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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 ATA 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 AY ’ 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, teemu.roos@cs.helsinki.fi(but see below how to reach us)

• TAs: – Jarkko Savela – Matti Leinonen

• How to reach us: – link to Telegram group: https://t.me/IAI19 – you need to be registered; if you have any questions about

registration, please send email to Reijo.Siven@helsinki.fi

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 – 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 are downloaded bu NOT submitted through TMC

• Java or Python

• Instructions for installing the TMC to NetBeans for Java, or using a command-line TMC client for Python/Java are 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

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 grade 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: – Skyner (Terminator movies) – Winston (Dan Brown’s Origin) – HAL9000 (Space Odyssey) – Isaac Asimov’s Robot series (short stories) – Samantha (HER movie) 

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 TRANSLATION

RECOMMENDATION

GAMES

MACHINE LEARNING

DATA MINING

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