Kyung Eun Park provides an overview of problem solving with artificial intelligence and machine learning. The document defines AI and discusses its evolution from early concepts in the 1950s to modern machine learning approaches. It describes how machine learning uses data to allow machines to learn without being explicitly programmed and provides examples of applications like self-driving cars and medical diagnosis. The document concludes by discussing interactive learning platforms that can recognize brainwaves and motions to enable behavior training.
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Ai history to-m-learning
1. Problem Solving with Knowledge
From Artificial Intelligence
To Machine Learning
Kyung Eun Park, D.Sc.
Augusta Ada King, Countess of Lovelace
2. Contents
1. AI Overview
2. How AI is implemented?
3. From AI to Machine Learning
4. Machine Learning
5. Examples of AI and Machine Learning
6. Behavior Training with BCI and Motion Recognition
7. Conclusion
3. What is Artificial Intelligence?
D E F I N I T I O N
“It is the science and engineering of making intelligent machines, especially
intelligent computer programs. It is related to the similar task of using computers
to understand human intelligence, but AI does not have to confine itself to
methods that are biologically observable,” by John McCarthy, 1956.
…
“Broadly, AI is the computer-based exploration of methods for solving challenging
tasks that have traditionally depended on people for solution. Such tasks include
complex logical inference, diagnosis, visual recognition, comprehension of natural
language, game playing, explanation, and planning” by Eric Horvitz, 1990.
4. AI Timeline
Ada
(1842)
Alan Turing
(1950)
The first
conference on
AI by John
McCarthy,
Marvin
Minsky (1956)
Demonstrated
by Newell
(1957)
Unimations
working on
GE (1961)
Joseph
Weizenbau
m (1965), E.
Geigenbau
m (1965)
Chess-
playing
program by
Greenblatt
at MIT
(1968)
Jack
Myers
Harry
Pople
(1979)
1980s Ian
Horswill
(1993)
TiVo
Suggestions
(2005)
Apple,
Google,
Micorsoft
(2011)
Machine
Learning,
Deep
Learning
(2013 ~)
5. Knowledge in AI
• Human knowledge
• Converted into a format suitable for use by an AI system
• AI generated/learned knowledge
• Generated by an AI system
• By gathering data and information, and
• By analyzing data, information, and knowledge at its disposal
Knowledge acquisition process is pretty similar to the normal learning procedure.
In brief, AI stores and uses the knowledge to solve problems.
6. Predicate Logic
Object Properties:
Is-a relationship
Instance-of relationship
ex) isSymptomOf: …
maybeSymptomOf: …
mayHaveSymptom: …
shouldHaveSymptom: …
Knowledge Representation by Healthcare Example
Classes:
SuperclassOf
SubclassOf
ex) Disease Class
Symptom Class
Object:
Sym
Tachycar
dia
Subject:
Hypo
perfusion
shouldHave
Symptom
predicate
7. Semantic Network
• Building relationship between Diseases and Symptoms
• Constructing semantic graph with Nodes (instance objects) and
Edges (object properties)
Sym
Tachycardia
Congestive
HeartFailure
HeatStroke
Hypo
perfusion
Overdose Acute
Myocardial
Infarction
shouldHave
Symptom
maybe
SymptomOf
maybe
SymptomOf
maybe
SymptomOf
maybe
SymptomOf
8.
9.
10.
11.
12.
13.
14. Minsky’s Insights into Human and Machine Intelligence
• Computer’s role in this context:
• It will help us to understand our own brains, to learn what is the
nature of knowledge.
• It will teach us how we learn to think and feel.
• This knowledge will change our views of Humanity and enable us to
change ourselves.
… in an interview in 1998, Sabbatini
17. Machine Learning
D E F I N I T I O N
“Machine learning is a type of
artificial intelligence that provides
computers with the ability to
learn without being explicitly
programmed. Machine learning
focuses on the development of
computer programs that can
teach themselves to grow and
change when exposed to new
data”
18. In 1956, he wanted this
computer to beat him at
checkers. He made the computer
play against itself thousands of
times and learn how to play
checkers, and indeed IT WORK!
By 1962 this computer had
beaten the Connecticut state
champion.
Arthur Samuel
19. Machine Learning
NOW
Machine learning is actively
being used today.
• The search engine
• The spam filter
• The recommender system
• The face/handwriting
/fingerprint recognition
• The location/context-based
security system
• The disease diagnosis &
prediction
• The weather forecast, etc.
22. • Turning data via information into knowledge
• A tool that can be applied to many problems.
• Uses statistics for solving the problem of not being able to
model the problem fully.
• ex) Maximize human’s happiness
• For these problems, we need to use some tools from
statistics.
Machine Learning Process
23. • Human-created data from the World Wide Web
• More non-human generated data coming online
• Challenge & Opportunity:
How to connect the data to the WWW and use them?
Sensors and Data Deluge
24. Key Terminologies by Example
Bird Classification System
• Expert system: ornithologist
• Features (or attributes): Weight, Wingspan, Webbed feet, Back color
• Target variable: Species (predicted)
• Instance: each row made up of features
• The first two features: numeric
• The 3rd feature: binary (0 or 1)
• The 4th feature: enumeration (integer)
25. • How do we decide if a bird is an Ivory-Billed Woodpecker or something
else?
Classification task is needed!
• Many machine learning algorithms good at classification
Choose a machine learning algorithm (Classifier) to use
• Train the classifier
Feed it quality data known as a training set
Classification as a Machine Learning Algorithm
Bird Classification
26. • Training set of data and a separate dataset, called a test set
• Multi-step Machine Learning
TRAINING/LEARNING ▬ TESTING ▬ USING
Testing a Machine Learning Algorithm
Raw Data
(Training Set)
ClassifierFeature
Raw Data
(Test Set)
ClassifierFeature
Feature
Extraction
Feed
Data
Acquisition
Data
Acquisition
Feature
Extraction
Training
Feed
Classification
Result
Training Phase:
Testing Phase:
Knowledge
Representation
27. Key Tasks of Machine Learning
• In the previous classification task,
The job is to predict what class an instance should fall into.
• Another task, regression,
The prediction of a numeric number
• Both classification and regression are examples of Supervised Learning
We are telling the algorithm what to predict.
• Unsupervised Learning
There’s no label or target value given for the data
• Clustering
Group of similar items in unsupervised learning
• Density estimation
Statistical values that describe data in unsupervised learning
28. Supervised learning tasks
Classification Regression
k-Nearest Neighbors Linear
Naïve Bayes Locally weighted linear
Support vector machines Ridge
Decision trees Lasso
Unsupervised learning tasks
Clustering Density estimation
k-Means Expectation maximization
DBSCAN Parzen window
Machine Learning Algorithms
29. Behavior Training Platform
NeuroSky Interface
Narrative
Contents
Manager
Interactive Intervention
Controller Sensor &
Intervention
Data CenterScene
Manager
Kinect Interface
Brainwaves & Motion Recognition Interface
Sensor &
Intervention
Data
Repository
Scene 3
Brainwaves
Scene 2
Scene 1
. . .
Motion
Character,
Space,
Action,
Item, Quest,
Contexts, etc.
Behavior Training Platform
30. Motion Recognition Learning
Skeletal tracking
with Kinect:
recognizing 22
different
motions
Head tracking
with Kinect:
recognizing 6
different
motions
32. Interactive Game Scenario
Scene Purpose
Graphic
Character Space Item Action
Text
Interactive
Intervention
2 Go to the
sea with
fishing
bag on
the
shoulder
Via Kinect:
Monitors the
player’s motion
and has the
character pause
when the player
moves.
Via MindWave:
Increase the
character’s
moving speed
when the
attention level
increases.
Let’s go to
the sea for
fishing.
Can you
help me
with the
fishing bag?
You sit still
and see
the way to
go.
Walking
or
running
Fishing
bag
Mountai
n path
to
the
sea
Fisherman
(Example: a set of fairy tale contents within a scene)
36. Summary
• Problem solving with knowledge from
through AI to through Machine Learning
• Knowledge learned by machine itself using
Big data of IoT/IoE
• AI Machine Learning Deep Learning
Internet of Everything
37. R E F E R E N C E S
1. John McCarthy, “What is Artificial Intelligence?” http:// www. formal. Stanford.
EDU/ jmc/ whatisai/
2. Wiki, “Timeline of Artificial Intelligence,”
http://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence
3. Eric Horvitz, “Computation and action under bounded resources,” 1990
4. David Moursund, “Brief Introduction to Educational Implications of Artificial
Intelligence,” 2005, 2006
5. Peter Harrington, “Machine Learning in Action,” Manning Publications, 2012
6. Henrik Brink, Joseph W. Richards, "Real-World Machine Learning,”Manning
Publications, 2015
7. IBM Watson, http://www.ibm.com/smarterplanet/us/en/ibmwatson/
8. Google’s IoT operating system, Brillo, http://www.techspot.com/news/60753-
google-iot-operating-system-codenamed-brillo-may-arrive.html
Notes de l'éditeur
This details of the individual object properties with their domain and range information.