Insight
Habib: Next-word prediction at scale forces LLMs to develop reasoning
“They are able to do this task extremely well. And the only way to do that is to have gotten better at under, you know, some form of reasoning and some form of knowledge.”
Insight
Habib: Senior developers benefit more from GitHub Copilot than junior developers
“One thing that is surprising to me is that the people who say to me they use it the most are some of the people I consider to be better or more senior developers. You might've thought this tool would help juniors more, but I think people who are more accustome…”
Prediction Not checkable as stated
Habib: Software engineers will be among the first jobs automated by AGI
“When we do get towards things that look like AGI, I suspect that developers will actually be one of the first jobs to see large fractions of their job be automated, which I think is very counterintuitive but also predicting the future is hard, so.”
Opinion
Habib: Foundation model barriers are capital and talent, not secret sauce
“Like to me, the barriers to entry of training, one of these models are mostly capital and talent. Like the people needed are still very specialized and very smart and you need lots of money to pay for GPUs. But beyond that, I don't see that much secret sauce, …”
Opinion
Habib: Skeptical feedback data gives general model creators an insurmountable flywheel
“There's some question about, you know, whether or not the feedback data might give them a flywheel. I'm a little bit skeptical of that, that it would give them so much that no one could catch up.”
Insight
Habib: Scaling LLM parameters and data consistently improves prediction accuracy
“As you scale the language models, both in terms of the number of parameters they have, but also in the size of the data set that they're trained on, it turns out that they continue to get better and better at this prediction task.”
Insight
Habib: ChatGPT's obsequious tone drove the need for custom, use-case-specific models
“I think when ChatGPT came out, there was a lot of frustration from people who didn't like its personality. The tone was a bit obsequious and it's, you know, it'll defer. It doesn't want to give strong opinions on things. And to me that demonstrates the need fo…”
Assertion Supported
Habib: Fine-tuning is what separated ChatGPT from earlier GPT models
“If you look at what the difference is between ChatGPT or the most recent OpenAI Text DaVinci Three model, and what's been in the platform for two years and has not gotten as much attention, the difference is fine tuning. Like it's the same base model, more or …”
Assertion Supported
Habib: Anthropic matched RLHF performance using AI-generated feedback
“Anthropic had this very exciting paper just a couple of weeks ago where actually we're able to get similar results to RLHF without the H. So just actually having a second model provide the evaluation feedback as well. And that's obviously a lot more scalable.”
Insight
Habib: LLM evaluation is harder than traditional ML due to subjectivity
“Then the use cases that people are building now tend to be a lot more subjective than you might have done with machine learning before. And so evaluation is a lot harder. You can't just calculate accuracy on a test set.”
Insight
Habib: Action-Taking Capabilities Shift LLMs from Text Generators to Agents
“One thing that I'm really excited about is actually augmenting large language models with the ability to take actions. And so we've seen a few examples of this at the startup called adapt AI that are doing this and a few others where you essentially let the la…”
Insight
Habib: AI models bake in creator and dataset biases
“The models bake in biases and preferences that were in the model and the data and the team that built it at the time that it was being constructed.”
Assertion Supported
Habib: OpenAI's 1.3B InstructGPT beat the 100x larger GPT-3 via RLHF
“In the InstructGPT paper that OpenAI released, they compared, you know, a one or two billion parameter model with instruction tuning and RHF to the full GPT-III model and people preferred that despite the fact it was a hundred times smaller.”
Insight
Habib: User Edits and Send Actions Serve as Model Improvement Signals
“They probably edit it, so you can capture the edited text, and they maybe get a response or they don't get a response. So all of those bits of feedback are things we would capture and then use to drive improvements of the underlying model.”
