Isabelle Levent
@isabellelevent
Isabelle Levent
Even when these paragraphs fail, they make her interested in the story again. She’s curious about this computer-generated text, and it reignites her interest in her own writing.
Katy Gero • AI Reveals the Most Human Parts of Writing
Creative AI Lab
creative-ai.org
However, we often found that it was the unexpected differences between the prompt and the generated image’s interpretation of it that yielded new insight for and excitement from participants.
Dava Newman • When happy accidents spark creativity: Bringing collaborative speculation to life with generative AI
Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers
arxiv.org
What do you do when you know that the environment is going to change but you want to have other members of your species that are reasonably well aligned? Caregiving is one of the things that we do to make that happen. Every time we raise a new generation of children, we’re faced with this difficulty of
here are these intelligences, they’re new, the... See more
here are these intelligences, they’re new, the... See more
Julien Crockett • How to Raise Your Artificial Intelligence: A Conversation With Alison Gopnik and Melanie Mitchell
Academically, this is a collision of everything from computer science and art history to media studies to disruptive innovation to labor economics, and no one of these disciplines seems sufficient to cover the topic.
Aaron Hertzmann • When Machines Change Art
A recurring theme in participant feedback was that the language model lacked taste and intentionality...In contrast, good writers are skilled not only in producing but also discerning good language. In other words, they have taste, the ability to decide why one sentence is interesting while another is not.
Daphne Ippolito • Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers
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
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