A Brown University undergraduate, Noah Solomon, disproved a long-standing conjecture in data science known as the "conjecture of Kahan." This conjecture, which had puzzled researchers for 40 years, stated that certain algorithms used for floating-point computations could only produce a limited number of outputs. Solomon developed a novel geometric approach to the problem, discovering a counterexample that demonstrates these algorithms can actually produce infinitely many outputs under specific conditions. His work has significant implications for numerical analysis and computer science, as it clarifies the behavior of these fundamental algorithms and opens new avenues for research into improving their accuracy and reliability.
Tufts University researchers have developed an open-source software package called "OpenSM" designed to simulate the behavior of soft materials like gels, polymers, and foams. This software leverages state-of-the-art numerical methods and offers a user-friendly interface accessible to both experts and non-experts. OpenSM streamlines the complex process of building and running simulations of soft materials, allowing researchers to explore their properties and behavior under different conditions. This freely available tool aims to accelerate research and development in diverse fields including bioengineering, materials science, and manufacturing by enabling wider access to advanced simulation capabilities.
HN users discussed the potential of the open-source software, SOFA, for various applications like surgical simulations and robotics. Some highlighted its maturity and existing use in research, while others questioned its accessibility for non-experts. Several commenters expressed interest in its use for simulating specific materials like fabrics and biological tissues. The licensing (LGPL) was also a point of discussion, with some noting its permissiveness for commercial use. Overall, the sentiment was positive, with many seeing the software as a valuable tool for research and development.
AI tools are increasingly being used to identify errors in scientific research papers, sparking a growing movement towards automated error detection. These tools can flag inconsistencies in data, identify statistical flaws, and even spot plagiarism, helping to improve the reliability and integrity of published research. While some researchers are enthusiastic about the potential of AI to enhance quality control, others express concerns about over-reliance on these tools and the possibility of false positives. Nevertheless, the development and adoption of AI-powered error detection tools continues to accelerate, promising a future where research publications are more robust and trustworthy.
Hacker News users discuss the implications of AI tools catching errors in research papers. Some express excitement about AI's potential to improve scientific rigor and reproducibility by identifying inconsistencies, flawed statistics, and even plagiarism. Others raise concerns, including the potential for false positives, the risk of over-reliance on AI tools leading to a decline in human critical thinking skills, and the possibility that such tools might stifle creativity or introduce new biases. Several commenters debate the appropriate role of these tools, suggesting they should be used as aids for human reviewers rather than replacements. The cost and accessibility of such tools are also questioned, along with the potential impact on the publishing process and the peer review system. Finally, some commenters suggest that the increasing complexity of research makes automated error detection not just helpful, but necessary.
Nadia Eghbal's 2018 post, "The Independent Researcher," explores the emerging role of individuals conducting research outside traditional academic and institutional settings. She highlights the unique advantages of independent researchers, such as their autonomy, flexibility, and ability to focus on niche topics. Eghbal discusses the challenges they face, including funding, credibility, and access to resources. The post ultimately argues for the increasing importance of independent research, its potential to contribute valuable insights, and the need for structures and communities to support this growing field.
Hacker News users discussed the challenges and rewards of independent research. Several commenters emphasized the difficulty of funding such work, especially for those outside academia or established institutions. The importance of having a strong network and collaborating with others was highlighted, as was the need for meticulous record-keeping and intellectual property protection. Some users shared personal experiences and offered advice on finding funding sources and navigating the complexities of independent research. The trade-off between freedom and financial stability was a recurring theme, with some arguing that true independence requires accepting a lower income. The value of independent research in fostering creativity and pursuing unconventional ideas was also recognized. Some users questioned the author's advice on avoiding established institutions, suggesting that they can offer valuable resources and support despite potential bureaucratic hurdles.
The blog post "Please Commit More Blatant Academic Fraud" argues that the current academic system, particularly in humanities, incentivizes meaningless, formulaic writing that adheres to rigid stylistic and theoretical frameworks rather than genuine intellectual exploration. The author encourages students to subvert this system by embracing "blatant academic fraud"—not plagiarism or fabrication, but rather strategically utilizing sophisticated language and fashionable theories to create impressive-sounding yet ultimately hollow work. This act of performative scholarship is presented as a form of protest, exposing the absurdity of a system that values appearance over substance and rewards conformity over original thought. The author believes this "fraud" will force the academy to confront its own superficiality and hopefully lead to meaningful reform.
Hacker News users generally agree with the author's premise that the current academic publishing system is broken and incentivizes bad research practices. Many commenters share anecdotes of questionable research practices they've witnessed, including pressure to produce positive results, manipulating data, and salami slicing publications. Some highlight the perverse incentives created by the "publish or perish" environment, arguing that it pushes researchers towards quantity over quality. Several commenters discuss the potential benefits of open science practices and pre-registration as ways to improve transparency and rigor. There is also a thread discussing the role of reviewers and editors in perpetuating these problems, suggesting they often lack the time or expertise to thoroughly evaluate submissions. A few dissenting voices argue that while problems exist, blatant fraud is rare and the author's tone is overly cynical.
Mathematicians and married couple, George Willis and Monica Nevins, have solved a long-standing problem in group theory concerning just-infinite groups. After two decades of collaborative effort, they proved that such groups, which are infinite but become finite when any element is removed, always arise from a specific type of construction related to branch groups. This confirms a conjecture formulated in the 1990s and deepens our understanding of the structure of infinite groups. Their proof, praised for its elegance and clarity, relies on a clever simplification of the problem and represents a significant advancement in the field.
Hacker News commenters generally expressed awe and appreciation for the mathematicians' dedication and the elegance of the solution. Several highlighted the collaborative nature of the work and the importance of such partnerships in research. Some discussed the challenge of explaining complex mathematical concepts to a lay audience, while others pondered the practical applications of this seemingly abstract work. A few commenters with mathematical backgrounds offered deeper insights into the proof and its implications, pointing out the use of representation theory and the significance of classifying groups. One compelling comment mentioned the personal connection between Geoff Robinson and the commenter's advisor, offering a glimpse into the human side of the mathematical community. Another interesting comment thread explored the role of intuition and persistence in mathematical discovery, highlighting the "aha" moment described in the article.
An analysis of top researchers across various disciplines revealed that approximately 10% publish at incredibly high rates, likely unsustainable without questionable practices. These researchers produced papers at a pace suggesting a new publication every five days, raising concerns about potential shortcuts like salami slicing, honorary authorship, and insufficient peer review. While some researchers naturally produce more work, the study suggests this extreme output level hints at systemic issues within academia, incentivizing quantity over quality and potentially impacting research integrity.
Hacker News users discuss the implications of a small percentage of researchers publishing an extremely high volume of papers. Some question the validity of the study's methodology, pointing out potential issues like double-counting authors with similar names and the impact of large research groups. Others express skepticism about the value of such prolific publication, suggesting it incentivizes quantity over quality and leads to a flood of incremental or insignificant research. Some commenters highlight the pressures of the academic system, where publishing frequently is essential for career advancement. The discussion also touches on the potential for AI-assisted writing to exacerbate this trend, and the need for alternative metrics to evaluate research impact beyond simple publication counts. A few users provide anecdotal evidence of researchers gaming the system by salami-slicing their work into multiple smaller publications.
PhD enrollment is declining globally, driven by several factors. The demanding nature of doctoral programs, coupled with often-meager stipends and uncertain career prospects outside academia, is deterring potential applicants. Many are opting for higher-paying jobs in industry directly after their master's degrees. Additionally, concerns about work-life balance, mental health, and the increasing pressure to publish are contributing to this trend. While some fields, like engineering and computer science, remain attractive due to industry demand, the overall appeal of doctoral studies is diminishing as alternative career paths become more appealing.
Hacker News users discuss potential reasons for the PhD decline, citing poor academic job prospects, low pay compared to industry, and lengthy, often stressful, programs. Some argue that a PhD is only worthwhile for those truly passionate about research, while others suggest the value of a PhD depends heavily on the field. Several commenters point out that industry increasingly values specialized skills acquired through shorter, more focused programs, and the financial burden of a PhD is a major deterrent. Some suggest the "lustre" hasn't faded for all PhDs, with fields like computer science remaining attractive. Others propose alternative paths like industry-sponsored PhDs or more direct collaborations between academia and industry to increase relevance and improve career outcomes. A few commenters also highlight the potential impact of declining birth rates and the rising cost of higher education in general.
Japan's scientific output has declined in recent decades, despite its continued investment in research. To regain its position as a scientific powerhouse, the article argues Japan needs to overhaul its research funding system. This includes shifting from short-term, small grants towards more substantial, long-term funding that encourages risk-taking and ambitious projects. Additionally, reducing bureaucratic burdens, fostering international collaboration, and improving career stability for young researchers are crucial for attracting and retaining top talent. The article emphasizes the importance of prioritizing quality over quantity and promoting a culture of scientific excellence to revitalize Japan's research landscape.
HN commenters discuss Japan's potential for scientific resurgence, contingent on reforming its funding model. Several highlight the stifling effects of short-term grants and the emphasis on seniority over merit, contrasting it with the more dynamic, risk-taking approach in the US. Some suggest Japan's hierarchical culture and risk aversion contribute to the problem. Others point to successful examples of Japanese innovation, arguing that a return to basic research and less bureaucracy could reignite scientific progress. The lack of academic freedom and the pressure to conform are also cited as obstacles to creativity. Finally, some commenters express skepticism about Japan's ability to change its deeply ingrained system.
The original poster is deciding between Physics PhD programs at Stanford and UC Berkeley, having been accepted to both. They're leaning towards Stanford due to perceived stronger faculty in their specific research interest (quantum computing/AMO physics) and the potential for better industry connections post-graduation. However, they acknowledge Berkeley's prestigious physics department and are seeking further input from the Hacker News community to solidify their decision. Essentially, they are asking for perspectives on the relative strengths and weaknesses of each program, particularly regarding career prospects in quantum computing.
The Hacker News comments on the "Ask HN: Physics PhD at Stanford or Berkeley" post largely revolve around the nuances of choosing between the two prestigious programs. Commenters emphasize that both are excellent choices, and the decision should be based on individual factors like specific research interests, advisor fit, and departmental culture. Several commenters suggest visiting both departments and talking to current students to gauge the environment. Some highlight Stanford's stronger connections to industry and Silicon Valley, while others point to Berkeley's arguably stronger reputation in certain subfields of physics. The overall sentiment is that the OP can't go wrong with either choice, and the decision should be based on personal preference and research goals rather than perceived prestige. A few commenters also caution against overemphasizing the "prestige" factor in general, encouraging the OP to prioritize a supportive and stimulating research environment.
A Nature survey of over 7,600 postdoctoral researchers across the globe reveals that over 40% intend to leave academia. While dissatisfaction with career prospects and work-life balance are primary drivers, many postdocs cited a lack of mentorship and mental-health support as contributing factors. The findings highlight a potential loss of highly trained researchers from academia and raise concerns about the sustainability of the current academic system.
Hacker News commenters discuss the unsurprising nature of the 40% postdoc attrition rate, citing poor pay, job insecurity, and the challenging academic job market as primary drivers. Several commenters highlight the exploitative nature of academia, suggesting postdocs are treated as cheap labor, with universities incentivized to produce more PhDs than necessary, leading to a glut of postdocs competing for scarce faculty positions. Some suggest alternative career paths, including industry and government, offer better compensation and work-life balance. Others argue that the academic system needs reform, with suggestions including better funding, more transparency in hiring, and a shift in focus towards valuing research output over traditional metrics like publications and grant funding. The "two-body problem" is also mentioned as a significant hurdle, with partners struggling to find suitable employment in the same geographic area. Overall, the sentiment leans towards the need for systemic change to address the structural issues driving postdocs away from academia.
Summary of Comments ( 2 )
https://news.ycombinator.com/item?id=43378256
Hacker News commenters generally expressed excitement and praise for the undergraduate student's achievement. Several questioned the "40-year-old conjecture" framing, pointing out that the problem, while known, wasn't a major focus of active research. Some highlighted the importance of the mentor's role and the collaborative nature of research. Others delved into the technical details, discussing the specific implications of the findings for dimensionality reduction techniques like PCA and the difference between theoretical and practical significance in this context. A few commenters also noted the unusual amount of media attention for this type of result, speculating about the reasons behind it. A recurring theme was the refreshing nature of seeing an undergraduate making such a contribution.
The Hacker News post titled "Undergraduate Upends a 40-Year-Old Data Science Conjecture" has generated a number of comments discussing the Wired article about Miles Edwards's work on the Conjecture.
Several commenters express admiration for Edwards's achievement. One notes the impressive nature of disproving a conjecture at the undergraduate level, highlighting the rarity of such accomplishments. Another emphasizes the significance of finding a counterexample in a widely accepted theory.
Some comments delve into the specifics of the conjecture and Edwards's work. One commenter discusses the implications for k-means clustering, suggesting that while Lloyd's algorithm is still practically useful, the conjecture's disproof raises theoretical questions. Another commenter, claiming expertise in the area, points out that the conjecture was already known to be false in high dimensions and clarifies that Edwards's work focuses on the previously unexplored low-dimensional case. This commenter further details that Edwards's counterexample used only six points and five clusters in two dimensions.
There's discussion on the practical implications of the discovery. A commenter questions the real-world impact, arguing that constant factors are often more important than asymptotic complexity in practice, particularly in machine learning. Another echoes this sentiment, suggesting that the theoretical breakthrough might not translate into significant improvements in everyday clustering applications.
One commenter expresses skepticism about the Wired article's portrayal of Edwards's discovery as "upending" the field, arguing that such framing is overblown and misleading.
Finally, some comments provide additional context, including links to Edwards's paper and his advisor's blog post. This supplementary material allows interested readers to delve deeper into the technical details of the work.