Artificial Intelligence in One Hour

A Tech Talk I gave to engineers at LinkedIn. Many of them hadn't encountered concepts in A.I. before, so I gave a soft introduction.

Transcript

1. Artificial Intelligence
   in
   One Hour Casidhe Lee Content adapted from Prof. Klein’s CS 188 course at UC Berkeley
2. What is AI?
3. What is Not AI?
4. A.I. is not about copying humans, it’s about building rational agents
5. sin(0.532343) / ln(343)
6. Rational The best sequence of actions toward a goal independent of the thought process Goals are expressed using utility functions
7. MAXIMIZE YOUR EXPECTED UTILITY Prof. Dan Klein
8. The Bellman Equations  Definition of “optimal utility” leads to a simple one-step lookahead relationship amongst optimal utility values: Optimal rewards = maximize over first action and then follow optimal policy  Formally: a s s, a s,a,s’ s’
9. What You’ll Get From this Talk A high level summary of the field No code, but a bit of math How to approach certain problems using AI techniques
10. The Two Big Topics of AI... Search ! & Learning
11. A Simple Search Problem...
12. ... versus this
13. Off-the-shelf search algorithms take too long on very large graphs. AI approach: estimate the path beforehand
14. A* Search Assign a path cost p(n) to node n. Assign an estimated cost h(n) to go from node n to goal node. This is called a heuristic. Let f(n) = p(n) + h(n) be the cost from current node to node n. Perform a search by choosing adjacent node n with smallest f(n) at each step. Return when we hit the goal.
16.                                                          DFS BFS A*
17. Why does it work? Admissibility h(n) < distance(n, goal) Consistency h(x) <= distance(x, y) + h(y)                         Warning: 3e book has a more complex, but also correct, variant A* Graph Search Gone Wrong? S A B C G 1 1 1 2 3 h=2 h=1 h=4 h=1 h=0 S (0+2) A (1+4) B (1+1) C (2+1) G (5+0) C (3+1) G (6+0) State space graph Search tree
18. Purely greedy algorithm gone wrong
19. Search is also useful for games Minimax                                     Alpha Beta Pruning
20. Learning commonly known as... Machine Learning
21. The science of getting computers to learn, without being explicitly programmed ! Prof. Andrew Ng
23. Machine learning is often coupled with classification Classifiers are implemented with linear or probabilistic methods, amongst others The challenge is to train these classifiers
24. Classifiers rely on features to reach conclusions A feature is any signal of the input’s property
25. Naive Bayes                               “Given observations of Features F1 ... Fn , what’s the probability we see event Y”
26. But how do you train it to do that? With this model, whenever we see feature, we can conclude a possible Y. This is a probabilistic classifier
27. How to train naive bayes Another method: Maximum Likelihood Estimation ! L(Y) = P(F1 , F2 , ... Fn | Y) Give it a test data set and tune parameters until we satisfy success metric One method: Find probabilities using counting
28. Beware of overfitting to data Beware of underfitting to data
29. Courtesy Autonomous Helicopter Flight group from Stanford
30. Other Topics to Cover Constraint Satisfaction Problems Reinforcement Learning Markov Decision Processes Decision Trees NLP, Computer Vision, and Robotics Hidden Markov Models SVM, Perceptron, Mira, and Linear Classification Models
31. Question How can I learn more about A.I.? Answer Brush up on probability and linear algebra Go ask your local expert Think about these techniques in your daily work
32. MAXIMIZE YOUR EXPECTED UTILITY Remember...
33. QUESTIONS?