This is a raw stream of working notes. Some may be wrong, and mostly these will have little context, but I believe sharing raw thoughts is valuable in a way that polished blog posts are not.
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&
There is a massive AI overhang today. The term is analogous to snow overhang. When snow slides down a roof, sometimes a section goes over the edge and is seemingly supported by nothing. You know it will eventually fall; the tension is in the air. It's a question of time. I have the same feeling with applying AI
There's a now-famous classification of different levels of self-driving: * Level 0: No automation * Level 1: Driver assistance * Level 2: Partial automation * Level 3: Conditional automation (human fallback always available) * Level 4: High automation (can safely pull over) * Level 5: Full automation The full definition is more detailed but I won't replicate it here. Now
Probably 80% of (mainstream, non-ML) Twitter is excited about the potential of GPT and ChatGPT. The other 20% is skeptical. Few are indifferent. But both the optimists and pessimists seem to focus on the obvious set of use cases: text generation and chatbots. That is a failure of imagination. Text is just an interface to those models, and chat
