11–20 of 43 posts
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 descriptor' that any other use is not a vector any more?
Earlier quoted context omitted.
10? no, it's more like 20+. lsh was a core piece of the google crawler. it was used for high performance fuzzy deduplication. see ullman's text: mining massive datasets. it's free on the web.
I think LSH was only introduced in 99 by Indyk et. al. I would say it was a pretty active research area 10 years ago.
i don't even think they called it ANN. it was high performance, scalable deduplication. (which is, in fact, just fast/scalable lossy clustering)
collaborative filtering was kind of a cute joke at the time. meanwhile they had lsh, in production, actually deduplicating the internet.
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…
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…
I used random projection hashing to increase the search speed because you can just match directly (or at least narrow down search) instead of calculating the euclidean distance for each row.
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…
I was as confused and annoyed as you were, though, since I don't have a machine learning background
Am I incorrect in thinking we are headed to future AIs that jump to conclusions? Or is it just my "human neural hash" being triggered in error?!
Seems the author is proposing LSH instead of vectors for doing ANN? There are benchmarks here, http://ann-benchmarks.com/ , but LSH underperforms the state of the art ANN algorithms like HNSW on recall/throughput. LSH I believe was state of the art 10ish years ago, but has since been surpassed. Although the caching aspect is really nice.
HNSW index is slow to construct, so it is best suited for search or recommendation engines where you build the index and serve. For workloads where you continuously mutate the index, like streaming clustering/deduplication LSH outperforms HNSW.