Training训练
Learning model parameters from examples. A training result is not proof that the model will perform equally well on new material.
Further reading ↗ — Google — Machine Learning GlossaryRead technology claims with more context. A compact glossary, a verification checklist and practical templates for your next reporting idea.
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Learning model parameters from examples. A training result is not proof that the model will perform equally well on new material.
Further reading ↗ — Google — Machine Learning GlossaryUsing a trained model to produce an output. In AI reporting, this does not mean the output is a logically verified conclusion.
Further reading ↗ — Google — Machine Learning GlossaryAdditional training of an existing model for a particular task or style. It changes parameters, unlike simply writing a different prompt.
Further reading ↗ — Google — Fine-tuning, distillation, and prompt engineeringTraining a student model using signals from a teacher model. A smaller student does not automatically retain every teacher capability.
Further reading ↗ — Google — Fine-tuning, distillation, and prompt engineeringA unit of text handled by a language model: sometimes a word, fragment or character. Token counts are not interchangeable with word counts.
Further reading ↗ — Google — Introduction to Large Language ModelsThe token capacity available for a model interaction. A larger limit does not guarantee reliable recall of every supplied detail.
Further reading ↗ — Google AI — Long contextRetrieving external material to help a model answer. Check whether the retrieved sources actually support the answer; retrieval alone is not verification.
Further reading ↗ — Google Cloud — Retrieval-Augmented GenerationA plausible-sounding model output that is unsupported or incorrect. Fluent wording, confidence and a citation-shaped link do not establish truth.
Further reading ↗ — Google Cloud — AI hallucinationsModel weights made available for use. Inspect the license and restrictions: accessible weights alone do not establish that the whole system is open source.
Further reading ↗ — Open Source Initiative — Open Source AI DefinitionSoftware that can plan or take actions toward a goal, sometimes using tools. Ask what it may access, which actions need approval and how failures are handled.
Further reading ↗ — Google Cloud — AI agentsA defined task and measurement procedure for comparison. Read the workload, quality target and test conditions before calling one system better overall.
Further reading ↗ — MLCommons — MLPerf Inference: DatacenterTime taken for a specified request or operation. Ask where timing starts and ends, and whether queueing and network time are included.
Further reading ↗ — NVIDIA — Triton Inference Server metricsWork completed per unit of time. Its unit and concurrency matter; high throughput does not necessarily mean short waits for each user.
Further reading ↗ — NVIDIA — Triton Inference Server metricsA weakness that can be exploited or triggered to cause harm. Distinguish a reported weakness, a demonstrated exploit and confirmed real-world abuse.
Further reading ↗ — NIST — Vulnerability glossaryTransforming readable data into ciphertext using cryptography. Ask which data is protected, who controls keys and where decryption happens.
Further reading ↗ — NIST — Encryption glossaryA research manuscript shared before formal journal publication, often before peer review. Repository moderation is not a substitute for scientific peer review.
Further reading ↗ — arXiv — Content moderationFill in what you know; explicitly mark gaps. This is a preparation template, not an online submission form.
Agree recording, attribution and publication ground rules before starting. Ask open questions; do not invent answers or consent.
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