376 Unit 6: Miscellaneous Topics

Background

Review my blog post on Mapping to Mimicry. I wrote it in one short sprint; feedback welcome!

• More resources: Ai generative art tools

Robotics

Rather than study theory, let’s look at two recent blog advances:

• An upgrade on a classic RL approach (Q-learning): Pre-training generalist agents using offline reinforcement learning – Google AI Blog
• A new kind of approach: just use Transformers for everything. PaLM-E: An embodied multimodal language model – Google AI Blog

Explainable and Human-Centered AI

ACM Selects: Trustworthy AI in Healthcare #02

What happens when AI meets people? How can we ensure that AI results are:

• Correct,
• Just, and
• Useful?

The first two are the subject of a subfield called Fairness, Accountability, and Transparency; the last is the subject of much research in human-computer interaction (HCI) and computer-supported cooperative work (CSCW). We’ll explore all three in these last two weeks of class.

Correctness and Transparency / Explainability

Read one or more of these:

• Explainable AI Guide (“Your high-level guide to the set of tools and methods that helps humans understand AI/ML models and their predictions.”)
• Interpreting Neural Nets: Skim one of these articles:
  • The Building Blocks of Interpretability
  • Activation Atlas
  • Visualizing Neural Networks with the Grand Tour
  • Multimodal Neurons in Artificial Neural Networks
• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges | Abstract
• Techniques and limitations of post-hoc interpretability
  • Problems with Shapley-value-based explanations as feature importance measures
  • The Science Behind InterpretML: SHAP - YouTube
• Built-in interpretability: imodels: leveraging the unreasonable effectiveness of rules – The Berkeley Artificial Intelligence Research Blog
• Hundreds of AI tools have been built to catch covid. None of them helped. | MIT Technology Review

  Many unwittingly used a data set that contained chest scans of children who did not have covid as their examples of what non-covid cases looked like. But as a result, the AIs learned to identify kids, not covid.

Watch:

• Watch: Stop doing “explainable” ML

Supplemental Material

• See the “Hall of Fame” list at 5th VISxAI Workshop at IEEE VIS 2022 (also the list of examples under the Call for Participation)
• PyTorch implementation for Transformer Interpretability Beyond Attention Visualization

Justice (Fairness, Bias)

• Read one of the articles in ACM Selects on Algorithmic Fairness
• Prefer to watch something? 21 fairness definitions and their politics

Supplemental: The Effects of Regularization and Data Augmentation are Class Dependent | Abstract

Usability

Read or watch something from Human-Centered Artificial Intelligence.

The content may not be revised for this year. If you really want to see it, click the link above.