4 ms·
There is also the fact that AI lacks long term memory like humans do. If you consider context length long term memory, its incredibly short compared to that of
by robotnikman 1y ago
There is also the fact that AI lacks long term memory like humans do. If you consider context length long term memory, its incredibly short compared to that of a human. Maybe if it reaches into the billions or trillions of tokens in length we might have something comparable, or someone comes up with a new solution of some kind
- amelius 1y agoThe long term memory is in the training. The short term memory is in the context window.
- candiddevmike 1y agoI think it's more analogous to "intuition", and the text LLMs provide are the equivalent of "my gut tells me".
- mawax 1y agoThe comparison misses the mark: unlike humans, LLMs don't consolidate short-term memory into long-term memory over time.
- griffzhowl 1y agoOver time though, presumably LLM output is going into the training data of later LLMs. So in a way that's being consolidated into the long-term memory - not necessarily with positive results, but depending on how it's curated it might be.
- runako 1y ago> presumably LLM output is going into the training data of later LLMs The LLM vendors go to great lengths to assure their paying customers that this will not be the case. Yes, LLMs will ingest more LLM-generated slop from the public Internet. But as businesses integrate LLMs, a rising percentage of their outputs will not be included in training sets.
- mikepurvis 1y agoThe LLM vendors aren't exactly the most trustworthy on this, but regardless of that, there's still lots of free-tier users who are definitely contributing back into the next generation of models.
- runako 1y agoFor sure, although I'm fairly certain there is a difference in kind between the outputs of free and paid users (and then again to API usage).
- scottLobster 1y agoPlease describe these "great lengths". They allowing customer audits now? The first law of Silicon Valley is "Fake it till you make it", with the vast majority never making it past the "Fake it" stage. Whatever the truth may be, it's a safe bet that what they've said verbally is a lie that will likely have little consequence even if exposed.
- runako 1y ago> great lengths to assure is not incompatible with > "Fake it till you make it" I don't know where they land, but they are definitely telling people they are not using their outputs to train. If they are, it's not clear how big of a scandal would result. I personally think it would be bad, but I clearly overindex on privacy & thought the news of ChatGPT chats being indexed by Google would be a bigger scandal.
- ako 1y agoThat is easily fixed, ask it to summarize it's learnings, store it somewhere, and make it searchable through vector indexes. An LLM is part of a bigger system that needs not just a model, but context and long term memory. Just like human needs to write things down. LLMs are actually pretty good at creating knowledge: if you give it a trial and error feedback loop it can figure things out, and then summarize the learnings and store it in long term memory (markdown, RAG, etc).
- Q6T46nT668w6i3m 1y agoYou’re making the assumption that there’s one, and only one, objective summarization, this is entirely different than “writing things down.”
- ako 1y agoWhy do you assume i assume that?
- Q6T46nT668w6i3m 1y agoMy bad if I misunderstood. I assumed by your use of “it” and approximation methods.
- imtringued 1y agoThis runs into the limitation that nobody has RL'd the models to do this really well.
- bfuller 1y agomy knowledge graph mcp disagrees
- yellow_postit 1y agoIs this not a tool that could be readily implemented and refined?
- enraged_camel 1y agoHumans have the ability to quickly pass things from short term to long term memory and vice versa, though. This sort of seamlessness is currently missing from LLMs.
- FollowingTheDao 1y agoNo, it’s not in the training. Human memories are stored via electromagnetic frequencies controlled by microtubules. They’re not doing anything close to that in AI.
- maldonad0 1y agoIt's not that either.
- dvfjsdhgfv 1y agoI don't believe this has been really proved yet.
- Difwif 1y agoAnd LLM memories are stored in an electrical charge trapped in a floating gate transistor (or as magnetization of a ferromagnetic region on an alloy platter). Or they write CLAUDE.md files. Whatever you want to call it.
- lawlessone 1y agoare ANN memories not also stored in loops like recurrent nets?
- FollowingTheDao 1y agoThat was my point, they’re stored in a totally different way. And that matters because being stored in microtubules infers quantum entanglement throughout the brain.
- yellow_postit 1y agoWhether QE is a mechanism in the brain still seems up for debate from the quick literature review I tried, but would love to learn more. Given the pace of quantum computing it doesn’t seem out of the realm of possibility to “wire up” to LLMs in a couple years.
- deleted 1y ago[deleted]
- jjfoooo4 1y agoThere are many folks working on this, I think at the end of the day the long term memory is an application level concern. The definition of what information to capture is largely dependent on use case. Shameless plug for my project, which focuses on reminders and personal memory: elroy.bot But other projects include Letta, mem0, and Zep
- rixrax 1y agoWhat is the current hypothesis on if the context windows would be substantially larger, what would this enable LLMs to do that is beyond capabilities of current models (other than the obvious the now getting forgetful/confused when you’ve exhausted the context)?
- jjfoooo4 1y agoI mean, not getting confused / forgetful is a pretty big one! I think one thing it does is help you get rid of the UX where you have to manage a bunch of distinct chats. I think that pattern is not long for this world - current models are perfectly capable of realizing when the subject of a conversation has changed
- danielbln 1y agoI wonder if there will be some sort of bitter lesson, generalized memory beating specialized memory.
- jjfoooo4 1y agoYeah to some degree that's already happened. Anecdotally I hear giving your whole iMessage history to Gemini results in pretty reasonable results, in terms of the AI understanding who the people in your life are (whether doing so is an overall good idea or not). I think there is some degree of curation that remains necessary though, even if context windows are very large I think you will get poor results if you spew a bunch of junk into context. I think this curation is basically what people are referring to when they talk about Context Engineering. I've got no evidence but vibes, but in the long run I think it's still going to be worth implementing curation / more deliberate recall. Partially because I think we'll ultimately land on on-device LLM's being the norm - I think that's going to have a major speed / privacy advantage. If I can make an application work smoothly with a smaller, on device model, that's going to be pretty compelling vs a large context window frontier model. Of course, even in that scenario, maybe we get an on device model that has a big enough context window for none of this to matter!
- JohnBooty 1y agoWell here's the interesting thing to think about for me. Human memory is.... insanely bad. We record only the tiniest subset of our experiences, and those memories are heavily colored by our emotional states at the time and our pre-existing conceptions, and a lot of memories change or disappear over time. Generally speaking even in the best case most of our memories tend to be more like checksums than JPGs. You probably can't name more than a few of the people you went to school with. But, if I showed you a list of people you went to school with, you'd probably look at each name and be like "yeah! OK! I remember that now!" So. It's interesting to think about what kind of "bar" AGI would really need to clear w.r.t. memories, if the goal is to be (at least) on par with human intelligence.
- BizarroLand 1y agoInsanely bad compared to what else in the animal kingdom? We are tool users. We use tools, like language, and writing, and technology like audio/video recording to farm out the difficulties we have with memory to things that can store memory and retrieve them. Computers are just stored information that processes. We are the miners and creators of that information. The fact that a computer can do some things better than we can is not a testament to how terrible we are but rather how great we are that we can invent things that are better than us at specific tasks. We made the atlatl and threw spears across the plains. We made the bow and arrow and stabbed things very far away. We made the whip and broke the sound barrier. Shitting on humans is an insult your your ancestors. Fuck you. Be proud. If we invent a new thing that can do what we do better it only exists because of us.
- joshmarinacci 1y agoInsanely bad compared to books or other permanent records. The human memory system did not evolve to be an accurate record of the past. It evolved to keep us alive by remembering dangerous things.
- caconym_ 1y agoAnd yet I have vivid memories of many situations that weren't dangerous in the slightest, and essentially verbatim recall of a lot of useless information e.g. quotes from my favorite books and movies. I am not sure exactly what point you're trying to make, but I do think it's reductive at best to describe memory as a tool for avoiding/escaping danger, and misguided to evaluate it in the frame of verbatim recall of large volumes of information.
- Difwif 1y agoMy mental model is a bit different: Context -> Attention Span Model weights/Inference -> System 1 thinking (intuition) Computer memory (files) -> Long term memory Chain of thought/Reasoning -> System 2 thinking Prompts/Tool Output -> Sensing Tool Use -> Actuation The system 2 thinking performance is heavily dependent on the system 1 having the right intuitive models for effective problem solving via tool use. Tools are also what load long term memories into attention.
- yellow_postit 1y agoI like this mental model. Orchestration / Agents and using smaller models to determine the ideal tool input and check the output starts to look like delegation.
- mark_l_watson 1y agoVery cool, good way to think about it. I wouldn’t be surprised if non-AGI LLMs help write the code to augment themselves into AGI. The unreasonable effectiveness of deep learning was a surprise. We don’t know what the future surprises will be.