Untangling AGI Terms: Reasoning, Memory, Continual Learning Metrics
A decision memo separating reasoning, long-term memory, and continual learning into testable metrics to reduce AGI narrative confusion.
A decision memo separating reasoning, long-term memory, and continual learning into testable metrics to reduce AGI narrative confusion.
How AI automation turns speed into new baselines, raising pressure, and how to redesign sustainable standards using risk-based governance.
Use Roofline (I ≤ π/β) to classify LLM inference kernels as memory- or compute-bound, and guide bandwidth, cache, and interconnect decisions.
How hidden sampling controls and unreliable web search can raise hallucination risk and verification costs in paid AI chat.
Generative AI recommendations can vary by default. Measure variance via reruns, improve reproducibility with seed and system_fingerprint, and add constraints and checklists.
Turn “no web browsing” claims into a repeatable grading protocol using accuracy, consistency, calibration, and leakage checks.
A curated link roundup from recently collected official updates and tech news.
Remote sensing lead time drops by narrowing candidate areas, prioritizing HITL review, and measuring preprocessing, co-registration, and QA.
AI firms define political neutrality via guardrails: election interference, impersonation, deception, and violence limits, plus logging and transparency.
Reporting exists, but unclear SLA, ownership, and evidence requirements for imminent threats make operational protocols central to AI safety.
Explains how public political criticism can translate into contract risk, triggering termination processes and vendor switching in AI procurement.
How small prompt shifts can amplify into risky robot actions, and why alignment alone can’t guarantee physical safety.
In high-risk deployments, prioritize uncertainty, false positives/negatives, and closed-loop failure propagation over single-model scores.
Examines OpenAI’s defense agreement: three redlines, verifiable safety controls, and contract-driven audit and liability allocation.
“AI-sounding” content is mainly a QA failure: missing editing, verification, and accountability. Measure claims, cite sources, and document review.
A curated link roundup from recently collected official updates and tech news.
AI abuse is shifting from text generation to channel-linked TTPs. Defend with multi-signal detection and rapid takedowns plus appeals.
A curated link roundup from recently collected official updates and tech news.
Explain 120B local LLM bottlenecks on 128GB: quantization, KV cache, context length, concurrency, and backend overhead.
In defense, pressure for full commercial AI use collides with FASCSA exclusion/removal, DPA priority orders, and governance logging controls.
In defense AI procurement, operations win: deployment, access control, logging, retention, liability, plus DFARS 72-hour reporting and 90-day retention, and 5-year rights terms.
Domain shift, post-processing, and adversarial attacks weaken detection. Treat scores as evidence and add provenance and stress tests.
DFARS 252.204-7012 can drive audit logging, 90-day retention, and forensic access requirements in DoD AI contracts.
Compares EU, US, and China rules on high-risk AI and critical infrastructure, highlighting regulators’ access to docs, data, and code.