LLMs

I’ve been giving talks and speaking with engineers and non-technical audiences about interpretability since 2022, and I still struggle to explain exactly what a “feature” is. I often use words like “concept” or “style”, or establish metaphors to debugging programs or making fMRI scans of brains. Both metaphors help people outside of the subfield... See more

“I think a lot of people obviously want to talk about the sexy kind of new consumer applications. I would tell you that I think that the earliest and most significant effect that AI is going to have on our company is actually going to be as it relates to our developer productivity. Some of the tools that we’re seeing are going to allow our devs to... See more

Two ways for an AI company to protect itself from competition: (a) depend not just on AI but also deep domain knowledge about a particular field, (b) have a very close relationship with the end users.

A core research interest of mine is imagining new kinds of interfaces to text documents that are made possible by modern AI and software. I think an interesting place to look for such ideas may be interface designs for reading and writing legal documents .

Legal document-wrangling tools have a handful of properties that make it fertile ground for... See more

core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.

Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more

Top considerations when choosing foundation models

Accuracy

Cost

Latency

Privacy

Top challenges when deploying production AI

Serving cost

Evaluation

Infra reliability

Model quality

So right now, LLMs (Large Language Models) are all the rage. But in the future, it’s possible that the way we get things done is composing things with a combination of LLMs, SMMs (Small, Mighty Models), agents and tools.

It’s what I call Cognitive Composition (because it sounds cool and I have a longtime love affair with alliteration).

This is how we... See more

The new seed parameter enables reproducible outputs by making the model return consistent completions most of the time. This beta feature is useful for use cases such as replaying requests for debugging, writing more comprehensive unit tests, and generally having a higher degree of control over the model behavior. We at OpenAI have been using this... See more

What is Substrate?

Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models – tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more