4005PY Artificial Intelligence
What is Artificial Intelligence?
Machine-based system that can, for a given set of human-
defined objectives, make predictions, recommendations, or
decisions influencing real/ virtual environments. AI
systems are designed to operate with varying levels of
autonomy.
(OECD, 2019)
*can look at Hao 2018a
Examples of AI: Roomba, self-driving cars (ie. Tesla),
voice-systems (ie. Alexa, google), tiktok, smart-home (ie.
Thermostats), facial recognition
*refer to slides for case study
Particularly in Japan – robots – older population
Machine Learning
(Hao, 2018c) Machine-learning algorithm uses statistics to
find patterns In massive amounts of data.
Encompasses lots of things – numbers, words,
images, clicks…it can be digitally stored, fed into a
machine-learning algorithm.
4 types of Machine Learning (WHO, 2021)
, (1) Supervised learning – data used to train the
model are labelled (outcome variable is known).
a. Model infers a function from the data that can be
used for predicting outputs from different inputs.
(2) Unsupervised learning – labelling not included,
instead identifies hidden patterns in the data by the
machine.
(3) Reinforcement learning – machine learning by
trial & error to achieve an objective for which the
machine is ‘rewarded’ or ‘penalised’, depending on
whether inferences reach or hinder achievement of an
objective.
(4) Deep learning/ deep-structured learning – family
of machine learning based on multi-layered models =
progressively extract features from the data
a. Can be supervised, unsupervised, semi-
supervised.
b. Requires large amounts of data to be fed into the
model
What is an Algorithm
Algorithm – process/ set of rules to be followed in
calculations or other problem-solving operation, especially
by a computer (Oxford English Dictionary)
2
What is Artificial Intelligence?
Machine-based system that can, for a given set of human-
defined objectives, make predictions, recommendations, or
decisions influencing real/ virtual environments. AI
systems are designed to operate with varying levels of
autonomy.
(OECD, 2019)
*can look at Hao 2018a
Examples of AI: Roomba, self-driving cars (ie. Tesla),
voice-systems (ie. Alexa, google), tiktok, smart-home (ie.
Thermostats), facial recognition
*refer to slides for case study
Particularly in Japan – robots – older population
Machine Learning
(Hao, 2018c) Machine-learning algorithm uses statistics to
find patterns In massive amounts of data.
Encompasses lots of things – numbers, words,
images, clicks…it can be digitally stored, fed into a
machine-learning algorithm.
4 types of Machine Learning (WHO, 2021)
, (1) Supervised learning – data used to train the
model are labelled (outcome variable is known).
a. Model infers a function from the data that can be
used for predicting outputs from different inputs.
(2) Unsupervised learning – labelling not included,
instead identifies hidden patterns in the data by the
machine.
(3) Reinforcement learning – machine learning by
trial & error to achieve an objective for which the
machine is ‘rewarded’ or ‘penalised’, depending on
whether inferences reach or hinder achievement of an
objective.
(4) Deep learning/ deep-structured learning – family
of machine learning based on multi-layered models =
progressively extract features from the data
a. Can be supervised, unsupervised, semi-
supervised.
b. Requires large amounts of data to be fed into the
model
What is an Algorithm
Algorithm – process/ set of rules to be followed in
calculations or other problem-solving operation, especially
by a computer (Oxford English Dictionary)
2
