Jeremy Howard (co-founder of Answer.AI and fast.ai) describes the extreme full-stack software and infrastructure development done in-house by their small team.
Assertion Not publicly verifiable
Howard: Alec Radford Built OpenAI's GPT After Reading ULMFiT
“I organized a chat for both of us with Kate Metz in the New York Times, and Kate Metz answered, sorry, and Alec answered this question for Kate, and Kate just like, so how did, you know, GPT come about? And he said, well, I was pretty sure that pre-training on…”
Opinion
Howard: Meta 'blew it' on Code Llama due to catastrophic forgetting
“So Code Llama was a, I think it was like a five hundred billion token fine-tuning of Llama II using code. And also prose about code that Meta did. And honestly, they kind of blew it. Because Code Llama is good at coding, but it's bad at everything else.”
Opinion
Howard: TensorFlow 2 was a failure that Google avoided internally
“Then in the end, you know, Google didn't follow through, which is fair enough, like, asking everybody to, you know, learn a new programming language is going to be tough, but, like, it was very obvious, very, very obvious at that time that TensorFlow II was go…”
Assertion Not checkable as stated
Howard: JAX was a grassroots Google reaction against TensorFlow 2
“But I mean, in the meantime, I will say, you know, Google now does have a backup plan. You know, they have JAX, which was never a strategy. It was just a bunch of people who also recognized TensorFlow two as shit, and they just decided to build something else.”
Opinion
Howard: RAG is an inefficient hack compared to fine-tuning
“RAG is like such a inefficient hack, really, isn't it? It's like, You know, segment up my data in some somewhat arbitrary way, embed it, ask questions about that, you know, hope that my embedding, you know, model embeds questions in the same embedding space as…”
Insight
Howard: Correcting LLM errors in chat history degrades subsequent model answers
“The autoregressive nature of language models means that if they make a mistake, and you correct it, and then say, no, that was a mistake, please do it this way instead. The more often you do that, the worse the dialogue answers get. Because it's in the trainin…”