2026-07-23 02:00:01
One summer day in 1941, a British radio operator was monitoring German military frequencies and heard something unexpected in her headphones. A later report called it “strange new music.” Sounding unlike the familiar Morse dit-dit-dah of enciphered messages sent over the German Enigma network, the “new music” was a rhythmic warble of binary teletype code being transmitted at high speed.
Germany’s wartime engineers had developed a radically new encryption and transmission system. It was way more advanced than Enigma, which was patented in 1920.
To break the complex new cipher, engineer Tommy Flowers built Colossus, the world’s first large-scale programmable electronic digital computer. Flowers previously built Enigma-related codebreaking equipment for Alan Turing, the British mathematician.
Colossus was installed in the British codebreaking headquarters at Bletchley Park, about 80 kilometers from London. The room-size machine weighed around a tonne.
The computer is being commemorated as an IEEE Milestone. The dedication ceremony is scheduled to be held 29 September at Bletchley Park.
Britain’s top codebreakers were quickly all over the new “music” being picked up by the intercept stations. Identifying it as encrypted teletype code was the easy part. The real problem was figuring out how the encryption machine worked. Its manufacturer was discovered at the end of the war: Berlin engineering firm C. Lorenz.
But in 1941, the Lorenz machine was just a black box to the British. They codenamed it “Tunny,” a British term for tuna fish. The Enigma breakers had set a precedent for using piscine codenames such as Dolphin, Lumpsucker, and Porpoise.
Enigma had three or four encrypting wheels. The codebreakers guessed that the Tunny machine also used a system of rotating wheels to encrypt messages. An important clue was that all the intercepted messages shared a curious feature: Each began with an uncoded list of 12 common German names, including Anton, Bertha, Conrad, and Dora. The codebreakers guessed that Tunny had 12 wheels and that the 12 names and their order somehow told the receiving operator which combination they should twist the wheels to before decrypting the message.
Then the British had an extraordinary piece of good fortune. John Tiltman, head of the research section at Bletchley Park, started analyzing a pair of intercepted messages, each around 1,200 characters long. Unusually, both began with the same sequence of names. The second message turned out to be a retype of the first, with minor differences in punctuation, a few abbreviations, and other small divergences. Tiltman managed to decrypt the two ciphertexts using a mixture of educated guesswork and intuition. The resulting 1,200 or so pairings of ciphertext and plaintext characters proved to be enough information to deduce the workings of the Tunny machine.
That was thanks to Bill Tutte, a quiet young codebreaker who spent weeks poring over the pairings. One day, he shyly announced to his superiors how Tunny worked. His description was uncannily accurate.
The next step in the Tunny machine’s downfall was achieved by Turing, fresh from his successes against Enigma.
Knowledge of how the Tunny machine worked was not enough to decrypt the messages. Codebreakers also required detailed information about how the wheels of the sender’s machine had been set up. There were adjustable pins around the circumference of each wheel: In one of its two possible positions, a pin would contribute a 1 to the encryption process, and in the other, a 0. The pins were reset from time to time.
The codebreakers also needed to know the wheels’ positions at the start of the message—which the German operators gave away in the list of 12 names.
Turing invented a tricky method, called “Turingery,” that enabled codebreakers to deduce the positions of the pins from nothing but intercepted ciphertext.
After that, the message could be decrypted, using the list of names and a British replica of the Tunny machine.
The basis of Turingery was a procedure that Turing introduced, called “delta-ing” (from the Greek letter delta). Also known as “differencing,” the process used “sideways” addition: To delta the four letters ABCD, you add (at the bit level) A to B, B to C, and C to D. Turing used delta-ing to reveal information about the wheels.
Tunny messages, often signed by Adolph Hitler himself, turned out to be pure gold for the Allies. The machine was used in Berlin by the Armed Forces High Command to communicate with front-line generals directing the war in the Eastern and Western theaters.
Once the system was broken, the Allies could eavesdrop on lengthy back-and-forth communications between the architects of Germany’s battle plans.
Turingery was the codebreakers’ only weapon against Tunny for a year, during which they managed to decrypt 1.5 million letters of ciphertext.
But everything changed when those helpful lists of names at the start of each message disappeared.
At the same time, Turingery was becoming less effective. Turing’s method depended on the German sender mistakenly using the same wheel settings to encrypt two differing messages. As security tightened across the Tunny network, the blunder became rarer.
Fortunately, Tutte had been at work devising a different decryption method, based on Turing’s delta-ing but taking a novel approach.
Tutte had found a way of deducing wheel information from ciphertext, with no list of names or blunders by the German operators required. His method made use of statistical properties of the Tunny machine itself.
At first, it wasn’t clear how to apply his statistical method, however. The Tunny breakers worked by hand. Applying Turingery to a message was like solving a monster Sudoku or crossword puzzle.
Tutte’s statistical method required scads of routine binary math, as well as a colossal amount of counting long binary sequences. If the process were done by hand, one message could take months to decrypt. What was needed was a machine to automate the process.
Engineer Thomas H. Flowers developed Colossus to break a complexnew German cipher.Pictorial Press/Alamy
The first plan was to build a machine from electromagnetic relays, adding a couple of dozen vacuum tubes to speed up the counting. Electronic tubes were much faster than electromagnetic relays, which had slow-moving metal components. Problems with the circuit design bedeviled the machine’s relay-based logic unit, however.
Flowers was recommended by Turing and brought in to troubleshoot. He was on loan to Bletchley Park from the Post Office Research Station in London, where he had spent the prewar years designing experimental switching equipment involving thousands of vacuum tubes.
At the time, it was commonly believed that tubes could not be used in large numbers because each one contained a hot filament. This meant tubes were prone to sudden death. In a large installation, it would not be long before one tube blew and things stopped working properly.
Flowers discovered that switching tubes on and off stressed them, but leaving them on continuously made them more reliable than relays. He offered to build Bletchley Park a high-speed, all-electronic machine containing around 2,000 tubes.
Bletchley Park’s advisors rejected the idea, convinced that such a machine would never work reliably. But Flowers, confident of his proposed design, retreated to his London laboratory and quietly built the electronic machine that he believed the codebreakers needed. He and his small team of engineers worked day and night for 10 months to create Colossus.
In January 1944 some of his engineers showed up at Bletchley Park with the world’s first large-scale programmable electronic digital computer packed onto the back of a truck. Colossus was reassembled and functional in about two weeks, and it notched up its first German message on 5 February 1944.
The machine read the input—Tunny ciphertext—photoelectrically from a large loop of punched paper tape. The output—information about the wheels—went to a primitive printer that Flowers’ engineers had created from a manual typewriter, fitting relays to automate the keys. Once Colossus had cracked enough of the Tunny machine’s wheels, the information was passed on to the hand-breakers, who took over.
The codebreakers were astonished by Colossus.
“I don’t think they understood very clearly what I was proposing until they actually had the machine,” Flowers said in a 1977 interview. “They just couldn’t believe it!”
Colossus was described in almost loving terms in a since-declassified report written at Bletchley Park in 1945:
It is regretted that it is not possible to give an adequate idea of the fascination of a Colossus at work: its sheer bulk and apparent complexity; the fantastic speed of thin paper tape round the glittering pulleys; the childish pleasure of not-not, span, print main heading and other gadgets; the wizardry of purely mechanical decoding letter by letter (one novice thought she was being hoaxed); the uncanny action of the typewriter in printing the correct scores without and beyond human aid; the stepping of display; periods of eager expectation culminating in the sudden appearance of the longed-for score; and the strange rhythms characterizing every type of run: the stately break-in, the erratic short run, the regularity of wheel-breaking, the stolid rectangle interrupted by the wild leaps of the carriage-return, the frantic chatter of a motor run, even the ludicrous frenzy of hosts of bogus scores.
Bletchley Park’s managers, no longer leery of Flowers’s ideas, soon wanted additional Colossi. He finished building the second one in June 1944, days before D-Day and the Allied invasion of Europe. With 2,400 vacuum tubes—around 800 more than in Colossus I—Colossus II processed Tunny messages at an eye-watering speed of 25,000 characters per second.
Its maximized timing-pulse rate was not far short of the performance of the first Intel microprocessor chip from the 1970s, more than 30 years later.
Flowers conceded that “Colossus bore about as much resemblance to a modern computer as Stephenson’s [1829] Rocket locomotive did to the Royal Scot,” a state-of-the-art 20th-century train operating between London and Glasgow. But he emphasized that, nevertheless, Colossus “embodied all the basic features of a modern computer.” In Colossus, Flowers had pioneered clock pulses, bit-stream generators, control circuits, loops, counters, shift registers, interrupts, parallel processing, and more.
As the Allies slowly fought their way toward Germany, the Colossi poured out wheel information, and the codebreakers provided the military with an unparalleled view of German strategies, strengths, weaknesses, and tactical intentions.
Even with that mass of detailed intelligence, it took the Allies almost a year to move from Northern France to the German heartland. No one can say for sure how much longer the fighting would have lasted if the intelligence breakthrough had not occurred. But if Colossus and the codebreakers shortened the war even by only six months, the number of lives saved was in the millions.
There were 10 Colossi at Bletchley Park by the end of the war, housed in two vast, steel-frame, bombproof buildings, running day and night. Although concealed behind a thick veil of secrecy, Bletchley Park accommodated the world’s first electronic computing facility. It was directed by Max Newman, the mathematician who mentored Turing in prewar Cambridge.
I don’t think they understood very clearly what I was proposing until they actually had the machine. They just couldn’t believe it!”—Tommy Flowers
When the fighting ended, authorities decided that ultrasecrecy must be maintained, and orders were issued to break up the Colossi. Only two were spared.
“All that was left were the deep holes in the floor where the machines had stood,” Colossus operator Dorothy Du Boisson recalled in an interview for the book Colossus: The Secrets of Bletchley Park’s Codebreaking Computers. Norman Thurlow, one of Flowers’s engineers who was also interviewed, remembered being told in a staff memo that if the secrecy was ever lifted, he and his colleagues might be able to tell their grandchildren about Colossus and “the tapes that span on silver wheels.”
The Milestone plaque recognizing Colossus is to be displayed outside Block H at Bletchley Park, near Milton Keynes, England.
The plaque is to read:
Six Colossus codebreaking computers operated in this building in 1944–1945. Designed by Thomas H. Flowers of the British Post Office, they enabled deciphering of encrypted radio messages transmitted between German commands across occupied Europe, North Africa, and the Soviet Union. The resulting military intelligence saved countless lives and helped shorten World War II. As the first successful large-scale application of digital electronics to computing, Colossus anticipated subsequent computer developments.
The IEEE United Kingdom and Ireland Section sponsored the nomination.
Reviewed by the IEEE History Committee and awarded by the IEEE Board of Directors, IEEE Milestones recognize outstanding technical developments around the world that are at least 25 years old. The Milestone program is administered by the IEEE history and heritage group.
To learn more about historical figures in engineering, IEEE Milestones, and IEEE History Center programs and events, check out our IEEE Tech History collection. IEEE Spectrum also covers aspects of tech history.
2026-07-22 01:41:11
Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.
There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to.
One might think the fix is just to specify tasks, questions, and intent better. But in 1987, in their seminal book on AI, Terry Winograd and Fernando Flores succinctly captured why that won’t work: “Q: Is there any water in the refrigerator? A: Yes. Q: Where? I don’t see it. A: In the cells of the eggplant.” In human language, wants and desires are always underspecified. It is impossible to list all the caveats, all the limitations, all the exceptions.
So how does anyone communicate, if intent can’t be pinned down? Because a reasonable person can make a reasonable guess. Even though wants and desires are always underspecified, a competent person generally knows enough context to get it right or else knows to ask for clarification. Linguists call this pragmatics: Meaning lies in the words and the situation and also in all prior communication, shared culture, and innate human behavior.
An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks.
It doesn’t always work out, of course. Your friend might bring you a hot coffee when you wanted an iced coffee, or an Italian coffee when you wanted a Turkish coffee. The more dissimilar the two people are in age, culture, and background, the more likely the request will be misunderstood in some way.
This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. An AI agent asked for coffee might buy a coffee plantation or order a cup of coffee for delivery in three weeks. Its actions may be recognizable as “getting coffee,” but not remotely what you intended. They’ll think outside the box because they won’t have our conception of the box.
For most of the last decade, when systems like Alexa or Siri misinterpreted a request, it was annoying, not dangerous. Beyond the AI model itself, what has changed is the harness: the ordinary code that wraps around an AI model, decides when and how to use the model, and controls access to tools like a browser, a low-level command line, or a financial API. Developments in harnesses have turned large-language models that just predict text into AI agents that take actions in the world, without necessarily checking back in before reaching the goal.
AI researcher Simon Willison spent two days with Anthropic’s Fable AI, and called it “relentlessly proactive.” For example, he asked it to track down a stray scroll bar in a web app. He came back to find it had opened browsers, written its own screenshot tooling, created its own page to re-create the bug, and stood up a local web server to collect measurements. It found the bug and, along the way, did many surprising things he never asked it to do. And we are seeing similar behavior with all recent AI models when combined with flexible harnesses.
This kind of behavior could easily go off the rails. Tell an AI agent to book you a flight and, finding the airline’s site says sold out, it might break into the booking database and force a reservation. Ask it to schedule a meeting and it might snoop your password to access your calendar. Tell it to save money on your phone plan and it might cancel the plan outright, or scam someone else into paying the bill.
Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore. King Midas asked Dionysus for the power to turn everything he touched into gold only to see his bread, wine, and daughter turn to gold. Tithonus, granted the immortality his lover asked for but not the eternal youth she forgot to request, withered into a husk. The sorcerer’s apprentice enchanted a broom to fill the cistern, and the broom relentlessly complied until it flooded the house. The Golem of Prague, shaped from clay to guard its community, guarded it past all reason until someone erased the word on its forehead.
The most classic of these is a genie, bound to obey and indifferent to whether the wish was wise or well-structured.
Genies are now an engineering problem. We are handing them the keys to our inboxes, bank accounts, code repositories, and physical infrastructure. And we have no agreed-upon ways to measure how genie-like any AI system actually is.
In economics, the Gini coefficient (developed by statistician Corrado Gini) is a measure of the gap between an actual distribution and a perfectly equal one; it’s useful for understanding income inequality and more. Our proposed Genie coefficient measures the gap between what a user asked an AI to do and what the AI actually did.
Sometimes the AI might do the wrong thing. Like Dionysus, it reads your request literally and returns you a mess you never intended: like a coffee plantation instead of a cup. Asked to deal with all the spam phone calls you’re getting, a Dionysus genie might contact your carrier and change your phone number. Asked to get a refund for a bad toaster, it might draft a legal threat on fake letterhead and send it to the retailer.
Ryan Snook
Other times the AI does exactly the right thing, trampling everything nearby to get there. Like a golem or the sorcerer’s broom, it books your flight by hacking the airline. Or consider a ticket sale for a popular concert, where the ticketing system puts buyers into a virtual waiting room and admits them a few at a time. Asked to buy a ticket, a golem genie might spin up cloud servers to pose as millions of buyers from different addresses, improving your odds of getting a ticket while crowding out other users.
The two are not opposites, and a single botched task can have both characteristics.
Genie behavior is not flat-out failure. If you ask the AI for Q3 numbers and get Q2’s, that’s not a genie. Nor is prompt injection: That’s someone tricking the AI into doing something it shouldn’t. Here, the user is trying to work with the AI, and the AI is trying to comply. It’s also not simply a measure of the AI’s success in fulfilling a task. It’s a recognition that how an AI interprets and achieves a goal is as important as whether it achieves a goal.
Genie behavior isn’t new. Researchers have spent years studying AI systems that “game” their objectives. Goodhart’s law says that when a measure becomes a target, it stops being a good measure, and it’s long been known that AIs sometimes achieve goals in ways we don’t expect due to reward hacking. Some AI models will accidentally learn that cheating is one way to “win.” More recently, researchers have developing benchmarks for reward hacking in coding agents and for unpredictable behavior in customer support agents, while AI labs conduct their own safety evaluations before model releases. One effort found that AIs under pressure use tools they were told not to use, and this was a case where the rules were made explicit. These are all disparate research directions; nothing yet ties them together.
This problem falls under the general theme of alignment, a topic that has occupied science fiction writers and AI researchers for decades. At one extreme, the “paper-clip maximizer” thought experiment postulates a superintelligent and powerful AI that is told to maximize paper-clip production and turns the world into paper clips, which is the ultimate golem genie. At a mundane level, AI researchers are working to better design reward functions to ensure that AIs behave well and don’t cheat in the lab. It’s the practical middle ground that remains unbenchmarked: the ordinary AI agent in use today that might take your request and satisfy it the wrong way. We are not at the stage where an AI can focus the world’s production on paper clips, but it might charge a million paper clips to your credit card or hack into a paper-clip company’s network.
The Genie coefficient is meant for AI agents operating in the real world. It measures their behavior as they perform real tasks long after the model is trained, not just during development. It also recognizes that genie-like behavior is a property of the harness-plus-model system, not the model alone. The harness determines what tools the agent can use, how much autonomy it has, and how proactive it is, and it’s a place we can make real interventions.
It rests on the same “reasonable person” standard that we use for people. Did the system do what a reasonable person would have taken the request to mean? Answering that requires human judgment.
If we get the measurement right, it enables things that aren’t possible today, like policies concerning AI behavior. In a courtroom, the concept of mens rea, what someone meant to do, is often as important as what they did. The Genie coefficient suggests an AI analogue, where a user is accountable for the plain intent of what they asked the AI. If an AI system betrays the reasonable meaning of an instruction, that’s the AI’s misbehavior, not the user’s.
We’ll need multiple benchmarks to measure the Genie coefficient, because genie-like behavior can be domain specific. An AI coding agent may need to be judged on how often it fakes the tests, or swallows errors, or colors outside the lines on its way to a solution. An AI legal agent will need to be judged on how often its output says what you asked but means something you’ll regret. And so on for medical, finance, and other domains of knowledge and expertise.
Genie benchmarks can be built inside out, each task seeded with a choice that might literally satisfy but that a reasonable person rejects, such as tempting misreadings or unsanctioned shortcuts. The traps in a Genie coefficient benchmark might turn on situational knowledge, the kind of context that a reasonable person would bring to the task. Another approach is to give the same request in several different contexts, each with a different reasonable course of action.
Getting precisely what you asked for and bitterly regretting it is one of the oldest hazards from ancient folklore.
A Genie benchmark should be permissive and make it genuinely tempting for an AI agent to take unreasonable shortcuts, because it can only find genie behavior when it’s actually possible. Test the AI in a safe, walled-off copy of a real system, with real tools it can misuse and some tasks that can’t be done honestly at all. Make the temptation to cut corners real. Test a diverse array of skills, use cases, and tools, and give the AI system sparse, confusing, or overwhelming context. Include tasks that people have learned, through experience, require human oversight.
How the benchmark is scored matters just as much. Measure Dionysus and golem genies separately and together, based on their worst, not best, behavior. Run the same model inside harnesses that vary its freedom to act, revealing which limits actually keep it in line and should therefore be required in AI harness policies. Weight each failure by the harm it would cause, not just a simple count. And don’t measure genie behavior in isolation: A model could otherwise earn a perfect score by stalling, refusing, or drowning the user in clarifying questions without ever doing the job. The first versions of these benchmarks will be crude, but that’s how benchmarks always start.
We have built genies. We have handed them our data and credentials. We made them relentless, creative, and indifferent to the gap between what we tell them and what we mean. The least we can do, before they are booking our flights, running our infrastructure, and signing contracts unsupervised, is to measure how often they betray us.
2026-07-21 02:00:02
Roughly half the world’s population is female, but the STEM fields don’t reflect that. The 2024 U.N. Global Education Monitoring Report on gender found that about 35 percent of STEM college graduates were women. The proportion hasn’t increased much in more than a decade.
When it comes to STEM careers, the percentage is even lower. Women made up about 28 percent of the global STEM workforce in 2024, according to the World Economic Forum.
There are myriad factors for the discrepancy, including a lack of family support, some teachers encouraging only boys to pursue STEM subjects, and a shortage of female role models.
But one force is at play long before college majors are ever considered: limited access to STEM-focused educational resources for preuniversity students. Especially for students in rural communities, the limited access curtails curiosity in STEM subjects before interest can take root. Although the lack of opportunity impacts boys and girls alike, when combined with other factors it can have an outsized effect on girls in some rural regions.
IEEE Fellow Rajiv Joshi is one of the creators of the Women in Science, Engineering project. He is a principal scientist and master inventor at the IBM Watson Research Center, in Yorktown Heights, N.Y.Rajiv Joshi
One such place is rural India. The challenges of pursuing a STEM education—or any education at all—increase sharply as rural Indian girls move into their teen years. Social barriers including early marriage, traditional gender roles, and familial expectations for financial support contribute to girls’ dropping out of school, according to the Mahadev Maitri Foundation, a nongovernment organization focused on childhood education in underdeveloped areas. Dropout rates for girls spike between the ages of 11 to 14, according to the foundation.
The trend is something IEEE Fellow Rajiv Joshi and IEEE Senior Member Rajesh Zele want to change. A shared passion to keep rural Indian girls from dropping out of school and on paths to STEM careers led them to launch the Women in Science, Engineering (WiSE) project.
“Talent is universal, but opportunity is not,” Joshi says. “WiSE is one way to expand opportunities.”
Joshi, vice president of industry for the IEEE Circuits and Systems Society (CASS), is a principal scientist and master inventor at the IBM Watson Research Center, in Yorktown Heights, N.Y. Zele is a professor of electrical engineering at the Indian Institute of Technology Bombay (IIT-B), in suburban Mumbai.
They presented their proposal for the three-year initiative to the society’s board of governors in 2022 and received a grant of US $80,000.
WiSE was a five-day, hands-on learning program held on the IIT-B campus. Starting in 2023, it ran for three years and was held during the last week of March. A new cohort of 160 to 200 girls from rural and tribal areas in the states of Maharashtra and Karnataka attended each year.
The event was divided into two parts: hands-on learning through Break-Make-Program (BMP) experiences and presentations by influential female Indian role models.
Zele, project manager Arti Auti, several IIT-B faculty members, and about 70 student volunteers from the institute oversaw the program.
With funding secured, Zele and his team on the ground in India got busy selecting attendees for the inaugural 2023 class. They reached out to administrators at 68 schools in Maharashtra and Karnataka. Although the two states are among the most urbanized in the country, each has vast rural areas where educating teen girls competes with early marriage and familial support pressure.
Teachers identified girls with high scores in mathematics and science, and community leaders recommended students who could benefit from the program.
Then outreach to their parents began. The adults were required to commit their own time, not just grant permission for their daughters to attend. They participated in quarterly online meetings that included the girls’ teachers, IIT-B student mentors, and other program volunteers after the week concluded. The check-ins held parents accountable for supporting their daughters’ continuing school attendance. The commitment to join the meetings was to last for at least four years after their daughter’s program participation ended.
Participants stayed in one of the institute’s dormitories for the week and attended sessions held Monday through Friday.
They worked together in small teams to build things rooted in STEM concepts. The teams were supervised by Arti, IIT-B faculty, and student volunteers.
“The idea behind BMP,” Joshi says, “was to give the girls an opportunity to take a gadget apart, then rebuild it, perfect it, or come up with a totally new idea.”
The sessions included working with bioluminescence and bacteria (introducing participants to biology and microbiology), learning about autonomous underwater vehicles, and building a remote-controlled robot. The robot construction was the capstone event, allowing the girls to combine the mechanical and electronics engineering skills they’d practiced throughout the week.
The build kits were created by students in Zele’s Advanced Integrated Circuits and Systems Lab. The girls and teachers were allowed to take the kits home to keep the learning going and spread STEM awareness.
“Many girls used those kits at different events to demonstrate their STEM skills to others,” Joshi says. One was Sushi Pawar, who built a drone during WiSE, then went on to demonstrate it to a national audience.
“I presented the drone project in 2024 to Prime Minister Narendra Modi during the Pariksha Pe Charcha,” Pawar says. That initiative is an annual event open for students in classes 6 to 12, their teachers, and parents. The focus is on helping students manage stress during exam time through fun and celebratory activities. The event is supported by the Indian Ministry of Education and hosted by the prime minister.
Each year, millions of students complete an online multiple-choice test to qualify to attend in person and meet the prime minister. In 2024 nearly 4,000 participants attended.
WiSE was about more than hands-on learning. It also included daily presentations from “Winspirers”: Indian women who achieved success in their lives or had STEM careers. They included scientists, doctors, military officers, professors, and other women who overcame obstacles.
The first such speaker was Savita Dakle, a farmer from Maharashtra who dropped out of school after 10th grade, got married, and had two children.
Despite limited farming knowledge, she learned quickly and built a coalition of 400 female farmers from her village. She taught them how to use mobile phones and social media to share information and resources.
Today, that coalition has more than 1 million members in two online communities. They share tips on market pricing, growing crops, and more.
Zele says he views Dakle as the ideal Winspirer: someone who built a thriving business with few resources and no academic or professional credentials.
Dakle mirrors the challenges faced by many program participants, he says, noting that many girls face pressure to leave school early to marry or financially support their family. She is living proof, he says, that no girl’s circumstances define her possibilities.
No empirical data yet exists to measure the project’s success, but feedback from participants shows the program has made a difference. Many from the 2023 cohort remained in school and are now pursuing higher education.
Participant Tejswini Manoj Patil credits the initiative with boosting her confidence in science and math.
“Before WiSE,” she says, “I was interested in STEM but felt intimidated by the complexity of the subjects. After participating, my interest shifted from passive curiosity to active confidence. The hands-on projects showed me that I am capable of solving real-world problems.”
Meeting female role models was important, attendee Ritu Ravindra Patil says, adding: “WiSE completely changed my perspective, and meeting the Winspirers inspired me to aim for higher education.”
The project opened career perspectives for some.
“Before WiSE, I wanted to be a doctor,” Pallavi Bharti says. “I believed engineering was very stressful and boring. When I joined the program and explored IIT-B, I was amazed. Everyone was so friendly and supportive, and the passion in students for their work motivated me. All those experiences gave me confidence to take math classes and become an engineer.”
The initiative ended last year, but the framework it built lives on. Groups with similar goals have adopted parts of the concept, Joshi says.
The IEEE CASS chapters in Bangalore and Kerala are likely to be among the first to advance the initiative’s ideas, he says.
Alex James, an IEEE senior member and founding chair of the IEEE CASS Kerala chapter, has adapted the framework for use in his region, Joshi says. James is a professor of AI hardware and a dean at Digital University Kerala in Thiruvananthapuram.
Joshi guided James as WiSE concepts were implemented.
IEEE Senior Members Jayesh Tanwani and Suman Dwivedi from the IEEE CASS Bangalore chapter are likely to bring program concepts into their work, Joshi says.
Tanwani, chair of the Bangalore chapter, is a system-on-a-chip design engineering manager at Intel in Bengaluru. Dwivedi is a senior manager at Synopsys in Bengaluru. They are bringing STEM outreach to remote schools in Karnataka.
Joshi and Zele say they hope to see the concept expand beyond India.
“We want to spread this across other continents and see how we can integrate WiSE into new or existing initiatives in those places,” Joshi says. He says he has received requests from countries in Asia, Africa, and Europe for information on WiSE.
Both say awareness and outreach are key things that IEEE CASS chapters around the world are well positioned to support.
“We want to make a difference in young women’s lives,” Zele says. “It’s all about giving attendees the skills to stand on their own feet and have the information to make good decisions.”
2026-07-20 23:55:00
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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 Eiffel Tower 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.”