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But it's not so simple to just "program the memory logic". The hand-holding offered here is sufficient to navigate this restricted simulated world, but what wou

by Imnimo 4y ago

But it's not so simple to just "program the memory logic". The hand-holding offered here is sufficient to navigate this restricted simulated world, but what would be required to achieve increasingly complex behaviors? If a ChatGPT agent can't even handle this simple simulation without all this assistance, what hope does it have to act effectively in the real world?

dragonwriter 4y ago
> But it’s not so simple to just “program the memory logic”. But, it is. The application domain here is fairly trivial, but the logic is both simple and highly general. > but what would be required to achieve increasingly complex behaviors? Basically, three things on top of this: (1) more input adaptors to map external data into language, and (2) a bigger context space to process more current & retrieved data simultaneously, and (3) more output adaptors to map intentions expressed in language to substantive action. But the basic memory/recall system seems fairly robust and general, as does the basic interaction system.
- Imnimo 4y ago
  I think you're ignoring a lot of ways in which this system will not easily extend to more complex tasks. -While the retrieval heuristic is sensible for the domain, it's not applicable to all domains. In what situations should you favor more recent memories over more relevant ones? -The prompt for evaluating importance is domain-specific, asking the model to rate on a scale of 1 to 10 how important a life event is, giving examples like "brushing teeth" (a specific action in the domain) as a 0, and college acceptance as a 10. How do you extend that to a real-world agent? -The process of running importance evaluation over all memories is only tractable because the agents receive a very small number of short memories over the course of a day. This can't scale to a continuous stream of observations. -Reflections help add new inferences to the agent's memory, but they can only be generated in limited quantities, guided by a heuristic. In more complex domains where many steps of reasoning may be required to solve a problem, how can an agent which relies on this sort of ad hoc reflection make progress? -The planning step requires that the agent's actions be decomposable from high-level to fine-grained. In more challenging domains, the agent will need to reason about the fine-grained details of potential plan items to determine their feasibility.
  - Aeolun 4y ago
    > This can't scale to a continuous stream of observations. My mind doesn’t scale to a continuous stream either. While I’m typing this on my phone 99.99% of all my observations are immediately discarded, and since this memory ranks as zero, I very much doubt I’ll remember writing this tomorrow.
  - didnotreadit 4y ago
    I did not read the original post, but your reflections are a great enrichment to what I think the post is about, so congratulations for this good addition.

by Imnimo 4y ago

dragonwriter 4y ago

> But it’s not so simple to just “program the memory logic”. But, it is. The application domain here is fairly trivial, but the logic is both simple and highly general. > but what would be required to achieve increasingly complex behaviors? Basically, three things on top of this: (1) more input adaptors to map external data into language, and (2) a bigger context space to process more current & retrieved data simultaneously, and (3) more output adaptors to map intentions expressed in language to substantive action. But the basic memory/recall system seems fairly robust and general, as does the basic interaction system.

Imnimo 4y ago
I think you're ignoring a lot of ways in which this system will not easily extend to more complex tasks. -While the retrieval heuristic is sensible for the domain, it's not applicable to all domains. In what situations should you favor more recent memories over more relevant ones? -The prompt for evaluating importance is domain-specific, asking the model to rate on a scale of 1 to 10 how important a life event is, giving examples like "brushing teeth" (a specific action in the domain) as a 0, and college acceptance as a 10. How do you extend that to a real-world agent? -The process of running importance evaluation over all memories is only tractable because the agents receive a very small number of short memories over the course of a day. This can't scale to a continuous stream of observations. -Reflections help add new inferences to the agent's memory, but they can only be generated in limited quantities, guided by a heuristic. In more complex domains where many steps of reasoning may be required to solve a problem, how can an agent which relies on this sort of ad hoc reflection make progress? -The planning step requires that the agent's actions be decomposable from high-level to fine-grained. In more challenging domains, the agent will need to reason about the fine-grained details of potential plan items to determine their feasibility.
- Aeolun 4y ago
  > This can't scale to a continuous stream of observations. My mind doesn’t scale to a continuous stream either. While I’m typing this on my phone 99.99% of all my observations are immediately discarded, and since this memory ranks as zero, I very much doubt I’ll remember writing this tomorrow.
- didnotreadit 4y ago
  I did not read the original post, but your reflections are a great enrichment to what I think the post is about, so congratulations for this good addition.