7 ms·

As long as there's no solution to the long-term memory problem, we will have a "country of geniuses in a data center" that are all suffering from anterograde am

As long as there's no solution to the long-term memory problem, we will have a "country of geniuses in a data center" that are all suffering from anterograde amnesia (movie: Memento), which requires human hand-holding. I have experimented with a lot of hacks, like hierarchies of indexed md files, semantic DBs, embeddings, dynamic context retrieval, but none of this is really a comprehensive solution to get something that feels as intelligent as what these systems are able to do within their context windows. I am als a touch skeptical that adjusting weights to learn context will do the trick without a transformer-like innovation in reinforcement learning. Anyway, I‘ll keep tinkering…

ambewas 5mo ago
You're right to be skeptical. Without a way to actually implement how the human brain processes experiences into a consolidated memory, we won't be able to solve the long term memory problem at all. Not with the current technology. An LLM context is a pretty well extended short term memory, and the trained network is a very nice comprehensive long term memory, but due to the way we currently train these networks, an LLM is just fundamentally not able to "move" these experiences to long term, like a human brain does (through sleep, among others). Once we can teach a machine to experience something once, and remember it (preferably on a local model, because you wouldn't want a global memory to remember your information), we just cannot solve this problem. I think this is probably the most interesting field of research right now. Actually understanding in depth how the brain learns, and figuring out a way to build a model that implements this. Because right now, with backtracking and weight adjustments, I just can't see us getting there.
- loehnsberg 5mo ago
  I think if we want to build on what we have, instead of compaction at the end of the context window, the LLM would have to 'sleep', i.e. adjust its weights, then wake up with the last bits of the old context window in the new one, and have a 'feel' for what it did before through the change in weights. I just sense it's not that simple to get there, because simply updating the weights based on a single context sample risks degrading the weights of the whole network. I like the idea of using small local model (or several) for tackling this problem, like low rank adaptation, but with current tech, I still have to piece this together or the small local models will forget old memories.
  - SeriousM 5mo ago
    Couldn't fitting solve the problem? That's what companies do: take a model as a base and train it on the specific data long enough so that it prefers the new data. Overfitting may be a thing but for personal use, I may want to have it work as I expected, every time.
  - ambewas 5mo ago
    Sleep would probably be a part of the equation for consolidating , but there's still the question of how exactly does the brain process the information during sleep in a way that it permanently consolidates the information. It's not how an llm can work right now, it needs too much iterations & a much bigger dataset than what we can work with. A single time experiencing something and we can remember it. That's orders of magnitude more efficient than an LLM right now can achieve.
- airstrike 5mo ago
  > I think this is probably the most interesting field of research right now. Actually understanding in depth how the brain learns, and figuring out a way to build a model that implements this. This field of research has been around for decades, so who's to say when there'll be a breakthrough. In fact, LLMs are great despite our very limited understanding, and not because we had some breakthrough about the human brain.
  - ambewas 5mo ago
    Exactly. It's been around so long and we still don't know how to mimic it. The way an llm learns is a very interesting way of doing it, but it sure isn't what the brain is doing. But it's indisputable.. We can get enormous results with this technique. It's just probably not the way forward for faster learning to remediate the issue of context loss.
- catlifeonmars 5mo ago
  Why does a language model have to be monolithic? I think retraining a model is expensive (relatively speaking). Is there some way to bolt on specialization?
  - ambewas 5mo ago
    That's exactly the issue. Retraining is too expensive & needs too much iteration to work efficiently I think.
- Leynos 5mo ago
  How well do LoRAs work for this using something like Thinking Machine's Tinker?
- saltcured 5mo ago
  It's kind of fascinating that everyone is trying to build a Chinese Room agent with stateless models, since we don't know how to produce a stateful model with continuous, incremental training. It's like spontaneous implemention of thought experiments from yesteryear. I wonder if all this product-focused experimentation will accidentally impact philosophy of mind after all...
gbro3n 5mo ago
I've used open claw (just for learning, I agree with the author it's not reliable enough to do anything useful) but also have a similar daily summary routine which is a basic gemini api call to a personal mcp server that has access to my email, calendar etc. The latter is so much more reliable. Open claw flows sometimes nail it, and then the next day fails miserably. It seems like we need a way to 'bank' the correct behaviours - like 'do it like you did it on Monday'. I feel that for any high percentage reliability, we will end up moving towards using LLMs as glue with as much of the actual work as possible being handed off to MCP or persisted routine code. The best use case for LLMs currently is writing code, because once it's written, tested and committed, it's useful for the long term. If we had to generate the same code on the fly for every run, there's no way it would ever work reliably. If we extrapolate that idea, I think it helps to see what we can and can't expect from AI.
- Gareth321 5mo ago
  That unreliability was why I gave up on OpenClaw. I tried hard to give it very simple tasks but it had a high degree of failure. Heartbeats and RAG are lightyears away from where they need to be. I'm not sure if this can be overcome using an application layer right now, but I trust that many people are trying, and I'm eager to see what emerges in the next year. In the mean time I know that they're working very hard on continuous learning - real-time updates to weights and parametric knowledge. It could be that in a year or so, we can all have customised models.
  - gbro3n 5mo ago
    That would be great if that comes to fruition. Investing in a model with weights updates would be like investing in employee training, rather than just giving the same unreliable employee more and more specific instructions.
- mike_hearn 5mo ago
  This is interesting. I haven't used OpenClaw but I set up my own autonomous agent using Codex + ChatGPT Plus + systemd + normal UNIX email and user account infrastructure. And it's been working great! I'm very happy with it. It's been doing all kinds of tasks for me, effectively as an employee of my company. I haven't seen any issues with memory so far. Using one long rolling context window, a diary and a markdown wiki folder seems sufficient to have it do stuff well. It's early days still and I might still encounter issues as I demand more, but I might just create a second or third bot and treat them as 'specialists' as I would with employees.
sonink 5mo ago
[dead]
Gareth321 5mo ago
I agree. A key to human intelligence is our ability to adjust our weights in real-time. All knowledge becomes parametric knowledge - the knowledge stored inside the model. RAG is a messy workaround which requires making assumptions about what is needed to load from external sources before it is clear what is needed. Agentic loops can go some way to overcome this, but they are resource intensive, slow, prone to mistakes and deviations, and far less accurate. The secret sauce of an LLM is the vectorised weights. RAG is like putting a 1990s Honda Civic engine into a Ferrari. You can do it, but the result is quite terrible. I think we will eventually end up with models which can be individually trained and customised on regular schedules. After that, real-time.
azmz 5mo ago
[flagged]
vdelpuerto 5mo ago
[flagged]

ambewas 5mo ago

You're right to be skeptical. Without a way to actually implement how the human brain processes experiences into a consolidated memory, we won't be able to solve the long term memory problem at all. Not with the current technology. An LLM context is a pretty well extended short term memory, and the trained network is a very nice comprehensive long term memory, but due to the way we currently train these networks, an LLM is just fundamentally not able to "move" these experiences to long term, like a human brain does (through sleep, among others). Once we can teach a machine to experience something once, and remember it (preferably on a local model, because you wouldn't want a global memory to remember your information), we just cannot solve this problem. I think this is probably the most interesting field of research right now. Actually understanding in depth how the brain learns, and figuring out a way to build a model that implements this. Because right now, with backtracking and weight adjustments, I just can't see us getting there.

loehnsberg 5mo ago
I think if we want to build on what we have, instead of compaction at the end of the context window, the LLM would have to 'sleep', i.e. adjust its weights, then wake up with the last bits of the old context window in the new one, and have a 'feel' for what it did before through the change in weights. I just sense it's not that simple to get there, because simply updating the weights based on a single context sample risks degrading the weights of the whole network. I like the idea of using small local model (or several) for tackling this problem, like low rank adaptation, but with current tech, I still have to piece this together or the small local models will forget old memories.
- SeriousM 5mo ago
  Couldn't fitting solve the problem? That's what companies do: take a model as a base and train it on the specific data long enough so that it prefers the new data. Overfitting may be a thing but for personal use, I may want to have it work as I expected, every time.
- ambewas 5mo ago
  Sleep would probably be a part of the equation for consolidating , but there's still the question of how exactly does the brain process the information during sleep in a way that it permanently consolidates the information. It's not how an llm can work right now, it needs too much iterations & a much bigger dataset than what we can work with. A single time experiencing something and we can remember it. That's orders of magnitude more efficient than an LLM right now can achieve.
airstrike 5mo ago
> I think this is probably the most interesting field of research right now. Actually understanding in depth how the brain learns, and figuring out a way to build a model that implements this. This field of research has been around for decades, so who's to say when there'll be a breakthrough. In fact, LLMs are great despite our very limited understanding, and not because we had some breakthrough about the human brain.
- ambewas 5mo ago
  Exactly. It's been around so long and we still don't know how to mimic it. The way an llm learns is a very interesting way of doing it, but it sure isn't what the brain is doing. But it's indisputable.. We can get enormous results with this technique. It's just probably not the way forward for faster learning to remediate the issue of context loss.
catlifeonmars 5mo ago
Why does a language model have to be monolithic? I think retraining a model is expensive (relatively speaking). Is there some way to bolt on specialization?
- ambewas 5mo ago
  That's exactly the issue. Retraining is too expensive & needs too much iteration to work efficiently I think.
Leynos 5mo ago
How well do LoRAs work for this using something like Thinking Machine's Tinker?
saltcured 5mo ago
It's kind of fascinating that everyone is trying to build a Chinese Room agent with stateless models, since we don't know how to produce a stateful model with continuous, incremental training. It's like spontaneous implemention of thought experiments from yesteryear. I wonder if all this product-focused experimentation will accidentally impact philosophy of mind after all...

gbro3n 5mo ago

I've used open claw (just for learning, I agree with the author it's not reliable enough to do anything useful) but also have a similar daily summary routine which is a basic gemini api call to a personal mcp server that has access to my email, calendar etc. The latter is so much more reliable. Open claw flows sometimes nail it, and then the next day fails miserably. It seems like we need a way to 'bank' the correct behaviours - like 'do it like you did it on Monday'. I feel that for any high percentage reliability, we will end up moving towards using LLMs as glue with as much of the actual work as possible being handed off to MCP or persisted routine code. The best use case for LLMs currently is writing code, because once it's written, tested and committed, it's useful for the long term. If we had to generate the same code on the fly for every run, there's no way it would ever work reliably. If we extrapolate that idea, I think it helps to see what we can and can't expect from AI.

Gareth321 5mo ago
That unreliability was why I gave up on OpenClaw. I tried hard to give it very simple tasks but it had a high degree of failure. Heartbeats and RAG are lightyears away from where they need to be. I'm not sure if this can be overcome using an application layer right now, but I trust that many people are trying, and I'm eager to see what emerges in the next year. In the mean time I know that they're working very hard on continuous learning - real-time updates to weights and parametric knowledge. It could be that in a year or so, we can all have customised models.
- gbro3n 5mo ago
  That would be great if that comes to fruition. Investing in a model with weights updates would be like investing in employee training, rather than just giving the same unreliable employee more and more specific instructions.
mike_hearn 5mo ago
This is interesting. I haven't used OpenClaw but I set up my own autonomous agent using Codex + ChatGPT Plus + systemd + normal UNIX email and user account infrastructure. And it's been working great! I'm very happy with it. It's been doing all kinds of tasks for me, effectively as an employee of my company. I haven't seen any issues with memory so far. Using one long rolling context window, a diary and a markdown wiki folder seems sufficient to have it do stuff well. It's early days still and I might still encounter issues as I demand more, but I might just create a second or third bot and treat them as 'specialists' as I would with employees.

sonink 5mo ago

[dead]

Gareth321 5mo ago

I agree. A key to human intelligence is our ability to adjust our weights in real-time. All knowledge becomes parametric knowledge - the knowledge stored inside the model. RAG is a messy workaround which requires making assumptions about what is needed to load from external sources before it is clear what is needed. Agentic loops can go some way to overcome this, but they are resource intensive, slow, prone to mistakes and deviations, and far less accurate. The secret sauce of an LLM is the vectorised weights. RAG is like putting a 1990s Honda Civic engine into a Ferrari. You can do it, but the result is quite terrible. I think we will eventually end up with models which can be individually trained and customised on regular schedules. After that, real-time.

azmz 5mo ago

[flagged]

vdelpuerto 5mo ago

[flagged]