Explore >> Select a destination
|
You are here 
|
lambdalabs.com | ||
| | | | |
gwern.net
|
|
| | | | | On GPT-3: meta-learning, scaling, implications, and deep theory. The scaling hypothesis: neural nets absorb data & compute, generalizing and becoming more Bayesian as problems get harder, manifesting new abilities even at trivial-by-global-standards-scale. The deep learning revolution has begun as foretold. | |
| | | | |
blog.moonglow.ai
|
|
| | | | | Parameters and data. These are the two ingredients of training ML models. The total amount of computation ("compute") you need to do to train a model is proportional to the number of parameters multiplied by the amount of data (measured in "tokens"). Four years ago, it was well-known that if | |
| | | | |
lacker.io
|
|
| | | | | I've been playing around with OpenAI's new GPT-3 language model. When I got beta access, the first thing I wondered was, how human is GPT-3? How close is it ... | |
| | | | |
www.nbcnews.com
|
|
| | | OpenAI on Thursday released the newest version of the artificial intelligence model that powers its popular ChatGPT chatbot, with CEO Sam Altman promoting it as like having a "team of Ph.D. | ||
