LLMs

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

The multiple cantilevered AI overhangs:

Compute overhang. We have much more compute than we are using. Scale can go much further.

Idea overhang. There are many obvious research ideas and combinations of ideas that haven’t been tried in earnest yet.

Capability overhang. Even if we stopped all research now, it would take ten years to digest the new... See more

We identified 30 types of tasks that UX professionals used generative AI tools for in their work. We grouped these tasks under four roles: content editor, research assistant, ideation partner, or design assistant.

• Content editor : Generating and editing text, from microcopy to social media posts, based on specifications or copy given by UX

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For the deployment side of things, we found that the performance of our training process was quite slow, especially when it gets into these large language models and when you train from scratch. MosaicML offers what's called programmatic optimization, which is not so much on the hardware side of things, but rather on the algorithmic side. Can you... 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

Study finds RLHF reduces LLM creativity and output variety : A new research paper posted in /r/LocalLLaMA shows that while alignment techniques like RLHF reduce toxic and biased content, they also limit the creativity of large language models, even in contexts unrelated to safety.

Some tasks are easier to improve via system design. While LLMs appear to follow remarkable scaling laws that predictably yield better results with more compute, in many applications, scaling offers lower returns-vs-cost than building a compound system. For example, suppose that the current best LLM can solve coding contest problems 30% of the

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Mem0: The Memory Layer for Personalized AI

Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.

Note: The Mem0 repository now also includes the Embedchain project. We continue to maintain and support Embedchain ❤️. You can find the Embedchain codebase in the embedchai

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