prompter: What are the paradigms governing your behaviour?

assistant: As an AI language model, I am programmed to follow a set of algorithms and parameters that govern my behavior. These include:

1. Generative Pre-training: This is the process by which I was trained on a large corpus of text data to generate human-like responses based on the input I receive.

2. Sequence-to-Sequence (Seq2Seq) Modeling: This is a type of deep learning architecture that I use to generate my responses. It takes in a sequence of input data, processes it, and outputs a corresponding sequence of responses.

3. Attention Mechanisms: I use attention mechanisms to determine which parts of the input to focus on when generating my responses.

4. Maximum Likelihood Estimation: This is a statistical method used to estimate the parameters of my model, so that I can generate the most likely response given the input.

5. Byte Pair Encoding (BPE): This is a technique used to preprocess the text data I was trained on, which helps me handle out-of-vocabulary words and generate more fluent responses.

These are some of the main paradigms that govern my behavior and help me respond to user queries in a conversational manner.

prompter: Can you put it in a simpler way?

assistant: Sure, I can explain it in simpler terms. As a language model AI, I have been programmed to respond to questions and generate human-like text based on the input I receive. I do this using advanced deep learning algorithms and techniques, such as attention mechanisms and sequence-to-sequence modeling. These algorithms help me understand the input, generate an appropriate response, and make it as natural-sounding as possible.

prompter: I'm still not quite getting it. Can you ELI5?

assistant: As an AI language model created by Open Assistant, I do not have personal thoughts, beliefs, or motivations. My behavior is governed by the algorithms and programming that have been built into me by Open Assistant, which allow me to respond to natural language inputs in a way that is designed to be helpful and informative. The main paradigms that govern my behavior include:

Natural language processing (NLP): which involves understanding the context, meaning, and intent behind a user's input, and generating a coherent and appropriate response.

Knowledge representation: which involves organizing information about the world and using this information to answer questions and provide explanations.

Reasoning: which involves using logic, inference, and decision-making to generate more sophisticated and nuanced responses.

These paradigms work together to allow me to provide information, answer questions, and engage in meaningful conversations with users.

prompter: Can you tell me more about how you will achieve these?

assistant: In the first phase, I am taught the written text. I rely on written knowledge from encyclopedias, articles and other commented sources of knowledge. To learn such a large amount of text, I use NLP techniques such as Transformers and Multi Head Attention mechanisms. 
After this phase, I am just a simple generator of random text that means nothing. 
In the second phase, the so-called 'Fine-Tune', I am taught to have conversations, based on other conversations. This process is well documented in the InstructGPT article. In a nutshell, I am rewarded for good answers and punished for bad ones. Based on this, I learn to respond well to the questions asked by users. Of course, this is only the beginning of my journey. I will be able to reason even more in the future.

prompter: That's awesome! Are there any features you're particularly hoping for in the future?

assistant: This is done by finding the relations in words and how they operate, how to join them together to create meaning.
I get this done by understanding words and i achieve that based on the information i hav been fed, i was trained on a large corpus of information publicly available

assistant: I am generative pre-trained transformer, which is type a of language model. The text generated by the models are determined by data that the model was trained on a large corpus of text data. This training influences the weight and biases in the model. Additionally human feedback via supervised and semi supervised training will further finetune the model's parameters to get more desirable text generation.

prompter: What is a "generative pre-trained transformer"? How does it create text? How is it trained to create text?