Research
Longer-form notes on the trajectory of artificial intelligence and the infrastructure underneath it. where the field has been, and where the evidence suggests it's headed.
Where Is AI Going?
The evolution of artificial intelligence. from hand-coded rules to autonomous systems
Expert Systems
Hand-coded rules and decision trees. Limited to narrow domains.
Machine Learning
Statistical models learned patterns from data. SVMs, random forests, gradient boosting.
Deep Learning
Neural networks with many layers. CNNs for vision, RNNs for sequences. GPU-accelerated training.
Transformers
Self-attention mechanism eliminated sequential processing. BERT, GPT-2, T5.
Foundation Models
GPT-4, Claude, Gemini. Multimodal, instruction-following, tool-using.
AI Agents
Autonomous systems that plan, use tools, and execute multi-step workflows.
Multi-Agent
Coordinated agent teams with specialized roles, shared memory, and negotiation.
Autonomous AI
Self-improving systems with long-term goals, continuous learning, and minimal human oversight.
Open Research Threads
Scaling Laws & Test-Time Compute
How model performance scales with parameters, data, and compute. and why spending more compute at inference time (not just training) is now a core lever.
Retrieval & Memory Architectures
Long-term memory for agents beyond a single context window: retrieval, summarization, and structured memory stores.
Interpretability
Techniques for understanding what is happening inside a model. feature visualization, probing, and mechanistic interpretability.
Alignment & Safety
RLHF, constitutional AI, and red-teaming methods used to make model behavior match intended values under adversarial pressure.
Note: this page reflects ongoing analysis and community discussion, not a settled scientific consensus. treat forward-looking eras as informed projection, not fact.