> demanding new —yes-destroy-my-computer-dangerously-and-step-on-my-face-daddy flags and crashing my automated scripts from last year.
Literally, my case. I recently had to compile an abandoned six-year-old scientific package written in C with Python bindings. I wasn’t aware that modern versions of pip handle builds differently than they did six years ago — specifically, that it now compiles wheels within an isolated environment. I was surprised to see a message indicating that %package_name% was not installed, yet I was still able to import it. By the second day, I eventually discovered the --no-build-isolation option of pip.
Today I tried with an old (1935) fundamental Bell Labs paper on harmonic distortions caused by ferromagnets in communication lines. It is for sure cited more times than it appears in Google Scholar: 25. DOI:10.1002/j.1538-7305.1935.tb00418.x
Here is the list of what the system proposed to take a look at:
1. Vascular at‐risk genotypes and disease severity in Lebanese sickle cell disease patients
2. Narrow band filter for solar spectropolarimetry based on Volume Holographic Gratings
3. Communication from Space: Radio and Optical by S. Weinreb
4. An Adaptive and High Coding Rate Soft Error Correction Method in Network-on-Chips
5. Plasma hemostasis in patients with coronavirus infection caus...
I would expect more papers like 3rd as the topic is communication systems. Unfortunately, inside the ref.3 there is noting said about distortions.
Maybe it is indeed a linguistical curse of this topic...
Because this is precisely what the word counterfeit means, an imitation that deceives you. The functionality of counterfeit can be from 0% to 100%, depending on your luck. If you accidentally bought a fake iPhone, it can still make calls. Chat output is something that looks like a literature review, some collection of summaries of relevant papers. But the problem is that a review is not just text compression. It is also about rejecting low-quality research, considering the historical context, identifying contradictions, etc. No machine can do that analysis for you, yet. I have quite a confidence in that.
Thank you for sharing! I like your dendrogram-like circular graphs! They are way more intuitive. That could be a nice companion for a bibliometrix/biblioshiny library for bibliometric analysis https://www.bibliometrix.org/. I tried "Deep Dive" with my own request, and ... it unfortunately stops at the end of "Organizing results". Maybe I should try again later.
I think you have a very good point here: a semantic search would be the best option for such a search. The items would have unique identifiers so the language variations can be avoided. But unfortunately, I am not aware of any of these kinds of publicly available projects, except DBpedia and some biology-oriented ontologies that would massively analyze scientific reports.
Currently, I am applying RDF/OWL to describe some factual information and contradictions in the scientific literature. On an amateur level. Thus I do it mostly manually. The GPT-discourse somehow brings up not only the human-related perception problems, such as cognitive biases, but also truly philosophical questions of epistemology that should be resolved beforehand. LLM developers cannot solve this because it is not under their control. They can only choose what to learn from. For instance, when we consider a scientific text, it is not an absolute truth but rather a carefully verified and reviewed opinion that is based on the previous authorized opinions and subject to change in the future. So the same author may have various opinions over time. More recent opinions are not necessarily more "truthful" ones. Now imagine a corresponding RDF triple (subject-predicate-object tuple) that describes that. Pretty heavy thing, and no NLTK can decide for us what the truth is and what is not.
It is excellent when just finding something is enough. Most often in my practice, I am dealing with questions that have no written-down answers, meaning the probability of finding a book/article that provides one is negligible. Instead, I am looking for indirect answers or proofs before I make a final engineering decision. Yet another problem is that the language itself changes over time. For instance, at the beginning of the 20th century, the integers were called integral numbers. IMHO, LLMs poorly handle such cases when considered as a substitute for search engines.
For full-text vector search, I am using https://www.recoll.org/ a real time saver for me, especially for desktop search.
> GPT-5 is proving useful as a literature review assistant
No, it does not. It only produces a highly convincing counterfeit. I am honestly happy for people who are satisfied with its output: life is way easier for them than for me.
Obviously, the machine discriminates me personally. When I spend hours in the library looking for some engineering-related math made in the 70s-80s, as a last resort measure, I can try to play this gambling with chat, hoping for any tiny clue to answer my question. And then for the following hours, I am trying to understand what is wrong with the chat output. Most often, I experience the "it simply can't be" feeling, and I know I am not the only one having it.
> We don’t need to worry about AI itself, we need to be concerned about what “humans + AI” will do. Humans will do what they’ve always tried to do—gain power, enslave, kill, control, exploit, cheat, or just be lazy and avoid the hard work—but now with new abilities that we couldn’t have dreamed of.
Starting with the AI itself: LLMs sold as AI are the greatest mislead. Text generation using Markov chains is not particularly intelligent, even when it is looped back through itself a thousand times and appears alike an intelligent conversation. What is actually being sold is an enormous matrix trained on terabytes of human-written, high-quality texts, obviously in violation of all imaginable copyright laws.
Here is a gedanken experiment to test if an AI has any intelligence: until the machine starts to determine and resolve contradictions in its own outputs w/o human help, one can sleep tight. Human language is a fuzzy thing that is not quite suitable for a non-contradictory description of the world. Building such a machine would require resolving all the contradictions humanity has ever faced in a unified way. Before it happens, humanity will be drowned in low-quality generated LLM output.
Your article strongly consonated with the ideas I often try to communicate to those around me. I wish there were more open discussion about biases related to PhD qualifications and the growing influence of venture-capital-style practices in science. Many researchers dedicate themselves to exploring new and uncertain areas of knowledge, yet their work is sometimes undervalued by hiring managers and industrial professionals. I personally know HRs who consider the "PhD" tag in a CV a red flag.
There is also a tendency to overlook the fact that pursuing a PhD is a form of professional work, comparable in commitment and responsibility to other careers. Sometimes 699, sometimes 700. Academia can indeed be a challenging environment, and not everyone contributes in the same way—some may prioritize credentials over substance, and cases of research misconduct do exist. However, these examples definitely do not represent the academic community as a whole.
Regarding the venture-capital approach, in applied sciences, researchers are increasingly expected to present their ideas in short, pitch-style formats. At some point, this process itself becomes the goal of the work. I can imagine this encouraging concise communication. Still, it also shifts part of the management responsibilities—such as market analysis and outreach—onto scientists, which does not align with their core expertise or professional goals.
YC remains a great source of creative inspiration for me. However, I tend to skip most AI-related content on HN, as the topic does more harm than good to me in my daily life. Some people around me delegate more and more decisions to the chat, and that frightens. Especially if you are somehow dependent on them or your work gets evaluated by some creepy AI-driven bossware. We should admit that AI, particularly LLMs, is not just eating: it is destroying society, human communications, the education system, and the scientific community. This enumeration is merely the sides that I personally faced.