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AIGP; (1)
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Exam of 4 pages for the course Biochemistry Module 3 at Biochemistry Module 3 (AIGP; (1))
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✅✅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
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.
Machine learning - ✅✅ -A subfield of AI involving algorithms that enable computer
systems to iteratively learn from and then make decisions, inferences or predictions
based on input data. These algorithms build a model from training data to perform a
specific task on new data without being explicitly programmed to do so.
Machine learning implements various algorithms that learn and improve by
experience in a problem-solving process that includes data cleansing, feature
selection, training, testing and validation. Companies and government agencies
deploy machine learning algorithms for tasks such as fraud detection, recommender
systems, customer inquiries, health care, or transport and logistics.
→ Acronym: ML
Algorithm -✅✅ -A procedure or set of instructions and rules designed to perform a
specific task or solve a particular problem, using a computer.
Supervised learning - ✅✅ -A subset of machine learning where the model (see
machine learning model) is trained on labeled input data with known desired outputs.
These two groups of data are sometimes called predictors and targets, or
independent and dependent variables, respectively. This type of learning is useful for
classification or regression. The former refers to training an AI to group data into
specific categories and the latter refers to making predictions by understanding the
relationship between two variables.
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.
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