4.4k citations · 7.3k across the 20 of their papers we have counts for
4 papers · 1 filter
Filling gaps in trustworthy development of AI
Shahar Avin, Haydn Belfield, Miles Brundage +9
The range of application of artificial intelligence (AI) is vast, as is the potential for harm. Growing awareness of potential risks from AI systems has spurred action to address t…
Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications
Sandhini Agarwal, Gretchen Krueger, Jack Clark +3
Recently, there have been breakthroughs in computer vision ("CV") models that are more generalizable with the advent of models such as CLIP and ALIGN. In this paper, we analyze CLI…
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun +55
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex p…
Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models
Alex Tamkin, Miles Brundage, Jack Clark +1
On October 14th, 2020, researchers from OpenAI, the Stanford Institute for Human-Centered Artificial Intelligence, and other universities convened to discuss open research question…