MemGPT: Towards LLMs as Operating Systems
arxiv.org
arxiv.org
What do you think about the tradeoff of having an agent manage its own memory as opposed to having a separate agent whose job it is to manage the memory and the other agent just focuses on the conversation?
This article seems pretty cool but in no way has it anything to do with its title. “Operating system” has a clearly defined meaning and it’s not “a thing that has memory”. To me these kinds of grandiose claims with no substance undermine the credibility of the authors.
I mean what’s wrong with “Tiered memory layers to provide extended AI context windows”?
Strange reality we live in.
People are joking about the crypto bros funding it, but the leap in profit suggests it's not crypto fueled at all.
1. Use you hands
2. Use a pick or shovel in to a bucket and carry it
3. Use a wheelbarrow
4. Use a mine cart
5. Put a steam engine on your minecart
Cryptocurrencies modernized the malware industry.
Before cryptocurrencies, ransomware used to require victims to pay using gift cards or other ridiculous means.
After cryptocurrencies, victims could now pay their "captors" directly over the Internet in cryptocurrency.
Before cryptocurrencies, malware entities and actors acted largely independently unless they were part of a state-sponsored group or larger organized crime "business".
After cryptocurrencies, "ransomware as a service" emerged where different groups specialize in particular components and services of ransomware: tools to establish footholds, tools to take over and encrypt victim systems, services to demand and take payments, and services to wash and launder payments. Payments between these groups are made in cryptocurrency.
please use the right words when you have to communicate, otherwise you just sound like scammers who want to sell pots with holes by passing them off as colanders
The fact that the current title got onto the HN front page suggests otherwise when it comes to incentives for picking submission titles.
Very interesting.
So to answer your question--perhaps cynically--“Tiered memory layers to provide extended AI context windows” is not the best title you could have for an AI paper because it has all the rizz of a shipping manifest. If you want to maximize citations, you need to market more.
I also considered whether to blame the field's very young reviewer population for not having the proper disdain for sensationalization that I'd expect from an older researcher who would surely have more restraint than to speak so much more of the sizzle than of the steak, but then I remembered that the paper which introduced the Transformer model was titled "Attention Is All You Need".
The bugs in programs aren't actual bugs either, windows aren't actual windows, and many would argue that computer science is still more of an art than a science.
The OS in the title is making a comparison to how page caches work. Relevant data is moved into the context the llm needs to answer chat questions. When it runs out of memory it moves a condensed, searchable version of the content to another data store. Its trying to solve the problem of relatively low max token limits.
I wonder if this makes it easier to run on small devices?
Thankfully, this paper is not that; the name is just misleading.
which is not that hard of an idea to come up with. Generally in each instance of LLM + ???? = "thing", the first papers that come out to fill the ???? with an answer do that in a rush to get on arxiv and so naturally the work is lackluster (since they have barely had time to think about an actual good solution for the ????).
At least LLMs have a very obvious use from day1… but an OS is not one of them.
> To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory.
Given how little progress (relatively) was made until transformers, it seems totally reasonable to pursue att ention models.
I know they suck for Serbian, but I wonder what kind of corpus they need to become useful?
ChatGPT always mixes these up, hallucinates a bunch of words (inappropriate prefixes, declensions etc and is very happy to explain the meaning of these imaginary words), and I can imagine smaller, more complex languages like Serbian needing even larger corpuses than English, yet that's exactly the hard part: there is simply less content to go off of.
Weird thing is it was designed to model language. It’s surprising that it returns sound answers as often as it does. But that’s also kind of the problem, it’s “surprising”, i.e. we don’t really know what happened.
You wouldn’t fly on a jetliner that’s “surprising it flies without disintegrating midair”.
Is this surprising? Can you point to researchers in the field being “surprised” by LLMs returning sound answers?
> “surprising”, i.e. we don’t really know what happened.
This ie reads like a sort of popsci conclusion.
We know exactly what happened. We programmed it to perform these calculations. It’s actually rather straightforward elementary mathematics.
But, what happens is so many interdependent calculations grow the complexity of the problem until we are unable to hold it in it our minds, and to analyze its decisions computationally necessitates similar levels of computation for each decision being made as what was used to compute the weights.
As for its effectiveness, familiarity with the field of computational complexity points to high dimensional polynomial optimization problems being broadly universal solvers.
It's surprising because it wasn't the intent of LLMs. LLMs are just predictive models that guess the most likely next word. Having the results make sense was never a priority. Early version, GPT1/2, all return mostly complete nonsense. It was only with GPT3 when the model got large enough that it started returning results that are convincing and might even make sense often enough.
Even more mind boggling is the fact that randomness is part of its algorithm, i.e. temperature, and that without it the output is kind of meh.
If you took the same amount of data for the GPT3+ but scrambled it's tokenization before training THEN I would agree with you that its current behaviour is surprising, but the model was fed data that has large swaths that are literal question and answer constructions. It's over fitting behavior is largely why it's parent company is facing so much legal backlash.
> Even more mind boggling is the fact that randomness is part of its algorithm
The randomness is for token choice rather than any training time tunable so fails to support the "i.e. we don’t really know what happened" sentiment. We do know, we told it to flip a coin, and it did.
> i.e. temperature, and that without it the output is kind of meh.
Both without it and with it. You can turn up the temperature and get bad results as well as you can turn it down and get bad results.
If adding a single additional dimension to the polynomial of the solution space turned a nondeterministic problem into a deterministic one, then yes, I would agree with you, that would be surprising.
I’m arguing against the breathless use of “surprising”.
My gp explains what I think you overlooked in this dismissive response.
> to analyze its decisions computationally necessitates similar levels of computation for each decision being made as what was used to compute the weights.
Explainable but intractable is still far from surprising for me.
If you read through what Hinton or any of his famous students have said, it genuinely was and is surprising. Everything from AlexNet to the jump between GPT-2 to GPT-3 was surprising. We can't actually explain that jump in a formal way, just reasonable guesses. If something is unexplainable, it's unpredictable. Prediction without understanding is a vague guess and the results will come as a surprise.
It's less that we don't know what's happening on a micro-level but more that it's surprising that it's producing anything coherent at all on a macro-level - especially with a (necessary) element of randomness in the process.
For most part we don't seem particularly knowledgeable about what happens on a macro-level. Hallucinations remain an unsolved problem. AI companies can't even make their "guardrails" bulletproof.
Lol researchers were surprised by the mostly incoherent nonsense pre-transformer RNNs were spouting years go, nevermind the near perfect coherency of later GPT models. To argue otherwise is just plain revisionism.
> What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I’ve in fact reached the opposite conclusion). Fast forward about a year: I’m training RNNs all the time and I’ve witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me.
This reads more like humanizing the language of the post then any legitimate surprise from the author.
The rest of the post then goes into great detail showing that “we DO really know what happened” to paraphrase the definition the op provides for their use of “surprise”.
> Conclusion We’ve learned about RNNs, how they work, why they have become a big deal, we’ve trained an RNN character-level language model on several fun datasets, and we’ve seen where RNNs are going.
I am pushing back on people conflating the innate complexity of a high dimensional polynomial with a misplaced reverence of incomprehensibility.
> In fact, it is known that RNNs are Turing-Complete in the sense that they can to simulate arbitrary programs (with proper weights).
Mathematically proven to be able to do something is about as far from surprise as one can get.
Lol Sure
>I am pushing back on people conflating the innate complexity of a high dimensional polynomial with a misplaced reverence of incomprehensibility.
We don't know what the models learn and what they employ to aid in predictions. That is fact. Going on a grad descent rant is funny but ultimately meaninglessness. It doesn't tell you anything about the meaning of the computations.
There is no misplaced incomprehensibility because the internals and how they meaningfully shape predictions is incomprehensible.
>Mathematically proven to be able to do something is about as far from surprise as one can get.
Magic the gathering is turing complete. I'm sorry but "therotically turing complete" is about as meaningless as it gets. Transformers aren't even turing complete.
Not exactly. They are designed to perform natural language processing (NLP) tasks. That includes understanding language and answering questions.
If the object of hype adds useful novelty, the interest could be justified. If, as it's often the case, it is not quite known - it's a question to figure out.
Granted, intuition is worth something, but it's still not a certainty, so somebody having a different opinion still could see something useful here.
I did not read the paper, but it could be that the authors find it is not effective.
To put it in layman's terms, LLMs are to software developers what power tools were for tradesmen.
Sure you can still use your old screw driver, and for some work, it's not worth getting your electric drill out; but it's a game changer.
Sometimes, the hype is justified. I believe at the OS layers, LLM support would make sense. It's full of old mental constructs and "I have to remember how to do this".
For example, this week several papers on time series forecasts indicate they may have use there.
For example they seem to do better job on translation than previous approaches.
For example they seem to do a better job at transcription than previous approaches.
Probably will do better on OCR than previous approaches.
Probably has flaws that limits scenarios requiring high precision we may or may not overcome
Possibly will better on autonomous decision making (Does x include a privacy leak should be investigated?) than previous approaches (keyword scanning).
We are in a technological wave of discovery and experimentation, calls for restraint of curiosity and research betray fears
yes, they're exciting, and they are the most general architecture we've found so far, but there are important problems in AI (like anything continuous), that they're really not suited for.
I think there's better architectures out there for many tasks, and I'm a little dismayed that everyone seems to be cargo-culting the GPT architecture rather than taking the lessons for transformers and experimenting with more specialized algorithms.
*btw they don't need quantized tokens, there's no reason they can't just work on continuous vectors directly, and they don't have to be causal or limited to one sequence, but "transformer" seems to mean GPT in everyone's mind, and even though the original transformer was an encoder-decoder model we rarely seem to see those these days for some reason.
Transformers can do Reinforcement Learning yes.
https://arxiv.org/abs/2106.01345
https://arxiv.org/abs/2205.14953
>they can handle continuous domains, like robot motion?
Yes they can handle it just fine. Excellently in fact.
https://www.deepmind.com/blog/scaling-up-learning-across-man...
https://tidybot.cs.princeton.edu/
https://general-pattern-machines.github.io/
https://wayve.ai/thinking/lingo-natural-language-autonomous-...
I don't know if anyone is saying they're the best at or have "solved" everything but they can damn near do anything.
That kind of rhetoric is dangerously close to the cryptocurrency shills saying all critics are people who are annoyed because they “missed the boat” and didn’t get rich. It’s the kind of generic comment which can be used to discredit anything the interlocutor wants.
That's the difference.
And even when we hit the limit of improvement on those models, when scaling up won't make a qualitative difference, we can expect many years of further breakthroughs in applications, as R&D focuses on less obvious applications and making more efficient use of the capabilities available.
That’s not the part of the comment I had an issue with, which is why it’s not the part I quoted. What I commented on is the end, which implies negative intentions on the part of the original commenter.
Given the context of the original comment this was a response to, you’d have to take things out of context to construct this as a generic comment
And what the original comment boils down to is “let’s not make LLMs and transformers solutions in search of problems, let’s not try to fit them to solve everything”. It seems you might agree.
My issue with the response has nothing to do with specific technologies, but that it painted another view as having an agenda (fear). That is the generic defence. You take something you believe and then say those who disagree do so due to <negative connotation>.
By the way, this is not the point but there are plenty of “solution in search of a problem” and “get instantly rich” (including full-on scams) cases in the current wave of AI.
Every single problem does not need to be solved using an LLM, which this paper tells us the amount of desperation in this hype cycle.
Those reading this paper and giving it credibility have fallen for this nonsense and are probably gullible enough to believe in this non use-case.
How do you know if a problem is suitable for transformers/LLMs unless you try? We have this great generalized architecture, I would hope people throw everything at it and see what sticks, because what's the downside? Less focus on bespoke predictive models? Oh no.
It's perfectly natural and allows us to figure out what does and doesn't work, even if it means sometimes we have to deal with empty hype projects.