT YOU ARE, and CONGRATULATIONS. These create the sense of a supportive instructor cheering the student on. Such politeness has been taken up in contemporary bots like ChatGPT, which has been shown to perform better when people are polite back to it.
ELIZA could become a tutor more effectively as the system grew in its capabilities, another valuable reminder that ELIZA was not one program but a family of programs. After the publication of the 1966 CACM article, Weizenbaum continued to develop the systems for interaction and understanding. As an experiment, Weizenbaum wrote the Arithm script less as a tutor and more so to “to illustrate the power of the evaluator to which ELIZA has access.” It uses a friendly, plain language interface to let users do simple programming. The script can do calculations, assign variables to values, and perform operations on them. Math problems can be described in sentence form:
The radius of a globe is 10.
A globe is a sphere. A sphere is an object.
What is the area of the globe.
IT’S 1256.635916
The updated 1967 version of the ELIZA system can accumulate facts and store additional information. In this later version of ELIZA, when the system does not recognize information, it asks follow-up questions to gain data. As Weizenbaum explains, “The present script is designed to reveal, as opposed to conceal, lack of understanding and misunderstanding. Notice, for example, that when the program is asked to compute the area of the ball, it doesn’t yet know that a ball is a sphere and that when the diameter of the ball needs to be computed the fact that a ball is an object has also not yet been established.” Unlike Doctor, which asks questions to keep the conversation going, Arithm is building its store of, if not knowledge, then data and logic statements.
Although the variety of scripts helps us to see how a range of personas could be constructed through script programming ELIZA, they represent only half of the conversational process. A script can establish a foundation for a persona, but that persona only emerges fully through interaction with users who engage with it, interpret it, and respond to it in ways that may confirm, challenge, or transform the script’s implicit character.