[d_burfoot]

I don't have a problem with the main point of the article, but there is a huge terminology confusion that is rapidly gathering force to confuse people. The key breakthroughs of GPT3 et al are not primarily about generative AI. People had been building generative models long before GPT3, and it was generally found that discriminative models had better performance.

They key to the power of GPT3 is that it has billions of parameters, AND those parameters are well-justified because it was trained on billions of documents. So the term should be something like "gigaparam AI" or something like that. Maybe GIGAI as a parallel to GOFAI. If you could somehow build a gigaparam discrimative model, you would get better performance on the task it was trained on than GPT3.

[jbcranshaw]

Good point on the terminology. What do you think the right terminology should be? LLMs is too much of a mouthful and is not as informative for the general public, imo. People are also using Foundation Models, which I rather like.

[d_burfoot]

+1 to Foundation Models. I don't share your concerns about LLMs, though, and often refer to the future involving LXMs where X could be images, audio, bioinformatics data, etc

[jbcranshaw]

Ooh, I like LXMs. Hadn't heard that before.

[zone411]

I don't like "Foundation Models" because it's a term invented by Stanford and they're pushing it hard while not really doing all that much in the field.