Nature reports that Microsoft's claim of creating a topological qubit, a key step towards fault-tolerant quantum computing, remains unproven. While Microsoft published a paper presenting evidence for the existence of Majorana zero modes, which are crucial for topological qubits, the scientific community remains skeptical. Independent researchers have yet to replicate Microsoft's findings, and some suggest that the observed signals could be explained by other phenomena. The Nature article highlights the need for further research and independent verification before Microsoft's claim can be validated. The company continues to work on scaling up its platform, but achieving a truly fault-tolerant quantum computer based on this technology remains a distant prospect.
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.
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 Night Watch" argues that modern operating systems are overly complex and difficult to secure due to the accretion of features and legacy code. It proposes a "clean-slate" approach, advocating for simpler, more formally verifiable microkernels. This would entail moving much of the OS functionality into user space, enabling better isolation and fault containment. While acknowledging the challenges of such a radical shift, including performance concerns and the enormous effort required to rebuild the software ecosystem, the paper contends that the long-term benefits of improved security and reliability outweigh the costs. It emphasizes that the current trajectory of increasingly complex OSes is unsustainable and that a fundamental rethinking of system design is crucial to address the growing security threats facing modern computing.
HN users discuss James Mickens' humorous USENIX keynote, "The Night Watch," focusing on its entertaining delivery and insightful points about the complexities and frustrations of systems work. Several commenters praise Mickens' unique presentation style and the relatable nature of his anecdotes about debugging, legacy code, and the challenges of managing distributed systems. Some highlight specific memorable quotes and jokes, appreciating the blend of humor and technical depth. Others reflect on the timeless nature of the talk, noting how the issues discussed remain relevant years later. A few commenters express interest in seeing a video recording of the presentation.
The blog post "The Cultural Divide Between Mathematics and AI" explores the differing approaches to knowledge and validation between mathematicians and AI researchers. Mathematicians prioritize rigorous proofs and deductive reasoning, building upon established theorems and valuing elegance and simplicity. AI, conversely, focuses on empirical results and inductive reasoning, driven by performance on benchmarks and real-world applications, often prioritizing scale and complexity over theoretical guarantees. This divergence manifests in communication styles, publication venues, and even the perceived importance of explainability, creating a cultural gap that hinders potential collaboration and mutual understanding. Bridging this divide requires recognizing the strengths of both approaches, fostering interdisciplinary communication, and developing shared goals.
HN commenters largely agree with the author's premise of a cultural divide between mathematics and AI. Several highlighted the differing goals, with mathematics prioritizing provable theorems and elegant abstractions, while AI focuses on empirical performance and practical applications. Some pointed out that AI often uses mathematical tools without necessarily needing a deep theoretical understanding, leading to a "cargo cult" analogy. Others discussed the differing incentive structures, with academia rewarding theoretical contributions and industry favoring impactful results. A few comments pushed back, arguing that theoretical advancements in areas like optimization and statistics are driven by AI research. The lack of formal proofs in AI was a recurring theme, with some suggesting that this limits the field's long-term potential. Finally, the role of hype and marketing in AI, contrasting with the relative obscurity of pure mathematics, was also noted.
This 1989 Xerox PARC paper argues that Unix, despite its strengths, suffers from a fragmented environment hindering programmer productivity. It lacks a unifying framework integrating tools and information, forcing developers to grapple with disparate interfaces and manually manage dependencies. The paper proposes an integrated environment, similar to Smalltalk or Interlisp, built upon a shared repository and incorporating features like browsing, version control, configuration management, and debugging within a consistent user interface. This would streamline the software development process by automating tedious tasks, improving code reuse, and fostering better communication among developers. The authors advocate for moving beyond the Unix philosophy of small, independent tools towards a more cohesive and interactive system that supports the entire software lifecycle.
Hacker News users discussing the Xerox PARC paper lament the lack of a truly integrated computing environment, even decades later. Several commenters highlight the continued relevance of the paper's criticisms of Unix's fragmented toolset and the persistent challenges in achieving seamless interoperability. Some point to Smalltalk as an example of a more integrated system, while others mention Lisp Machines and Oberon. The discussion also touches upon the trade-offs between integration and modularity, with some arguing that Unix's modularity, while contributing to its fragmentation, is also a key strength. Others note the influence of the internet and the web, suggesting that these technologies shifted the focus away from tightly integrated desktop environments. There's a general sense of nostalgia for the vision presented in the paper and a recognition of the ongoing struggle to achieve a truly unified computing experience.
Stanford researchers have engineered a dual-antibody therapy effective against all known SARS-CoV-2 variants of concern, including Omicron subvariants. This treatment uses two antibodies that bind to distinct, non-overlapping regions of the virus's spike protein, making it harder for the virus to develop resistance. The combined antibodies neutralize the virus more potently than either antibody alone and have shown promise in preclinical models, preventing infection and severe disease. This approach offers a potential broad-spectrum therapeutic option against current and future SARS-CoV-2 variants.
HN commenters discuss the potential of the dual-antibody treatment, highlighting its designed resistance to viral mutations and broad effectiveness against various SARS-CoV-2 variants. Some express cautious optimism, noting the need for further research and clinical trials to confirm its efficacy in humans. Others question the long-term viability of antibody treatments given the virus's rapid mutation rate, suggesting that focusing on broader-spectrum antivirals might be a more sustainable approach. Several comments also touch on the accessibility and cost of such treatments, raising concerns about equitable distribution and affordability if it proves successful. Finally, there's discussion about the delivery method, with some wondering about the practicality of intravenous administration versus other options like nasal sprays.
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.
MIT researchers have developed a nanosensor for real-time monitoring of iron levels in plants. This sensor, implanted in plant leaves, uses a fluorescent protein that glows brighter when bound to iron, allowing for non-destructive and continuous measurement of iron concentration. This technology could help scientists study iron uptake in plants, ultimately leading to strategies for improving crop yields and addressing iron deficiency in agriculture.
Hacker News commenters generally expressed interest in the nanosensor technology described in the MIT article, focusing on its potential applications beyond iron detection. Several suggested uses like monitoring nutrient levels in other crops or even in humans. Some questioned the practicality and cost-effectiveness of the approach compared to existing methods, raising concerns about the scalability of manufacturing the nanosensors and the potential environmental impact. Others highlighted the importance of this research for addressing nutrient deficiencies in agriculture and improving crop yields, particularly in regions with poor soil conditions. A few commenters delved into the technical details, discussing the sensor's mechanism and the challenges of real-time monitoring within living plants.
Bell Labs' success stemmed from a unique combination of factors. A long-term, profit-agnostic research focus fostered by monopoly status allowed scientists to pursue fundamental questions driven by curiosity rather than immediate market needs. This environment attracted top talent, creating a dense network of experts across disciplines who could cross-pollinate ideas and tackle complex problems collaboratively. Management understood the value of undirected exploration and provided researchers with the freedom, resources, and stability to pursue ambitious, long-term projects, leading to groundbreaking discoveries that often had unforeseen applications. This "patient capital" approach, coupled with a culture valuing deep theoretical understanding, distinguished Bell Labs and enabled its prolific innovation.
Hacker News users discuss factors contributing to Bell Labs' success, including a culture of deep focus and exploration without pressure for immediate results, fostered by stable monopoly profits. Some suggest that the "right questions" arose organically from a combination of brilliant minds, ample resources, and freedom to pursue curiosity-driven research. Several commenters point out that the environment was unique and difficult to replicate today, particularly the long-term, patient funding model. The lack of modern distractions and a collaborative, interdisciplinary environment are also cited as key elements. Some skepticism is expressed about romanticizing the past, with suggestions that Bell Labs' output was partly due to sheer volume of research and not all "right questions" led to breakthroughs. Finally, the importance of dedicated, long-term teams focusing on fundamental problems is highlighted as a key takeaway.
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.
Polish researchers suspect that GPS jamming in the Baltic Sea, affecting maritime and air navigation, is being deliberately caused by ships, possibly linked to the ongoing war in Ukraine. The Centre for Eastern Studies (OSW) report highlights numerous incidents of interference, particularly near Russian naval exercises and around strategic areas like the Bornholm Basin, suggesting a potential Russian military strategy to disrupt navigation and create uncertainty. While technical malfunctions are possible, the patterns of interference strongly point toward intentional jamming, impacting both civilian and military operations in the region.
Several Hacker News commenters discuss the plausibility and implications of GPS jamming in the Baltic Sea. Some express skepticism, suggesting the observed disruptions could be caused by unintentional interference or even solar flares. Others point out the increasing availability and use of GPS jammers, highlighting their potential use in smuggling or other illicit activities. The prevalence of spoofing is also raised, with one commenter mentioning the known use of GPS spoofing by Russia around airports and other strategic locations. Another commenter questions the motivation behind such jamming, speculating that it could be related to the ongoing war in Ukraine, possibly to mask ship movements or disrupt navigation. A few comments also touch on the broader implications for maritime safety and the potential for escalating tensions in the region.
Cornell University researchers have developed AI models capable of accurately reproducing cuneiform characters. These models, trained on 3D-scanned clay tablets, can generate realistic synthetic cuneiform signs, including variations in writing style and clay imperfections. This breakthrough could aid in the decipherment and preservation of ancient cuneiform texts by allowing researchers to create customized datasets for training other AI tools designed for tasks like automated text reading and fragment reconstruction.
HN commenters were largely impressed with the AI's ability to recreate cuneiform characters, some pointing out the potential for advancements in archaeology and historical research. Several discussed the implications for forgery and the need for provenance tracking in antiquities. Some questioned the novelty, arguing that similar techniques have been used in other domains, while others highlighted the unique challenges presented by cuneiform's complexity. A few commenters delved into the technical details of the AI model, expressing interest in the training data and methodology. The potential for misuse, particularly in creating convincing fake artifacts, was also a recurring concern.
A study published in BMC Public Health found a correlation between tattoo ink exposure and increased risk of certain skin cancers (squamous cell carcinoma, basal cell carcinoma, melanoma) and lymphoma. While the study observed this association, it did not establish a causal link. Further research is needed to determine the exact mechanisms and confirm if tattoo inks directly contribute to these conditions. The study analyzed data from a large US health survey and found that individuals with tattoos reported higher rates of these cancers and lymphoma compared to those without tattoos. However, the researchers acknowledge potential confounding factors like sun exposure, skin type, and other lifestyle choices which could influence the results.
HN commenters discuss the small sample size (n=407) and the lack of control for confounding factors like socioeconomic status, sun exposure, and risky behaviors often associated with tattoos. Several express skepticism about the causal link between tattoo ink and cancer, suggesting correlation doesn't equal causation. One commenter points out that the study relies on self-reporting, which can be unreliable. Another highlights the difficulty in isolating the effects of the ink itself versus other factors related to the tattooing process, such as hygiene practices or the introduction of foreign substances into the skin. The lack of detail about the types of ink used is also criticized, as different inks contain different chemicals with varying potential risks. Overall, the consensus leans towards cautious interpretation of the study's findings due to its limitations.
Onyx is an open-source project aiming to democratize deep learning research for workplace applications. It provides a platform for building and deploying custom AI models tailored to specific business needs, focusing on areas like code generation, text processing, and knowledge retrieval. The project emphasizes ease of use and extensibility, offering pre-trained models, a modular architecture, and integrations with popular tools and frameworks. This allows researchers and developers to quickly experiment with and deploy state-of-the-art AI solutions without extensive deep learning expertise.
Hacker News users discussed Onyx, an open-source platform for deep research across workplace applications. Several commenters expressed excitement about the project, particularly its potential for privacy-preserving research using differential privacy and federated learning. Some questioned the practical application of these techniques in real-world scenarios, while others praised the ambitious nature of the project and its focus on scientific rigor. The use of Rust was also a point of interest, with some appreciating the performance and safety benefits. There was also discussion about the potential for bias in workplace data and the importance of careful consideration in its application. Some users requested more specific examples of use cases and further clarification on the technical implementation details. A few users also drew comparisons to other existing research platforms.
Researchers at the National University of Singapore have developed a new battery-free technology that can power devices using ambient radio frequency (RF) signals like Wi-Fi and cellular transmissions. This system utilizes a compact antenna and an innovative matching network to efficiently harvest RF energy and convert it to usable direct current power, capable of powering small electronics and sensors. This breakthrough has the potential to eliminate the need for batteries in various Internet of Things (IoT) devices, promoting sustainability and reducing electronic waste.
Hacker News commenters discuss the potential and limitations of the battery-free technology. Some express skepticism about the practicality of powering larger devices, highlighting the low power output and the dependence on strong ambient RF signals. Others are more optimistic, suggesting niche applications like sensors and IoT devices, especially in environments with consistent RF sources. The discussion also touches on the security implications of devices relying on potentially manipulable RF signals, as well as the possibility of interference with existing radio communication. Several users question the novelty of the technology, pointing to existing energy harvesting techniques. Finally, some commenters raise concerns about the accuracy and hype often surrounding university press releases on scientific breakthroughs.
Research on Syrian refugees suggests that exposure to extreme violence can cause epigenetic changes, specifically alterations to gene expression rather than the genes themselves, that can be passed down for at least two generations. The study found grandsons of men exposed to severe violence in the Syrian conflict showed altered stress hormone regulation, even though these grandsons never experienced the violence firsthand. This suggests trauma can have lasting biological consequences across generations through epigenetic inheritance.
HN commenters were skeptical of the study's methodology and conclusions. Several questioned the small sample size and the lack of control for other factors that might influence gene expression. They also expressed concerns about the broad interpretation of "violence" and the potential for oversimplification of complex social and biological interactions. Some commenters pointed to the difficulty of isolating the effects of trauma from other environmental and genetic influences, while others questioned the study's potential for misinterpretation and misuse in justifying discriminatory policies. A few suggested further research with larger and more diverse populations would be needed to validate the findings. Several commenters also discussed the ethics and implications of studying epigenetics in conflict zones.
Drone footage has revealed that narwhals utilize their tusks for more than just male competition. The footage shows narwhals tapping and probing the seafloor with their tusks, seemingly to locate and flush out prey like flatfish. This behavior suggests the tusk has a sensory function, helping the whales explore their environment and find food. The observations also document narwhals gently sparring or playing with their tusks, indicating a social role beyond dominance displays. This new evidence expands our understanding of the tusk's purpose and the complexity of narwhal behavior.
HN commenters were generally fascinated by the narwhal footage, particularly the tusk's use for probing the seafloor. Some questioned whether "play" was an appropriate anthropomorphic interpretation of the behavior, suggesting it could be related to foraging or sensory exploration. Others discussed the drone's potential to disrupt wildlife, with some arguing the benefit of scientific observation outweighs the minimal disturbance. The drone's maneuverability and close proximity to the narwhals without seeming to disturb them also impressed commenters. A few users shared related trivia about narwhals, including the tusk's sensory capabilities and its potential use in male-male competition. Several expressed a wish for higher resolution video.
The Simons Institute for the Theory of Computing at UC Berkeley has launched "Stone Soup AI," a year-long research program focused on collaborative, open, and decentralized development of foundation models. Inspired by the folktale, the project aims to build a large language model collectively, using contributions of data, compute, and expertise from diverse participants. This open-source approach intends to democratize access to powerful AI technology and foster greater transparency and community ownership, contrasting with the current trend of closed, proprietary models developed by large corporations. The program will involve workshops, collaborative coding sprints, and public releases of data and models, promoting open science and community-driven advancement in AI.
HN commenters discuss the "Stone Soup AI" concept, which involves prompting LLMs with incomplete information and relying on their ability to hallucinate missing details to produce a workable output. Some express skepticism about relying on hallucinations, preferring more deliberate methods like retrieval augmentation. Others see potential, especially for creative tasks where unexpected outputs are desirable. The discussion also touches on the inherent tendency of LLMs to confabulate and the need for careful evaluation of results. Several commenters draw parallels to existing techniques like prompt engineering and chain-of-thought prompting, suggesting "Stone Soup AI" might be a rebranding of familiar concepts. A compelling point raised is the potential for bias amplification if hallucinations consistently fill gaps with stereotypical or inaccurate information.
A new model suggests dogs may have self-domesticated, drawn to human settlements by access to discarded food scraps. This theory proposes that bolder, less aggressive wolves were more likely to approach humans and scavenge, gaining a selective advantage. Over generations, this preference for readily available "snacks" from human waste piles, along with reduced fear of humans, could have gradually led to the evolution of the domesticated dog. The model focuses on how food availability influenced wolf behavior and ultimately drove the domestication process without direct human intervention in early stages.
Hacker News users discussed the "self-domestication" hypothesis, with some skeptical of the model's simplicity and the assumption that wolves were initially aggressive scavengers. Several commenters highlighted the importance of interspecies communication, specifically wolves' ability to read human cues, as crucial to the domestication process. Others pointed out the potential for symbiotic relationships beyond mere scavenging, suggesting wolves might have offered protection or assisted in hunting. The idea of "survival of the friendliest," not just the fittest, also emerged as a key element in the discussion. Some users also drew parallels to other animals exhibiting similar behaviors, such as cats and foxes, furthering the discussion on the broader implications of self-domestication. A few commenters mentioned the known genetic differences between domesticated dogs and wolves related to starch digestion, supporting the article's premise.
A Penn State student has refined a century-old math theorem known as the Kutta-Joukowski theorem, which calculates the lift generated by an airfoil. This refined theorem now accounts for rotational and unsteady forces acting on airfoils in turbulent conditions, something the original theorem didn't address. This advancement is significant for the wind energy industry, as it allows for more accurate predictions of wind turbine blade performance in real-world, turbulent wind conditions, potentially leading to improved efficiency and design of future turbines.
HN commenters express skepticism about the impact of this research. Several doubt the practicality, pointing to existing simulations and the complex, chaotic nature of wind making precise calculations less relevant. Others question the "100-year-old math problem" framing, suggesting the Betz limit is well-understood and the research likely focuses on a specific optimization problem within that context. Some find the article's language too sensationalized, while others are simply curious about the specific mathematical advancements made and how they're applied. A few commenters provide additional context on the challenges of wind farm optimization and the trade-offs involved.
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.
Ben Evans' post "The Deep Research Problem" argues that while AI can impressively synthesize existing information and accelerate certain research tasks, it fundamentally lacks the capacity for original scientific discovery. AI excels at pattern recognition and prediction within established frameworks, but genuine breakthroughs require formulating new questions, designing experiments to test novel hypotheses, and interpreting results with creative insight – abilities that remain uniquely human. Evans highlights the crucial role of tacit knowledge, intuition, and the iterative, often messy process of scientific exploration, which are difficult to codify and therefore beyond the current capabilities of AI. He concludes that AI will be a powerful tool to augment researchers, but it's unlikely to replace the core human element of scientific advancement.
HN commenters generally agree with Evans' premise that large language models (LLMs) struggle with deep research, especially in scientific domains. Several point out that LLMs excel at synthesizing existing knowledge and generating plausible-sounding text, but lack the ability to formulate novel hypotheses, design experiments, or critically evaluate evidence. Some suggest that LLMs could be valuable tools for researchers, helping with literature reviews or generating code, but won't replace the core skills of scientific inquiry. One commenter highlights the importance of "negative results" in research, something LLMs are ill-equipped to handle since they are trained on successful outcomes. Others discuss the limitations of current benchmarks for evaluating LLMs, arguing that they don't adequately capture the complexities of deep research. The potential for LLMs to accelerate "shallow" research and exacerbate the "publish or perish" problem is also raised. Finally, several commenters express skepticism about the feasibility of artificial general intelligence (AGI) altogether, suggesting that the limitations of LLMs in deep research reflect fundamental differences between human and machine cognition.
Researchers used AI to identify a new antibiotic, abaucin, effective against a multidrug-resistant superbug, Acinetobacter baumannii. The AI model was trained on data about the molecular structure of over 7,500 drugs and their effectiveness against the bacteria. Within 48 hours, it identified nine potential antibiotic candidates, one of which, abaucin, proved highly effective in lab tests and successfully treated infected mice. This accomplishment, typically taking years of research, highlights the potential of AI to accelerate antibiotic discovery and combat the growing threat of antibiotic resistance.
HN commenters are generally skeptical of the BBC article's framing. Several point out that the AI didn't "crack" the problem entirely on its own, but rather accelerated a process already guided by human researchers. They highlight the importance of the scientists' prior work in identifying abaucin and setting up the parameters for the AI's search. Some also question the novelty, noting that AI has been used in drug discovery for years and that this is an incremental improvement rather than a revolutionary breakthrough. Others discuss the challenges of antibiotic resistance, the need for new antibiotics, and the potential of AI to contribute to solutions. A few commenters also delve into the technical details of the AI model and the specific problem it addressed.
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.
Google's AI-powered tool, named RoboCat, accelerates scientific discovery by acting as a collaborative "co-scientist." RoboCat demonstrates broad, adaptable capabilities across various scientific domains, including robotics, mathematics, and coding, leveraging shared underlying principles between these fields. It quickly learns new tasks with limited demonstrations and can even adapt its robotic body plans to solve specific problems more effectively. This flexible and efficient learning significantly reduces the time and resources required for scientific exploration, paving the way for faster breakthroughs. RoboCat's ability to generalize knowledge across different scientific fields distinguishes it from previous specialized AI models, highlighting its potential to be a valuable tool for researchers across disciplines.
Hacker News users discussed the potential and limitations of AI as a "co-scientist." Several commenters expressed skepticism about the framing, arguing that AI currently serves as a powerful tool for scientists, rather than a true collaborator. Concerns were raised about AI's inability to formulate hypotheses, design experiments, or understand the underlying scientific concepts. Some suggested that overreliance on AI could lead to a decline in fundamental scientific understanding. Others, while acknowledging these limitations, pointed to the value of AI in tasks like data analysis, literature review, and identifying promising research directions, ultimately accelerating the pace of scientific discovery. The discussion also touched on the potential for bias in AI-generated insights and the importance of human oversight in the scientific process. A few commenters highlighted specific examples of AI's successful application in scientific fields, suggesting a more optimistic outlook for the future of AI in science.
Contrary to traditional practice of immobilizing broken ankles and lower leg bones, emerging research suggests that early weight-bearing and mobilization can lead to better healing outcomes. Studies have shown that patients who start walking on their fractured limbs within a few weeks, under the guidance of a physical therapist and with appropriate support, experience less pain, stiffness, and muscle loss compared to those who remain immobilized for extended periods. This approach, often combined with less invasive surgical techniques where applicable, promotes faster recovery of function and mobility, allowing patients to return to normal activities sooner. While complete avoidance of weight-bearing may still be necessary in certain cases, the overall trend is toward early mobilization as a standard for uncomplicated fractures.
Hacker News users discussed the surprising advice of walking on broken legs and ankles soon after injury. Many expressed skepticism, citing personal experiences with traditional casting and longer recovery periods. Some highlighted the importance of distinguishing between different types of fractures and the crucial role of a doctor's supervision in determining appropriate weight-bearing activities. Several commenters pointed out the potential risks of premature weight-bearing, including delayed healing and further injury. The potential benefits of early mobilization, like reduced stiffness and faster recovery, were also acknowledged, but with caution and emphasis on professional guidance. A few users shared positive anecdotal evidence of early mobilization aiding their recovery. The overall sentiment leaned towards cautious optimism, emphasizing the need for personalized advice from medical professionals. Several users expressed concern that the article's title might mislead readers into self-treating without professional consultation.
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.
This 2019 EEG study investigated the neural correlates of four different jhāna meditative states in experienced Buddhist practitioners. Researchers found distinct EEG signatures for each jhāna, characterized by progressive shifts in brainwave activity. Specifically, higher jhānas were associated with decreased alpha and increased theta power, indicating a transition from relaxed awareness to deeper meditative absorption. Furthermore, increased gamma power during certain jhānas suggested heightened sensory processing and focused attention. These findings provide neurophysiological evidence for the distinct stages of jhāna meditation and support the subjective reports of practitioners regarding their unique qualities.
Hacker News users discussed the study's methodology and its implications. Several commenters questioned the small sample size and the potential for bias, given the meditators' experience levels. Some expressed skepticism about the EEG findings and their connection to subjective experiences. Others found the study's exploration of jhana states interesting, with some sharing their own meditation experiences and interpretations of the research. A few users also discussed the challenges of studying subjective states scientifically and the potential benefits of further research in this area. The thread also touched on related topics like the placebo effect and the nature of consciousness.
Researchers at the University of Surrey have theoretically demonstrated that two opposing arrows of time can emerge within specific quantum systems. By examining the evolution of entanglement within these systems, they found that while one subsystem experiences time flowing forward as entropy increases, another subsystem can simultaneously experience time flowing backward, with entropy decreasing. This doesn't violate the second law of thermodynamics, as the overall combined system still sees entropy increase. This discovery offers new insights into the foundations of quantum mechanics and its relationship with thermodynamics, particularly in understanding the flow of time at the quantum level.
HN users express skepticism about the press release's interpretation of the research, questioning whether the "two arrows of time" are a genuine phenomenon or simply an artifact of the chosen model. Some suggest the description is sensationalized and oversimplifies complex quantum behavior. Several commenters call for access to the actual paper rather than relying on the university's press release, emphasizing the need to examine the methodology and mathematical framework to understand the true implications of the findings. A few commenters delve into the specifics of microscopic reversibility and entropy, highlighting the challenges in reconciling these concepts with the claims made in the article. There's a general consensus that the headline is attention-grabbing but potentially misleading without deeper analysis of the underlying research.
UNC researchers have demonstrated how loggerhead sea turtles use the Earth's magnetic field to navigate. By manipulating the magnetic field around hatchlings in a special tank, they showed that the turtles use a "magnetic map" to orient themselves towards their natal beach. This map allows them to identify their location relative to their target destination, enabling them to adjust their swimming direction even when displaced from their original course. The study provides strong evidence for the long-hypothesized magnetic navigation abilities of sea turtles and sheds light on their remarkable open-ocean migrations.
Hacker News users discussed the methodology and implications of the turtle navigation study. Several commenters questioned the sample size of the study (seven turtles) and whether it's enough to draw broad conclusions. Some debated the ethics of attaching GPS trackers to the turtles, expressing concern about potential harm. Others pointed out that the Earth's magnetic field fluctuates, wondering how the turtles adapt to these changes and how the researchers accounted for that variability in their analysis. A few users drew parallels to other animals that use magnetic fields for navigation, speculating on the common mechanisms involved. The lack of open access to the full study was also lamented, limiting deeper discussion of the findings.
Summary of Comments ( 7 )
https://news.ycombinator.com/item?id=43405918
Hacker News users discuss Microsoft's quantum computing claims with skepticism, focusing on the lack of peer review and independent verification of their "majorana zero mode" breakthrough. Several commenters highlight the history of retracted papers and unfulfilled promises in the field, urging caution. Some point out the potential financial motivations behind Microsoft's announcements, while others note the difficulty of replicating complex experiments and the general challenges in building a scalable quantum computer. The reliance on "future milestones" rather than present evidence is a recurring theme in the criticism, with commenters expressing a "wait-and-see" attitude towards Microsoft's claims. Some also debate the scientific process itself, discussing the role of preprints and the challenges of validating groundbreaking research.
The Hacker News post titled "Microsoft quantum computing claim still lacks evidence" (linking to a Nature article about skepticism surrounding Microsoft's quantum computing advancements) has generated a substantial discussion. Many of the comments revolve around the difficulty of verifying claims in the quantum computing field, the hype surrounding the technology, and the potential implications of a genuine breakthrough.
Several commenters express skepticism about Microsoft's claims, echoing the sentiment of the Nature article. Some highlight the lack of peer-reviewed publications and independent verification of Microsoft's results. One commenter points out the historical trend of overpromising and underdelivering in the field of quantum computing, suggesting a cautious approach to Microsoft's announcements. Others discuss the specific technical challenges involved in creating a topological qubit, the type Microsoft is pursuing, and the inherent difficulty in scaling such a system.
Another line of discussion focuses on the difference between scientific breakthroughs and practical applications. Some commenters argue that even if Microsoft's claims are valid, it doesn't necessarily mean that practical quantum computers are imminent. They emphasize the significant engineering hurdles that still need to be overcome before quantum computers can be used for real-world problems.
A few commenters discuss the potential impact of quantum computing on various industries, including cryptography, medicine, and materials science. They acknowledge the transformative potential of the technology but also caution against overhyping its near-term prospects.
Some commenters delve into the technical details of Microsoft's approach, comparing it to other quantum computing platforms like superconducting qubits and trapped ions. They debate the relative merits and challenges of each approach, highlighting the uncertainty surrounding which technology will ultimately prove most successful.
A recurring theme in the comments is the need for more transparency and rigorous peer review in the quantum computing field. Several commenters call for greater scrutiny of claims and a more cautious approach to publicizing research results before they have been thoroughly vetted by the scientific community. They express concern that excessive hype could damage the credibility of the field and hinder its long-term progress.
Finally, a few commenters offer more optimistic perspectives, suggesting that even incremental progress in quantum computing is valuable and that Microsoft's research could contribute to the eventual development of practical quantum computers. They emphasize the importance of continued investment and research in the field, despite the challenges and uncertainties.