1. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
11/28/2018
Today’s Topics
Exam: two pages of notes and simple calculator (log, e, * / + -) allowed
Final List of Topics Covered this Term (to various levels of depth, of course)
Review of Rest of Fall 2015 Final
The Turing Test
Strong vs Weak AI Hypotheses
Searle’s Chinese Room Story
Future of AI? - As a science/technology Its impact of society
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

2. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
An Informal Survey
First, be sure to do on-line course eval!
Does the ‘singularity’ seems NEARER or farther now than it did on Day 1 of the class?
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

3. Main Topics Covered Since Midterm (incomplete sublists)
But don’t forget that ML and search played major role in second half of class as well!
Bayesian Networks and Bayes’ Rule
Full joint, Naïve Bayes, odds ratios, statistical inference
Artificial Neural Networks
Perceptrons, gradient descent, HUs, linear separability, deep nets
Support Vector Machines
Large margins, penalties for outliers, kernels (for non-linearity)
First-Order Predicate Calculus
Representation of English sentences, logical deduction, prob logic
Unsupervised ML, ILP, MLNs, AI & Philosophy
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

4. Detailed List of Course Topics: Final Version
Learning from labeled data
Experimental methodologies for choosing parameter settings and estimating future accuracy
Decision trees and random forests
Probabilistic models
Nearest-neighbor methods
Genetic algorithms
Neural networks
Support vector machines
Reinforcement learning (reinforcements are ‘indirect’ labels)
Inductive logic programming
Markov logic networks
Theory refinement
Learning from unlabeled data
K-means
Hierarchical clustering
Expectation maximization
Auto association neural networks
Searching for solutions
Heuristically finding (shortest) solutions
Algorithms for playing games like chess
Simulated annealing
Reasoning probabilistically
Probabilistic inference
Bayes' rule, Bayesian networks, Naïve Bayes, MLNs
Reasoning from concrete cases
Cased-based reasoning
Nearest-neighbor algorithm
Kernels
Reasoning logically
First-order predicate calculus
Representing domain knowledge using mathematical logic
Logical inference
Probabilistic logic
Problem-solving methods based on the biophysical world
Genetic algorithms
Simulated annealing
Neural networks
Reinforcement learning
Philosophical aspects
Turing test
Searle's Chinese Room thought experiment
The coming singularity
Strong vs. weak AI
Societal impact and future of AI
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

5. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Suggestions
Be sure to carefully review all the HW solutions, especially HWs 3, 4, & 5 (out soon)
Imagine a HW 6 on last few lectures (and see worked examples in lec notes)
My old cs540 exams highly predictive of my future cs540 exams
Some things to only know at the ‘2 pt’ level
Calculus: have an intuitive sense of slope in non-linear curves (only need to know well: algebra, exp & log’s, arithmetic, (weighted) sums and products:  and )
Matrices and using linear programming to solve SVMs (do know dot product well)
How to build your own walking-talking robot :-)
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

6. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Bayesian Networks A B C D
a) P(A, B,  C, D)
= P(A) x P(B | A) x (1- P(C | A, B)) x P(D |  C, B) = 0.028
b) P(A,  C, D)
= P(A,  C, D, B) + P(A,  C, D,  B) // Create complete world states!
= 0.129
c) P( B | A,  C, D)
= P( B, A,  C, D) / P(A,  C, D) ) // Get rid of the GIVEN!
= 0.783
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

7. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Ex # A B
Output 1
True
False - 2 3 +
Naïve Bayes
Pseudo Ex’s: FF+, FF-, TT+, TT-
Odds(+ | A,  B) = P(+ | A,  B) / P(- | A,  B)
= [ P(A | +) x P( B | +) x P(+) ] / [ P(A | -) x P( B | -) x P(-) ]
= 2/9
We also know P(+ | A,  B) + P(- | A,  B) = 1
Two equations, two unknowns
Solve to get P(+ | A,  B) = 2/11
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

8. Neural Networks (learning rate = 0.1)4 2
Teacher = 1 2 4 -1 -5
2.4 2
1.5
1.9 2 -5 2 -1
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

9. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Value of HUs
Perceptrons
Three HUs - - + - + - - - + + - - + - + - + + + + - + - + - + - + + + + + - - - - - -
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

10. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Ex # A B C
Output 1 + 2 3 -
SVMs
New Data Set
Ex #
Kex1
Kex2
Kex3
Output 1 3 2 + -
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

11. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Ex # P Q R
Output 1 2 - -1 4 3 +
SVMs
Assume model is
if (4 P – 3 Q + 1 R ≥ 5) then + else –
Cost?
sum of abs value of weights + 2 (sum of slacks)
Unslacked weighted sums
ex1: 0 – = -1 (no slack needed)
ex2: = 3 (no slack needed)
ex3: = 3 (need slack of 3 BECAUSE WE NEED TO EXCEED THRESHOLD BY 1)
cost = x 3 = 14
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

12. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Misc Questions
Assume we have a binary classification problem where Feature A has 3 values, Feature B has 5 values, and Feature C has 4 values. How big is a full joint probability table for this problem? 2 x 3 x 5 x 4 = 120
An unavoidable weakness of SVMs is that the kernel matrix produced when using kernels is of size N 2, where N is the number of examples: FALSE (can sample columns)
Often data sets have missing values for some features in some examples. Circle the method below that is the best way to ‘fill in’ these missing values.
Drop In
Expectation-Maximization
K-Means
Transfer Learning
A ‘complete world state’ that makes every WFF in a set of WFFs true is called a/an:
Interpretation
Model
Skolemizer
Tautology
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

13. Another Worked MLN Example
Given these three rules, what is the prob of P?
wgt = 2 P  R
wgt = 3 R  Q [ same as  R  Q ]
wgt = 1 Q
P Q R Unnormalized Prob
F F F exp( )
F F T exp( )
F T F exp( )
F T T exp( )
T F F exp( )
T F T exp( )
T T F exp( )
T T T exp( )
To get Z, sum all the unnormalized probs
Then divide all the probs by Z to normalize
Finally sum prob’s of those cells where P is true
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

14. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
The Turing Test
Says intelligence is judged based on behavior (rather than inspecting internal data structures)
Focus is on cognition, rather than perception, so use a simple ‘ascii’ interface
If human judge interacting (via a teletype) with two ‘entities’ cannot accurately say which is the human and which is the computer, then the computer is intelligent (visualized on next slide)
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

15. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Not a serious concern of nearly all AI researchers
The Turing Test
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

16. Strong vs. Weak AI Hypotheses
Weak AI Hypothesis we can accurately simulate animal/human intelligence in a computer STRONG AI Hypothesis we can create algo’s that are intelligent (conscious?)
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

17. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Searle’s Chinese Room
What does it mean to ‘understand’?
Assume non-Chinese speaker is in a room with a bunch of IF-THEN rules written in Chinese (see next slide)
Questions come in written in Chinese
Human inside room matches symbols, adding intermediate deductions to some ‘scratch space’
Some rules say (in English) in their THEN part, ‘send this Chinese symbol … out to the user’
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

18. Searle’s Chinese Room (1980)
If person inside does a great job of answering questions,
can we say he or she understands?
Even if she or he is only blindly following rules?
(Of course the ‘person inside’ is acting like an AI program)
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

19. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Some Debate
Your thoughts/comments?
Is the room + the human intelligent?
After all, no one part of an airplane has the property flies, but the whole thing does
This is called ‘the systems reply’ (see
The ‘robot reply’ says that the problem is that the person doesn’t sense/interact with the real world – ‘symbols’ would be grounded to actual physical things and thereby become meaningful
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

20. Future of AI? (Remember everyone’s bad at predicting the future!)
Your comments/questions?
ML, ML, and more ML?
Ditto for Data? Scaling up even more? Specialized h/w for AI/ML algorithms?
Personalized s/w, learns from our every action? Our watches predict heart attacks N minutes in advance? Etc
Will ‘knowledge’ (ever) play a bigger role?
Eg, can we train our s/w agents and robots by talking to them, like humans teach humans?
Robots becoming ubiquitous? (Eg, self-driving cars)
More natural interaction with computers?
Language(text, voice), gestures, sketches, images, brain waves?
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

21. Robots Teaching Robots
How much time did we, as a group, spend me teaching you a fraction of what I (and the authors of assigned readings) know about AI?
40 hrs of class time x 85 humans  150 days
Plus, no doubt, 10x that time outside of class :-)
How long will it take one robot that has learned ‘a lot’ to teach 84 robots? 8M robots? 8B?
A few seconds?
Or will robots+owners have ‘individual differences’ that preclude direct brain-to-brain copying?
Remember : predictions (a) for nuclear power leading to “electricity to cheap to meter” and (b) `the war to end all wars’
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

22. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Societal Impact of AI?
When will majority of highway miles be robot driven?
When will most of the 'exciting new' S/W come from ML?
When will half of current jobs be fully automated?
For every job automated by AI/ML, how many new jobs will be created?   0.8?  1.5?
Will there be a minimal ‘basic’ income?  Proposed in Finland For ‘industrialized’ countries?  All countries?
Do we really all want to retire at 30? Humanities majors victorious?
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

23. Societal Impact of AI? (2)
Ever watch “Planet of the Apes”?
Societal Impact of AI? (2)
When will owning a car be a hobby?
When will communication between human speakers of any two natural (and non-rare) languages be as easy as communication in the same one?
When will our 'digital doubles' and robots do all our
Travel planning?  Entertainment planning?  Financial decision making?
Medical decision making? Shopping? Cooking? Cleaning?
When will the average human life span grow faster than one year per year? (Will AI drive med?) Robot care and engagement in nursing homes?
AI and war? AI and privacy? AI and income distribution?
Others? Comments or Questions?
What is the prob we will all look back at these questions in 25 years and see them as naively optimistic? Seems likely (but other things will happen faster than we expect)
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15

24. CS 540 - Fall 2016 (Shavlik©), Lecture 27, Week 15
Final Comments
Even if you don’t work in AI in the future, hopefully the class helps you understand the AI news, technological opportunities, and social impacts
If you do/will work in AI, seems to be an exciting time! (Hope there’s no lurking ‘AI Winter 2’ due to over-hyped expectations)
Good luck on the exam and keep in touch, especially if working in AI!
12/15/16
CS Fall 2016 (Shavlik©), Lecture 27, Week 15