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Vectors are over, hashes are the future

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Re: Vectors are over, hashes are the future

#31

Post by ummonk »

robotresearcher wrote: post #11

A state vector can represent a point in the state space of floating-point representation, a point in the state space of a hash function, or any other discrete space. Vectors didn't go anywhere. The article is discussing which function to use to interpret a vector. Is there a special meaning of 'vector' here that I am missing? Is it so synonymous in the ML context with 'multidimensional floating point state space desc…

Keep in mind that this is the same field which uses multidimensional arrays that fail to obey tensor transformation laws (because ML requires the kind of nonlinear structure introduced by functions such as ReLU that requires a preferred basis and cannot be transformed between bases) but insists on calling them tensors.

Re: Vectors are over, hashes are the future

#32

Post by gk1 »

packetlost wrote: post #22
gk1 wrote: post #4

This is a rehash (pardon me) of this post from 2021: https://www.search.io/blog/vectors-versus-hashes The demand for vector embedding models (like those released by OpenAI, Cohere, HuggingFace, etc) and vector databases (like https://pinecone.io -- disclosure: I work there) has only grown since then. The market has decided that vectors are not, in fact, over.

PineCone seems interesting. Is the storage backend open source? I've been working on a persistent hashmap database that's somewhat similar (albeit not done) that should have less RAM requirements than bitcask (ie. larger than RAM keysets)

Although we may open-source parts in the future, currently no part of Pinecone is open-sourced. Instead, there are several proprietary index types available, packaged along with hardware/compute resources into what we call “pods.”

Re: Vectors are over, hashes are the future

#33

Post by evrydayhustling »

fzliu wrote: post #9

Hashes are fine, but to say that "vectors are over" is just plain nonsense. We continue to see vectors as a core part of production systems for entity representation and recommendation (example: https://slack.engineering/recommend-api ) and within models themselves (example: multimodal and diffusion models). For folks into metrics, we're building a vector database specifically for storing, indexing, and searching acr…

Yes this is funny to read when (a) embeddings are such a huge leap in reusable machine learning investment and (b) almost nobody is using them yet. On the other hand, neural hashes do look similar to the density tree analysis that is the first step in many of our applications of language embeddings. It makes sense to me that some of this might be incorporated into vector dbs in the near future. Do you have plans to?

Re: Vectors are over, hashes are the future

#34

Post by hansvm »

mrkeen wrote: post #24

> The analogy here would be the choice between a 1 second flight to somewhere random in the suburb of your choosing in any city in the world versus a 10 hour trip putting you at the exact house you wanted in the city of your choice. Wouldn't the first part of the analogy actually be: A 1 second flight that will probably land at your exact destination, but could potentially land you anywhere on earth?

To be fair, that sounds like an extraordinarily fun prospect.

Re: Vectors are over, hashes are the future

#35

Post by [deleted] »

ummonk wrote: post #31
robotresearcher wrote: post #11

A state vector can represent a point in the state space of floating-point representation, a point in the state space of a hash function, or any other discrete space. Vectors didn't go anywhere. The article is discussing which function to use to interpret a vector. Is there a special meaning of 'vector' here that I am missing? Is it so synonymous in the ML context with 'multidimensional floating point state space desc…

Keep in mind that this is the same field which uses multidimensional arrays that fail to obey tensor transformation laws (because ML requires the kind of nonlinear structure introduced by functions such as ReLU that requires a preferred basis and cannot be transformed between bases) but insists on calling them tensors.

[deleted]

Re: Vectors are over, hashes are the future

#39

Post by 8note »

hansvm wrote: post #34
mrkeen wrote: post #24

> The analogy here would be the choice between a 1 second flight to somewhere random in the suburb of your choosing in any city in the world versus a 10 hour trip putting you at the exact house you wanted in the city of your choice. Wouldn't the first part of the analogy actually be: A 1 second flight that will probably land at your exact destination, but could potentially land you anywhere on earth?

To be fair, that sounds like an extraordinarily fun prospect.

That somewhere could be the middle of the Pacific, and it just drops you without a boat

Re: Vectors are over, hashes are the future

#40

Post by psteitz »

packetlost wrote: post #22
gk1 wrote: post #4

This is a rehash (pardon me) of this post from 2021: https://www.search.io/blog/vectors-versus-hashes The demand for vector embedding models (like those released by OpenAI, Cohere, HuggingFace, etc) and vector databases (like https://pinecone.io -- disclosure: I work there) has only grown since then. The market has decided that vectors are not, in fact, over.

PineCone seems interesting. Is the storage backend open source? I've been working on a persistent hashmap database that's somewhat similar (albeit not done) that should have less RAM requirements than bitcask (ie. larger than RAM keysets)

Have a look at Milvus (BSD license) and Weviate (Apache 2)
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