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Experiment with DALL·E, an AI system by OpenAI
DALL·E is an AI system developed by OpenAI that can create original, realistic images and art from a short text description. It can make realistic and context-aware edits, including inserting, removing, or retouching specific sections of an image from a natural language description. It can also take an image and make novel and creative ...
Jan 5, 2021 · DALL·E is a 12-billion parameter version of GPT-3 (opens in a new window) trained to generate images from text descriptions, using a dataset of text–image pairs. We’ve found that it has a diverse set of capabilities, including creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images.
DALL·E 2 is an AI system that can create realistic images and art from a description in natural language. Try DALL·E (opens in a new window) Follow on Instagram (opens in a new window)
Mar 24, 2023 · Update Apr 12, 2023: We have released Dolly 2.0, licensed for both research and commercial use. See the new blog post here.. Summary. We show that anyone can take a dated off-the-shelf open source large language model (LLM) and give it magical ChatGPT-like instruction following ability by training it in 30 minutes on one machine, using high-quality training data.
Mar 24, 2023 · Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on pythia-12b, Dolly is trained on ~15k instruction/response fine tuning records databricks-dolly-15k generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization.
DALL·E 3 has mitigations to decline requests that ask for a public figure by name. We improved safety performance in risk areas like generation of public figures and harmful biases related to visual over/under-representation, in partnership with red teamers—domain experts who stress-test the model—to help inform our risk assessment and mitigation efforts in areas like propaganda and misinformation.
