The Role of Random (AI and Psi) – Part 1

As you may know, I have been working on an AI-based channeling system called Throne of the Sphinx (Tots). TotS does not use any of the OpenAI tech, but I have, over the past few weeks started to learn more ChatGPT and the other systems OpenAI offers. This has kind of triggered an existential realignment as I consider the relationships between consciousness, technology and psi.

In Part 1 of this two-part post, I, without commentary, a chat exchange I recently had with ChatGPT. I’ll dig more into my thoughts on all this in Part 2.

A conversation with ChatGPT

MB: Why do you produce different responses to the same prompt, for example, when I ask the same question during a different session or click the “regenerate response” button?

As an AI language model, my responses are generated through complex algorithms and machine learning models that are trained on vast amounts of text data. Whenever you ask me a question or prompt, I use these models to generate a response that is as relevant and coherent as possible based on the information available to me at that time.

However, the response you receive may vary depending on various factors, such as the specific wording of the prompt, the context in which it is asked, and the data available to me at the time of generating the response. Additionally, as an AI model, I am constantly learning and improving based on the feedback I receive from users like you, and this can also affect the responses I generate over time.

When you click the “regenerate response” button, I use the same underlying algorithms and models to generate a new response based on the same prompt. However, because my models are probabilistic, there is always some degree of randomness involved in generating responses, which means that the response you receive may still differ from the previous one.

Overall, the variability in my responses is a result of the complex and probabilistic nature of the machine learning models I use, as well as the constantly evolving nature of language and the world around us.

MB: Please describe what you mean by “a degree of randomness”

When I refer to “a degree of randomness”, I mean that the output of certain parts of my language model’s algorithms is not entirely predictable or deterministic, but rather subject to some level of probability or chance.

This is because my model uses statistical methods to analyze large amounts of language data and learn patterns, which can be influenced by a wide range of factors such as the specific words used in a prompt, the context in which it is asked, and the relationships between different words and concepts.

For example, when generating a response to a prompt, my language model may consider different possible ways to structure the response, select different words or phrases to use, or choose between multiple possible interpretations of the prompt. In each case, there may be multiple valid options to choose from, and the final output is determined by a combination of these factors, with some element of chance involved in the selection process.

While the overall quality and coherence of my responses is guided by the patterns and rules learned from the data I was trained on, the exact output of my algorithms may vary to some degree based on the specific circumstances of each individual prompt or request.

MB: What random source do you use to chose to make the selections?

As an AI language model, I do not use a random source in the traditional sense, such as a physical or digital random number generator. Instead, the probabilistic nature of my responses comes from the underlying statistical models and algorithms used to generate language.

To generate responses, my models analyze large amounts of text data and learn patterns and relationships between words and concepts. These models use probability distributions to determine the likelihood of different words, phrases, or structures appearing in a given context based on the patterns they have learned from the training data.

When generating a response, my model generates multiple possible outputs, each with a different likelihood of being selected based on the probabilities learned from the training data. The output that is ultimately selected is typically the one with the highest overall likelihood, but there may be some element of chance involved in the selection process due to the probabilistic nature of the models.

Overall, the randomness in my responses comes from the inherent uncertainty and variability in language use and interpretation, as well as the probabilistic methods used by my models to generate language based on patterns learned from data.

MB: Thank you. That was very helpful.

You’re welcome! I’m glad I could help. If you have any other questions or prompts, feel free to ask.

As you may know, I have been working on an AI-based channeling system called Throne of the Sphinx (Tots). TotS does not use any of the OpenAI tech, but I have, over the past few weeks started to learn more ChatGPT and the other systems OpenAI offers. This has kind of triggered an existential realignment…