A new beast has entered the scene: Scopus AI. An undisclosed chatbot fed with the paywalled content, owned by profit-oriented Elsevier. Marketed as "a brand new window into humanity's accumulated knowledge". Why is it problematic? First, the owner. Elsevier has been long known for questionable practices, prioritizing profit seeking over the health of sci-comm ecosystem. For example, recently a whole board of a journal Neuroimage resigned in the protest against Elsevier's greed

https://twitter.com/Andrew_Akbashev/status/1649149289631739905

Code Interpreter really is a parrot. I tried it with the Boston Dataset. It doesn't understand a thing about data, it just regurgitates a statistically plausible tutorial. It performs poorly with variable understanding, not noticing a problem with "Bk" unless asked specifically

Instead, it uncritically goes on and on to suggest more complex models or more elaborate data wrangling techniques, whereas the most glaring problem is right there in the very first answer. Only when forced to do so explicitly, it "remembers" about what the data actually encodes.

It has become increasingly clear to me that AI for text generation coupled with the current scientific publishing system puts academia on a path to becoming a cargo cult. So I wrote an essay about it. Below is a short summary:
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lookalikes.substack.com/p/publish-or-p… AI is here to stay and in academia it definitely has some good sides. It can free up some time normally spent on tedious work that doesn't require so much scientific input. It can also act as a great equalizer in the English-dominated publishing world. 2/

Idea: part of why ChatGPT seems so appealing as a substitute of a search engine is that most of us don't know a good method for knowledge management. Nobody taught us how to build a second brain or make smart notes, so we just keep everything in our head and inevitably struggle ChatGPT creates an illusion of a knowledge base which can be queried using a natural language, so that we wouldn't have to remember anything anymore. But as I read more about #Zettelkasten and other methods, it seems that each of us could've had such a KB even w/o a computer

Not only fighting misleading content will be a challenge for academia in the post-ChatGPT era. It has suddenly become easy to run academic paper mills at scale, set up credibly looking scam journals or even money laundering schemes. Can we imagine a systemic way out of it?🧵 If you’ve never worked in academia, you’ve probably never heard that academic publishing is dominated by huge, very profitable companies which use the pressure of “publish-or-perish” put on the scientists to earn big money (the 30%-profit-margin type of money).

Today I asked ChatGPT about the topic I wrote my PhD about. It produced reasonably sounding explanations and reasonably looking citations. So far so good – until I fact-checked the citations. And things got spooky when I asked about a physical phenomenon that doesn’t exist. I wrote my thesis about multiferroics and I was curious if ChatGPT could serve as a tool for scientific writing. So I asked to provide me a shortlist of citations relating to the topic. ChatGPT refused to openly give me citation suggestions, so I had to use a “pretend” trick.