https://arxiv.org/abs/2204.13807 close this message arXiv smileybones icon Giving Week! Show your support for Open Science by donating to arXiv during Giving Week, April 25th-29th. DONATE Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation and member institutions. arxiv logo > cs > arXiv:2204.13807 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computer Vision and Pattern Recognition arXiv:2204.13807 (cs) [Submitted on 25 Apr 2022] Title:A very preliminary analysis of DALL-E 2 Authors:Gary Marcus, Ernest Davis, Scott Aaronson Download PDF Abstract: The DALL-E 2 system generates original synthetic images corresponding to an input text as caption. We report here on the outcome of fourteen tests of this system designed to assess its common sense, reasoning and ability to understand complex texts. All of our prompts were intentionally much more challenging than the typical ones that have been showcased in recent weeks. Nevertheless, for 5 out of the 14 prompts, at least one of the ten images fully satisfied our requests. On the other hand, on no prompt did all of the ten images satisfy our requests. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2204.13807 [cs.CV] (or arXiv:2204.13807v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2204.13807 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ernest Davis [view email] [v1] Mon, 25 Apr 2022 18:54:39 UTC (3,158 KB) Full-text links: Download: * PDF only [by-sa-4] Current browse context: cs.CV < prev | next > new | recent | 2204 Change to browse by: cs cs.AI References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export bibtex citation Loading... Bibtex formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) [ ] scite.ai Toggle scite Smart Citations (What are Smart Citations?) ( ) Code & Data Code and Data Associated with this Article [ ] arXiv Links to Code Toggle arXiv Links to Code & Data (What is Links to Code & Data?) ( ) Demos Demos [ ] Replicate Toggle Replicate (What is Replicate?) ( ) Related Papers Recommenders and Search Tools [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) ( ) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs and how to get involved. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack