Context: first thoughts on Pause Giant AI experiments. I will refine my thinking over time. * I had not thought about AI safety much since ~2017, after thinking a lot about it in 2014-2017. In 2017, I defended my MSc thesis on an AI-safety-inspired topic (though very narrow and technical in nature), but decided to take some distance
Chain-of-thought reasoning is surprisingly powerful when combined with tools. It feels like a natural programming pattern of LLMs: thinking by writing. And it's easy to see the analogy to humans: verbalizing your thoughts in written (journalling) or spoken ("talking things through") form is a good way to actually make progress. To put another way:
There is a very simple, standardized way of solving the problem of too small GPT context windows. This is what to do when the context window gets full: Indexing: 1. Chunk up your context (book text, documents, messages, whatever). 2. Put each chunk through the text-embedding-ada-002 embedding model, and store the resulting vectors -- each consisting of 1536
I was building an agent with langchain today, and was very frustrated with the developer experience. It felt hard to use, hard to get things right, and overall I was confused a lot while developing (for no good reason). Why is it so hard to use? Here are some of my observations. 1. Verbose. If I have done something once
Microsoft recently held an event where they announced the "Office 365 Copilot". It was extremely impressive to me, to the extent that (when this launches) I will consider switching from Google's work suite to Microsoft's. Why is this announcement so impressive? In one word, integration. Pretty much every Office app can refer to documents,
"What's the Moat of your AI company?" That seems to be top of mind for founders pitching their novel idea to VCs -- and the most common question they get. But I think it might not be the right question. Right now, nobody seems to have a good answer. a16z doesn't. At the same time,
GPT-4 came out yesterday and overshadowed announcements, each of which would have been bombshell news otherwise: * Anthropic AI announcing their ChatGPT-like API -- likely the strongest competitor to OpenAI today (only waitlist) * Google announcing PaLM API (currently it's just marketing -- no public access yet) * Adept AI announcing their $350M round (they build foundation models to interact with
"Alpaca is just $100 and competitive with InstructGPT" -- takes like this are going around Twitter, adding to the (generally justified!) hype around AI models. It is indeed a very encouraging result. Specifically, that it took so little compute to train something that achieves competitive results on benchmarks and is a relatively small model at 7B parameters. I haven&
Matt Rickard has a concise overview of Chain-of-thought: the design pattern of having an LLM think step by step. To summarize, the four mentioned approaches from simpler to more nuanced are: 1. Add "Let's think step-by-step" to the prompt. 2. Produce multiple solutions, have the LLM self-check on each one, pick
I've been experimenting with embedding ideas into GPT-space, and using the resulting vectors to visualize. For example, you could plot different activities based on two axes: how safe they are, and how adrenaline-inducing they are. How does it work? The intuition is the following. In words, you can define an axis, e.g. "unsafe activity&
