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The blog post format is so much nicer than a PDF paper! More seriously, this does feel like a real advance. Vector search+context stuffing (RAG) is clearly a h

The blog post format is so much nicer than a PDF paper! More seriously, this does feel like a real advance. Vector search+context stuffing (RAG) is clearly a hack that doesn't resemble how we actually think or do things in reality. I've been wondering for the last year whether it's possible to extend the attention mechanism to more naturally connect to a bigger set of per-session weights or activations. The moment you encounter the query/key/value analogy it's an obvious idea, the problem being that you need a very strong grip on the low level details of neural architecture to actually do it. Now apparently it is possible! And the way the topk knob actually maps to abstraction is quite amazing. Still, this doesn't eliminate context window constraints. The memories themselves have a form of context window in this technique. Context size (what they call sequence length) does still matter. Additionally, the memories have to actually fit in GPU memory (at least in their implementation). And the memories appear to be nearly full snapshots of the network, so they will get quite large (much larger than text grabbed using RAG). So there's going to be a painful tradeoff here for the forseeable future where you'll have to decide whether you want a bigger smarter base model with a bigger context window but less space for memory, or a smaller model with a smaller context window but bigger memory. This is the first I've heard of Normal Computing, who are these guys/gals exactly? > Normal is a deep-tech startup founded by former Google Brain & X engineers Ah. That explains it. "X" here also refers to Google, not Twitter/Musk devs.

naasking 3y ago
> Vector search+context stuffing (RAG) is clearly a hack that doesn't resemble how we actually think or do things in reality. How do you know?
- mike_hearn 3y ago
  Well, RAG is a form of lookup operation from an external set of documents that tries to work around anterograde amnesia. Humans don't suffer from that condition unless something has gone badly wrong. We don't carry around sets of documents and constantly consult them because we forgot what we read 10 seconds ago. Instead we have instead short term memory (e.g. what was the last paragraph I just read), a sort of medium-term memory that can absorb lots of information (e.g. the contents of a textbook if you're studying a subject) but which will eventually be forgotten, and a general long term memory that is rarely forgotten (e.g. riding a bike, how to speak your mother tongue ....). In LLM terms the parameters are the long term memory, the activations whilst processing a prompt are the short term memory, but there's no medium term. This paper is about adding that sort of medium term memory, if I understood it correctly. RAG is quite the hack. All you start with is the question, but why should the documents you need for the answer be similar to the question? It can be true for some kinds of search-engine like queries, but it'll be false for others. And then the results are attempted to be stuffed into the short term memory, of course it doesn't fit so then RAG gets done at the paragraph level or whatever, at which point you may have lost vital context. It's just going to be a set of paragraphs that happen to resemble the question. And finally word vector similarity isn't necessarily useful and can easily hurt, e.g. France and Germany are considered close conceptually but if your question is about France then stuffing the context with stuff related to Germany is going to fail much harder than a classical keyword based search engine would. If you integrate memory with the attention mechanism then you can use "memories" captured at higher levels of abstraction and not just word-level documents, so it is potentially much more powerful.
  - kristiandupont 3y ago
    I don't know, it actually feels quite similar to something that seems to happen in my head. In a certain context, my brain resurfaces a bunch of memories and thoughts of things that are similar, even if they are not necessarily valuable to the current situation. I am not saying that the how is equivalent, but the what certainly seems at least related.
    - mike_hearn 3y ago
      Yes but RAG isn't re-surfacing memories, it's re-surfacing documents i.e. the source of memories. That's what this new paper tackles: how do you resurface actual memories or the closest analogy at the neural architecture level.
  - naasking 3y ago
    > Instead we have short term memory [...], a sort of medium-term memory that can absorb lots of information [...], and a general long term memory that is rarely forgotten This is basically folk psychology though, not necessarily representative of the actual mechanisms that are being used. This is a common mistake I see in comparisons of ML and neuroscience or psychology. ML is all about mechanistic models for things, where we know all the details. We simply don't have any such mechanistic models of how the brain works, all we have are speculative models and folk concepts, ie. how things seem to us not how they are. I'm just very skeptical that these are enough to be confident in claims like this not being how we actually do things in reality. For instance, the anteretrograde amnesia you describe certainly isn't a common feature of the brain as a whole, but can you really say that it isn't a feature of some parts of the brain responsible for cognition, and then some RAG-like mechanism isn't used to eliminate that problem to give the outward appearance of no amnesia?
    - mike_hearn 3y ago
      Sure it's just an analogy. We clearly don't have 3 distinct separate levels, it's more like one memory where recalling memories strengthens them (but can also change them). But whatever we have, it's clearly very different to an LLM. We don't even want to replicate the human mind exactly. If we did that what'd be the point? AI is useful exactly because it's not the same as how people work. I don't think RAG is a good analogy for how the brain works because RAG is about finding original concrete sources (like books), not about matching and recalling digested and abstracted thoughts inside the "mind" itself. The article does talk about this a bit at the beginning.
btbuildem 3y ago
I've always considered the human ability to abstract things away - the capacity to say "this is like that" - as a kind of data compression mechanism. Instead of memorizing everything, we remember the first few things of that kind that we encountered, and then the differences between them and everything else that's sort of like them. The core memories being the root, and the differences being steps along the path of a trie -- both space and time efficient in storage and retrieval. I think the addition of medium-term memory to an LLM system would mean an individualization / giving that system instance the means to become unique from its common starting point. We can already consider the context window as a short-term memory of sorts, but it's lobotomized every time. Having a persistent medium-term memory window (eg, via collecting summaries / tries of abstraction "keyframes" + diffs) would begin to pose some serious questions. If a mechanism was further developed to persist these medium-term data summaries into "long term memory" (adjusting weights and parameters), we would have continuous learning. It's pretty wild stuff.
dragonwriter 3y ago
> Vector search+context stuffing (RAG) is clearly a hack that doesn't resemble how we actually think or do things in reality. Its actually a very close model of how people use external data sources to supplement thought in reality. Its a hack, but it is very much the same hack (indexing external data, and looking up and stuffing some in our brain temporarily at need) that humans have used forever (with a novel indexing method.) Its not (intentionally, at least) a model of anything that happens inside the human brain, but what humans do in reality extends beyond what happens inside the brain.
- vorticalbox 3y ago
  I've noticed this as I've gotten older, I have started to remember "where" to find information rather than remembering it. I end up with a lot of book marks as a result.
  - _boffin_ 3y ago
    I have severe ADHD and have never been good at tests due to this issue right here. I’m now a professional and I carry my books, notes, articles, papers, etc… with me everywhere via my iPad and margin notes. I’ve found, since a young age that knowing where to find the information I need vs memorization is how my brain works best. Wish it was the opposite though.

naasking 3y ago

> Vector search+context stuffing (RAG) is clearly a hack that doesn't resemble how we actually think or do things in reality. How do you know?

mike_hearn 3y ago
Well, RAG is a form of lookup operation from an external set of documents that tries to work around anterograde amnesia. Humans don't suffer from that condition unless something has gone badly wrong. We don't carry around sets of documents and constantly consult them because we forgot what we read 10 seconds ago. Instead we have instead short term memory (e.g. what was the last paragraph I just read), a sort of medium-term memory that can absorb lots of information (e.g. the contents of a textbook if you're studying a subject) but which will eventually be forgotten, and a general long term memory that is rarely forgotten (e.g. riding a bike, how to speak your mother tongue ....). In LLM terms the parameters are the long term memory, the activations whilst processing a prompt are the short term memory, but there's no medium term. This paper is about adding that sort of medium term memory, if I understood it correctly. RAG is quite the hack. All you start with is the question, but why should the documents you need for the answer be similar to the question? It can be true for some kinds of search-engine like queries, but it'll be false for others. And then the results are attempted to be stuffed into the short term memory, of course it doesn't fit so then RAG gets done at the paragraph level or whatever, at which point you may have lost vital context. It's just going to be a set of paragraphs that happen to resemble the question. And finally word vector similarity isn't necessarily useful and can easily hurt, e.g. France and Germany are considered close conceptually but if your question is about France then stuffing the context with stuff related to Germany is going to fail much harder than a classical keyword based search engine would. If you integrate memory with the attention mechanism then you can use "memories" captured at higher levels of abstraction and not just word-level documents, so it is potentially much more powerful.
- kristiandupont 3y ago
  I don't know, it actually feels quite similar to something that seems to happen in my head. In a certain context, my brain resurfaces a bunch of memories and thoughts of things that are similar, even if they are not necessarily valuable to the current situation. I am not saying that the how is equivalent, but the what certainly seems at least related.
  - mike_hearn 3y ago
    Yes but RAG isn't re-surfacing memories, it's re-surfacing documents i.e. the source of memories. That's what this new paper tackles: how do you resurface actual memories or the closest analogy at the neural architecture level.
- naasking 3y ago
  > Instead we have short term memory [...], a sort of medium-term memory that can absorb lots of information [...], and a general long term memory that is rarely forgotten This is basically folk psychology though, not necessarily representative of the actual mechanisms that are being used. This is a common mistake I see in comparisons of ML and neuroscience or psychology. ML is all about mechanistic models for things, where we know all the details. We simply don't have any such mechanistic models of how the brain works, all we have are speculative models and folk concepts, ie. how things seem to us not how they are. I'm just very skeptical that these are enough to be confident in claims like this not being how we actually do things in reality. For instance, the anteretrograde amnesia you describe certainly isn't a common feature of the brain as a whole, but can you really say that it isn't a feature of some parts of the brain responsible for cognition, and then some RAG-like mechanism isn't used to eliminate that problem to give the outward appearance of no amnesia?
  - mike_hearn 3y ago
    Sure it's just an analogy. We clearly don't have 3 distinct separate levels, it's more like one memory where recalling memories strengthens them (but can also change them). But whatever we have, it's clearly very different to an LLM. We don't even want to replicate the human mind exactly. If we did that what'd be the point? AI is useful exactly because it's not the same as how people work. I don't think RAG is a good analogy for how the brain works because RAG is about finding original concrete sources (like books), not about matching and recalling digested and abstracted thoughts inside the "mind" itself. The article does talk about this a bit at the beginning.

btbuildem 3y ago

I've always considered the human ability to abstract things away - the capacity to say "this is like that" - as a kind of data compression mechanism. Instead of memorizing everything, we remember the first few things of that kind that we encountered, and then the differences between them and everything else that's sort of like them. The core memories being the root, and the differences being steps along the path of a trie -- both space and time efficient in storage and retrieval. I think the addition of medium-term memory to an LLM system would mean an individualization / giving that system instance the means to become unique from its common starting point. We can already consider the context window as a short-term memory of sorts, but it's lobotomized every time. Having a persistent medium-term memory window (eg, via collecting summaries / tries of abstraction "keyframes" + diffs) would begin to pose some serious questions. If a mechanism was further developed to persist these medium-term data summaries into "long term memory" (adjusting weights and parameters), we would have continuous learning. It's pretty wild stuff.

dragonwriter 3y ago

> Vector search+context stuffing (RAG) is clearly a hack that doesn't resemble how we actually think or do things in reality. Its actually a very close model of how people use external data sources to supplement thought in reality. Its a hack, but it is very much the same hack (indexing external data, and looking up and stuffing some in our brain temporarily at need) that humans have used forever (with a novel indexing method.) Its not (intentionally, at least) a model of anything that happens inside the human brain, but what humans do in reality extends beyond what happens inside the brain.

vorticalbox 3y ago
I've noticed this as I've gotten older, I have started to remember "where" to find information rather than remembering it. I end up with a lot of book marks as a result.
- _boffin_ 3y ago
  I have severe ADHD and have never been good at tests due to this issue right here. I’m now a professional and I carry my books, notes, articles, papers, etc… with me everywhere via my iPad and margin notes. I’ve found, since a young age that knowing where to find the information I need vs memorization is how my brain works best. Wish it was the opposite though.