AIGP
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Terms in this set (246)
, The obligation and responsibility of the creators,
operators and regulators of an AI system to
ensure the system operates in a manner that is
ethical, fair, transparent and compliant with
Accountability applicable rules and regulations (see fairness
and transparency). Accountability ensures the
actions, decisions and outcomes of an AI system
can be traced back to the entity responsible for
it
A subfield of AI and machine learning where an
algorithm can select some of the data it learns
from. Instead of learning from all the data it is
Active Learning
given, an active learning model requests
additional data points that will help it learn the
best. → Also called query learning.
, A machine learning technique that raises a safety
and security risk to the model and can be seen
as an attack. These attacks can be instigated by
manipulating the model, such as by introducing
malicious or deceptive input data. Such attacks
Adversarial Machine
can cause the model to malfunction and
Learning
generate incorrect or unsafe outputs, which can
have significant impacts. For example,
manipulating the inputs of a self-driving car may
fool the model to perceive a red light as a green
one, adversely impacting road safety.
, Artificial General Intelligence
AI that is considered to have human-level
intelligence and strong generalization capability
to achieve goals and carry out a variety of tasks
in different contexts and environments. AGI still
remains a theoretical field of research. It is
contrasted with "narrow" AI, which is used for
AGI
specific tasks or problems.
.beyond reach right now
.experts expect AGI systems to have strong
generalization abilities, the ability to think, learn
and perform complex tasks, and achieve goals
in different contexts and environments
Save
Terms in this set (246)
, The obligation and responsibility of the creators,
operators and regulators of an AI system to
ensure the system operates in a manner that is
ethical, fair, transparent and compliant with
Accountability applicable rules and regulations (see fairness
and transparency). Accountability ensures the
actions, decisions and outcomes of an AI system
can be traced back to the entity responsible for
it
A subfield of AI and machine learning where an
algorithm can select some of the data it learns
from. Instead of learning from all the data it is
Active Learning
given, an active learning model requests
additional data points that will help it learn the
best. → Also called query learning.
, A machine learning technique that raises a safety
and security risk to the model and can be seen
as an attack. These attacks can be instigated by
manipulating the model, such as by introducing
malicious or deceptive input data. Such attacks
Adversarial Machine
can cause the model to malfunction and
Learning
generate incorrect or unsafe outputs, which can
have significant impacts. For example,
manipulating the inputs of a self-driving car may
fool the model to perceive a red light as a green
one, adversely impacting road safety.
, Artificial General Intelligence
AI that is considered to have human-level
intelligence and strong generalization capability
to achieve goals and carry out a variety of tasks
in different contexts and environments. AGI still
remains a theoretical field of research. It is
contrasted with "narrow" AI, which is used for
AGI
specific tasks or problems.
.beyond reach right now
.experts expect AGI systems to have strong
generalization abilities, the ability to think, learn
and perform complex tasks, and achieve goals
in different contexts and environments
