The blog post "The Differences Between Deep Research, Deep Research, and Deep Research" explores three distinct interpretations of "deep research." The first, "deep research" as breadth, involves exploring a wide range of related topics to build a comprehensive understanding. The second, "deep research" as depth, focuses on intensely investigating a single, narrow area to become a leading expert. Finally, "deep research" as time emphasizes sustained, long-term investigation, allowing for profound insights and breakthroughs to emerge over an extended period. The author argues that all three approaches have value and the ideal "depth" depends on the specific research goals and context.
Researchers engaging in human subjects research generally need IRB approval. This includes studies involving interaction with individuals, or the use of their identifiable private information. While some activities like quality improvement projects, oral histories, or certain types of program evaluations might be exempt, it's crucial to consult with your institution's IRB to determine whether your project requires review. Ultimately, the IRB is responsible for ensuring ethical research practices and protecting the rights and welfare of human participants, so seeking their guidance is paramount.
HN commenters largely discuss the overreach and bureaucracy of IRBs, particularly for low-risk research. Many share anecdotes of seemingly unnecessary IRB hurdles for projects involving publicly available data or simple surveys. Some question the efficacy of IRBs in actually protecting participants, suggesting they're more focused on liability protection for institutions. A few commenters point out the chilling effect excessive IRB requirements can have on valuable research, especially for independent researchers and smaller institutions lacking dedicated IRB staff. Others offer advice on navigating the IRB process, including pre-registering studies and seeking out institutions with more streamlined procedures. The general sentiment is that IRB review is important for ethically sensitive research but the current system is often overly burdensome and needs reform.
Anthropic has launched a new Citations API for its Claude language model. This API allows developers to retrieve the sources Claude used when generating a response, providing greater transparency and verifiability. The citations include URLs and, where available, spans of text within those URLs. This feature aims to help users assess the reliability of Claude's output and trace back the information to its original context. While the API strives for accuracy, Anthropic acknowledges that limitations exist and ongoing improvements are being made. They encourage users to provide feedback to further enhance the citation process.
Hacker News users generally expressed interest in Anthropic's new citation feature, viewing it as a positive step towards addressing hallucinations and increasing trustworthiness in LLMs. Some praised the transparency it offers, allowing users to verify information and potentially correct errors. Several commenters discussed the potential impact on academic research and the possibilities for integrating it with other tools and platforms. Concerns were raised about the potential for manipulation of citations and the need for clearer evaluation metrics. A few users questioned the extent to which the citations truly reflected the model's reasoning process versus simply matching phrases. Overall, the sentiment leaned towards cautious optimism, with many acknowledging the limitations while still appreciating the progress.
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 ( 24 )
https://news.ycombinator.com/item?id=43236184
Hacker News users generally agreed with the author's distinctions between different types of "deep research." Several praised the clarity and conciseness of the piece, finding it a helpful framework for thinking about research depth. Some commenters added their own nuances, like the importance of "adjacent possible" research and the role of luck/serendipity in breakthroughs. Others pointed out the potential downsides of extremely deep research, such as getting lost in the weeds or becoming too specialized. The cyclical nature of research, where deep dives are followed by periods of broadening, was also highlighted. A few commenters mentioned the article's relevance to their own fields, from software engineering to investing.
The Hacker News post titled "The Differences Between Deep Research, Deep Research, and Deep Research" (linking to an article on deep research) has generated a moderate number of comments, exploring various facets of the topic.
Several commenters discuss the differing interpretations of "deep research" depending on the context. One points out the distinction between academic research, industrial research, and personal exploration, highlighting how the goals, methodologies, and expected outcomes vary significantly. They elaborate on the pressures and constraints within each setting, such as the publish-or-perish dynamic in academia versus the market-driven focus in industry.
Another commenter picks up on the author's mention of "exploratory research" and contrasts it with "exploitative research." They argue that genuine deep research often involves a blend of both, where initial exploration paves the way for focused exploitation of promising avenues. This commenter further suggests that the most impactful research often arises from a willingness to embrace uncertainty and delve into uncharted territory, rather than simply optimizing existing knowledge.
A few comments focus on the practical challenges of conducting deep research, particularly within a corporate environment. They discuss the difficulty of securing funding and resources for long-term, open-ended projects, especially when faced with pressure to deliver short-term results. One commenter shares personal anecdotes about navigating these challenges, emphasizing the importance of effectively communicating the value of deep research to stakeholders and demonstrating its potential impact, even if it's not immediately apparent.
The concept of "depth" itself is also debated. Some commenters argue that true depth isn't solely about the duration of a research project, but also about the level of intellectual rigor, the thoroughness of the investigation, and the novelty of the insights generated. They caution against equating long hours with deep work and emphasize the importance of focused effort and critical thinking.
Finally, a few commenters offer practical advice for aspiring researchers, such as the importance of building a strong foundation of knowledge, developing effective research habits, and cultivating a mindset of curiosity and perseverance. They also recommend seeking mentorship and collaborating with others to broaden perspectives and accelerate learning. One commenter suggests maintaining a research journal to document progress, reflect on learnings, and generate new ideas.