Artificial Intelligence in Academia
All You Need to Know About Generative AI in 17 Minutes!
AI Taxonomy
General Principles
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"A large language model (LLM) is a type of machine learning (ML) model that can perform a variety of natural language processing (NLP) tasks, such as generating and classifying text, answering questions in a conversational manner, and translating text from one language to another. LLMs are trained with immense amounts of data and use self-supervised learning (SSL) to predict the next token in a sentence, given the surrounding context. The process is repeated until the model reaches an acceptable level of accuracy. A LLM uses deep neural networks to generate outputs based on patterns learned from training data. Most LLMs are pre-trained on a large, general-purpose data set. The purpose of pre-training is for the model to learn high-level features that can be transferred to the fine-tuning stage for specific tasks." (Source) LLMs, as a subset of generative AI, have enabled the development of chatbots such as ChatGPT. |
In other words, it's important to know that these tools...
...are NOT information systems, and are therefore not designed to search for and obtain factual information, so it is better to use a search engine for this purpose.
...are NOT proficient in carrying out bibliographic searches for academic work, so it is better to use the bibliographic databases provided by the Library to this end.
...are NOT able to provide sound and safe advice, notably in areas such as health, finance or justice.
| Are these tools actually useful in an academic context? If so, how and how can we make the best use of them? |
Generative AI, and chatbots in particular can be very useful when it comes to doing creative things with text (as opposed to factual information retrieval).
More specifically, here is a non-exhaustive list of common uses:
• Generate ideas (ex.: find ideas for topics to study in a given field, suggest keywords on a given subject, etc.);
• Get an overview of a topic;
• Summarize information or a text;
• Explain a notion in a simple way, help analyze a text;
• Define unfamiliar concepts;
• Help with writing (draft a text, correct language, translate, etc.);
• Suggest a methodology or ways to organize or structure ideas;
• Help with writing or debugging computer code, etc.
