"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

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,

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

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.

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

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

I've built around 10 prototypes with GPT over the past couple weeks and it's surprisingly fun. Why is that? Obviously it's a shiny new toy. Whenever I discover a new tech capability, I get excited. I recall discovering D3.js, Bokeh, Keras... These appeal to the Technology enthusiast in me.

I hacked a bit on an AI experiment: idea generator. In general, working on these AI apps makes you think a lot about the work process and unique value add of Knowledge workers in general, and myself specifically. How predictable is it? Which parts are easy to automate; which are

NFTPort sells tools in web3. I've heard people say that this is a smart approach in a volatile market, because selling shovels in a gold rush is safer than looking for gold yourself. That seems wrong. Sure, during the rush your revenue is more stable, but you still depend on