"Real AGI"

Basically because the strengths of LLMs can be leveraged to do most of the work in each case.

OpenAI's o1 shows how straightforward reasoning was after reaching GPT4 etc levels of LLMs.

Reflection is merely allowing the system to apply its intelligence to its own cognition; it's questionable whether that's worth categorizing as a different ability at all.

Autonomy already exists, and existing LLMs do effectively choose subgoals; they need to get better at it to be useful or dangerous; but they are.

I gavve the rough scheme for all of the types of learning except episodic memory; I won't say more because I don't want to improve capabilities. I wouldn't even say this much if I didn't strongly suspect that language model agents/cognitive architectures are our best shot at aligned AGI. Suffice it to say that I expect people to be working on the improvement to episodic memory I'm thinking of now and to release it soon.

I happen to have a little inside information that one of the other types of learning is working very well in at least one project.

If you don't believe me, I ask you: can you be sure they won't? Shouldn't we have some plans together just in case I'm right?

Good point on different teams simultaneously developing different cognitive capabilities. Further efforts will also copy the most successful techniques, and combine them in a single LMC architecture. The synergies might make the rate of progress surprising at some point. Hopefully not an important point...

I agree that the claims about rapidity and inevitability of real AGI is the important part.

But that's trickier, so it's not the point of this post.

One thing I feel stuck on is that part of why I'm so convinced this stuff happens pretty rapidly is that I see pretty direct routes to each. They're not that clever, but they're not totally obvious.

Beyond sharing good ideas, just spreading the belief that this stuff is valuable and achievable will speed up progress.

Any insight on either of these would be appreciated. But I will write another post posing those questions.

Hooking up ai subsystems is predictably harder than you’re implying. Humans are terrible at building agi, the only thing we get to work is optimization under minimal structural assumptions. The connections between subsystems will have to be learned not hardcoded, and that will be a bottleneck - very possibly somehow unified system trained in a somewhat clever way will get there first.

Right. That remains to be seen. Efforts are progressing in both directions, and either one could work.

Here is some rough draft of an expanded version that would try to justify the claims more rather than just stating the definition and the claims. 

Here are two starts at detailing those types of memory:

• and Learning (alternate draft)
  • Meaning continuous learning from ongoing experience
  • Humans have at least four types:
    • Episodic memory for specific snapshots of cognition
      • Useful for pinpointing important information, beliefs, and strategies for rapid single-shot learning
      • Currently implemented as vector-based memories for LLM agents/cognitive architectures;
        • better implementations are relatively straightforward and should be expected soon
    • Semantic learning/habit learning
      • Equivalent to continuously training an LLMs weights with examples from its deployment
    • Reinforcement learning
      • Learning not just what happened, but what lead to reward or situations that predict reward
      • Ongoing RLHF from user feedback is one of many possible straightforward implementations.
  • Working memory. 
    • Temporary storage
      • Implemented by short-term persistent activation and short-term weight changes in cortex
    • Roughly, the context window in LLMs
    • Curiously, the limitation in both seems to be analogous: it's not how much you can fit in there, which is a lot, but how much you can usefully get out.
      • Seven is NOT the correct average estimate of working memory capacity, except for things like phone numbers you can repeat vocally or subvocally.
      • Four is a more common general estimate, but it's clear that the measured capacity depends on retreival difficulty from proactive interference.
        • Most lab tasks use highly overlapping repeated stimuli, eg the same colors or numbers repeated in different patters
        • Measures of working memory in text comprehension can be interpreted as showing nearly unlimited capacity, probably because the semantics barely overlap among different items.
      • I know less about benchmarks for retrieval from context windows, but have heard that there are similar limitations on retrieving more than a handful of items appropriately.

• Learning (second alternate draft)
  • Here I mean continuous learning from experiences during deployment. Current LLMs barely do this; they accumulate contnext, but this isn't quite learning, just using more extensive inputs to make better outputs. GPT4o now makes guesses at important information to put into "memory" (probably just added to the context window/promp), which appears to be fairly useful.
  • Humans have multiple types of continuous learning. Each could be implemented in conjunction with LLM agents/cognitive architectures, so I expect them to be implemented soonish.
    • Episodic memory for specific events.
      • This is a "snapshot" of a moment of cognition
      • Current vector-based memory in LLM agents does this, but very poorly compared to the human implementation. A more human-like system is relatively straightforward, but explaining why I think that would require explaining how to implement it, which I'm not going to do. Plenty of people understand this, but they tend to not be the people implementing AGI agents.
    • Updating semantic knowledge continuously
      • Even if I'm a dense amnesic without any working episodic memory (because my medial temporal lobe has been severely damaged), I'll be able to use new knowledge if it's repeated a few times. This is something like adding every user correction to the training set for the next training run, or running training continuously.