James Cohan retweeted
Excited to share a new paper that aims to narrow the conceptual gap between the idealized notion of Kolmogorov complexity and practical complexity measures for neural networks.
James Cohan retweeted
Excited to share a new paper: “ALTA: Compiler-Based Analysis of Transformers” (w/ @James_Cohan, @jacobeisenstein, @kentonctlee, @JonathanBerant, @toutanova)
arxiv.org/abs/2410.18077
James Cohan retweeted
“Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach” is now available at arxiv.org/abs/2407.12687
#ICML2024: Irina Jurenka and Markus Kunesch will be demoing the LearnLM-Tutor at the GDM booth on Tues afternoon: deepmind.google/discover/eve…
What if everyone, everywhere could have their own personal AI tutor, on any topic? 💡
We’re making learning more engaging and personal with our new family of models, LearnLM.
Find out more → dpmd.ai/3wK6fBo #GoogleIO
James Cohan retweeted
What if everyone, everywhere could have their own personal AI tutor, on any topic? 💡
We’re making learning more engaging and personal with our new family of models, LearnLM.
Find out more → dpmd.ai/3wK6fBo #GoogleIO
James Cohan retweeted
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
Shows that, based on pixel-based pretraining, it is possible for an agent to outperform human crowdworkers on MiniWob++ of GUI-based instruction following tasks
arxiv.org/abs/2306.00245
James Cohan retweeted
Excited to present Pix2Act! An agent that can interact with GUIs using the same conceptual interface that humans commonly use — via pixel-based screenshots and generic keyboard and mouse actions -- arxiv.org/abs/2306.00245 (1/4)
