WGU D685 - Practical Applications of Prompt
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Terms in this set (252)
The limitations of AI can be - fundamental limitations of AI
categorized into three main areas: - practical limitations and challenges
- societal concerns and implications.
Fundamental limitations of AI - dependence on training data
include: - limited common sense
- lack of emotional sense.
Practical limitations of AI include: - perpetuating bias
- lack of ethics
- understanding nuances of language and humans.
Societal concerns on AI include: - data privacy
- safety
- security concerns
the study of creating machines and artificial intelligence (AI)
computer systems capable of
performing tasks that typically
require human intelligence
,artificial intelligence that is designed narrow AI
and trained for a specific task or
narrow set of tasks
a hypothetical future AI system that general AI
would possess human-level
intelligence
defined methods or processes algorithms
employed to train models, generate
predictions, and execute tasks using
data
a branch of AI that enables machine learning
computers to improve their
performance through experience
without needing explicit
programming
a computer program designed to AI model
make predictions or decisions based
on input data
a technique where a model is trained supervised learning
using data that includes labeled
examples, such as images with
tagged objects or text with marked
entities
a type of machine learning where unsupervised learning
the model is trained on unlabeled
data without explicit guidance or
supervision
, a type of machine learning wherein reinforcement learning
an AI agent learns through
interactions with an environment,
garnering rewards or penalties
contingent upon its actions
computational models inspired by neural networks
the structure and function of the
human brain's neural networks that
learn from data called training to
recognize patterns, make
predictions, and perform tasks such
as classification, regression, and
pattern recognition
a powerful subset of machine deep learning
learning that uses artificial neural
networks to learn from large
amounts of data
AI systems that can create new generative AI
content
a type of machine learning model large language models (LLMs)
that is trained on massive amounts of
text data to understand and
generate human-like language
the field of AI concentrated on natural language processing (NLP)
enabling computers to understand
and engage with human language,
mirroring the intricacies of human
communication
Exam | Questions and Answers | Verified
Solutions | 2026 Edition | Pass Guaranteed
Save
Terms in this set (252)
The limitations of AI can be - fundamental limitations of AI
categorized into three main areas: - practical limitations and challenges
- societal concerns and implications.
Fundamental limitations of AI - dependence on training data
include: - limited common sense
- lack of emotional sense.
Practical limitations of AI include: - perpetuating bias
- lack of ethics
- understanding nuances of language and humans.
Societal concerns on AI include: - data privacy
- safety
- security concerns
the study of creating machines and artificial intelligence (AI)
computer systems capable of
performing tasks that typically
require human intelligence
,artificial intelligence that is designed narrow AI
and trained for a specific task or
narrow set of tasks
a hypothetical future AI system that general AI
would possess human-level
intelligence
defined methods or processes algorithms
employed to train models, generate
predictions, and execute tasks using
data
a branch of AI that enables machine learning
computers to improve their
performance through experience
without needing explicit
programming
a computer program designed to AI model
make predictions or decisions based
on input data
a technique where a model is trained supervised learning
using data that includes labeled
examples, such as images with
tagged objects or text with marked
entities
a type of machine learning where unsupervised learning
the model is trained on unlabeled
data without explicit guidance or
supervision
, a type of machine learning wherein reinforcement learning
an AI agent learns through
interactions with an environment,
garnering rewards or penalties
contingent upon its actions
computational models inspired by neural networks
the structure and function of the
human brain's neural networks that
learn from data called training to
recognize patterns, make
predictions, and perform tasks such
as classification, regression, and
pattern recognition
a powerful subset of machine deep learning
learning that uses artificial neural
networks to learn from large
amounts of data
AI systems that can create new generative AI
content
a type of machine learning model large language models (LLMs)
that is trained on massive amounts of
text data to understand and
generate human-like language
the field of AI concentrated on natural language processing (NLP)
enabling computers to understand
and engage with human language,
mirroring the intricacies of human
communication
