Unlock the incredible world of visual creativity with DALL-E, the cutting-edge AI image generation model. Experience mind-boggling artistry and witness the future of digital content creation
One of the key enablers of the ChatGPT magic can be traced back to 2017 under the obscure name of reinforcement learning with human feedback(RLHF).
Large language models(LLMs) have become one of the most interesting environments for applying modern reinforcement learning(RL) techniques. While LLMs are great at deriving knowledge from vast amounts of text, RL can help to translate that knowledge into actions. That has been the secret behind RLHF.
Without RAG, an LLM is only as smart as the data it was trained on. Meaning, LLMs can only generate text based purely on what its “seen”, rather than pull in new information after the training cut-off. Sam Altman stated “the right way to think of the models that we create is a reasoning engine, not a fact database.” Essentially, we should only use the language model for its reasoning ability, not for the knowledge it has.
We’ve done a lot of looking over our shoulders at OpenAI. Who will cross the next milestone? What will the next move be?
But the uncomfortable truth is, we aren’t positioned to win this arms race and neither is OpenAI. While we’ve been squabbling, a third faction has been quietly eating our lunch.
I’m talking, of course, about open source. Plainly put, they are lapping us. Things we consider “major open problems” are solved and in people’s hands today.