Mar 12, 2025
Unveiling the Future of AI Cognition
We explore using thought data for the next wave of AI agents
AI Insights
Cognitive AI
Data Mapping
Generative AI
Capturing and structuring human thought processes from conversations can create more effective AI agents with improved reasoning capabilities, practical application frameworks, and governance benefits.
In last year’s article, I explored how one of AI’s most meaningful promises lies in thought partnership, helping people think, not just act. Since then, several research threads have started to converge. I’ve highlighted a few of them below.
These advances make it realistic to capture unstructured conversations, distill them into structured cognitive flows, and feed that data back into agents, closing the loop we sketched last year.
The Concept
The Concept
From messy chatter to machine-readable cognition
Step 1. Capture & segment
Source: call transcripts, chat logs, design reviews.
Tooling: diarisation → sentence‐level time-stamps → unsupervised topic-boundary detection (Bayesian surprise or windowed embedding drift).
Step 2. Classify into “thought steps”
A minimal taxonomy that ports well across domains:
1. Problem framing
2. Hypothesis generation
3. Information retrieval / query
4. Synthesis & sense-making
5. Decision / recommendation
6. Reflection & meta-comment
LLMs excel at labeling these spans in-context; you can bootstrap with weak heuristics and refine via reflection self-training.
Step 3. Build the problem-to-solution graph
Represent each session as a directed acyclic graph where nodes = labeled thought steps and edges = rhetorical moves (e.g., supports, contradicts, refines).
Serialise to JSON L or a property graph so it slots straight into vector + graph stores.
Latest Updates
(GQ® — 02)
©2024
Latest Updates
(GQ® — 02)
©2024
Mar 12, 2025
Unveiling the Future of AI Cognition
We explore using thought data for the next wave of AI agents
AI Insights
Cognitive AI
Data Mapping
Generative AI
Capturing and structuring human thought processes from conversations can create more effective AI agents with improved reasoning capabilities, practical application frameworks, and governance benefits.
In last year’s article, I explored how one of AI’s most meaningful promises lies in thought partnership, helping people think, not just act. Since then, several research threads have started to converge. I’ve highlighted a few of them below.
These advances make it realistic to capture unstructured conversations, distill them into structured cognitive flows, and feed that data back into agents, closing the loop we sketched last year.
The Concept
From messy chatter to machine-readable cognition
Step 1. Capture & segment
Source: call transcripts, chat logs, design reviews.
Tooling: diarisation → sentence‐level time-stamps → unsupervised topic-boundary detection (Bayesian surprise or windowed embedding drift).
Step 2. Classify into “thought steps”
A minimal taxonomy that ports well across domains:
1. Problem framing
2. Hypothesis generation
3. Information retrieval / query
4. Synthesis & sense-making
5. Decision / recommendation
6. Reflection & meta-comment
LLMs excel at labeling these spans in-context; you can bootstrap with weak heuristics and refine via reflection self-training.
Step 3. Build the problem-to-solution graph
Represent each session as a directed acyclic graph where nodes = labeled thought steps and edges = rhetorical moves (e.g., supports, contradicts, refines).
Serialise to JSON L or a property graph so it slots straight into vector + graph stores.
Latest Updates
(GQ® — 02)
©2024
Mar 12, 2025
Unveiling the Future of AI Cognition
We explore using thought data for the next wave of AI agents
AI Insights
Cognitive AI
Data Mapping
Generative AI
Capturing and structuring human thought processes from conversations can create more effective AI agents with improved reasoning capabilities, practical application frameworks, and governance benefits.
In last year’s article, I explored how one of AI’s most meaningful promises lies in thought partnership, helping people think, not just act. Since then, several research threads have started to converge. I’ve highlighted a few of them below.
These advances make it realistic to capture unstructured conversations, distill them into structured cognitive flows, and feed that data back into agents, closing the loop we sketched last year.
The Concept
From messy chatter to machine-readable cognition
Step 1. Capture & segment
Source: call transcripts, chat logs, design reviews.
Tooling: diarisation → sentence‐level time-stamps → unsupervised topic-boundary detection (Bayesian surprise or windowed embedding drift).
Step 2. Classify into “thought steps”
A minimal taxonomy that ports well across domains:
1. Problem framing
2. Hypothesis generation
3. Information retrieval / query
4. Synthesis & sense-making
5. Decision / recommendation
6. Reflection & meta-comment
LLMs excel at labeling these spans in-context; you can bootstrap with weak heuristics and refine via reflection self-training.
Step 3. Build the problem-to-solution graph
Represent each session as a directed acyclic graph where nodes = labeled thought steps and edges = rhetorical moves (e.g., supports, contradicts, refines).
Serialise to JSON L or a property graph so it slots straight into vector + graph stores.
Latest Updates
©2024