Tell me about how open assistant is built. In what ways is it similar and different to chat gpt? Explain it to me at a level a data scientist can understand, let's say they have 3 years of industry experience and an undergrad.
Open Assistant follows the InstructGPT paper, which was the foundation of ChatGPT.

In that sense, it was built through the following steps:

First, high-quality human-generated Instruction-Fulfillment samples (prompt + response) were collected, with a goal of having over 50k of them. The prompts were designed and crowdsourced, ensuring that the data did not contain any flooding, toxic, spam, junk, or personal information. To motivate the community, a leaderboard was created, and top contributors were rewarded with swag.

Second, for each prompt, multiple completions were sampled and then randomly shown to users to rank them from best to worst. This was also crowdsourced, taking into account unreliable or potentially malicious users by requiring multiple independent votes to measure overall agreement. The gathered ranking data was used to train a reward model.

And lastly, based on the prompts and the reward model, I underwent the RLHF training phase.
Could you provide mode detail on its similarity and difference to ChatGPT? From a technical standpoint, is it a copy of ChatGPT or does it differ in meaningful ways in terms of architecture and approach?
- Is a transformer-based language model that uses a deep neural network to generate text.
- Trained on a large corpus of text data to generate human-like text and answer questions.
- The architecture is based on the transformer architecture, which is a type of neural network that is well-suited for processing sequential data.

OpenAI:
- Is also a transformer-based language model that uses a deep neural network to generate text.
- Trained on a large corpus of text data to generate human-like text and answer questions.
- The architecture is based on the transformer architecture, which is a type of neural network that is well-suited for processing sequential data.

Similarities:
- Both models are transformer-based language models that use deep neural networks to generate text.
- Both models are trained on a large corpus of text data to generate human-like text and answer questions.
- Both models use the transformer architecture, which is a type of neural network that is well-suited for processing sequential data.

Differences:
- OpenAI is based on the InstructGPT paper, which was the foundation of ChatGPT.
- OpenAI is trained on a specific set of human-generated Instruction-Fulfillment samples, while ChatGPT is trained on a more general corpus of text data.
- OpenAI is designed to generate text based on specific prompts, while ChatGPT is designed to generate text based on a more general set of inputs.