Isabelle Levent
@isabellelevent
Isabelle Levent
Research talk: Prompt tuning: What works and what's next
youtube.comPrompting and
This builds on a growing body of work that our ‘‘mind perception’’ (which manifests as inferences of intentions, beliefs, and values) meaningfully varies across individuals and shapes our moral judgments
Sydney Levine • Who gets credit for AI-generated art? – MIT Media Lab
You must never think at the typewriter — you must feel. Your intellect is always buried in that feeling anyway. (Ray Bradbury)
Maria Popova • What Is Creativity? Cultural Icons on What Ideation Is and How It Works
A much better way of thinking about them is as a technology that allows humans to access information from many other humans and use that information to make decisions.
Julien Crockett • How to Raise Your Artificial Intelligence: A Conversation With Alison Gopnik and Melanie Mitchell
on metaphors for LLMs
Second, we should create a legal regime that can make our data’s collective value something we can bargain over as a group.
Hanlin Li • From My Data to Our Data: A Proposal to Equitably Distribute Wealth in a Digital Economy
Like oil and land, data are a common that is commodified by private actors for profits. The commons being commodified is our essence as humans: our interactions with society at large.
Hanlin Li • From My Data to Our Data: A Proposal to Equitably Distribute Wealth in a Digital Economy
Poetry and
We find that models learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively “good” prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on
... See moreAlbert Webson • Do Prompt-Based Models Really Understand the Meaning of their Prompts?
Tech Ethics and