Aims

Thinking Machines

Prof. Calvin

Introduction

  • Welcome to Willamette!
  • Welcome to Thinking Machines
  • We will learn:
    • Scientific computing, to learn…
    • Machine learning and artifical intelligence

Course Description

Artifical intelligence, machine learning, big data, and large scale computing have captured the collective imaginations of millions people across a range of technical expertise. Yet each of these buzzwords is rooted in a central promise: that machines may think.

About Me

About Me
Name: Calvin (Deutschbein)
Say: (Professor) Calvin
Pronoun: they/them
Office: Discord calvin2501
Email: ckdeutschbein@willamette.edu
Website: cd-public.github.io

Background

Specification mining can discover properties that can verify the secure behavior of closed-source CISC CPU designs, the temporal correctness of CPU designs, and hyperproperties for secure information flow in modules, SoCs, and CPUs.

Background

  • Plain English:
    Just as there are bugs in code that make software, modern hardware is also written in code and therefore may contain bugs. I find these bugs.
  • Of note: I found those bugs using scientific computing (thinking machines).

Modality

  • Lecture (Talk)
    • Monday & Wednesday
  • Exercise (Code)
    • Friday
  • Projects (Write-ups)
    • Friday, 10 Oct
    • Friday, 14 Nov

General Expectations

  • Students will begin the course with a grade of an “A”.
    • Students will be expected to:
      • Follow lecture.
      • Treat fellow students with respect.
      • Complete exercises.
      • Complete projects.

Class/Exercises

  • For an A (10/10)
    • Code that meets requirements.
    • On time.
  • For a B (9/10)
    • Code that runs without error.
    • On time
  • Erroneous code is an F (6/10)
  • Late work is a zero (0/10)

Feedback Structure

  • Students collectively within the class as a whole will receive feedback..
    • Individual feedback will be provided in limited and unique cases.
    • Students are expected to provide respectfully collaborate on exercises.
    • Students may request individual feedback from the instructor at any time.
    • Students will receive narrative rather than quantitative feedback.

Project/Website

  • Students will create a public-facing scientific publication via:
    • Midterm: GitHub, Markdown, Python
    • Final: Quarto, GitHub Pages, Python
  • Graded on the AP Scale as an argument essay and then curved.

Group Exercise:

  • Let’s start the class off right away with an exercise designed for:
    • Groups
    • Discussion
    • Disagreement
    • Deep critical thinking

Aims of Education

  • Before starting my first degree, we went to a lecture titled “Aims of Education”
  • I completed all remaining degrees during shutdowns/other crises!
  • I remember it fondly.
  • We will review “Explainable AI for Climate Science” by Elizabeth Barnes.
    • And then discuss.

Source

Question 0

What are the goals of a college education?

  • Think: about a possible answer individually.
  • Discuss: answers within the group.
  • Record: a summary of the discussion.

Question 1

How does a scientist learn something new?

  • Think: about a possible answer individually.
  • Discuss: answers within the group.
  • Record: a summary of the discussion.

Question 2

What do you reasonably expect to remember from your courses in 20 years?

  • Think: about a possible answer individually.
  • Discuss: answers within the group.
  • Record: a summary of the discussion.

Question 3

What is the value of making mistakes in the learning process?

  • Think: about a possible answer individually.
  • Discuss: answers within the group.
  • Record: a summary of the discussion.

Question 4

How do we create a safe environment where risk-taking is encouraged and productive failure is valued?

  • Think: about a possible answer individually.
  • Discuss: answers within the group.
  • Record: a summary of the discussion.

Closing Thoughts

  • Try! Hard work leads to innovative thinking.
  • Fail! Don’t fear failure. Be ready to redo from scratch.
  • Collaborate! Work with peers to succeed at this course and succeed at collaboration.
  • Enjoy! Experience the fun of being a scientist through hard work and exploration.