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I have yet to find LLMs useful beyond basic factoids (I have plans to deploy Llama 2 as an Alexa replacement), but the latest Stable Diffusion model (XL) has co

by NBJack 3y ago

I have yet to find LLMs useful beyond basic factoids (I have plans to deploy Llama 2 as an Alexa replacement), but the latest Stable Diffusion model (XL) has continued to impress me. It is clearly a game changer in the utility it provides to my interests. Admittedly, I'm having fun making high quality 8k wallpapers of abstract concepts and fictions places, but a truly honed creator could clearly work wonders with it. Bitcoin is a solution in search of a much bigger set of problems than it can currently solve now. The ML Renaissance we've hit right now has clear, immediate utility to many. The moves made by the stock art heavyweights is proof enough of how disruptive it is. Contrast this with how banks and financial institutions have responded to bitcoin. To my knowledge, there's yet to be a Bank of America Coin, a Wells Fargo blockchain solution, or non-fungible tokens offered by RBC. They clearly felt at least somewhat threatened, but not in any meaningful way.

j45 3y ago
Continued fun can lead to innovation and breakthroughs
AmIDev 3y ago
LLMs are extremely useful for ML classification. 1. If you have a large amount of unlabeled data, labeling it using LLMs(even a costly XXXB parameter model) can be significantly cheaper than using humans. 2. Text classification works far better with LLMs compared to usual techniques like random forests. 3. We are exploring using LLMs on structured data(tables) for tasks like clustering, where it is difficult to tune other unsupervised approaches. Similarly we are exploring using LLM embeddings for similarity on unsupervised datasets. 4. LLMs could explain their decisions to users(e.g. why was your comment removed?) although work needs to be done on verifying it's correctness. I wouldn't be surprised if LLM takes the spot of Random forest as the default go to for supervised ML.
- Tostino 3y ago
  Yup, and you can fine-tune an LLM to be a specialized classifier with relatively little data compared to building a classifier from scratch. I think that's the biggest benefit to LLMs, the data required to show it a behavior is pretty minuscule in the scheme of things.

by NBJack 3y ago

j45 3y ago

Continued fun can lead to innovation and breakthroughs

AmIDev 3y ago

LLMs are extremely useful for ML classification. 1. If you have a large amount of unlabeled data, labeling it using LLMs(even a costly XXXB parameter model) can be significantly cheaper than using humans. 2. Text classification works far better with LLMs compared to usual techniques like random forests. 3. We are exploring using LLMs on structured data(tables) for tasks like clustering, where it is difficult to tune other unsupervised approaches. Similarly we are exploring using LLM embeddings for similarity on unsupervised datasets. 4. LLMs could explain their decisions to users(e.g. why was your comment removed?) although work needs to be done on verifying it's correctness. I wouldn't be surprised if LLM takes the spot of Random forest as the default go to for supervised ML.

Tostino 3y ago
Yup, and you can fine-tune an LLM to be a specialized classifier with relatively little data compared to building a classifier from scratch. I think that's the biggest benefit to LLMs, the data required to show it a behavior is pretty minuscule in the scheme of things.