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I don't believe that's what they were saying at all though. The claim appears to be that it's near lossless relative to their own baseline that uses float. Which I'd grant, since a 32x storage reduction for 0.61% loss in quality is a reasonable trade off when you've already decided to accept that ~90% is "good enough".

>Saying "Near lossless" to mean 90% accurate retrieval of saved vectors is simply a lie. Lossy-ness is binary, not something you can paper over with getting close enough.

Lossy-ness is a binary, "near lossness" however is still valid (and is not the same as saying lossless).

How else, if not by comparing to "lossness" (whether with a more abstract qualitative term like "near" or with some distance or error measurement) do you report the level of fidelity to non-lossy results?

Depends on the context, but even in abstract for whatever domain, 90% sounds pretty close if we're talking about a linear level of degradation corresponding to each X% level.

In this case if this is calculated on a lossless baseline that's itself close to 90% - it's distance from it, it doesn't represent distance from some pure 100% perfect retrieval. So ~90% vs ~89% is very close to lossless capability.

near lossless refers to being 89.65/90.26 = 99.32% of baseline, i'm pretty sure.

Yeah, what bugs me about stuff like that is like they spend all this time and then they output several or minimal real testing to prove the theory It's like you're building your model to And just because it takes a long time to compute and do the testing, you'd rather publish your article and then try to get credit on something that hasn't really been proven. Look, prove your results. Study it. Ruggedize it. Make sure it works. Then, show us.

Ask a SOTA LLM when Newton was born without any access to internet : the answer is Lossless for our shared culture understanding of this question. Not Near-lossless, lossless. Ask the same LLM when YOU were born, the answer is just wrong for almost anyone in the world, not lossy. Between the two there is a whole new field of Lossyness to study.

90% depends entirely on what the measure means here, do you understand what "Normalized Discounted Cumulative Gain at rank 10" means to the set of data that we are comparing ?

Sometimes coming up with new codecs (compressors decompressors) means coming up with new ways to interpret artifacts of the real world. And this is exactly why LLM are so powerful and they are like a giant Lossy (but Near-Lossless for various use cases) ZIP file / Database of the whole knowledge of the training data.

Nobody is trying to manipulate you here, humanity just has to find new explanations for complex topics.

    Lossy-ness is binary

Lossless is objective for information theory. To get from the real world to digital world you need an analog to digital converter, this process is by definition lossy. We are interested in the real world, and information is pure but never represents exactly reality. Lossyness is baked into our problem statement here.

Using terms like near lossless means we think we are very close to reality for what we’re trying to do

Most of the fine distinctions are already lost when a document is processed through a pile of linear algebra to turn it into a fixed-size list of floating-point numbers, as you can see from the NDCG@10. Vector search is not a tool for fine distinctions. It's a tool for reducing a large pile of documents to a smaller selection of candidates, which you can then check individually with some more expensive method.

this is the reason why we report ndcg and not recall. ndcg respects fine grained details so you get the an overview of how much details you are trading off since it would hurt the ranking.

Any way I can read about this or the use case? I have a hobby interest

to which email did you send it? can u send it to support please?

Doubtful. The problem with the pi idea is that you need to include the offset, which will likely be as long as or longer than your data.

Not seeing a typo in your quote. Can you point it out?

Unfortunately as cost reduction trends to 100%, it comes along with an intrinsic high-pass sarcasm filter.

Reminds me of 'Learning to be me' by Greg Egan

You would obviously be trading storage for compute and time to retrieve the storage.

100% reduction is impossible for something which should work, because -100% means it is now 0

There is, after you define what you’re ready to loose and understand the lossy space. That’s how we came up with mobile cellphones, audio and video codecs etc. Literally powering all modern devices we use.

yes, your are right. what heading would you have taken here?