2026-07-29 22:55:45
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity and delivered to your inbox for free!
Scroll through LinkedIn right now and you may find the same advice repeated by well-meaning people: “In a market this rough, just be grateful anyone will hire you. Take the offer.”
I could not disagree more.
Negotiating your offer is not ungrateful, and it isn’t greedy. Done well, it’s good for you and good for the company hiring you. I misunderstood this early in my career, and it cost me.
When I got my first job in tech, I didn’t know negotiation was even on the table. The recruiter asked what salary I wanted, and I gave a number below the bottom of their range. They came back with the lowest number in their band—still more than I had asked for—and I was thrilled. I had no idea I’d left money on a table I couldn’t see.
Then I started teaching at a Bay Area coding bootcamp in the evenings. A coworker mentioned what he made and it was nearly double my salary for roughly the same work. My jaw dropped.
During that time, I began interviewing and got an offer. I handed in my resignation and my manager countered with an offer for nearly 30K more. That money had been there the whole time. At that moment, I realized my salary was a business decision, not a measure of my worth.
Years later, I became an engineering manager and saw the salary discussion from a different angle: A position would open. Many interviews later, we’d find someone we wanted, and HR would hand me a salary range to make an offer. I was encouraged to make an initial offer near the bottom to leave room for, you guessed it, negotiations.
Most applicants didn’t negotiate.
The first offer is rarely the ceiling. It’s usually the floor. Companies extend a reasonable number and quietly hope you say yes.
Negotiating isn’t only about a bigger paycheck. (But who doesn’t want that?)
Let’s say you’re on the job market, maybe recently laid off, and a low offer comes in. You take it out of relief. Then you start, you like the team, and you quietly resent the number. Now you’re stuck with it, and you’ll probably leave that role inside a year or whenever the market improves.
Nobody wins there. You’re back on the market starting over, and the company loses someone good and pays more to replace you, when a fair number up front would have cost far less.
Paying you fairly is cheaper than starting over.
People overcomplicate this. Once I have an offer, I say some version of this:
“Thank you so much for the offer, and I’m genuinely excited to join the team. I’m hoping we can come in around [10 to 20 percent higher than the original number]. Is there any wiggle room here?”
Then I stop talking and let them respond.
Why 10 to 20 percent and not double? The number you ask for is itself a signal. Ask for something wildly out of range and you’ve told them you never learned what the role pays, or that your expectations are miles from reality. That’s what makes a company walk away. A calibrated request reads as someone who knows their worth and did their homework.
You’ve probably heard a horror story about someone who asked for more and had the offer yanked. Any company that would pull an offer over a reasonable question about pay is telling you exactly how they’ll treat you once you’re inside.
If the salary can’t move, it isn’t the only lever. I’ve negotiated more remote days, a later start to drop my kids off, and a sign-on bonus when the base was locked. Most people negotiate none of these perks.
Negotiating can feel like something you can only do from a position of power. But if you’re in the final stages of an offer, you already have it. They want to hire you. They’ve spent weeks finding you. Now they’re hoping you say yes.
That’s true even if you were recently laid off. Even if it’s your first job. Even if the number already looks higher than you expected.
The game is being played whether or not you join in. Sit it out, and you’re not just leaving money on the table. You may be quietly shortening your own stay at a job you could have been happy in. So ask.
—Brian
If you’ve been on the job market for a software engineering role recently, you’ve probably encountered—or used—AI tools in the hiring process. From application filters to live interview assistants, both applicants and employers are trying to use generative AI to their advantage. Can real, human skills still shine through in this new reality?
Sarah Downs, a Ph.D. student in electrical engineering at Texas A&M University, has long been interested in robotics and dreamed of working with NASA. This year, she achieved that dream, collaborating with NASA and the U.S. Air Force on an algorithm that enables satellites to insert an antenna into the correct spot.
Women make up only about 28 percent of the global STEM workforce, in part because of limited access to educational resources for preuniversity students—especially in areas like rural India. An IEEE initiative, the Women in Science, Engineering (WiSE) project launched to help expand opportunities and hands-on learning for young women.
2026-07-29 22:00:02
Artificial intelligence is reshaping the skills employers expect from new graduates. In response, universities are scrambling to launch new courses, research centers, and industry partnerships that prepare students for today’s workforce. But building a cutting-edge AI curriculum demands funding and access to industry networks, resources that remain unevenly distributed across higher education.
At North Carolina Central University, Siobahn Day Grady is trying to change that equation.
In January 2025, Grady, an associate professor in the NCCU School of Library and Information Sciences, launched the first AI research institute at a historically Black college or university, or HBCU. The Institute for Artificial Intelligence and Emerging Research (IAIER) aims in part to help students and faculty across the university develop the skills needed to navigate a labor market increasingly transformed by AI.
“There used to be a time where people could say, ‘I don’t do tech,’ or ‘That’s not for me,’” Grady says. “But we’re in a stage now where you do need digital skills. Now it’s evolving into AI literacy.”
The approach reflects a broader shift in how many universities are thinking about AI education. AI skills are no longer confined to computer science and engineering departments—and at NCCU, they can’t be. The university does not yet have a dedicated computer science program, though it is developing one alongside a new AI minor.
The challenge of providing these resources is especially acute for historically Black institutions. Although HBCUs account for roughly 3 percent of four-year institutions in the United States, they receive less than 1 percent of federal research and development funding, according to a 2025 report by the Center for American Progress and the Thurgood Marshall College Fund. The same report found that 17 of the 43 federal agencies that distributed research funding to universities in 2023 awarded no funding to HBCUs.
Yet less than two years since its launch, IAIER has emerged as a powerhouse for interdisciplinary AI education. Backed by a US $1 million Google.org grant, the institute has engaged more than 2,800 students, faculty members, and community residents through research initiatives and training. Now the challenge is sustaining that momentum to keep up with rising demand.
“We have a guiding principle that we lead with on our campus,” Grady says. “AI is for everyone.”
The mission to expand AI literacy grew out of Grady’s lifelong curiosity about technology. “I was born during a time [when] the internet did not exist,” she says. “Ever since the internet came to be, it’s changed our entire world.”
Grady was particularly drawn to the questions tech raises about privacy, identity, and human behavior. After receiving her bachelor’s degree in computer science and master’s degrees in AI and information science, Grady pursued a Ph.D. in computer science at the North Carolina Agricultural and Technical State University to dig into those questions.
Her dissertation focused on authorship attribution in social media, using machine learning and natural-language processing to determine whether a person’s writing style could reveal their identity. “I’ve always been intrigued by how much data we give for free,” Grady says. That work introduced her to the power of AI systems to detect patterns hidden within large datasets.
“We have a guiding principle that we lead with on our campus: AI is for everyone.”
After completing her doctorate in 2018, Grady joined NCCU as an assistant professor in the School of Library and Information Sciences. There, she researched machine learning applications for health care and autonomous vehicles. In 2020, she launched the Laboratory for Artificial Intelligence and Emerging Research at NCCU, giving students opportunities to participate in hands-on projects and explore AI beyond the classroom.
Then in 2024, an opportunity emerged to apply for a Google grant, and Grady began thinking beyond a single research group. Rather than building another faculty lab, she envisioned an institute that could serve the entire university during the AI boom. “We wanted to capitalize on the moment and make sure we don’t get left behind,” Grady says.
Since receiving the $1 million grant, Grady and her team have built a university-wide AI initiative, launched new academic programs, organized conferences, secured external support, and created research opportunities.
“We’ve really operated like a startup,” Grady says.
As part of the institute’s goal of integrating AI education across disciplines, all NCCU freshmen are required to complete an introductory AI course, designed in partnership with IBM, to build foundational prompting skills. The institute has also worked with faculty development teams to help instructors integrate AI into their teaching.
Research is another part of the strategy. IAIER has awarded seed grants of up to $10,000 to faculty members exploring AI applications across departments. The first cohort funded 11 projects spanning social work, digital archiving, health care, and information science. One project, for instance, is creating an AI lab where students in social work courses can practice client interactions through simulations.
“It’s really interesting to see the lens that our researchers take in trying to solve complex problems and also bring our students along with them,” Grady says.
The institute’s growth has been fueled by a mix of workforce training, interdisciplinary research, and, especially important, industry engagement. “Industry is where the advancements are really moving at that very fast rate,” Grady says, “not necessarily higher ed.”
To bridge that gap, IAIER hosts events that connect students and faculty with researchers, employers, and technology leaders. It has held sessions with companies including Deloitte, FICO, and Anthropic. Partnerships with Google and IBM let students gain recognized certificates and credentials. And last year, the institute hosted the first OpenAI Academy Summit held at an HBCU, drawing 444 participants from more than 40 institutions.
The institute’s rapid growth has created a new challenge: continuing its momentum.
“Funding right now is the biggest barrier for [IAIER] to remain sustainable,” Grady says. As interest in the institute continues to grow, demand for its programs is beginning to outpace its capacity. “People just want more,” she says.
The bottleneck reflects a broader tension across higher education. AI is evolving quickly, while developing new academic programs, training faculty, and building research capacity takes time. The uncertainty is compounded by a shifting political landscape. As a whole, U.S. universities are grappling with proposed cuts to federal research spending and increased scrutiny of diversity-focused initiatives under the Trump administration. However, in September 2025, the administration also announced a $500 million one-time investment in HBCUs and higher-ed institutions chartered by Native American tribal governments.
Meanwhile, NCCU has continued to attract new investment. Last September, in a collaboration with Howard University and two other institutions, IAIER received a nearly $500,000 award through a National Science Foundation research coordination network program to help define emerging AI jobs, identify in-demand skills, and inform future credentials and curricula. That work will continue this fall when IAIER opens its first dedicated physical space on campus, Grady says.
Over the next several years, Grady plans to expand academic programming, launch the university’s computer science major and its AI minor, increase faculty research opportunities, and integrate AI more deeply across campus operations. She also plans to deepen the institute’s collaborations with industry partners.
Beyond program expansion, Grady sees the institute’s long-term success as linked to building a model other universities can adapt. “We’re creating a framework that can help not only HBCUs,” she says, “but also help any university looking to do similar work.”
2026-07-29 19:00:04
Artificial intelligence is rapidly becoming part of everyday infrastructure–in some places. It helps write emails and software code, filters job applications, powers recommendation systems, and is increasingly being integrated into education, healthcare, finance, and public administration. Industry leaders talk about “AI for everyone,” while governments rush to publish national AI strategies and build sovereign compute.
Yet over the past decade, working on digital inclusion and digital literacy projects in regions from Europe to sub-Saharan Africa and Southeast Asia, I’ve seen the same pattern repeat: each new wave of “transformative” technology lands on a landscape already stratified by connectivity, skills, and institutional capacity. The current AI wave is no exception. If anything, it amplifies those underlying fractures.
Still, some countries areexploring ways of participating in AI development without directly replicating the frontier model race dominated by the United States and China. Recent developments in South Africa and Indonesia illustrate both the possibilities and challenges. The stakes extend far beyond access to AI. Countries that remain primarily consumers rather than creators of AI risk losing opportunities to build local innovation ecosystems, strengthen public-sector capacity, and ensure that their own languages, cultures, and societal priorities are reflected in AI systems. In this sense, the AI divide is also becoming a divide in economic opportunity and technological influence.
Recent analyses from Stanford University’s 2026 AI Index report that the United States alone hosts more than 5,000 data centers, over ten times as many as any other single country. Because AI workloads are increasingly performed on cloud platforms rather than local infrastructure, this concentration of compute also becomes a concentration of dependency. According to World Bank data, in 2023 the United States accounted for roughly 87 percent of global exports of cloud computing and data storage services.
For most countries, this means that AI development is not just technologically, but commercially and geopolitically outsourced and out of their control. The result is an AI ecosystem where a small number of states and firms host the computational engines that power globally deployed systems.
Systems trained, standardized, and governed within a narrow set of institutional and linguistic environments may struggle to serve a genuinely global public.
Even where connectivity and cloud access exist, not everyone is equally positioned to make use of them. Across OECD countries, only around 40 percent of adults possess more than basic digital problem-solving skills, while advanced computational and AI-related competences remain concentrated among highly educated workers and technology-intensive sectors.
At the same time, governments are racing to integrate AI into education, often starting at higher levels of schooling. UNESCO has reported growing efforts worldwide to integrate AI into education, while support for AI literacy in primary and lower secondary education, as well as ethical training for educators, remains uneven.
Those with robust schooling, advanced digital skills, and stable connectivity are best positioned to treat AI as a tool to extend their capabilities. Recent OECD survey data show that participation in AI-related training remains strongly stratified by educational attainment: 36 percent of respondents with tertiary education reported undertaking AI-related training in the previous year, compared with just 18 percent of those with upper secondary education. Those on the wrongt side of the divide are more likely to experience AI as an opaque system acting upon them, from algorithmic welfare systems such as the Dutch childcare benefits scandal to AI-assisted hiring tools such as Amazon’s discontinued AI recruiting system, rather than as a technology they can actively interrogate or shape.
The core agenda-setting power often remains with a narrow set of industry actors and a small group of technologically advanced states. Most other countries remain in a perpetual catch-up posture, adapting imported models, standards, and templates for “trustworthy AI” to their own contexts, and may have limited local capacity to assess trade-offs or propose alternatives.
In countries such as Indonesia and South Africa, communities generate data at massive scale yet still have little voice in how AI systems are designed, governed, or deployed. Their languages are underrepresented in training data; their institutions are under-resourced in regulatory forums; their experiences rarely feature in benchmark datasets. For many countries in the Global South, participation in AI still occurs largely through adapting imported systems rather than shaping how those systems are designed, governed, or deployed.
In South Africa, the Department of Communications and Digital Technologies released a draft national AI policy in April 2026, proposing new oversight institutions. The department withdrew the draft days later after a journalist discovered that at least six of its academic citations did not exist, apparently AI-generated hallucinations. The minister called it “an unacceptable lapse.“ The episode sharply illustrates the gap between AI governance ambition and the institutional capacity needed to implement it, though the new AI panel the country has since constituted has a chance to use South Africa’s unique leverage.
Indonesia presents a case of deliberate, if constrained, public-sector agency. The National Research and Innovation Agency (BRIN) which now leads AI implementation under the national strategy, has built practical AI tools aimed at underserved communities rather than frontier capabilities such as an app that uses satellite data and machine learning to help artisanal fishermen locate schools of fish, multilingual language models trained on Indonesian and local languages such as Javanese and Sundanese, and AI chatbots deployed in government services. In August 2025, the Ministry of Communication and Digital Affairs released a national AI roadmap with a target of training 100,000 AI-skilled workers annually.
The choice is not simply between “AI superpower” and “passive recipient.”
Regional cooperation may also become increasingly important. In 2024 African ministers adopted a Continental AI Strategy and African Digital Compact and participants in the April 2025 Global AI Summit on Africa in Kigali explored how regional coordination, local-language AI models, public universities, and open-source ecosystems might reduce long-term dependence on externally developed AI systems.
None of this means that people should slow or abandon AI, nor that cloud concentration or venture capital are inherently bad. Instead, when we talk about an “AI revolution,” we should also ask who can shape it and who can merely adapt to it.
Digital divide debates once focused on devices and connectivity, later expanding toward skills and outcomes. But the current AI wave adds another layer: disparities in who can meaningfully participate in deciding what AI is for, which problems it is meant to solve, and which social priorities it ultimately serves.
For engineers and policymakers, this raises difficult but necessary questions. Are they designing AI systems and infrastructures that broaden, rather than narrow, participation in shaping technological change? When governments roll out national AI strategies or integrate AI into public services, whose constraints, languages, and institutional realities are they including?
Many observers frame the current AI moment as a competition. But technological competition is never only about speed. It is also about who can influence the direction of change.
AI is already spreading globally. The deeper question is whether the technologists and policymakers responsible for it will ensure that meaningful participation in shaping that future will spread as well.
2026-07-29 02:00:01
In shaping her career, Peru native Mariana Costa has asked herself a question: What can I do to make life better for women in Latin America?
The answer she landed on was training them for tech jobs.
Employer
Laboratoria
Title
Co-founder and president
Alma Maters
London School of Economics; Columbia
Such positions pay well and are in demand. And for too long, women across the region have been locked out of them, she says.
Costa is president of Laboratoria, a U.S.-registered nonprofit based in Miami that she helped found. Laboratoria has trained thousands of women in 11 Latin American countries for technology careers. She has built training centers in the countries and has placed graduates at major companies. Meanwhile, she has become one of the most recognized voices in the region on workforce equity and tech education for women.
IEEE recognized her work with its President’s Award this year for her “distinguished leadership and contributions to the betterment of society.” Recipients of the award are selected by the IEEE president with the consent of the IEEE Board of Directors.
Costa says the recognition came as a surprise because she is not an engineer by training and had never considered becoming affiliated with IEEE.
She was presented with the award at the IEEE Honors Ceremony on 24 April in New York City.
Costa grew up in Lima, Peru’s capital, in a household with no connection to engineering or technology. Her mother was an art historian and professor, and her father was a lawyer. The family was financially comfortable and traveled abroad regularly. Costa attended well-resourced schools.
That economic stability came with a reckoning, Costa says, in that she recognized early on that economic inequality had created separate societies inside Peru. Her parents, she says, made it “clear that my reality wasn’t the reality of most people in my country.”
Lima is a microcosm of the country, she says. The divide in the capital city is visible: A kilometers-long concrete wall topped with barbed wire separates wealthier neighborhoods from shantytowns, where residents lack running water.
Nationally, there are splits along ethnic and geographic lines. The highland and jungle regions remain home to mostly indigenous communities with limited educational access and a deep cultural distance from the Hispanic-dominated coast.
The questions that stirred in her as a child never left, she says.
“Why do I live in a country where so much depends on where you’re born?” she asked herself. “What does it mean to be Peruvian when individual realities are strikingly different?”
Those questions followed her to the London School of Economics, where she studied international relations, graduating with a bachelor’s degree in 2007. She held onto the questions when she moved to Washington, D.C., where she spent the next four years working for the Organization of American States, helping Latin American governments improve public services that fall under the heading of civil registration.
“I said, ‘How can it be? The tech space has so many rich opportunities. Why aren’t any women here?’”
The OAS Universal Civil Identity Program in the Americas provides technical support to national civil registry institutions, modernizing them to foster social inclusion and ensuring the right to civil identity for all people. Without civil identity, a person can’t access education, health care, legal employment, social services, or the right to vote. People without the classification don’t exist in the eyes of the government. They also can’t own property, get married officially, or pass citizenship rights to their children.
Doing that work deepened her concern about the socioeconomic disparities in her homeland, she says. In search of practical solutions to those problems, she went to New York City in 2011 to further her education. She earned a master’s degree in public administration and development from Columbia in 2013.
Technology was not yet part of a solution. But Costa already had met someone who would change that.
While working in Washington, Costa met Herman Marìn, a software engineer who used digital tools in support of social causes. Because he was doing work she had never associated with programmers before, her assumptions about the field dissolved quickly.
“I had a vision of [programmers doing] something not very social—strictly technical,” she says. “And my then-boyfriend, now husband, actually worked for different social movements that used technology to address social causes.”
That realization cracked something open, she says: “I said, ‘Oh! Technology can actually be a tool to address some of the more stubborn problems in our societies.’”
After earning her degree at Columbia, Costa returned to Lima with her husband. She had been abroad for nearly a decade and felt the pull of home.
“The thought of not moving back to my country was something that tormented me a bit,” she says. “I really felt I had to move back, at least to try it out and contribute somehow.”
When Costa, her husband, and a friend from graduate school moved to Lima, they had modest savings and big ambitions. They wanted to build something that combined technology with social impact.
They started with what they had: a small digital services agency, where they built websites for clients.
The business grew, and they hired more employees. Their team expanded to a dozen software engineers. And as it did, Costa noticed three things.
First, there weren’t enough trained developers to meet the demand. Second, many of their best hires did not have traditional computer science degrees. Some had never even finished college.
“There was no other space where you could actually build an amazing career and get a well-paying job without a good degree from a good school,” she says. “The tech world was different. It was open in ways other fields weren’t.”
Thirdly, she noticed that there were no women on the team. In the first six months, Costa says, they didn’t interview a single female developer.
Her colleagues shrugged. It’s just how it is, they told her.
Costa, the outsider, didn’t accept that.
“I said, ‘How can that be? The tech space has so many rich opportunities,’” she says. “‘Why aren’t there any women?’”
In 2014 she decided to launch Laboratoria. The business model was simple: Find talented women who hadn’t yet broken into tech, train them quickly on practical skills, and connect them with employers who needed developers.
Laboratoria started offering a six-month immersive boot camp that covered Web development, UX design, data literacy, strategic use of artificial intelligence, and soft-skills coaching such as interview prep and projecting confidence.
Just as important for career success, Costa says, is a user-centered mindset. She says Laboratoria’s program emphasizes the discipline of keeping the client’s needs in mind when designing the work.
The teaching model has evolved beyond the boot-camp structure, but the organization still focuses on helping Latin American women develop tech skills and land quality jobs in the digital age. These days, the training, conducted via twice-weekly live Zoom sessions, lasts six weeks.
“We needed developers ourselves,” she says of the company’s original logic. “I said, ‘Why don’t we run a program to train women—women who are incredibly talented but haven’t been given a chance yet—and help them gain the skills they need to get a great job as quickly as possible?’”
Mariana Costa [seated, right] poses with Laboratoria co-founder and CEO Gabriela Rocha and co-founder and chief product officer Rodulfo Prieto.Valeria Martens
It worked. Laboratoria expanded from Lima to Santiago, Chile; Mexico City; São Paulo, Brazil; and Bogotá, Colombia. The organization eventually incorporated as a nonprofit in the United States. Today its programs are held remotely in Latin America’s major job markets. So far, Laboratoria has opened the doors to tech careers for more than 3,500 women.
Costa says she believes the most important skills Laboratoria’s graduates need aren’t purely technical. Close behind the growth mindset is self-confidence, she says.
“Knowing who you are, valuing who you are, and learning to trust yourself and your capacities are indispensable traits,” she says.
Networking, she adds, is the third pillar, and often the hardest to build for women without access to elite schools or flexible work schedules.
“When you go out in the market,” she says, “you realize that having a network of people who trust you and know your work is such a valuable and critical asset.”
Costa’s introduction to IEEE came late—but it landed hard.
She is not an IEEE member, so when she was contacted this year about receiving the President’s Award, she did her homework on the organization. What she found, she says, was a public charity whose reach and values aligned with her mission.
“IEEE is about expanding access to opportunities in the world of technology,” she says. “And that’s also the core of what we do at Laboratoria.”
She says she also sees IEEE as a living example of something her company preaches every day: “I was talking about the value of professional networks, and I think IEEE is such an amazing reference in that space. It exemplifies this belief that human connection—not only doing your work but also sharing and learning with others—is at the core of building thriving technology careers.”
The engineering organization found her well after she launched her tech-related career. But it wasn’t too late. She says she intends to make the most of the connection.
2026-07-28 21:00:01
Physicists have been trying to measure the fundamental gravitational constant for well over two centuries. The current accepted value of big G, as it’s known, is 6.67430 × 10-11 cubic meters per kilogram per square second. It also has an uncertainty of ±0.00015 × 10-11 m3/(kg s2). As far as constants of the universe go, that’s very uncertain.
Schlamminger is a physicist at the U.S. National Institute of Standards and Technology.
Stephan Schlamminger recently completed a 10-year effort at the U.S. National Institute of Standards and Technology to replicate an earlier measurement of big G from the International Bureau of Weights and Measures, or BIPM (located near Paris) that’s notably higher than most measurements. He spoke with IEEE Spectrum about why it took so long to get a number—6.67387 x 10-11 m3/(kg s2)—and why it’s notably lower than the BIPM result, to the tune of 0.0235 percent.
Why is it so difficult to measure big G?
Stephan Schlamminger: Gravity is very weak. When you were a kid, you probably played with fridge magnets, and it was a force you could feel. But if you have two coffee cups, you can try all you want—you can’t feel the force between them. It is there, but it’s so, so weak.
How did you attempt to measure big G?
NIST used a torsion balance with a fourfold geometry. This animation shows an exaggerated version of how the outer green masses gravitationally attract the inner blue masses.S. Kelley/NIST
Schlamminger: We used what’s called a torsion balance. The key idea in the torsion balance is that it decouples vertical gravity that you have from Earth from horizontal gravity, and that makes it sensitive to masses that are around the torsion balance but not the Earth below.
Ours had a fourfold geometry. It has a very thin torsion strip, then four cylinders in a “plus sign” arrangement. All of this is inside a vacuum. Outside, we have four larger cylinders that gravitationally attract the four smaller masses to them. If I move the outer masses just a tiny little bit, the plus sign will rotate, and we measure that angle that it moves. That angle is proportional to the gravitational torque.
Why try to replicate the BIPM value?
Schlamminger: We could move the field forward. The measurements have been plagued with inconsistencies, so by redoing an experiment, we hoped to shed light on the inconsistencies.
We did not find a smoking gun, so there’s no single reason why it’s different—our value versus their value. It’s still a big question mark.
What was it like spending 10 years on this?
Schlamminger: It’s a bit like herding cats. I’ve measured other fundamental constants, like Planck’s constant, and for most experiments, they have some sort of self-calibration built in. But with the gravitational constant, you have to keep track of every single mass that moves—where they are, how big they are, and weigh them.
How does your result compare to the rest?
Schlamminger: Our result is a little bit below the standard accepted literature value. I was disappointed because it doesn’t agree with the BIPM value, nor with the literature value. If there’s something wrong with the BIPM experiment, then the literature value—which includes that result—probably ought to come down a bit. But that is not for me to say. I think somebody else, independent, should figure out what the new mean value ought to be.
2026-07-28 01:54:07
An overview of how mode-agile threats challenge static library radar/EW systems, and how AI/ML cognitive architectures enable adaptive, real-time countermeasures.
What Attendees will Learn