Notes

Leaving a note here so I can say “I was right” later: by the time I retire, it will seem quaint that we thought global economics could be understood as the US vs China, ignoring the self-determination of India and African societies.

A major theme in dismissing LLMs as “AI” has been their transactional nature. And it’s quite reasonable to correct popular understanding over how these large models learn and their read-only nature in inference!

But RAG techniques are already well-established and there’s no reason to expect we won’t find even more useful architectures, so the “they don’t remember anything so can’t be smart” arguments feel naive or disingenuous. Of course, ChatGPT is the only service that matters in public opinion, so maybe it’s new memory will put these to rest.

I knew the Apple Vision Pro’s EyeSight looked familiar.

Rendering text has been a big challenge for the image generation models (which work very differently than the GPT language models), but it looks like that’s temporary: https://github.com/tyxsspa/AnyText

We’ve scaled up the capabilities of individual human intelligence by forming teams, it seems obvious this will be a way forward with AI: https://arstechnica.com/science/2023/12/large-language-models-can-figure-out-how-to-do-chemistry/

Finally, an LLM that works in SVG: https://arxiv.org/abs/2312.11556