Modul Topik 1 - Kecerdasan Buatan

Topik 1
Definisi, Topik, dan Terminologi
Kecerdasan Buatan
Dr. Sunu Wibirama
Modul Kuliah Kecerdasan Buatan
Kode mata kuliah: UGMx 001001132012
March 31, 2022

March 31, 2022
1 Capaian Pembelajaran Mata Kuliah
Topik ini akan memenuhi CPMK 1, yakni mampu mendefinisikan pengertian sistem cer-
das, berbagai macam terminologi di bidang kecerdasan buatan, dan berbagai macam aplikasi
sistem berbasis kecerdasan buatan. Adapun indikator tercapainya CPMK tersebut adalah
mampu membedakan konsep narrow AI dan general AI, mengerti konsep umum machine
learning (supervised, unsupervised, reinforcement learning), mengerti contoh penerapan AI
dan peluang AI di dunia kerja.
2 Cakupan Materi
Cakupan materi dalam topik ini sebagai berikut:
a) What is AI: mengenal konsep dasar kecerdasan buatan dan aplikasinya dalam kehidu-
pan sehari-hari.
b) AI and The Future of Jobs: mengenal dampak dari penerapan kecerdasan buatan
terhadap dunia kerja dan peluang digantikannya sumber daya manusia dengan mesin.
c) Learning AI: materi ini berisi seputar berbagai macam cara untuk mempelajari kecer-
dasan buatan, mulai dari pendekatan secara konseptual (conceptual approach), secara
algoritmis (algorithmic approach), secara matematis (mathematical approach), sampai
dengan melaui praktik nyata untuk memecahkan permasalahan tertentu (real case
study).
d) Machine Intelligence Continuum: materi ini berisi tentang pembagian kecerdasan
mesin menjadi beberapa tingkatan. Kecerdasan mesin dibagi berdasarkan kemam-
puan mesin untuk menyelesaikan tuga dalam domain pengetahuan yang sempit, luas,
dan bagaimana mesin mampu beradaptasi dengan domain pengetahuan yang baru.
e) Expert Systems and Machine Learning: materi ini berisi penjelasan tentang teknologi
kecerdasan buatan paling awal, yang dikenal dengan expert systems. Kelemahan expert
systems pada akhirnya mendorong para peneliti untuk menemukan teknik lain yang
dapat digunakan untuk melatih mesin melakukan suatu tugas secara otomatis tanpa
harus diprogram secara eksplisit. Metode inilah yang disebut dengan machine learning.
f) Supervised Learning, Unsupervised Learning and Reinforcement Learning: materi ini
membahas tipe-tipe machine learning yang sering dijumpai dalam pemecahan problem-
problem praktis. Tipe-tipe machine learning ini dijelaskan dengan ilustrasi yang in-
tuitif untuk memudahkan pemahaman.
g) Various AI Applications: materi ini menjelaskan tentang berbagai implementasi ke-
cerdasan buatan di bidang kesehatan (healthcare), pengawasan lalu-lintas (traffic mon-
itoring), keamanan (security), dan interaksi manusia-komputer (human-computer in-
teraction).
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Copyright © 2022 Sunu Wibirama | Do not distribute without permission @sunu_wibirama 1
Sunu Wibirama
sunu@ugm.ac.id
Department of Electrical and Information Engineering
Faculty of Engineering
Universitas Gadjah Mada
INDONESIA
What is Artificial Intelligence?
Kecerdasan Buatan | Artificial Intelligence
Version: January 2022
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AI and your daily activities
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Artificial intelligence (AI)
• Intelligence: the ability to acquire and apply knowledge
• Artificial intelligence is created to simulates human
intelligence processes by machines, especially computer
systems.
• These processes include learning, decision-making, and
self-correction. Particular applications of AI include expert
systems (e.g., Google Maps), speech recognition
(e.g.,: Apple Siri) and machine vision (e.g., Facebook’s face
recognition).
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[Source: Frost and Sullivan, “Artificial Intelligence-R&D and Applications Road Map”, 2016]
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Copyright © 2022 Sunu Wibirama | Do not distribute without permission @sunu_wibirama
Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
Artificial Intelligence and Future Jobs
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Businesses affected by AI
AI value creation by 2030:
$13 trillion
[Source: McKinsey and Co, “Notes from the AI frontier: Applications and value of deep learning”, 2018]
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How AI improves business
[Source: Marco K., Medium, 2019] [Source: World Economic Forum, Future of Jobs Report, 2020]
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[Source: Harvard Business Review, 2017]
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INDONESIA
Learning AI: How to Get Started
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Approaches on learning AI
Conceptual approach
Illustrations and
visualizations are helpful
Pros:
• Intuitive understanding
• The easiest way to learn AI
• Helping you explaining AI
to non-specialist
Cons:
• Shallow understanding, not
enough if you aim for career as
ML researcher/engineer/data
scientist
• No practical experience (coding,
algorithm design, etc.)
Algorithmic approach
Learn from pseudocode
or source code
Pros:
• Understanding both concept and
process in AI techniques
• Fastest way to get your hands wet
with programming exercise
• Entry point for most AI professional
career
Cons:
• Rarely uncovering what’s under the
hood (math), which is important if
you want to be
ML researcher/academician
Mathematical approach
Demystifying AI using
mathematics
Pros:
• Providing strong theoretical
background of AI techniques
• Necessary if you aim for career in
academia / RnD division in tech
industry.
Cons:
• You have to be strong in at least
three parts of mathematics:
probability and statistics, linear
algebra, and calculus
Case study
Real case problem solving
Pros:
• Developing strong practical skills
• You may need to blend other three
approaches to be successful in
solving problem
• Widening experience, useful in
professional career
Cons:
• Real world dataset with
noises/errors may be not so easy to
obtain
• You need strong background in
programming, conceptual
understanding, and math
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INDONESIA
Learning AI: How to Get Started (Part 2)
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Conceptual approach
[Source: Annalyn Ng, Decision Trees Tutorial, Algobeans.Com, 2015]
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Algorithmic approach
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Mathematical approach
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Case study
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More recommended resources (1)
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https://www.kdnuggets.com/
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https://www.coursera.org/learn/machine-learning
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https://machinelearningmastery.com/start-here/
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Online datasets
https://archive.ics.uci.edu/ml/index.php
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Live coding courses
http://datacamp.com
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INDONESIA
Machine Intelligence Continuum (Part 1)
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Narrow vs. General AI
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Machine Intelligence Continuum
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[Source: M. Yao, “The machine intelligence continuum”, TopBots.Com, 2017]
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Level 1: Systems that act
• Lowest level of the continuum: rule-based
automatons.
• Hand engineered, following if-then rules.
• Cruise control in your car:
• The system monitors your automobile’s
speed and uses a motor to vary throttle
position to maintain a constant speed
• Remember: this is not a “self-driving car”,
you cannot take your hands off the wheel.
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Level 2: Systems that predict
• The system analyzes the data and produces
probabilistic predictions based on the data.
• Prediction does not necessarily need to be a
future event, but rather a mapping of known
information to unknown information
• If your data is flawed, or you choose a sample
set to analyze that does not represent your
target population as a whole, you will get
erroneous results (case study: The US 2016
election polls).
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Level 3: Systems that learn
• The system performs particular tasks
without being explicitly programmed.
• These systems are mostly powered by two
notorious AI branches: machine learning
and deep learning.
• The systems are used in many enterprise
applications to improve the process of
turning data into predictions:
• Predictive marketing
• Netflix’s movies recommendation
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INDONESIA
Machine Intelligence Continuum (Part 2)
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Machine Intelligence Continuum
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Level 4: Systems that create
• Recent breakthroughs in neural network
models have inspired a resurgence of
computational creativity, with computers
now capable of producing original writing,
imagery, music, and industrial designs.
• A profound example is “automated
image-based story teller” using deep
neural networks.
• Berlin-based engineer Samim trained a
neural network on 14 million lines of
passages from romance novels and
asked the model to generate stories
about images.
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[Source: Samim, “Generating Stories about Images”, Medium.Com, 2015]
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Level 5: Systems that relate
• Since digital assistants like Apple’s Siri and Amazon
Echo’s Alexa infiltrate our personal lives, machines will
also need to be emotionally intelligent to succeed in our
society.
• Sentiment analysis, also known as opinion mining or
emotion AI, extracts and quantifies emotional states from
our text, voice, facial expressions, and body language.
• This AI systems are commonly used in user experience
research and AI-powered interview software.
• Affectiva—a leading emotion AI company—helps
advertisers improve the effectiveness of brand content by
assessing and adapting to consumer reactions.
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Level 6: Systems that master
• A human toddler only needs to see a single tiger to develop a
mental construct of the animal and recognize other tigers.
• If humans needed to see thousands of tigers before learning to
run away, our species would have died out long ago.
• By contrast a deep learning algorithm needs to process
thousands of tiger images to recognizing them in images and
video. Even, the algorithm does not reliably recognize other
abstractions and representations of tigers, such as cartoons or
costumes.
• A “System That Masters” is an intelligent agent capable of
constructing abstract concepts and strategic plans from sparse
data. By creating modular conceptual representations of the
world around us, humans are able to transfer knowledge from
one domain to another.
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Level 7: Systems that evolve
• This final category refers to systems that exhibit
superhuman intelligence and capabilities.
• “Systems That Evolve” are entities capable of
dynamically changing their own architecture and
design to adapt to environmental needs.
• Computers are currently constrained by both hardware
and software, while humans and other biological
organisms are constrained by wetware.
• Some futurists hypothesize that we may be able to
achieve superhuman intelligence by augmenting
biological brains with synthesized technologies, but
currently this research is more science fiction than
science.
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[Source: Michael Copeland, 2016]
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INDONESIA
Expert Systems and Machine Learning
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[Source: Michael Copeland, 2016]
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Early AI: Expert Systems
Suppose you want to recognize a digit of 7
You can tell computer three rules
on how to write a “7”
[Source: Andrew Glassner, 2021]
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Early AI: Expert Systems
Suddenly, your computer sees this number.
Do you think the computer can recognize
this number?
I don’t think so.
You have to “teach” the computer to
recognize an object automatically without
being explicitly programmed à machine
learning
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Machine Learning
“A computer program is said to
learn from experience (E) with
some class of tasks (T) and a
performance measure (P) if its
performance at tasks in T as
measured by P improves with E”
(Tom Mitchell, 1997)
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Some important terminologies
• Training/Evolution set
Set of data to discover potentially predictive relationships.
• Instances
A sample is an item to process (e.g. classify). It can be a document, a picture, a sound, a
video, a row in database or CSV file, or whatever you can describe with a fixed set of
quantitative traits.
• Features / attributes
The number of features or distinct traits that can be used to describe each item in a
quantitative manner.
• Feature vector
is an n-dimensional vector of numerical features that represent some object.
• Feature extraction
Preparation of feature vector, transforms the data in the high-dimensional space to a
space of fewer dimensions.
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(class / label)
(features)
(instance)
7
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(class / label)
(features)
(instance)
8
?
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sunu@ugm.ac.id
INDONESIA
Supervised Learning
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How students learn
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Can we treat machine with same process?
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Apple
What do you mean by
Simple example
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Define how apple looks
Features:
1. Color: Radish/Red
2. Type: Fruit
3. Shape: Round
Features:
1. Color: Sky Blue
2. Type: Logo
3. Shape: Half-bitten
Features:
1. Color: Yellow
2. Type: Fruit
3. Shape: Round
Label : red apple Label : apple logo Label : green apple
Notes: features can be numerical of categorical
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Define how apple looks
2. Type: Fruit
3. Shape: Round
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Train the machine learning system
2. Type : Fruit
3. Shape Learning: update the
algorithm’s parameters
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Test the machine learning system
Unseen data
Features
1. Color: Green
2. Type : Fruit
3. Shape: Half-bitten
Red apple/Apple logo/Green apple?
Green apple
Accuracy: 80%
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Applying supervised learning
Input Output Application
email spam? (yes/no) spam filtering
audio text transcript speech recognition
ads, user info click? (yes/no) online advertising
Indonesian Japanese machine translation
plate number pics plate number computer vision
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sunu@ugm.ac.id
INDONESIA
Unsupervised Learning and Reinforcement Learning
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Data without label
What if, we have no labeled data?
Or, we have very large data that it is almost impossible to label it.
Can we do something with the data?
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Data without label
Yes, we do! We can find some similarities,
or at least we can provide better representation to the data
[Source: scikit-learn.org]
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Clustering
Automatic grouping based on similar features among data
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Dimensionality reduction
Better representation based on dominant features
[Source: https://towardsdatascience.com/a-one-stop-shop-for-principal-component-analysis-5582fb7e0a9c]
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Association rules
Uncovering relationship between frequently bought items
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In March 2016, Lee Sedol, the Korean Go 18-
times world champion, played and lost a five-
game match against DeepMind’s AlphaGo—a
deep reinforcement learning based Google
DeepMind’s project.
[Source: Aurelien Geron, 2021]
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Reinforcement learning
Learning based on optimal policy
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Initial Performance After 15 minutes of training After 30 minutes of training
[Source: https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/]
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Reinforcement learning in action
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Sunu Wibirama
sunu@ugm.ac.id
INDONESIA
AI Applications in Medical Diagnosis and Healthcare Systems
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Roles of AI in healthcare systems
[Source: DataFlair, 2021]
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AI applications in healthcare systems
• Machine learning in medical eye tracking
• AI for covid-19 detection
• Self-early screening app for oral cancer
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Machine learning in medical eye tracking
San Diego, US
Industrial partner:
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Reference:
S. Wibirama, I. Ardiyanto, T. Satriya, T.B. Adji, N. A. Setiawan, M. T. Setiawan, “An
Improved Pupil Localization Technique for Real-Time Video-Oculography Extreme
Eyelid Occlusion”, International Journal of Innovative Computing, Information and
Control, Vol. 15, No. 4, 2019, pp. 1547-1563.
Common problem in this
research: how to handle
noises caused by eyelid
occlusion?
Machine learning in medical eye tracking
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Pupil Detection with Automatic Thresholding
Reference:
S. Wibirama, I. Ardiyanto, T. Satriya,
T.B. Adji, N. A. Setiawan, M. T.
Setiawan, “An Improved Pupil
Localization Technique for Real-Time
Video-Oculography Extreme Eyelid
Occlusion”, International Journal of
Innovative Computing, Information and
Control, Vol. 15, No. 4, 2019, pp. 1547-
1563.
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AI for Covid-19 detection
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AI for Covid-19 detection
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Self-early screening app for oral cancer
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oral cancer,
malignancy
oral squamous
cell carcinoma
oral lesion
Text books
Scientific journal
Dataset: 1154 pictures
517 Cancer
259 Normal
258 Non-cancer lesion
Dataset acquisition
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Cancer images
Non-cancer images
Google Colaboratory
Images dataset Screening parameters
Prediction process
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Screening
photo
Results of
prediction
Probability of
cancer
occurrence
Recommended action:
Cancer case:
Medical observation
with dentist/medical
doctor
Non-cancer case:
Presenting information
on avoiding oral cancer
Prediction result
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INDONESIA
AI Applications in Traffic Monitoring and Security
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AI applications in traffic monitoring and security
• Vehicle number plate recognition
• Real-time car counting
• Twitter-based geolocation for traffic monitoring
• Self-driving car
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Vehicle number plate recognition
DIP DIP
DIP
Machine learning
Reference:
D.Sihombing, H.A. Nugroho, S. Wibirama, “Perspective rectification in vehicle number plate recognition using 2D-2D transformation of planar
homography”, in 2015 International Conference on Science in Information Technology, pp. 237-240, 2015.
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Real-time car counting
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Reference:
Hudalizaman, I. Ardiyanto, S. Wibirama, “Image Enhancement on You Only Look Once (YOLO) Method to Detect Public
Transportation of CCTV Image”, presented in The 6th International Conference Science and Technology, 2020.
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How does YOLO (You Only Look Once) work?
• Step 1: The image is divided into grid cells. Each grid
cell forecasts B bounding boxes and provides their
confidence scores. The cells predict the class
probabilities to establish the class of each object.
• Step 2: We can notice at least three classes of objects:
a car, a dog, and a bicycle. Note that for one object,
there will be more than one bounding boxes because an
object may overlap on several grid cells.
• Step 3: Intersection Over Union (IOU) ensures that the
predicted bounding boxes are equal to the real boxes of
the objects.
• Step 4: If IOU is greater than 0.5, we can say that the
prediction is good enough.
• Step 5: Remove unnecessary bounding boxes that do
not meet the threshold.
• Step 6: The final detection will consist of unique
bounding boxes that fit the objects perfectly.
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IoU = Area of yellow box / Area of green box
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Twitter-based geolocation for traffic monitoring
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Reference:
D.A. Kurniawan, S. Wibirama, N.A. Setiawan, “Real-
time traffic classification with Twitter data mining”, in
2016 8th International Conference on Information
Technology and Electrical Engineering, pp. 1-5, 2016.
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Reference:
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Reference:
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Self-driving car
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How does self-driving car work?
High-level view of the data collection system. Input
Actual output by
human driver
Computed
output
Input Computed output
Training the neural network.
The trained network is used to generate steering
commands from a single front-facing center camera.
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AI Applications in Human-Computer Interaction
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Touchless technology: from laboratory to necessity
Public display has been an inseparable part of smart
societies around the world (i.e. in the airport, toilet,
and other public facilities)
However, implementing an informative public display
during pandemic posses a challenging interaction
circumstances.
To avoid Covid-19, there is a strong need to develop
an intelligent touchless technology to minimize
customer interactions with high contact surfaces.
Our proposal: spontaneous gaze-based interaction
powered with artificial intelligence
Source of photo:
https://www.enr.com/articles/49734-airports-ponder-touchless-technology-installations-in-bathrooms
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Gaze-based interactive application
A brief illustration
Application content
User (passerby)
User’s object of interest
(dynamic button)
Eye Tracker
(Suatmi Murnani & Sunu Wibirama, 2020)
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Three types of eye movement
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1. Fixation
*eyes remain still for
more than 200-300 ms
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2. Saccade
*rapid eye movement from
an object to another object
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3. Smooth pursuit
*slower eye movement while pursuing an object
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Eye tracking calibration: fixation-based
• Goal: improving spatial accuracy during gaze
interaction
• User is asked to fixate their gaze on calibration
targets (animated white or red circle).
• Mapping from eye position to calibration target:
second order polynomial regression
Gaze calibration target
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Ø Alternative method:
Uncalibrated gaze-based
interaction
(Smooth pursuit-based
gaze interaction)
Spatial accuracy
issue
*limitation of calibration:
-time consuming
-tiring / fatigue
- impractical for interaction in public space
Can not meet
the challenge in
spontaneous
interaction
Challenges of spontaneous gaze-based interaction
Eye tracking calibration
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Eye tracking data processing
Hidden Markov
Models
Exponential
Moving
Average (EMA)
Pearson
Product-
Moment
Correlation
(PPMC)
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Probabilistic events detection using Hidden Markov Models
• Objectives: eye movement classification to improve accuracy of spontaneous object selection
• Hidden states: Fixation (Fix), Saccades (Sac), Post-Saccadic Oscillation (PSO), and Smooth Pursuit (SP)
• Observable states: segment velocity and inter-segment angle from gaze position signal
Our choice on NSLR and HMM was based on:
J. Pekkanen and O. Lappi, “A new and general approach to signal denoising and eye movement classification based on
segmented linear regression,” Scientific reports, vol. 7, no. 1, p. 17726, 2017.
(Suatmi Murnani, 2019)
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Pearson Product-Moment Correlation (PPMC)
• Pearson Product-Moment Correlation measures the
similarity of eye movements (e.g. smooth pursuit) and
object trajectories based on their spatial pattern
correlation.
• PPMC for objects that moved along X-axis direction
was calculated from their X coordinates.
• This approach was also applied for objects that
moved along Y-axis direction.
• We computed PPMC scores for all buttons and
chose the largest PPMC score. This score resembled
the button that was gazed by the user.
Max(C1, C2, C3) = the triggered button
with Cn denotes PPMC score and n is
the dynamic button on screen
C1
C2
C3
9
sunu@ugm.ac.id
coviDisplayPRO: gaze-controlled smart public display
10

Modul Topik 1 - Kecerdasan Buatan

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