Intelligence
Independent guides to artificial intelligence technology, machine learning lifecycles, model evaluation, monitoring, deployment, and responsible AI.
ExplainerA retrieval-augmented generation system can fail before a model writes a word. A practical test program should inspect the corpus, retrieval step, context assembly, response, and monitoring loop.
GuideAn agent that can act on behalf of a person needs more than an instruction to be careful. Design boundaries around authorization, evidence, human review, and recovery before expanding its reach.
AnalysisA multimodal system can perform well on a benchmark yet fail when cameras, microphones, documents, timing, or human workflows interact. Evaluation should follow the real task and its consequences.
ExplainerMixture-of-experts models activate only selected subnetworks for each input. That conditional computation can increase capacity, but routing, balance, communication, and operations determine the real engineering result.
AnalysisSynthetic data can make rare, sensitive, or expensive scenarios easier to study. Its value depends on a specific validation purpose, because a realistic-looking simulation can still omit the variation that matters.
GuideQuantization reduces numerical precision to make some models smaller and faster. The durable practice is to match the method, calibration data, hardware, and quality tests to the deployment target.
AnalysisState-space models, sparse attention, and hybrid memory systems are changing how engineers think about long-context artificial intelligence.
GuideLocal inference can improve privacy and resilience, but model selection should begin with memory, task quality, and operational constraints.