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Ai assurance
| AI Glossary |
The defensive science of protecting ai applications from attack or malfunction.
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Assurance, английский
- (see marine insurance.) conveyance or deed: in which light shakspeare makes tranio say that his father will “pass assurance.”
- Страхование (в основном страхование жизни)
Assurance certificate, английский
Страховой сертификат
Assurance company; offi ce, английский
Assurance engagement, английский
Assurance factor, английский
Assurance level, английский
Assurance of salvation, английский
Assurance service, английский
Malfunction, английский
- Detection, analysis and recording (system) (бортовая) система обнаружения неисправностей, регистрации и анализ данных «мадар»
- Неисправность
- A situation in which a particular organ does not work in the usual way her loss of consciousness was due to a malfunction of the kidneys or to a kidney malfunction. verb to fail to work correctly during the operation his heart began to malfunction.
- Нарушение
- Сбой; неисправность; неправильное срабатывание; ложное срабатывание; неправильной функционирование; нарушение работоспособности
- Неисправность; отказ mall 1. тяжёлая деревянная киянка 2. деревянная трамбовка 3. молл (свободная от транспорта пешеходная зона с выходящими на неё предприятиями розничной торговли)
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Ai digital ecosystem, английский
A technology stack driving the development, testing, fielding, and continuous update of ai-powered applications. the ecosystem is managed as a multilayer collection of shared ai essential building blocks (e.g., data, algorithms, tools, and trained ai models) accessed through common interfaces.
Adversarial machine learning, английский
A broad collection of techniques used to exploit vulnerabilities across the entire machine learning stack and lifecycle. adversaries may target the data sets, algorithms, or models that an ml system uses in order to deceive and manipulate their calculations, steal data appearing in training sets, compromise their operation, and render them ineffective.1 adversarial ai may be used as a phrase that broadens the considerations to attacks on ai systems, including approaches that are less dependent on data and machine learning.
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