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Nate Bargatze Finally Explained Why He's Getting Into The Theme Park Business, But The Internet Still Seems Confused
Nateland is in the works!
‘Euphoria’: Is Nate Jacobs Dead? That Rattlesnake Moment Explained
Inside that rattlesnake moment.
‘Euphoria’: Is Nate Jacobs Dead? That Rattlesnake Moment Explained
Inside that rattlesnake moment.
‘Euphoria’: Is Nate Jacobs Dead? That Rattlesnake Moment Explained
Inside that rattlesnake moment.
‘Euphoria’: Is Nate Jacobs Dead? That Rattlesnake Moment Explained
Inside that rattlesnake moment.
How integrating tradition and self-care has strengthened public health in the Americas
On 13 May 2026, a regional dialogue for the Americas titled, Innovative Experiences to Strengthen Health and Self-Care through Traditional, Complementary, and Integrative Medicine, was hosted by EsSalud, the government agency responsible for social security and health care in Peru.The event brought together representatives from the World Health Organization (WHO), the Pan American Health Organization (PAHO), WHO collaborating centres, the health sector and academia. It presented regional perspectives from BrBolivia, Brazil, Mexico and Peru to explore how self-care and traditional medicine can strengthen public health, empower communities and support more inclusive models of care. Across all four countries, a clear message emerged: traditional medicine is not being positioned as an alternative to formal systems, but as a way to strengthen them. In the Americas region, the challenge is to consolidate intercultural and holistic care, with the aim of strengthening the healthcare system – making care more preventive, culturally relevant and community-based.Peru: institutionalizing complementary care over 28 years Dr Martha Villar López from EsSalud Peru demonstrated how integration can be institutionalized over time. With nearly three decades of investment, including a research programme dating back to 1998, traditional medicine is now embedded within public health infrastructure. EsSalud’s flagship programme, Reforma de Vida, operating as Prevenir, takes multidisciplinary teams into workplaces to reduce and detect risk factors for chronic noncommunicable diseases. Education cycles are structured around three pillars: celestial nourishment, focused on life purpose and movement; terrestrial nourishment, centred on regional food and healthy diets; and human nourishment, addressing identity, relationships, stress and spirituality. Grounded in Andean cosmovision and the principle of the ayllu, the programme aligns with local cultural values. Since 2007, it has trained over 6600 health leaders, certified more than 3100 health-friendly organizations and built alliances with nearly 11 000 employers.Bolivia: legal recognition and medical pluralism Adelino Pau Carpacheco, an 82-year-old Callawaya healer, described working with more than 220 medicinal plants, supported by deep generational knowledge. Traditional healers and plant medicine are highly respected in the country. Bolivia’s contribution also highlighted the role of policy in enabling integration. Through Law 459, traditional medicine has been formally recognized within a legal framework and led the country to medical pluralism where traditional medicine coexists with biomedicine, and both are now offered free of charge through the public health system. Mexico: preserving Indigenous knowledge and reinventing food culture Mexico presented two complementary approaches focused on knowledge preservation and food systems. In Veracruz, communities partnered with a university to safeguard Totonac traditional medicine through a diploma programme launched in 2019. This collaboration has already produced practical outputs, including manuals and a dictionary, helping to sustain knowledge at risk due to migration and generational change. Alongside this, Dr Hernán García, Directorate of Traditional Medicine and Intercultural Development – Mexico, explained how the Dieta de la Milpa programme addresses Mexico’s dual burden of malnutrition and rising noncommunicable diseases. Rooted in traditional Mayan cultivation systems and ingredients, it promotes healthier diets through community-led, intercultural approaches involving local authorities, Indigenous leaders and cooks. By reconnecting food, culture and health, it offers a practical pathway to prevention. Brazil: community therapy as a public health tool Brazil’s experience centred on Integrative Community Therapy, a methodology developed over 40 years ago by Dr Adalberto Barreto and now used in more than 47 countries. Therapy circles create spaces where participants share experiences and coping strategies, building social support networks at very low cost. Within Brazil’s Unified Health System, the approach has expanded rapidly since its inclusion in national policy in 2017. By 2025, more than five million integrative practice procedures were delivered annually in primary care, with registrations for this therapy increasing by over 500% in eight years. The main challenge now is equity, with access still concentrated in urban areas such as São Paulo. A shared regional direction against a global backdrop Drawing these experiences together, Dr Natalia Aldana of the TCIM Network of the Americas highlighted the shared outcomes of the interventions discussed. Such approaches strengthen healthy behaviours, social networks and community resilience; they are low-cost, adaptable and enable active participation in care, while showing potential to reduce the burden of noncommunicable diseases.The regional momentum mirrors a broader global shift. Dr João Paulo Souz, Director of BIREME (Latin American and Caribbean Center on Health Sciences Information), the specialized center of PAHO/WHO, emphasized that the Global Traditional Medicine Strategy 2025–2034 promotes a vision centred on people, evidence and integrated use. He noted that health should be understood through the relationships between people, community, nature and spirituality. Self-care interventions as a critical pathway towards universal health coverage Dr Manjulaa Narasimhan, Unit Head for Sexual Health and Well-being across the Life-course at WHO, delivered a presentation titled Advancing Self-Care in Traditional Medicine: Aligning the WHO Traditional Medicine Strategy 2025–2034 with the WHO Self-Care Framework, co-authored by Dr Geetha Krishnan Gopalakrishna Pillai, Unit Head for Research, Data and Innovation at the WHO Global Traditional Medicine Centre. WHO recommends self-care interventions for every country and economic setting as a critical path to reach universal health coverage – and traditional medicine acts as a bridge for self-care. Dr Narasimhan explained that traditional medicine contributes most effectively to self-care when embedded directly into primary health care, not operating in parallel, siloed systems. This, she stated, “is possibly the only sustainable way to deliver continuity of care that meets the needs, rights and priorities of communities and individuals”. The Global Traditional Medicine Strategy 2025–2034 is a move towards a more people-centred approach to health care that champions holistic approaches to health equity and patient autonomy.The integration of traditional medicine and self-care requires collaboration across disciplines, clear referral pathways, trained health and care workers, and strong leadership that supports integration. The WHO self-care competency framework defines 10 key competencies for health and care workers to support self-care in their clinical practice, supported by the WHO guideline on self-care interventions for health and well-being. These documents help health-care workers safeguard their own well-being and ensure that care is consistent, safe and grounded in shared standards and values.Together, the discussion through this dialogue points to a fundamental shift in how health systems are designed in the region, and globally, towards models that are more inclusive, culturally-grounded and responsive to the needs of the communities they serve. It is a move towards a system where traditional medicine is respected and responsibly integrated, where self-care provides empowerment and evidence-based care, and where health systems are supportive rather than being distant from these systems.Traditional medicine perspectives from Bolivia, Mexico and Peru - WHO's "Restoring Balance" seriesInnovative Experiences to Strengthen Health and Self-Care through Traditional, Complementary, and Integrative Medicine was streamed in Spanish, with Spanish and Portuguese subtitles. Recordings are available through the CABSIN YouTube channel with Portuguese subtitles and on the BVS MTCI Americas YouTube channel with Spanish subtitles. Dr Narasimhan’s session can be watched in English on both channels.
One UI 8.5 and One UI 9 Explained: What's the Difference -
One UI 8.5 runs on Android 16 and is here now. One UI 9 runs on Android 17 and is still in beta. Here is the difference.
Who (or What) Is ‘Mother’ on ‘The Boroughs’? The Creature, Explained
The series begs the question, 'What is a monster?'
Ubisoft Confirms Black Flag And Its Remake Are Both Canon
The leads on Assassin's Creed Black Flag Resynced explained how both the original and remake are canon
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Why are the Enhanced Games, taking place in Las Vegas this weekend, so contentious, and how have governing bodies have responded to the event?
‘The Boys’: Homelander’s Gruesome Ending Explained by Creator Eric Kripke
Plus, Eric Kripke reveals the reason behind that Hughie and Annie reveal in the finale's closing moments.
Curiosity-driven research explained: Basic science lays the foundation for life-saving discoveries - Morgridge Institute for Research
When scientists ask fundamental questions about life, biomedical breakthroughs follow — often at a distance. Why should we embrace the long, unpredictable path from curiosity to cure?
Why Warren Buffett's meeting guide expects bigger crowd next year
Tilman Versch, who's published numerous guides to Berkshire Hathaway's annual meeting, explained why he's optimistic about attendance next year.
Science behind sobriety field tests
Albany High students in Elizabeth Lopatniuk's health classes participated in a dynamic lesson about the effects of alcohol on the body. After taking an in-depth look at the science behind why and how alcohol slows the activities of the brain and central nervous system, students learned about how a variety of factors can impact the level of intoxication and how blood alcohol concentration can impair one's ability to operate a motor vehicle. Albany High Student Resource Officer Nick Schuler led students through an overview of field sobriety tests that are routinely performed during a traffic stop when an officer suspects a driver may be under the influence of alcohol. Officer Schuler explained the three elements of a physical sobriety test, including walking a straight line with turns and checks for motor skill patterns, standing on one foot to check for balance and following a moving stimulus with eyes only to check for Horizontal Gaze Nystagmus (HGN), an involuntary, rhythmic jerking of the eyes as they look to the side. Students volunteered to try walking a straight line both with and without alcohol impairment simulation goggles. Officer Schuler pointed out particular behaviors and patterns that would lead to a failed field sobriety test during the simulation. As prom and graduation festivities approach, we applaud our Albany High staff for empowering our Falcons with valuable lessons so they can make wise and safe choices.
Science behind sobriety field tests
Albany High students in Elizabeth Lopatniuk's health classes participated in a dynamic lesson about the effects of alcohol on the body. After taking an in-depth look at the science behind why and how alcohol slows the activities of the brain and central nervous system, students learned about how a variety of factors can impact the level of intoxication and how blood alcohol concentration can impair one's ability to operate a motor vehicle. Albany High Student Resource Officer Nick Schuler led students through an overview of field sobriety tests that are routinely performed during a traffic stop when an officer suspects a driver may be under the influence of alcohol. Officer Schuler explained the three elements of a physical sobriety test, including walking a straight line with turns and checks for motor skill patterns, standing on one foot to check for balance and following a moving stimulus with eyes only to check for Horizontal Gaze Nystagmus (HGN), an involuntary, rhythmic jerking of the eyes as they look to the side. Students volunteered to try walking a straight line both with and without alcohol impairment simulation goggles. Officer Schuler pointed out particular behaviors and patterns that would lead to a failed field sobriety test during the simulation. As prom and graduation festivities approach, we applaud our Albany High staff for empowering our Falcons with valuable lessons so they can make wise and safe choices.
Study: Younger scientists produce more disruptive research
“You stick to a certain kind of idea or taste, and as time goes by you keep sticking to that," explained one of the researchers.
Bioshock creator Ken Levine Reveals Why Judas Took a Decade to Develop: "We Kissed Many Frogs"
In a recent interview with IGN, Ken Levine explained why his next game Judas has been ten years in the making.
The National Science Board purge, explained
Amid the many attention-grabbing headlines of 2026, there is a recent one that may have flown under the radar but shouldn't have. On April 24, the White House dismissed the entire 22-person board that ...
“Cannot be explained” – New ultra stainless steel stuns researchers
A team at the University of Hong Kong has developed a new “super steel” that can survive the harsh conditions needed to make green hydrogen from seawater. The material uses an unexpected double-protection mechanism that resists corrosion far better than conventional stainless steel. Even more impressive, it could replace costly titanium parts used in today’s hydrogen systems.
The National Science Board purge, explained
Amidst the many attention-grabbing headlines of 2026, there is a recent one that may have flown under the radar but shouldn’t have. On April 24, the White House dismissed the entire 22-person board that oversees the National Science Foundation. The NSF is an independent federal agency that supports science and engineering in all 50 states and U.S. territories.
Resident Evil Requiem Leon Must Die Forever DLC, explained
Enjoy more hours of Leon S. Kennedy killing zombies
NBC Sports' Full WNBA Broadcast Team, Explained: Play-By-Play, Commentators & More
With the WNBA returning to NBC and debuting on Peacock for the 2026 season, here's NBC Sports' all-star roster of broadcasters, analysts and commentators.
Amazon CEO Andy Jassy explains why Amazon is opening its supply chain to every business
In a recent interview, Jassy explained how Amazon built its logistics network for its own retail business—and why it's now offering those services to companies of all sizes.
Trump Sparks Health Concerns With Weird Food Boast
The president appears to have inadvertently explained one of his lingering ailments.
Advanced AI helps 3D imaging labs evolve with the times
“Technology grows and grows,” as one lab director explained. “Our job is to keep up with it and use it in the most efficient and effective way.”
HEALTH Yeah! Episode 30: Protecting Your Skin: Melanoma and Other Skin Cancers Explained
The discussion covers common skin cancers, as well as one of the most dangerous types of skin cancer, melanoma.
'Watson' ending explained: How the CBS series says farewell to John Watson
The 'Watson' finale features a long-awaited reunion — as well as the arrival of a famous villain from the Sherlock Holmes canon. Here's how the CBS series, which was canceled earlier this year, says farewell to John Watson.
How Perimenopause Became America's New Health Scare
If you’re a woman who speaks English, it sometimes feels like you can’t spend time on the internet without being bombarded with all the ways in which your body or your mind could be just a little broken. A few years ago, Instagram was full of women talking about attention deficit disorder, and lo and behold: From 2020 to 2022, the percentage of women (between the ages of 23 and 49) newly diagnosed with it nearly doubled. Not long ago, premenstrual syndrome was all the rage, with the algorithm pushing videos that explained why it’s okay to fly off the handle once a month. These days, the word perimenopause is everywhere. Perhaps you’ve seen the headlines. “Perimenopause is having a millennial moment. Here are 7 ways to cope,” says The Washington Post. Or perhaps you’ve seen Apple TV’s glossy drama Your Friends & Neighbors, which is back for its second season; the female protagonist Mel is explicitly perimenopausal, and in the first season, she keyed a car for no apparent reason. She’s played by Amanda Peet, who talks about being perimenopausal in real life (“God knows I’ve wanted to key a lot of cars in the last three years,” she’s said.) In fact, it sometimes feels like every celebrity interview with a forty- or fiftysomething woman requires her to open up about what’s going on with her hormones. Drew Barrymore joked on CBS Mornings that when she found out how long perimenopause can last, she thought she’d “never make it 10 years like this.” Katherine Heigl told Business Insider that “whoever designed it so that women would be going through perimenopause while raising teenagers should be sent a strongly worded letter.” At the same time, influencers who talk about perimenopause seem to have cropped up out of nowhere with something to sell you, causing people online to wonder where this all came from. It’s trickled down from Instagram to tchotchke shops, which sell fridge magnets that say “Perimenopause Is Hot.” You can buy a T-shirt that proclaims “Perimenopause Made Me Do It!” My mom, who is 48, started uncontrollably shaking her leg at night a few years ago. “It’s perimenopause,” she told me when I complained about how annoying it was. But what exactly is perimenopause—and why are we all talking about it all of a sudden?
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field
‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field ‘The reality, for better or worse’: Columbia comp sci students and faculty grapple with AI’s disruption of the field A field that once seemed like a direct path to stable, lucrative work is becoming less certain under AI, students and faculty told Spectator. By Ria Vasishtha and Arjun Menon May 3, 2026 Shanying Liu / Deputy Illustrations Editor Asia Genawi, SEAS ’29, first encountered artificial intelligence during her sophomore year at her high school in Indiana. Back in 2023, policies on AI varied widely—even within the same district—and some schools had no guidance at all. As Genawi began to see the effects of AI trickle into her classroom, she turned to guidance from her home state’s Department of Education, which she said she found “outdated and inaccessible.” Genawi spent the rest of her high school career dedicated to the issue, including writing a 15-page memo exploring varying AI policies across Indiana schools, which she sent to every Indiana state senator and representative whose contact information she was able to find. She later helped draft a bill that would have required public high schools to establish and share their own AI policies with students in an accessible format. But with so many other bills already awaiting referral, hers never made it to committee. When Genawi arrived at Columbia to study computer science, she found that the gaps she had spent years trying to address in Indiana had followed her there. Many students and professors were also grappling with what AI meant for the field. Across Columbia’s undergraduate schools, computer science has become the most popular major—accounting for around 11 percent of degrees awarded in Columbia College, 32 percent in the School of Engineering and Applied Science, and 12 percent in the School of General Studies in 2024. While the number of computer science majors in SEAS grew from 166 to 173 during the 2024-25 academic year, the total number across the three schools fell around 4.5 percent from 393 to 375, driven by declines at Columbia College and General Studies. At Barnard, it has grown to become the second most popular major as of 2025, up from third the year prior. The number of degrees awarded have grown fivefold since 2016. Faculty members and students in the department told Spectator that the surge in the major’s popularity has been driven in large part by its apparent promise of a secure, well-paid career path. However, they noted that the recent rise of generative AI has compromised the sense of stability that once defined the field, even as interest in technical skills among students persists. According to data from the Federal Reserve Bank of New York in 2024, out of 74 majors tracked, computer science had the fifth highest unemployment rate among majors at 7 percent, while computer engineering had the second highest at 7.8 percent. “Anecdotally, people are moving away from CS as a major,” Daniel Bauer, a senior lecturer in the computer science department, said. “They’re still taking some of our classes, right, because they think they need that, but they’re instead majoring in other things.” Bauer described a field that had shifted from what he called a “nerdy outlier discipline” to one that “everyone needs,” a sign that technical skills have become a baseline expectation across industries with the emergence of AI. “A lot of people thought, ‘Oh, it’s a guaranteed path to a stable income. You get a six-figure job right out of your undergrad,’” Bauer said. Now, companies may see AI as a way to reduce demand for entry-level programmers, especially in what he calls “code monkey jobs,” roles where a large number of employees are “just writing out code nine-to-five.” Chris Murphy, a senior lecturer in the computer science department, described the current moment as the convergence of two forces: the downturn of the job market and generative AI coming onto the scene. “Things kind of started to really pick up in 2014, 16,” he said, recalling the years after the weaker 2010 market. Murphy added that this was also a period where “students were getting more experience outside of class than they were inside of class” through internships, jobs, and side projects. Murphy suggested that the earlier momentum helped make computer science feel like both an academic program and a reliable pathway into work. That feeling is harder to sustain now as the industry reconfigures, which is a shift that students are seeing firsthand. Rebecca Yu, SEAS ’27, said that during a recent internship in Big Tech, she saw that “every single tech company was racing to integrate” large language models. She added that her manager said her intern class was the first to complete their assigned projects, which she attributed to the increased efficiency that comes with AI. Frank Liu, SEAS ’29, said that with AI lowering the barrier to coding, companies now expect everyone to write code. “A lot of companies outright say if you’re not using coding in your job, you might get fired for not being productive enough,” Liu said. Yu similarly expressed that students increasingly have to accept AI being a part of industry workflow, adding that many also “make use of it to maximize their productivity.” Richard Li, CC ’28, described a “franticness” among his peers as the pace of change accelerates. He explained how “some new tool” is released regularly, noting that tools rise and fall in prominence within weeks. He has come to believe that he doesn’t need to “match that speed all the time” and keep up with every development. Li has learned that AI tools allow him to “take a breath” and “get back into the loop much easier than before” when he needs to. While some companies specifically target Columbia when hiring, sending representatives to campus career fairs and recruiting large numbers from Columbia, a computer science degree cannot be treated as a static credential, Yu described. “You can’t just get your degree and stop learning after that. You have to always be learning,” Yu said. “That is something you basically have to expect for your job.” AI has not only affected students’ decisions to major in computer science, but also reshaped how the subject is taught. Across departments, faculty members no longer treat work done outside the classroom as a reliable measure of understanding, shifting instead toward in-person exams, quizzes, and attendance checks. While homework once made up as much as 60 to 70 percent of a student’s grade in computer science classes, Murphy said that breakdown has since flipped, with exams and quizzes now accounting for the majority. “The assumption—especially among pessimists—is that they’re using it to do their homework,” Murphy said. “They take the homework assignment. They put it into generative AI. Out comes the solution. Submit solution. The end.” Murphy said that faculty members ranged in their responses from fully prohibiting AI use to actively teaching students how to effectively work with AI. He said he shifted his approach this year after his teaching assistants called his belief that students weren’t using the technology “naïve,” and now permits students to use AI on assignments. His surveys of students in his introductory classes have told a more complicated story than the one so-called pessimists offer: Around 60 to 70 percent of students who reported using AI said they used it to have concepts explained to them, to clarify assignment instructions, to generate test cases, or to create practice questions. “Those are all ways that you might have had a tutor in the past to do that, and now you have this AI essentially as your tutor,” he said. Bauer has taken a different approach, teaching students how to use AI productively without sacrificing the core principles of the discipline. “People who are trained to actually use AI tools productively, while being in charge of designing the overall project, will be very much in demand in the next couple of years,” he said. Over the past year, a working group of computer science faculty has been redesigning the introductory and intermediate programming sequence to integrate generative AI as both a subject and a tool, representatives from SEAS wrote in a statement to Spectator. The effort is “coordinated” across the department, “not course-by-course,” with a systematic assessment of how AI integration is affecting student outcomes underway to shape the next round of changes. The goal, the department explained, is for students to be able to “read, verify, and reason about code, whether they or a model wrote it.” Tony Dear, senior lecturer in the computer science department, is piloting individualized AI feedback in a discrete mathematics course, while a machine learning course is also testing a Socratic AI dialogue system designed to “guide inquiry rather than supply answers,” the statement reads. Beyond individual courses, SEAS funded seven cross-departmental faculty projects in generative AI this past academic year, spanning biomedical engineering, chemical engineering, industrial engineering, and computer science, according to the statement. This phenomenon extends beyond the computer science department. SEAS’ new AI minor, which it announced in November 2025, reflects the University’s attempt to adapt. The minor is specifically designed for noncomputer science majors and aims to provide “essential AI knowledge” while emphasizing practical and responsible use across disciplines, according to the SEAS website. At the center of the minor is AI in Context, a course taught by a team of faculty from the departments of computer science, applied mathematics, philosophy, music, literature, and the Writing Center, the representatives told Spectator. Bauer described it as targeted at students who want to bring AI into fields like economics or biology, rather than as a path into software engineering itself. At the graduate level, SEAS announced a new Master of Science in artificial intelligence in February, with the inaugural cohort starting in the fall. While recognizing how AI has helped them navigate coursework, students also described its rise as isolating, even among members of the Application Development Initiative, one of Columbia’s largest computer science clubs. “As people start to ask AI for questions that they would normally ask students and members, it starts to isolate people” Li said. Alexa Kafka, BC ’27, echoed Li’s sentiment, pointing out that computer science has always been a collaborative discipline with code reviews and pull requests, even as AI makes independent work easier. Despite this, Kafka noted that she hasn’t been to office hours “in at least a year.” Bauer added that faculty are exploring long-term solutions to the assessment problem. “One of the things that we’re trying to do at the department or school level is to maybe move towards computerized testing facilities,” Bauer said, referring to ideas in the department to revitalize computer labs as an alternative to pen-and-paper exams. “We have to accept that this is the reality, for better or worse.” But more exams, students say, does not mean better learning, nor does it reflect how computer science is actually done. “Code is so variable and can be approached in so many directions, there’s never just going to be one correct answer. There can be multiple optimal solutions,” Kafka said. Students also expressed concern that the shift toward exam-heavy grading pulls focus away from the applied, project-based work that better reflects both discipline and industry expectations. ADI has tried to respond more quickly than the curriculum can. Through a vibe coding workshop this past semester, club members said they wanted to help students understand not just that AI exists, but how to use it deliberately. “Vibe coding” refers to the practice of prompting AI to write code for a task or project, and has become widespread enough that ADI felt the gap in formal instruction was worth addressing, according to Sophia Huang, BC ’29. The workshop introduced students to the concept of context engineering, which Shaina Sahu, SEAS ’27, described as not just writing a prompt, but optimizing “the rest of the relevant information that you would feed into the model.” Without that, students said that AI-generated code can fail at scale or introduce difficult-to-detect bugs. Genawi said the shift toward AI has changed what it means to be good at computer science. “I think the shift has kind of gone towards what can you create and what can you do, instead of, what do you know,” she said. Indeed, during ADI DevFest, its annual hackathon, Li said more teams are trying to complete their projects than in years past. While sign-ups have historically outpaced submissions, Li said this is beginning to change because teams “have a tool that completes the vision for them.” While many students see AI as an equalizer that lowers the barrier to entry for coding and technical work, Murphy said it also creates new equity concerns inside the classroom around who can afford stronger tools and who already knows how to use them. “The equity issues I worry about are students who have access to paid versions of things versus free versions of things,” he said. Although popular AI models like ChatGPT and Claude have free versions available, both have paid models that can reach over $100 per month, which output different qualities of code. Murphy also pointed to differences in prior exposure to AI literacy itself. “Students who go to high schools or K-12 education, where they’re taught AI literacy, prompt engineering, context engineering, they understand how to do this, as opposed to students who go to schools who don’t teach that,” he said. “Then they come in, and then they’re somehow expected to know how to use these tools.” For Genawi, those disparities begin long before students arrive on campus. “It’s very interesting to consider how lucky you were geographically,” she said. “To be born where you were is influencing your ability to get access to these spaces.” Students like Yu, though broadly apprehensive about the future of computer science, are not without hope. “I’m optimistic, because I think if you’re truly passionate about CS, there will definitely be a place in the field to do something you want to do,” she said. Senior Staff Writer Ria Vasishtha can be contacted at ria.vasishtha@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Staff Writer Arjun Menon can be contacted at arjun.menon@columbiaspectator.com. Follow Spectator on X @ColumbiaSpec. Want to keep up with breaking news? Subscribe to our email newsletter and like Spectator on Facebook. Produced with Spectate by Emily Ta
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This AI knew the answers but didn’t understand the questions
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Apple Testing Two Small Yet Useful iOS 27 Options
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Seniors: Dennis Ouellette, 80
Ontario Orchards owner not slowing down as he grows his farm market By Stefan Yablonski The seeds for Dennis Ouellette’s career path were sown when he was 6 years old — and he isn’t slowing down. “Since age 6, I grew up an agriculturalist. That is 72 years. Started out with a 37-acre farm when I bought it from my parents and built it up to almost 300 acres. Brought it back down to around 200 acres now,” he said. The location of his business was just a barn, a horse barn, he said. “In 1967, it officially became Ontario Orchards Farm Market. My wife [June] and I graduated [high school] in 1964 and three years later we became Ontario Orchards. We have been here more than 60 years,” he explained. “It is the same location. We have expanded over the years as the customers mentioned different things; we have expanded to bring in different products. “What keeps me going is I totally enjoy what I am doing. I enjoy the people that I work with. The customers are wonderful. Mostly, more important than anything, is my family — my wife, my daughters, grandchildren, son-in-law — we are still very much a family business I’m not going to retire just yet.” He also enjoys the changing seasons. “Ontario Orchards is a four-season business. There is always something new; more energy, more interest. Not only for the business but for my staff, the people that work here. Things keep changing, changing. We are always looking forward to something new and exciting,” he said. They work with quite a number of local farmers within the area and throughout the state, throughout the country and from around the world.” “We do have things from different countries around the world. But specifically, we are very excited about the local. We have an over 200-acre farm. We sell just about everything we raise on that farm right here at the farm market. For example, we press cider year-round. We make baked goods every day, year-round. We sell our apples 12 months a year.” “Today, there are so many products in Ontario Orchards that we never would have thought of in 1967,” he continued. “I attribute a lot of that to my daughters. I like the young blood and new energy. That is very crucial to a business, it keeps it vibrant.” What’s his favorite part of the job? “I don’t have a favorite part. I enjoy it all. I have a lot of intertest in other things,” he said. “I have staff that knows so much. I can go to the staff and say, ‘I don’t know what this is, you tell me what this is.’ They are so knowledgeable. It is impossible for one person to know everything in this place. In the summer, staff is about 60; in the winter about 20, 25.” He added that he plans on being around for several more years.
Seniors: John Lemp, 70
Has been working for more than 50 years in the business — M. Lemp Jewelers in Syracuse — that his great-grandfather started in 1890 By Mary Beth Roach Don Lemp owns and represents the fourth-generation of the family business, M. Lemp Jewelers, but he still calls himself “a counter guy.” “I love interacting with couples. It’s a wonderful business. You’re celebrating marriages, engagements, births, anniversaries — all these different significant moments that people invite you into their lives for whatever brief period of time it might be to help them celebrate whatever their important occasion is,” he said. This is one of several reasons why Lemp, at 70, has been working for more than 50 years in the business that his great-grandfather started in 1890. Always located in the downtown Syracuse area, the store has been situated in the building that the family owns on East Fayette and Warren streets since 1971. He jokes that he’s been in the business so long he is now selling engagement rings to the second generation in some families. He also loves the opportunity to learn about the ever-changing industry and gemstones. “You have to know not only diamonds, but all the colored stones, from A to Z, from andalusite to zircons,” he said. Lemp said he had originally thought he wanted to become an attorney, but while working part-time in the store while attending college at Syracuse University, he decided to follow in the family business. His father, Walter D. Lemp Sr., and his mother tried to discourage their son from pursuing retail, reminding him of the hectic hours and lifestyle intrinsic in this business. But the younger Lemp [whose full name is Walter D. Lemp Jr.] prevailed. So, over the years, father and son got to work together in the family business for 35 years. And while many other jewelers retreated from downtown to suburban malls in the 1970s, the Lemps decided to stay downtown. And it’s a decision that Lemp Jr. feels it has worked out well. As he explained, downtown Syracuse is seeing a rebirth. One website, Point2Home.com, has noted that more than 6,000 people live there and the median income is about $62,000. In addition, according to the website that Lemp built years ago, the store can cater to customers throughout the country. His customers, whether from across the country or across Onondaga County, are just one reason that Lemp stays engaged in the business. His employees, the opportunity to continue to learn in an industry, and the challenge to continue to improve business are all other reasons, he noted. He’s also been able to travel to India to buy diamonds and he enjoys buying and selling estates. “I’d rather gallop in the corral than ride into the sunset,” Lemp said.
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
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