Raj Venkat
Assistant Teaching Professor
Raj Venkat
Assistant Teaching Professor
RESEARCH AREAS
- Genomic Privacy
- Graph Theory & Networks
- Natural Language Processing
What does AI mean to you?
What is CS5100 all about?
What is CS5100 all about?
Computers are dumb...
...but we can make them do incredibly clever things
Link to ChatGPT
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Magic Eraser
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Link to lalal.ai
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This course covers:
Search, planning, constraint-satisfaction
Dealing with uncertainty
Machine learning, deep learning & RL
Ethics of AI
LOGISTICS
Course website:
venkat.prof/CS5100
Textbook
Artificial Intelligence: A Modern Approach
Russel and Norvig (Pearson, 4th Ed.)
Components
Final Project (40%)
(presentation, final report, modular code in GitHub repo)
Programming Assignments (30%)
Problem Sets (20%)
Labs (10%)
Grading
Note open and closed intervals
A | [93, 100] |
A- | [90, 93) |
B+ | [87, 90) |
B | [82, 87) |
B- | [80, 82) |
C+ | [77, 80) |
C | [72, 77) |
C- | [70, 72) |
F | [0, 70) |
Policies - Homework Submissions
Written (typed) submissions: Gradescope
Programming submissions: Refer to HW instructions
Deadlines: 6:00 pm on the due date
Conceptual discussions encouraged, disclose your collaborators
Grades released on Gradescope
Regrade Requests: submit within 1 week of receiving grade
Late Policy
You have 1 freebie - a 3-day extension on a single homework, no questions asked.
Cannot be used project submissions, labs or presentations.
If you have accomodations through DAS, get the notification letter sent ASAP.
Life happens, come talk to me!
Policy on Generative AI
For programming assignments, use whatever you'd like, but cite it.
For written assignments, the use of generative AI is prohibited.*
*If you use gen AI only to rephrase for writing clarity,
you must submit an appendix with your original answers.
Academic Integrity
Don't post your solutions anywhere!
Don't discuss code or actual written answers
Disclose your collaborators, cite your sources
Violations will lead to an OSCCR report being filed
Additional penalties, including failing the course
without the option to withdraw
Classroom Policies
Don't be late
Bring a laptop/tablet*
No phones please!
Be respectful
Don't distract your classmates
24/7 Discussion Forum
Campuswire
Campuswire
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Course Feedback
Anonymous Polls (in-class)
Reach out to TAs
Come talk to me!
Lab 1 will be released today!
Python & NumPy worksheet
Let's take a mental break!
Any questions for/about me?
Can Machines Think?
Alan Turing, 1950
Some Philosophical Questions...
Is a machine intelligent if it merely mimics human thinking?
Is a machine intelligent if it can fool a human into believing so?
Could you construct a machine whose workings you couldn't explain?
What does intelligence have to do with free will?
Thinking = symbol manipulation
"Intelligence can be replicated
using a computer program"
- Mathematicians in the 1950s
1956, Dartmouth College, workshop on AI
"The conjecture that every aspect of learning or any other feature of intelligence can be,
in principle, so precisely described that a machine can be made to simulate it"
What is an intelligent agent?
A system or program
that perceives and interacts with its environment
while making informed decisions
to achieve a certain goal or maximize some utility
A system or program
that perceives and interacts with its environment
while making informed decisions
to achieve a certain goal or maximize some utility
How does a program perceive its environment?
- Computer Vision
- Depth Sensors
- Language
- Explicit Representations
A system or program
that perceives and interacts with its environment
while making informed decisions
to achieve a certain goal or maximize some utility
How does a program interact with its environment?
- Physical Interactions
- Image Generation
- Language
A system or program
that perceives and interacts with its environment
while making informed decisions
to achieve a certain goal or maximize some utility
How does a program learn?
- Rule Based Systems
- Learning from Computation
- Learning from Data
- Learning from Mistakes
A system or program
that perceives and interacts with its environment
while making informed decisions
to achieve a certain goal or maximize some utility
How does a program reason?
- Objective Functions
- Utility
- Fairness
- Biases
Consider a Roomba (a robot vacuum)
Think about
- Percepts
- Interactions
- Learning/Inference
- Rationality
How about Netflix/Youtube/Amazon Recommendations?
Think about
- Percepts
- Interactions
- Learning/Inference
- Rationality
Categorizing AI Environments
- Fully Observable v/s Partially Observable
- Single Agent v/s Multi Agent
- Deterministic v/s Non-Deterministic
- Episodic v/s Sequential
- Static v/s Dynamic
- Discrete v/s Continuous
- Known v/s Unknown
Let's now think about building an AI
Consider the Missionaries and Cannibals game...
Consider the Missionaries and Cannibals game...
Representation: State Space Graph
Nodes: States or World Configurations
Edges: Actions
Edge Weights: Cost of Action
Successor/Transition Function: Set of Edges & Edge Weights
Path: Sequence of Actions
Solution: A Path leading from Start to Goal
SEARCH
Assume a Fully Observable, Static, Deterministic, Known environment (for now)
Let us try and formalize the following problems.
Route Planning
- State Space
- Start & End State
- Successor/Transition Function (Action & Cost)
- When riding the T?
- When driving?
Maze Solving
- State Space
- Start & End State
- Successor/Transition Function (Action & Cost)
8 Puzzle
- State Space
- How many possible states?
- Start & End State
- Successor/Transition Function (Action & Cost)
Chess
- State Space
- How many possible states?
- Start & End State
- Successor/Transition Function (Action & Cost)
Before next class, please review...
- Breadth First Search
- Depth First Search
- Uniform Cost Search
Notes posted on course website