@fivetroop@ylecun@vkhosla@GaryMarcus@yudapearl There really isn't a mystery or open problem here, in response to Dirk, and complementing
@yudapearl's answer. The question on how to incorporate causal assumptions on neural methods has been studied and implemented in practice, here's an example: https://t.co/Yx5VPyfOEs.
Just watched https://t.co/SpiOyI40e6, fantastic video!
Around 39 minutes, @karpathy mentions there is no simple evaluation of whether the model did well.
This may be naive, but can't the LLM give multiple answers to a question and argue against itself which is the best answer?
Hey @jobergum, what's the best way to create a nested structure of NN/ANN rankable tensors in Vespa, similar to how you would imagine tightly coupled parent/child documents to work, where you can choose to query only the parents or the children?
Hey @jobergum, is there any way to limit hnsw's space based on an attribute (userID where they should not access each other's documents for example) in @vespaengine?
@jobergum@vespaengine It's both, each user have their own set of documents that should not be accessed by others, I'm just looking for what the best way to set that up would be so I don't get hit by scaling issues.
Thanks for the blog, I've seen it earlier, by brute force you mean exact search?
@jobergum@vespaengine Yea, I'm looking to do pre-filtering, but worrying a bit about the latency impact at scale (high amount of users, few documents per user).
Correct me if I'm wrong, but it would not be sustainable to have one document type per user, right?
Many thanks for the reply! :)
🚨Biggest @langchain drop yet🚨
⚡️Dynamic, zero-shot composition of multiple chains
Easily plug in different subchains (google, REPL, wikipedia, DBs) just by telling the router LLM (in English!) when and how to use them
`pip install langchain==0.0.8`
https://t.co/vxBzH0v5F9
Are you struggling to validate pains or want to double-check that you are building something important for your users?
Try using Customer Problems Stack Rank (CPSR)!
Great visual thread: https://t.co/Ek9D1wrQ5i
How to execute: https://t.co/i0WblPl7tJ
With multiple foundation models “talking to each other”, we can combine commonsense across domains, to do multimodal tasks like zero-shot video Q&A or image captioning, no finetuning needed.
Socratic Models:
website + code: https://t.co/Zz0kbV5GTQ
paper: https://t.co/NpsW61Ka3s
NVIDIA open-source 3 versions of the StyleGAN architecture:
1. The original StyleGAN implementation
2. StyleGAN2
3. StyleGAN3
StyleGANs is a technique for creating photorealistic synthetic images.
Find links here: https://t.co/W6T4OXxO40
I start my day 🌅 by writing down everything that's in my "soon to be done" scope.
Then I categorizing them into different buckets "Do now", "Do after lunch" and "Do if time permits / another day".
This way I have a clear list of what to focus on. 🗒️
How do you start yours?