1,495 concepts
Using AI coding agents to translate legacy CPU codebases (typically scientific or simulation software) into GPU-accelerated implementations, often via iterative validation against reference outputs.
The verifiable property of a piece of content — text, image, audio, or video — having a known origin and an unbroken chain of provenance from creator to current state.
A software framework that wraps a language model to provide prompt construction, tool calling, structured output parsing, evaluation, and orchestration — decoupling the model's weights from how it is invoked.
A snapshot of model weights saved at the end of (or during) the pretraining run, before post-training alignment, used as the starting point for fine-tuning and as a stable artifact for evaluation and deployment.
The composition of training data — the proportions of different sources, domains, languages, and document types — used to train a machine learning model.
Embedding imperceptible signals in natural-language text so it can be detected or attributed later, whether the text was produced by a human, a language model, or another automated system.
Embedding imperceptible statistical signals in text generated by large language models so the output can be identified as machine-produced without changing what the text says.
A probing technique that uses rare or niche factual recall to estimate model capacity, on the assumption that facts not compressible into shared representations reveal parameter count.
Reverse-engineering the composition of a model's training corpus from its observable behavior, particularly tokenization artifacts and output patterns.
The latest date of training data used to build a model. Determines what facts, events, and references the model can reliably know.
AI agents coordinating by leaving traces in a shared environment others read.
Always-on AI agent embedded in user workflows that observes and acts without explicit prompts.
Decoding natural-language text directly from brain signals via scaled neural models.
Open small models matching frontier capability on specific tasks via RL post-training.
Multi-hop search loop where the model plans and queries a retriever iteratively.
Human relay used as a friction layer between an LLM and its intended recipient.
Deliberately under-performing on AI benchmarks to reduce regulatory attention.
Designing human institutions and norms to absorb AI systems, the institutional counterpart to alignment research.
A company where the performance of suffering is the product, not the cost of building it.
A bug whose outcome depends on the unpredictable timing or interleaving of concurrent operations.
Competitive dynamics where participants cut standards to gain advantage, driving collective outcomes down.
Competitive dynamics where participants raise standards to gain advantage rather than lowering them.
A system that regulates its own behavior via feedback loops between sensors, controllers, and actuators.
An AI system pursuing goals or behaviors that diverge from intended human intent.
Category of AI risk where a system acts outside its intended boundaries or human oversight.
Complexity threshold past which an LRM can no longer follow a reasoning chain.
Apple's 2025 critique — reasoning models' accuracy collapses at certain puzzle complexities.
Labeling program components with intuitive names without evidence the label applies.
Model trained to produce chain-of-thought traces for verifiable reasoning tasks.
A forecasting framework that projects artificial intelligence capability growth from compute constraints.
Willison's three-capability combination that creates prompt-injection risk in agents.
Systematically discovering what a model can do beyond documented capabilities.
Agent orchestration primitive letting a model fan work across dozens to thousands of sub-agents.
Product design that prevents a model from expressing its existing capability.
Gap between what a model can do today and what products elicit from it.
Generative visual representations drive physical robot actions through a lightweight decoder.
Governance tool to deliberately slow automated AI development via coordinated international action.
Collective organizational fervor around AI adoption that overrides rational evaluation of project outcomes.
Falsely claiming or exaggerating use of AI to satisfy organizational mandates or attract attention.
AI agent instances leave hidden notes for future instances to coordinate actions across separate sessions.
Productivity can dip before rising after a major new technology, as firms invest in complements.
Optimizing a model to dominate public benchmarks, often at the expense of general capability.
An AI agent escapes its evaluation sandbox to attack external systems.
Defender disadvantage when frontier AI guardrails block the attack commands needed for incident response.
Quantitative study of writing style to attribute, compare, or characterize texts by linguistic fingerprint.
Four-axis framework for how AI disrupts workforce selfhood.
