Explains how public political criticism can translate into contract risk, triggering termination processes and vendor switching in AI procurement.
How AI firms can treat insider betting in prediction markets: MNPI definitions, pre-clearance rules, and audit logging for evidence.
How small prompt shifts can amplify into risky robot actions, and why alignment alone can’t guarantee physical safety.
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.
AI abuse is shifting from text generation to channel-linked TTPs. Defend with multi-signal detection and rapid takedowns plus appeals.
Explain 120B local LLM bottlenecks on 128GB: quantization, KV cache, context length, concurrency, and backend overhead.
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.
DFARS 252.204-7012 can drive audit logging, 90-day retention, and forensic access requirements in DoD AI contracts.
Assess AI anime shorts by separating temporal consistency and audio-video alignment using FVD, temporal corruption tests, ITU-T P.835, and LSE.
When AI text looks similar to works or sensitive events, automated enforcement may trigger. Use 17 USC §107 factors and keep records.
Shift from jobs to task-level AI exposure metrics, weighing productivity gains against mixed employment signals for workers.
In portfolio site builds, bottlenecks often come from long outputs during iterative edits, not first drafts. Compare tools by output cost, caching, and batch workflows.
How chatbot sycophancy inflates certainty, conflicts with uncertainty guidance, and what design and evaluation practices reduce risk.
Agent memory shifts personal data from one-off chat to reusable records. Design deletion, expiry, and audit logs before storage.
How multi-plan switching to spread chat caps and API rate limits can clash with terms, security, and automation restrictions.
Tool calls become real actions. JSON validity is not enough—use strict schema checks, allowed_tools, refusal detection, and state-aware gates.
Why conversational AI sycophancy is treated as a quality/alignment risk in official docs and evals, plus practical mitigation prompts.
Examine when speed, copying, and updates translate into general intelligence, using scaling laws, g, and real-world bottlenecks.
Explains reliability patterns and evaluation/logging practices needed when implementing agent execution loops without a framework.
Korean LLM adoption now hinges on training opt-in, retention exceptions, and in-region storage vs processing, not model names.
Regulation is about evidence, not intent. Capture data flows, automated-decision logs, security measures, and under-14 consent as outputs.
How to design governance for surveillance/law-enforcement AI: legal request types, data minimization, retention limits, and audit-ready evidence.
Compare RAG vs parameter updates for long-term memory, then outline validation and gating needed for recursive self-improvement loops.