LLMs

“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

Memory Considerations

Since co-occurrence matrices are square, they grow exponential with the number of entities being embedded. For 50k entities and a 32-bit data format, a dense matrix will already be at 10GB. 100k entities puts it at 40GB.

If you are trying to embed even more entities than that or have limited RAM available, you may need to use a... See more

However, a key risk with several of these startups is the potential lack of a long-term moat. It is difficult to read too much into it given the stage of these startups and the limited public information available but it’s not difficult to poke holes at their long term defensibility. For example:

• If a startup is built on the premise of taking base

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However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or... See more

To do this, we employ a technique known as AI-assisted evaluation, alongside traditional metrics for measuring performance. This helps us pick the prompts that lead to better quality outputs, making the end product more appealing to users. AI-assisted evaluation uses best-in-class LLMs (like GPT-4) to automatically critique how well the AI's... See more

Google Deepmind used similar idea to make LLMs faster in Accelerating Large Language Model Decoding with Speculative Sampling. Their algorithm uses a smaller draft model to make initial guesses and a larger primary model to validate them. If the draft often guesses right, operations become faster, reducing latency.

There are some people speculating... See more

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

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