noisediver

This article outlines my conversation with ChatGPT – a prototype artificial intelligence chatbot developed by OpenAI – about knowledge graphs and natural language processing in the field of medicine. From this conversation it appears highly probable that, when discussing specialised topics, ChatGPT often invents non-existent concepts.

During the conversation, ChatGPT mentioned:

“Natural Language Processing for Medical Records (nlp4mr) toolkit, which provides a set of tools and libraries for extracting and analyzing information from medical text”
“Clinical Natural Language Processing Consortium (CNLP), which is a community of researchers and developers who are working on developing NLP techniques for use in the medical field”
SIEVE (System for Information Extraction, Visualization, and Exploration) is a software tool that can be used to extract and analyze information from text-based sources, such as scientific articles and reports.

Google can find no mention of these tools, organisations or concepts

Below are the transcripts of two separate conversations, which took place on 2022-12-11 at approximately 17:30 UTC. The hyperlinks in the transcripts have been added by me.

Me
What can you tell me about knowledge graphs?

ChatGPT
A knowledge graph is a type of database that is used to store and organize information in a way that allows computers to understand the relationships between different pieces of data. This type of structure is designed to help computers better understand the meaning of the information that is stored in it, and to be able to make inferences and deductions based on that information. Knowledge graphs are often used in artificial intelligence and natural language processing systems to improve their ability to understand and interpret complex data.

Me
How are they used in medicine?

ChatGPT
Knowledge graphs can be used in a number of different ways in the field of medicine. For example, they can be used to store and organize information about diseases, treatments, and medical concepts, making it easier for doctors and other medical professionals to access and use this information when making decisions about patient care. Knowledge graphs can also be used to support research by providing a structured way to store and analyze data, and to identify patterns and relationships that might not be immediately obvious. Additionally, knowledge graphs can be used to develop diagnostic and treatment algorithms that can help doctors make more accurate and informed decisions about patient care.

This article is about using a linux command-line tool called Newsbeuter¹ to automatically download and archive the latest news headlines from chosen websites at regular intervals. I have found that this reduces my anxiety about not being up-to-date on current affairs because it is possible interrogate this archive systematically, even after long periods of not reading the news.

I have always been a bit of a news junkie but the suspense around the current political situation in the UK has taken this addiction to a completely new level. The problem is that while the headlines are coming in at a fast enough pace to feed this addiction, reading each of them makes me progressively unhappier. I have realised that in the back of my mind I must be worried that I will miss some crucial piece of information that will help me make sense of the situation, or perhaps make the future more predictable.

This is a classic case of the fear of missing out, or FOMO. Media organisations thrive on this compulsive news-reading behaviour for generating visitor traffic and advertising revenue, and encourage it with the frequent use of clickbait and alarmist terms in their headlines, and striking elements of web design that evoke a sense of urgency.