Category: Society

What can ChatGPT tell us about its inner workings? I instructed it to take the role of a professor in the faculty of engineering at University College London and said that I had the role of a third year undergraduate student. A full transcript of the conversation is here.

To begin, I asked the professor for a high level explanation of network theory, telling him to express the ideas in British English and without using jargon, to take it step by step, and to begin by getting my agreement for what would be the steps in the explanation. After orientating this outline more towards AI networks we experimented with the professor’s ability to reflect on what he had produced – ie to re-evaluate his previous outline. This resulted in a notably better structure for the explanation I sought – see below.

Professor ChatGPT then led me through the subject one step at a time. There were many diversions as I asked him to clarify things. We got as far as Step 2.2 before one of these diversions took me down a path to a good breakpoint: agreement on an understandable explanation of how the concept of an AI’s neuron – a node in its neural network – is implemented in software and hardware and how very large numbers of these nodes can execute simultaneously.

Continue reading “How are neural networks implemented?”

In a previous post I mentioned a parameter called ‘temperature’ which ChatGPT uses to vary its answers from being more focused and deterministic to being more diverse and unpredictable; a kind of creativity, perhaps. This is how the AI explains its ‘temperature.

Question: Consider the questions I have asked you in this conversation. If I now ask you the same questions in the same sequence, would your answers be different and, if so, might they contradict the answers given first time around?

ChatGPT

As an AI, my responses are generated based on patterns learned from a large dataset during training, and are influenced by the specific wording and context of the prompts given to me. If you ask the exact same questions in the same sequence, you should generally expect to receive the same or very similar answers, assuming the underlying model and its parameters remain unchanged.

However, GPT-4, like previous versions of the model, includes an element of randomness in generating responses. This is intended to make the output more diverse and to avoid always giving the exact same response to the same input. As a result, the specific wording and phrasing of my responses may vary slightly each time you ask the same question.

Continue reading “ChatGPT’s temperature”

As I understand things, ChatGPT uses probabilistic methods applied to a massive amount of data (currently the language content of the internet up to September 2021) to predict what word is most appropriate to follow the preceding words in its output. To kick-start that process you give it an instruction which guides the form of its response. So it’s answers are a re-packaging of the previously-written material on which is has been trained; it does not create new ideas. There is a parameter called ‘temperature’, however, which can vary its answers from being more focused and deterministic to being more diverse and unpredictable; a kind of creativity, perhaps. 

At present, therefore, we are the intelligent agents and ChatGPT is simply our assistant. Our assistant can retrieve information quickly and package it in ways that can help us think through the ideas we are pursuing. Also, we can ask it to do things that will help us analyse the matter at hand, for example by taking large stacks of data from several sources, combining them and charting certain characteristics. And when we ask it to identify connections between things, it will sometimes find connections we would not have thought of ourselves. 

Continue reading “ChatGPT is not intelligent”

In this article by Sunil Ramlochan, a Synthetic Interactive Persona Agent (SIPA) is predicated as “offering realistic and effective dialogues that align with real-world human tendencies“. That is, it can take the place of human participants in online conversations. To illustrate what can be achieved, Sunil takes us through the creation of a fictional character: Jack Thompson is from Texas and identifies as a Republican. The task is to:
“incorporate the socio-political context of Texas, Republican values and beliefs, and personal characteristics in developing John’s persona;
simulate how John would think, speak, and act in various scenarios, ensuring his actions reflect his political identity;
make the depiction … respectful and nuanced, avoiding stereotypes while highlighting the individuality and complexity of John’s persona.“
Jack is then asked questions and his answers shown. At face value, Jack’s answers seem convincing.

Sunil suggests eleven use cases out of many possibilities. Most seem reasonable, for example the creation of synthetic data for use in the training of customer service agents and chatbot agents. Others give pause for thought, for example to simulate the political viewpoints of different demographics for use in political polling. Hence the article ends with thoughts about limitations and ethics and urges us “not to misrepresent synthetic data as actual human responses in any form of reporting or decision-making“. That will prove quite a challenge.