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I N T R O D U C T I O N T O A R T I F I C I A L I N T E L L I G E N C E

D A TA 1 5 0 0 1

E P I S O D E 1

1. L O G I S T I C S

2. W H AT I S A I ?

3. S C I F I V S R E A L I T Y

4. P H I L O S O P H Y O F A I

T O D A Y ’ S M E N U

D ATA 1 5 0 0 1 : I N T R O D U C T I O N T O A I

• Intermediate level course, 5 cu

• Organized by the Data Science MSc programme – Computers and Cognition module

• Elective course in the Computer Science BSc

T E A M

• Lecturer: Teemu Roos (teemuroos)

• TAs: – Nea Pirttinen (pinecone) – Juho Leinonen (Mession/Mession2) – Artem Chistiakov (ArtificialAnt) – special guest star: Henrik Nygren (hn/Henrik Nygren)

• Reach us through Riot Chat: – click 'Chat' in the top-right corner of the github material

L E C T U R E S + E X E R C I S E S

• Lectures are not obligatory

• Material will be online: – materiaalit.github.io/intro-to-ai-17 – these slides complement the material – additional material:

+ links to web sources, Youtube, literature + some of it is "nice-to-know", will be indicated if so

• Weekly exercises – make sure you are registered and attend the group to which

you registered – exercise points are gained by attending the exercise sessions

T E S T M Y C O D E

• Programming exercises can be submitted through TMC

• Java or Python – but tests currently only available for Java

• Instructions for installing the TMC to NetBeans, or using a command-line TMC client are (will be) given on the github material

• Preinstalled NetBeans environment is available in B221 and BK107

T E S T M Y C O D E

P R E R E Q U I S I T E S

• Data Structures and Algorithms (or equivalent knowledge/skills): – queue, stack, traversals (depth/breadth/best-first, A*)

• Some university level maths: – most notably: probability calculus, conditional probability – the basic concept of vector calculus (addition)

• Programming skills: – we'll do a bit larger programs than in the intro courses – Java is supported, but python is a good choice too

G R A D I N G

• Exercises are mandatory: minimum 50% required to pass

• Grading based 33% on exercise points, 67% on final exam

• Completing 5/6 exercises gives you max. exercise points

• Exact point limits will be decided later, "grading on a curve" to some extent – but typically people work so hard that the curve ends up being quite skewed (towards higher grades)

• A different AI course: – usually at a later stage in the degree – less maths than usual (but lots of probability)

• Diverse student basis (BSc/MSc, major/minor): – hard to find a balance – constructive criticism is warmly welcomed

• Our goal: 100% student satisfaction and 100% pass

• Workload: 5 cu / 7 weeks = 18 hr / week

• No pain, no gain

M I S C

T O P I C S

1. What is AI? History and Philosophy of AI

2. Games and Search

3. Logic (Programming)

4. Reasoning under Uncertainty and Machine Learning

5. Natural Language Processing

6. Robotics

"GOFAI"

"Modern AI"

FA C T V S F I C T I O N

• AI in scifi: – Skynet – Matrix  – Star Wars droids  – Ex machina  – Galactica 

FA C T V S F I C T I O N

Discuss the following points:

• What capabilities do the AIs in scifi have?

• Which of these are not yet reality?

• Bonus question: Are the scifi-AIs self-conscious?

T U R I N G T E S T

T U R I N G T E S T

T H E C H I N E S E R O O M ( S E A R L E )

• Is intelligent behavior possible without intelligence?

• Is a "mind" necessary for intelligent behavior?

• "Consciousness"?

P H I L O S O P H Y O F A I

BEHAVIORTHINKING

HUMAN-LIKE

INTELLIGENT

"strong AI" "weak AI" rational agents "swarm intelligence"

cognitive science neuroscience psychology

Turing test

W H AT I S A I , R E A L LY ?

MACHINE VISION

ROUTE PLANNING

NLP

SPEECH

LOGICAL REASONING

INFORMATION RETRIEVAL

MACHINE TRANSLATIONRECOMMENDATION

GAMES

MACHINE LEARNING

DATA MINING

W H AT I S A I , R E A L LY ?