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

Let's take a look!

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This course covers:

Search & planning
Dealing with uncertainty
Machine learning, deep learning & RL
Ethics of AI

24/7 Discussion Forum

Campuswire

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Office Hours Queue Management

Khoury Office Hours App

Requires Khoury Account

Link and Instructions on course website
(see office hours calendar)

Components

Attendance Requirements

Mandatory attendance - project presentations, last 2 weeks

Must be present even if you're not presenting on that day

No remote options for any lectures

Very high correlation between attendance in lectures/office hours and co-op offers!

Classroom Policies

Don't be late

Bring a laptop/tablet*

No phones please!

Be respectful

Don't distract your classmates

Policies - Homework Submissions

Deadlines: 6:00 pm, Friday

Assignments will be released on Canvas

Submissions: Gradescope

Grades released on Gradescope

Regrade Requests: submit within 1 week of receiving grade

Late Policy

Automatic extension until Sunday 6:00 pm

Warning: No office hours during this period

Does not apply to project submissions, labs or presentations

If you have accommodations through DAS, get the notification letter sent ASAP

Life happens, come talk to me!

Policy on Collaboration and Generative AI

Discussing concepts is okay.
Discussing the actual problem on the assignment is not.
If in doubt, just ask me!

For programming assignments (except assignment 0), use whatever you'd like, but cite it.

For problem sets, 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.

Course Rigor

This is not an easy course.
The name can be misleading...

AI is inherently complex and mathematical.
There will be equations, lots of them.

Most students who work hard get an A.
This is not because I'm lenient - they had to earn it.

Academic Integrity

If I catch you cheating...

• You will fail this course


• You will be reported to OSCCR
  Penalties ranging from warnings/probation to dismissal


• You will be reported to the Khoury Academic Integrity Committee
  Potential consequences include:
  
  
  • Immediate and permanent loss of TA/co-op
    (Most reinstatement applications are unsuccessful)
  • Dismissal


• Khoury has a "Rate my Students" for TA hiring

My Expectations From You

Come to class having studied notes in advance

Manage your time wisely - you will always have conflicting deadlines

Read all instructions, follow policies strictly, and do not cheat!

Come to office hours - get to know me outside class

Challenge yourselves - that's why you're here!

Ask questions - I'm here to help you learn!

My Commitments To You

I will always welcome student questions

I will foster a nonjudgmental environment

You can reach out to me for any reason*

*Faculty are mandatory reporters at Northeastern - see syllabus for details.

Required Background

Python 3, strong programming skills

Basics of Linear Algebra
(matrix and vector operations, dot products)

Basics of Probability & Statistics
(conditional probability, Bayes' theorem)

Basics of Calculus
(differentiation, partial derivatives)

Assignment 0 - out today!

Self-placement/evaluation

Locked until Syllabus Quiz is completed

Covers required background

Tests programming ability

Contains resources to build necessary skills

Due on Jan 17th

Attempt this honestly - I don't expect anyone to know everything!

Any questions for/about me?

Where did it all start?

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...

SEARCH

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

Let us try and formalize the following problems.