AIGP Exam With Complete Solutions Latest Update
AIGP Exam With Complete Solutions
Latest Update
Accountability - ✔✔✔ - 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 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
Active Learning - ✔✔✔ - 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
given, an active learning model requests additional data points that will help it
learn the best. → Also called query learning.
Adversarial Machine 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 can cause the model to malfunction and
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.
AI governance - ✔✔✔ - A system of laws, policies, frameworks, practices and
processes at international, national and organizational levels. AI governance helps
various stakeholders implement, manage and oversee the use of AI technology. It
also helps manage associated risks to ensure AI aligns with stakeholders'
objectives, is developed and used responsibly and ethically, and complies with
applicable requirements.
Algorithm - ✔✔✔ - A procedure or set of instructions and rules designed to perform
a specific task or solve a particular problem, using a computer.
AGI - ✔✔✔ - Artificial General Intelligence
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,AIGP Exam With Complete Solutions Latest Update
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 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
Artificial Intelligence - ✔✔✔ - Artificial intelligence is a broad term used to describe
an engineered system that uses various computational techniques to perform or
automate tasks. This may include techniques, such as machine learning, where
machines learn from experience, adjusting to new input data and potentially
performing tasks previously done by humans. More specifically, it is a field of
computer science dedicated to simulating intelligent behavior in computers. It may
include automated decision-making. → Acronym: AI
.has hallmarks of human intelligence: ability to think creatively; can consider
various possibilities; & keep a goal in mind while making short term decisions,
.common elements in a definition of AI:
1. Technology: use of technology and specified objectives for the technology to
achieve
2. Autonomy: level of autonomy by the technology to achieve defined objectives
3. Human Involvement: need for human input to train the technology and identify
objectives for it to follow
4. Output: technology produces output - performing tasks, solving problems,
producing content
Automated Decision Making - ✔✔✔ - The process of making a decision by
technological means without human involvement, either in whole or in part.
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,AIGP Exam With Complete Solutions Latest Update
Bias - ✔✔✔ - There are several types of bias within the AI field. Computational bias
is a systematic error or deviation from the true value of a prediction that originates
from a model's assumptions or the input data itself. Cognitive bias refers to
inaccurate individual judgment or distorted thinking, while societal bias leads to
systemic prejudice, favoritism and/or discrimination in favor of or against an
individual or group. Bias can impact outcomes and pose a risk to individual rights
and liberties.
Bootstrap Aggregating - ✔✔✔ - A machine learning method that aggregates
multiple versions of a model (see machine learning model) trained on random
subsets of a dataset. This method aims to make a model more stable and accurate.
→ Sometimes referred to as bagging
Chatbot - ✔✔✔ - A form of AI designed to simulate human-like conversations and
interactions that uses natural language processing and deep learning to
understand and respond to text or other media. Because chatbots are often used
for customer service and other personal help applications, chatbots often ingest
users' personal information.
Classification Model - ✔✔✔ - A type of model (see machine learning model) used in
machine learning that is designed to take input data and sort it into different
categories or classes. → Sometimes referred to as classifiers
Clustering - ✔✔✔ - An unsupervised machine learning method where patterns in
the data are identified and evaluated, and data points are grouped accordingly into
clusters based on their similarity. → Sometimes referred to as clustering
algorithms.
Compute - ✔✔✔ - Refers to the processing resources that are available to a
computer system. This includes the hardware components such as the central
processing unit or graphics processing unit. Computing is essential for memory,
storage, processing data, running applications, rendering graphics for visual media,
powering cloud computing, among others.
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, AIGP Exam With Complete Solutions Latest Update
Computer Vision - ✔✔✔ - A field of AI that enables computers to process and
analyze images, videos and other visual inputs.
Conformity Assessment - ✔✔✔ - An analysis, often performed by a third-party
body, on an AI system to determine whether requirements, such as establishing a
risk-management system, data governance, record keeping, transparency and
cybersecurity practices, have been met. Often referred to as an audit.
Contestability - ✔✔✔ - The principle of ensuring that AI systems and their decision-
making processes can be questioned or challenged. This ability to contest or
challenge the outcomes, outputs and/or actions of AI systems can help promote
transparency and accountability within AI governance. → Also called redress.
Corpus - ✔✔✔ - A large collection of texts or data that a computer uses to find
patterns, make predictions or generate specific outcomes. The corpus may include
structured or unstructured data and cover a specific topic or a variety of topics.
Decision Tree - ✔✔✔ - A type of supervised learning model used in machine
learning (see machine learning model) that represents decisions and their potential
consequences in a branching structure.
Deep Learning - ✔✔✔ - A subfield of AI and machine learning that uses artificial
neural networks. Deep learning is especially useful in fields where raw data needs
to be processed, like image recognition, natural language processing and speech
recognition.
Deepfakes - ✔✔✔ - Audiovisual content that has been altered or manipulated using
AI techniques. Deepfakes can be used to spread misinformation and
disinformation.
Discriminative Model - ✔✔✔ - A type of model (see machine learning model) used
in machine learning that directly maps input features to class labels and analyzes
for patterns that can help distinguish between different classes. It is often used for
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AIGP Exam With Complete Solutions
Latest Update
Accountability - ✔✔✔ - 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 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
Active Learning - ✔✔✔ - 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
given, an active learning model requests additional data points that will help it
learn the best. → Also called query learning.
Adversarial Machine 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 can cause the model to malfunction and
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.
AI governance - ✔✔✔ - A system of laws, policies, frameworks, practices and
processes at international, national and organizational levels. AI governance helps
various stakeholders implement, manage and oversee the use of AI technology. It
also helps manage associated risks to ensure AI aligns with stakeholders'
objectives, is developed and used responsibly and ethically, and complies with
applicable requirements.
Algorithm - ✔✔✔ - A procedure or set of instructions and rules designed to perform
a specific task or solve a particular problem, using a computer.
AGI - ✔✔✔ - Artificial General Intelligence
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,AIGP Exam With Complete Solutions Latest Update
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 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
Artificial Intelligence - ✔✔✔ - Artificial intelligence is a broad term used to describe
an engineered system that uses various computational techniques to perform or
automate tasks. This may include techniques, such as machine learning, where
machines learn from experience, adjusting to new input data and potentially
performing tasks previously done by humans. More specifically, it is a field of
computer science dedicated to simulating intelligent behavior in computers. It may
include automated decision-making. → Acronym: AI
.has hallmarks of human intelligence: ability to think creatively; can consider
various possibilities; & keep a goal in mind while making short term decisions,
.common elements in a definition of AI:
1. Technology: use of technology and specified objectives for the technology to
achieve
2. Autonomy: level of autonomy by the technology to achieve defined objectives
3. Human Involvement: need for human input to train the technology and identify
objectives for it to follow
4. Output: technology produces output - performing tasks, solving problems,
producing content
Automated Decision Making - ✔✔✔ - The process of making a decision by
technological means without human involvement, either in whole or in part.
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,AIGP Exam With Complete Solutions Latest Update
Bias - ✔✔✔ - There are several types of bias within the AI field. Computational bias
is a systematic error or deviation from the true value of a prediction that originates
from a model's assumptions or the input data itself. Cognitive bias refers to
inaccurate individual judgment or distorted thinking, while societal bias leads to
systemic prejudice, favoritism and/or discrimination in favor of or against an
individual or group. Bias can impact outcomes and pose a risk to individual rights
and liberties.
Bootstrap Aggregating - ✔✔✔ - A machine learning method that aggregates
multiple versions of a model (see machine learning model) trained on random
subsets of a dataset. This method aims to make a model more stable and accurate.
→ Sometimes referred to as bagging
Chatbot - ✔✔✔ - A form of AI designed to simulate human-like conversations and
interactions that uses natural language processing and deep learning to
understand and respond to text or other media. Because chatbots are often used
for customer service and other personal help applications, chatbots often ingest
users' personal information.
Classification Model - ✔✔✔ - A type of model (see machine learning model) used in
machine learning that is designed to take input data and sort it into different
categories or classes. → Sometimes referred to as classifiers
Clustering - ✔✔✔ - An unsupervised machine learning method where patterns in
the data are identified and evaluated, and data points are grouped accordingly into
clusters based on their similarity. → Sometimes referred to as clustering
algorithms.
Compute - ✔✔✔ - Refers to the processing resources that are available to a
computer system. This includes the hardware components such as the central
processing unit or graphics processing unit. Computing is essential for memory,
storage, processing data, running applications, rendering graphics for visual media,
powering cloud computing, among others.
©®™ Page 3
, AIGP Exam With Complete Solutions Latest Update
Computer Vision - ✔✔✔ - A field of AI that enables computers to process and
analyze images, videos and other visual inputs.
Conformity Assessment - ✔✔✔ - An analysis, often performed by a third-party
body, on an AI system to determine whether requirements, such as establishing a
risk-management system, data governance, record keeping, transparency and
cybersecurity practices, have been met. Often referred to as an audit.
Contestability - ✔✔✔ - The principle of ensuring that AI systems and their decision-
making processes can be questioned or challenged. This ability to contest or
challenge the outcomes, outputs and/or actions of AI systems can help promote
transparency and accountability within AI governance. → Also called redress.
Corpus - ✔✔✔ - A large collection of texts or data that a computer uses to find
patterns, make predictions or generate specific outcomes. The corpus may include
structured or unstructured data and cover a specific topic or a variety of topics.
Decision Tree - ✔✔✔ - A type of supervised learning model used in machine
learning (see machine learning model) that represents decisions and their potential
consequences in a branching structure.
Deep Learning - ✔✔✔ - A subfield of AI and machine learning that uses artificial
neural networks. Deep learning is especially useful in fields where raw data needs
to be processed, like image recognition, natural language processing and speech
recognition.
Deepfakes - ✔✔✔ - Audiovisual content that has been altered or manipulated using
AI techniques. Deepfakes can be used to spread misinformation and
disinformation.
Discriminative Model - ✔✔✔ - A type of model (see machine learning model) used
in machine learning that directly maps input features to class labels and analyzes
for patterns that can help distinguish between different classes. It is often used for
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