A new text-generating language model that combines Google’s own PaLM model and a technique known as Reinforcement Learning with Human Feedback to create an open source tool that can, in theory, do everything OpenAI’s ChatGPT can.
For most, however, this will remain a theory. Unlike ChatGPT, AI developer Philip Wang’s PaLM + RLHF(opens in new tab) doesn’t come trained on any text data required for the model to learn from. Users must compile their own data corpuses and use their own hardware to train the model and process requests.
The latest craze in artificial intelligence is text generation models that respond to human inputs, such as ChatGPT and PaLM + RLHF. Simply put, they predict the appropriate words by learning semantic patterns from a pre-existing data set that could include anything from ebooks to internet flame wars.
Developing usable artificial intelligence
Despite the fact that PaLM and RLHF come pre-trained, the Reinforcement Learning with Human Feedback technique is intended to provide a more intuitive user experience.
As explained by TechCrunch(opens in new tab), RLHF trains a language model by producing a wide range of responses to a human prompt, which are then ranked by human volunteers. Those rankings are then used to train a “reward model”, which sorts the responses by order of preference.
This is not a cheap process, which will prevent all but the wealthiest of AI enthusiasts from training the model. PaLM has 540 billion components of the language model (or parameters) that must be trained on data, and a 2020 study(opens in new tab) revealed that training only a 1.6 billion parameter model would cost anywhere from $80,000 to $1.6 million.
Right now, it seems as though we’re relying on a wealthy benefactor to get involved and train and release the model to the public. Such reliances have not ended well before(opens in new tab), but there are existing efforts by other companies looking to replicate ChatGPT’s capabilities and release them as free software.
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