Merge pull request #50 from plastic-labs/chl_notes

proofing

content/_index.md

@@ -28,7 +28,7 @@ It’s our mission to realize this future.
 [[LLMs excel at theory of mind because they read]]  
 [[Loose theory of mind imputations are superior to verbatim response predictions]]  
 [[Honcho name lore]]  
-[[Human-AI chat paradigm hamstrings the space of possibility]]. 
+[[Human-AI chat paradigm hamstrings the space of possibility]]  
 [[LLM Metacognition is inference about inference]]  
 [[Machine learning is fixated on task performance]]  
 

content/notes/Human-AI chat paradigm hamstrings the space of possibility.md

@@ -8,7 +8,7 @@ Here's a limited list of things *besides* a direct response we routinely want to
 - A list of items to search over storage
 - A 'plan' for how to approach a problem
 - A mock user response
-- A [[LLM Metacognition is inference about inference|metacogntive step]] to consider the product of prior inference
+- A [[LLM Metacognition is inference about inference|metacognitive step]] to consider the product of prior inference
 
 In contrast, the current state of inference is akin to immediately blurting out the first thing that comes into your mind--something that humans with practiced aptitude in social cognition rarely do. But this is very hard given the fact that those types of responses don't ever come after the special AI message token. Not very flexible.
 

content/notes/LLMs excel at theory of mind because they read.md

@@ -8,7 +8,7 @@ We know that in humans, we can strongly [correlate reading with improved theory
 
 The experience of such natural language narration *is itself a simulation* where you practice and hone your theory of mind abilities. Even if, say, your English or Psychology teacher was foisting the text on you with other training intentions. Or even if you ran the simulation without coercion to escape at the beach.
 
-It's not such a stretch to imagine that in optimizing for other tasks LLMs acquire emergent abilities not intentionally trained.[^3] It may even be that in order to learn natural language prediction, these systems need theory of mind abilities or that learning language involves specifically involves them--that's certainly the case with human wetware systems and theory of mind skills do seem to improve with model size and language generation efficacy.
+It's not such a stretch to imagine that in optimizing for other tasks LLMs acquire emergent abilities not intentionally trained.[^3] It may even be that in order to learn natural language prediction, these systems need theory of mind abilities or that learning language specifically involves them--that's certainly the case with human wetware systems and theory of mind skills do seem to improve with model size and language generation efficacy.
 
 ---
 

content/notes/Loose theory of mind imputations are superior to verbatim response predictions.md

@@ -14,22 +14,22 @@ So what are models good at predicting that's useful with limited context and loc
 
 Besides just being better at it, letting the model leverage what it knows to make open-ended theory of mind imputation has several distinct advantages over verbatim response prediction:
 
-1. Fault tolerance
+1. **Fault tolerance**
 	- Theory of mind predictions are often replete with assessments of emotion, desire, belief, value, aesthetic, preference, knowledge, etc. That means they seek to capture a range within a distribution. A slice of user identity.
 	- This is much richer than trying (& likely failing) to generate a single point estimate (like in verbatim prediction) and includes more variance. Therefore there's a higher probability you identify something useful by trusting the model to flex its emergent strengths.  
 
-2. Learning
+2. **Learning**
 	- That high variance means there's more to be wrong (& right) about. More content = more claims, which means more opportunity to learn. 
-	- Being wrong here is a a feature, not a bug; comparing those prediction errors with reality are how you know what you need to understand about the user in the future to get to ground truth.  
+	- Being wrong here is a feature, not a bug; comparing those prediction errors with reality are how you know what you need to understand about the user in the future to get to ground truth.  
 
-3. Interpretability 
+3. **Interpretability** 
 	- Knowing what you're right and wrong about exposes more surface area against which to test and understand the efficacy of the model--i.e. how well it knows the user.
-	- As we're grounded in the user and theory of mind, we're better able to assess this than if we're simply asking for likely human responses in massive space of language encountered in training.  
+	- As we're grounded in the user and theory of mind, we're better able to assess this than if we're simply asking for likely human responses in the massive space of language encountered in training.  
 
-4. Actionability
+4. **Actionability**
 	- The richness of theory of mind predictions give us more to work with *right now*. We can funnel these insights into further inference steps to create UX in better alignment and coherence with user state.
-	- Humans make thousands of tiny, subconscious interventions responsive to as many sensory cues & theory of mind predictions all to optimize single social interactions. It pays to know about the internal state of others.
-	- Though our lifelong partners from above can't perfectly predict each other's sentences, they can impute each other's state with extremely high-fidelity. The rich context they have on one another translates to as desire to spend most of their time together (good UX).  
+	- Humans make thousands of tiny, subconscious interventions resposive to as many sensory cues & theory of mind predictions all to optimize single social interactions. It pays to know about the internal state of others.
+	- Though our lifelong partners from above can't perfectly predict each other's sentences, they can impute each other's state with extremely high-fidelity. The rich context they have on one another translates to a desire to spend most of their time together (good UX).