John's Politics and Futurism blog

 Having fiddled with ChatGPT for a while now, I have identified the following two risks. 1. Hallucination . It sometimes just makes stuff up. This is particularly annoying when you give it the information and it goes on a tangent and makes stuff up that was not in the source information. For example, when I gave it some meeting notes, it went on a tangent about N-P completeness in computer science. OK?!? 2. Memory errors . The memory feature that they have added messes up a lot. I have noticed that it performs two mistakes: Recites what it gave you previously , even if the topic was utterly different . For example I gave it some meeting notes to summarise and it spat out some Python code that I had asked for last year. Totally irrelevant! Recites what it gave you previously, because the content was similar , in its opinion. I created a new chat, gave it the meeting notes, and it gave me the PREVIOUS summary of the previous set of meeting notes. In one case it was even worse! It g...

Had an interesting discussion and took the following notes.  Differentiating epistemic positions and beliefs What is the difference between fake news and a conspiracy theory? First, let me define my understanding of a conspiracy theory. I understand it to mean: a) There is a theory (belief, proposition, hypothesis) that b) There exists a conspiracy (secret plan amongst a group of two or more people) c) To do something malevolent (harmful) to society. So for example, if we know Lee Harvey Oswald or John Wilkes Booth worked with others, then they were conspirators. At the time they were planning to perform their malevolent acts (assassination), they were effectively, conspiring (Latin: Con- together; -spirare: to breathe or hope; compare e.g. to be inspired). So, if someone at the time suspected them of plotting murder, then, that person would have a theory that there was a conspiracy afoot. A person believing in that theory would, in fact, on my definition, have a conspiracy theory....

This is the second of two posts on Generative AI. Late last year (November), OpenAI released ChatGPT to the public. However, it took a few months for the internet at large to catch on and realise the potential of this new tool. The trend started to take off in December, and by February everyone was talking about it. ChatGPT is an example of a "Generative AI". To recapitulate, let's see what this means. For convenience, let's call AIs that are not generative, "regular" AIs. Regular or "Narrow" AI (Artificial Intelligence): Regular AI, often referred to as "narrow" or "specific" AI, focuses on building systems that can perform specific tasks or solve specific problems. These AI systems are designed to operate within predefined boundaries and excel at specialised tasks. For example, a regular AI could be created to classify images, play chess, or process natural language. Another example is the AI that chooses your words in predict...

This is the first of two posts on Generative AI. Late last year (November), OpenAI released ChatGPT to the public. However, it took a few months for the internet at large to catch on and realise the potential of this new tool. The trend started to take off in December, and by February everyone was talking about it. Well, what is ChatGPT? ChatGPT is an LLM, or Large Language Model, released by the company OpenAI, founded in 2015. A language model is a program that has been trained on a huge amount of text data, like books, articles, and websites. It learns patterns and relationships between words, so it can predict what words or phrases are likely to come next in a sentence. We see an example of this in predictive text in cellular telephone chat applications. The "large" part means that the model has been trained on a massive amount of data, which helps it become even better at understanding and generating human-like text. It's like having read millions of books and articl...