AIGP
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
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.
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.
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 text
, classification tasks, like identifying the language of a piece of text. Examples are
traditional neural networks, decision trees and random forests.
Disinformation - ✅✅ -Audiovisual content, information and synthetic data that is
intentionally manipulated or created to cause harm. Disinformation can spread
through deepfakes by those with malicious intentions.
Entropy - ✅✅ -The measure of unpredictability or randomness in a set of data used
in machine learning. A higher entropy signifies greater uncertainty in predicting
outcomes.
Expert System - ✅✅ -A form of AI that draws inferences from a knowledge base to
replicate the decision-making abilities of a human expert within a specific field, like a
medical diagnosis.
Explainability -✅✅ -The ability to describe or provide sufficient information about
how an AI system generates a specific output or arrives at a decision in a specific
context to a predetermined addressee. XAI is important in maintaining transparency
and trust in AI. → Acronym: XAI
Exploratory Data Analysis - ✅✅ -Data discovery process techniques that take place
before training a machine learning model in order to gain preliminary insights into a
dataset, such as identifying patterns, outliers, and anomalies and finding
relationships among variables.
Fairness - ✅✅ -An attribute of an AI system that prioritizes relatively equal
treatment of individuals or groups in its decisions and actions in a consistent,
accurate manner. Every model must identify the appropriate standard of fairness that
best applies, but most often it means the AI system's decisions should not adversely
impact, whether directly or disparately, sensitive attributes like race, gender or
religion.
Federated Learning - ✅✅ -A machine learning method that allows models (see
machine learning model) to be trained on the local data of multiple edge devices or
servers. Only the updates of the local model, not the training data itself, are sent to a
central location where they get aggregated into a global model — a process that is
iterated until the global model is fully trained.
Foundation Model - ✅✅ -A large-scale, pretrained model with AI capabilities, such
as language (see large language model), vision, robotics, reasoning, search or
human interaction, that can function as the base for use-specific applications. The
model is trained on extensive and diverse datasets
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