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rustastra commented on Automated reasoning to remove LLM hallucinations   aws.amazon.com/blogs/aws/... · Posted by u/rustastra
Metricon · 9 months ago
To be clear, my amusement isn't that I find this technique to not be useful for the purpose it was created, but that 40 years later, we find ourselves in pursuit for the advancement of AI to be somewhat back where we already were; albeit, in a more semi-automated fashion as someone still has to create the underlying rule-set.

I do feel that the introduction of generative neural network models in both natural language and multi-media creation has been a tremendous boon for the advancement of AI, it just amuses me to see that which was old is new again.

rustastra · 9 months ago
Same with symbolic systems!
rustastra commented on Automated reasoning to remove LLM hallucinations   aws.amazon.com/blogs/aws/... · Posted by u/rustastra
drew-y · 9 months ago
How does automation reasoning actually check a response against the set of rules without using ML? Wouldn't it still need a language model to compare the response to the rule?
rustastra · 9 months ago
aiui a natural language question e.g. "What is the refund policy?" gets matched against formalized contracts, and the relevant bit of the contract gets translated into natural language deterministically. At least this is the way I'd do it, but not sure how it actually works
rustastra commented on Happy Birthday ChatGPT: Looking Forward from Here   encord.com/blog/one-year-... · Posted by u/Buhljingo
rustastra · 2 years ago
I couldn't agree more with this take: > Our demand for stimulating content is being overtaken by supply. Analogously, with AI, we might be in a world where scientific progress is accelerated beyond our wildest dreams, where we have more answers than questions, and where we cannot even process the set of answers available to us.
rustastra commented on Learnings from employing ChatGPT as a ML Engineer for a day   encord.com/blog/we-employ... · Posted by u/ruinar50
rustastra · 3 years ago
Curious to learn more about prompt engineering takeaways here. Was feeding more context (or chapters of textbooks, bits of papers, documentation) helpful? It does seem like layering information and being very precise helps a lot. Eerily like with interns
rustastra commented on Launch HN: Cord (YC W21) – training data toolbox for computer vision    · Posted by u/ulrikhansen54
rustastra · 5 years ago
As a user, am I expected to write the labeling algorithms myself or do you offer some in-built ones?
rustastra commented on Label a Dataset with a Few Lines of Code   eric-landau.medium.com/la... · Posted by u/ulrikhansen54
elandau25 · 5 years ago
Really just depends on the task. For this particular case I used a Faster-RCNN model with weights pretrained on the COCO dataset
rustastra · 5 years ago
Could feature extraction be helpful here?
rustastra commented on Label a Dataset with a Few Lines of Code   eric-landau.medium.com/la... · Posted by u/ulrikhansen54
rustastra · 5 years ago
I am very curious to know which pre-trained models work better for this task and whether it's possible at all to do without a neural net...

u/rustastra

KarmaCake day32January 19, 2021View Original