The Human Element in Machine Learning w Catherine D’Ignazio, Jacob Andreas & Harini Suresh (S3:E5)
Skills:
ML Maths Basics90%LLM Foundations80%Supervised Learning80%Prompt Craft70%Research Methods60%
When computer science was in its infancy, programmers quickly realized that though computers are astonishingly powerful tools, the results they achieve are only as good as the data you feed into them. (This principle was quickly formalized as GIGO: “Garbage In, Garbage Out.”) What was true in the era of the UNIVAC has proved still to be true in the era of machine learning: among other well-publicized AI fiascos, chatbots that have interacted with bigots have learned to spew racist invective, while facial-recognition software trained solely on images of white people sometimes fails to recognize people of color as human. In this episode, we meet Prof. Catherine D’Ignazio of MIT’s Department of Urban Studies and Planning (DUSP) and Prof. Jacob Andreas and Harini Suresh of the Department of Electrical Engineering and Computer Science. In 2021, D’Ignazio, Andreas, and Suresh collaborated as part of the Social and Ethical Responsibilities of Computing initiative from the Schwartzman College of Computing in a project to teach computer science students in 6.864 Natural Language Processing to recognize how deep learning systems can replicate and magnify the biases inherent in the data sets that are used to train them.
Relevant Resources:
MIT OpenCourseWare
https://ocw.mit.edu/index.htm?utm_source=youtube&utm_medium=shownotes&utm_campaign=chalkradio&utm_term=s3e5
The OCW Educator Portal
https://ocw.mit.edu/educator?utm_source=youtube&utm_medium=shownotes&utm_campaign=chalkradio&utm_term=s3e5
Share your teaching insights
https://forms.gle/XBwUwqn35abSdjNs8
Case Studies in Social and Ethical Responsibilities of Computing
https://ocw.mit.edu/resources/res-tll-007-case-studies-in-social-and-ethical-responsibilities-of-computing-fall-2021/? utm_source=youtube&utm_medium=shownotes&utm_campaign=chalkradio&utm_term=s3e5
SERC website
https://computing.mit.edu/cross-cutting/social-and-ethical-responsibilities-of-computing/
Professor D’Ignazio’s faculty page
https://dusp.m
Watch on YouTube ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
Playlist
Uploads from MIT OpenCourseWare · MIT OpenCourseWare · 0 of 60
← Previous
Next →
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
21. Post Trade Clearing, Settlement & Processing
MIT OpenCourseWare
10. Financial System Challenges & Opportunities
MIT OpenCourseWare
7. Technical Challenges
MIT OpenCourseWare
3. Blockchain Basics & Cryptography
MIT OpenCourseWare
19. Primary Markets, ICOs & Venture Capital, Part 1
MIT OpenCourseWare
1. Introduction for 15.S12 Blockchain and Money, Fall 2018
MIT OpenCourseWare
Chalk Radio, A Podcast about Inspired Teaching at MIT (Teaser)
MIT OpenCourseWare
Nuclear Gets Personal with Prof. Michael Short (S1:E1)
MIT OpenCourseWare
How Africa Has Been Made to Mean with Prof. Amah Edoh (S1:E2)
MIT OpenCourseWare
Making Deep Learning Human with Prof. Gilbert Strang (S1:E3)
MIT OpenCourseWare
Social Impact at Scale, One Project at a Time with Dr. Anjali Sastry (S1:E4)
MIT OpenCourseWare
Film is for Everyone with Prof. David Thorburn (S1:E5)
MIT OpenCourseWare
Lecture 12: Aircraft Performance
MIT OpenCourseWare
Lecture 3: Learning to Fly
MIT OpenCourseWare
Lecture 13: Interpreting Weather Data
MIT OpenCourseWare
Lecture 21: Weather Minimums and Final Tips
MIT OpenCourseWare
Hand-on, Minds On with Dr. Christopher Terman (S1:E6)
MIT OpenCourseWare
Part 4: Eigenvalues and Eigenvectors
MIT OpenCourseWare
Part 5: Singular Values and Singular Vectors
MIT OpenCourseWare
Part 3: Orthogonal Vectors
MIT OpenCourseWare
Part 2: The Big Picture of Linear Algebra
MIT OpenCourseWare
Part 1: The Column Space of a Matrix
MIT OpenCourseWare
Intro: A New Way to Start Linear Algebra
MIT OpenCourseWare
9. Chromatin Remodeling and Splicing
MIT OpenCourseWare
28. Visualizing Life - Fluorescent Proteins
MIT OpenCourseWare
20. Roth's theorem III: polynomial method and arithmetic regularity
MIT OpenCourseWare
8. Szemerédi's graph regularity lemma III: further applications
MIT OpenCourseWare
19. Roth's theorem II: Fourier analytic proof in the integers
MIT OpenCourseWare
12. Pseudorandom graphs II: second eigenvalue
MIT OpenCourseWare
1. A bridge between graph theory and additive combinatorics
MIT OpenCourseWare
Special Episode: Teaching Remotely During Covid-19 with Prof. Justin Reich
MIT OpenCourseWare
Spring 2020 Update from Dean Rajagopal
MIT OpenCourseWare
S1E7: Unpacking Misconceptions about Language & Identities with Prof. Michel DeGraff
MIT OpenCourseWare
Climate 101 Live
MIT OpenCourseWare
Welcome for Volunteers (for EarthDNA's Climate 101)
MIT OpenCourseWare
Learning to Fly with Drs. Philip Greenspun & Tina Srivastava (S1:E8)
MIT OpenCourseWare
Thinking Like an Economist with Prof. Jonathan Gruber (S1:E9)
MIT OpenCourseWare
2. Cyber Network Data Processing; AI Data Architecture
MIT OpenCourseWare
1. Artificial Intelligence and Machine Learning
MIT OpenCourseWare
2: Resistor Capacitor Circuit and Nernst Potential - Intro to Neural Computation
MIT OpenCourseWare
14: Rate Models and Perceptrons - Intro to Neural Computation
MIT OpenCourseWare
4: Hodgkin-Huxley Model Part 1 - Intro to Neural Computation
MIT OpenCourseWare
18: Recurrent Networks - Intro to Neural Computation
MIT OpenCourseWare
3: Resistor Capacitor Neuron Model - Intro to Neural Computation
MIT OpenCourseWare
15: Matrix Operations - Intro to Neural Computation
MIT OpenCourseWare
13: Spectral Analysis Part 3 - Intro to Neural Computation
MIT OpenCourseWare
16: Basis Sets - Intro to Neural Computation
MIT OpenCourseWare
20: Hopfield Networks - Intro to Neural Computation
MIT OpenCourseWare
8: Spike Trains - Intro to Neural Computation
MIT OpenCourseWare
7: Synapses - Intro to Neural Computation
MIT OpenCourseWare
19: Neural Integrators - Intro to Neural Computation
MIT OpenCourseWare
5: Hodgkin-Huxley Model Part 2 - Intro to Neural Computation
MIT OpenCourseWare
6: Dendrites - Intro to Neural Computation
MIT OpenCourseWare
17: Principal Components Analysis_ - Intro to Neural Computation
MIT OpenCourseWare
12: Spectral Analysis Part 2 - Intro to Neural Computation
MIT OpenCourseWare
11: Spectral Analysis Part 1 - Intro to Neural Computation
MIT OpenCourseWare
9: Receptive Fields - Intro to Neural Computation
MIT OpenCourseWare
10: Time Series - Intro to Neural Computation
MIT OpenCourseWare
1: Course Overview and Ionic Currents - Intro to Neural Computation
MIT OpenCourseWare
The Power of OER with Profs. Mary Rowe and Elizabeth Siler (S1:E10)
MIT OpenCourseWare
More on: ML Maths Basics
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
H2O.ai launches tabH2O, a foundation model that makes predictions from tabular data without any training
The Next Web AI
How TraceML Measures PyTorch Training Time Without Stalling the GPU
Medium · AI
How TraceML Measures PyTorch Training Time Without Stalling the GPU
Medium · Machine Learning
How TraceML Measures PyTorch Training Time Without Stalling the GPU
Medium · Data Science
🎓
Tutor Explanation
DeepCamp AI