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Monday-Friday
Artificial intelligent (AI) driven language processing tools are becoming more relevant in the scientific writing world, with new tools emerging all the time. Their widespread availability and usage are causing both hopes and fears regarding their long-term impact on publishing, authorship, and scientific writing.
This page tries to give an overview of the most prevalent questions in the form of a living document. Please note that it is neither complete, nor an official position of Lib4RI or the four RIs.
Generative AI tools
ChatGPT is an AI language model developed by OpenAI. It is part of the GPT series, with GPT-4o being the latest version. It is designed to understand and generate natural language responses, and it is thus capable of engaging in conversations, answering questions, and providing information or assistance across a wide range of topics by following instructions from prompts by the user.
The model is trained on a diverse range of internet text, but it is important to keep in mind that it does not automatically browse the internet or access real-time information. This means that while it can provide information that is current up to its last training data in April 2023, it may not have the latest information on events or developments that occurred after that date. An option to use ChatGPT-4 with internet access for free, is Microsoft Copilot (formerly Bing Chat).
While the full functionalities of ChatGPT-4 (including Dall-E, plugins, custom GPTs etc.) can only be used via their subscription plan, there are several GPT-4 based services that you can use for free:
The following list is of course incomplete, it is just a selection of the most well known ones:
Yes, you can choose not to share your chat history with OpenAI to train their models via your account settings. For more information, see here.
AI tools in scientific writing
The use of generative AI tools in scientific writing poses a number of risks and it is crucial to be mindful of them. Here is a selection of some points to consider:
Further reading & general information
| Abbreviation | Meaning |
|---|---|
| AI detector | A tool designed to detect when a text was AI-generated (highly unreliable) |
| Algorithm | A finite sequence of instructions followed by a computer system |
| Alignment | The extent to which an AI’s goals are in line with its creators’ goals |
| Anthropomorphism | The attribution of human traits to non-human entities |
| Artificial general intelligence (AGI) | AI that surpasses human intelligence |
| Artificial intelligence (AI) | Intelligence demonstrated by machines |
| Automation | Handling a process with machines or software so that less human input is needed |
| Autonomous | If a machine is able to perform tasks without human input |
| Bias | Assumptions that an AI makes to simplify its tasks stemming from its training and training data |
| Big data | Very large datasets that normal data-processing software cannot handle |
| CAPTCHA | A test used online to ensure that the user is human |
| Chatbot | A software application that mimics human conversation, usually through text |
| Chinese room | A philosophical thought experiment about AI |
| Deep learning (DL) | A form of machine learning based on neural networks |
| Deepfake | AI-generated images and videos designed to look real |
| Emergent behavior | Complex behavior resulting from basic processes |
| Generative AI | AI systems that generate output in response to prompts |
| Generative pre-trained transformer (GPT) | A type of LLM used in ChatGPT and other AI applications |
| Hallucination | A tendency of AI chatbots to confidently present false information |
| Large language model (LLM) | A neural net trained on large amounts of text to imitate human language |
| Machine learning (ML) | The study of how AI acquires knowledge from training data |
| Machine translation | Use of software to translate text between languages |
| Natural language processing (NLP) | The study of interaction between computers and human language |
| Neural net(work) | Computer systems designed to mimic brain structures |
| Parameter | A variable in an AI system that it uses to make predictions |
| Perplexity | A measurement of how unpredictable a text is |
| Prompt | The input from the user to which the AI system responds |
| Reinforcement learning from human feedback (RLHF) | A training method used to fine-tune GPT model responses |
| Temperature | The level of randomness in an LLM’s output |
| Token | The basic unit of text (a word or part of a word) processed by LLMs |
| Training data | The dataset that was used to train an AI system |
| Turing test | A test of a machine’s ability to display human intelligence |
The discussion about usage of generative AI tools in scientific writing have greatly increased; regulations are being defined and its advantages and limitations evaluated. Below, you can find a selection of interesting articles on the topic:
Salvagno M., Taccone F.S. & Gerli, A.G, 2023. Can artificial intelligence help for scientific writing? Crit Care, 27 (75). https://doi.org/10.1186/s13054-023-04380-2.
As institutions and publishers have to face the fact that authors may use AI-based tools to (help) write scientific articles, they begin to set up guidelines, best practices and regulations regarding its usage. Here are some examples of information from publishers and Swiss universities: