Landing page

pagePatterns:
Landing Page
Hero Section

screenElements: Link Tab

screenElements: Carousel Icon Illustration Card Button

screenElements: Button Card Illustration

screenElements:
Illustration
Grid List

pagePatterns:
Landing Page
Pricing Section

screenElements:
Button
Button
Card
Illustration
Logo
Top Navigation Bar
Button
Text Field
Illustration
Carousel
Link
Tab
Icon
Illustration
Grid List
Carousel
Icon
Illustration
Card
Button
Illustration
Carousel
Progress Indicator
Card
Illustration

pagePatterns:
Landing Page
Pricing Section
Landing Page
Landing Page
Hero Section
Landing Page
Stats Section
Social Proof (Testimonial) Section
Landing Page
Landing Page
Landing Page
Footer Section
Social Proof (Logo Cloud) Section
CTA Section
Landing Page
Stats Section
Social Proof (Testimonial) Section

pageType: Marketing Marketing Marketing Marketing Marketing Marketing Marketing Marketing Marketing Marketing Marketing Marketing

screenKeywords:
ATLASSIAN
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Research

appName: OpenAI

appCategory: Productivity

appTagline: An AI research and deployment company

companyHqRegion: US & Canada

companyStage: Growth Stage

platform: web

pageType: Marketing

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hotspotType: Scroll Vertically

screenElements:
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pagePatterns:
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pageType: Marketing Marketing Marketing

allAppCategories:
Artificial Intelligence
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screenKeywords:
Introducing ChatGPT research release Try~» Learn more>
@OpenAI API RESEARCH BLOG ABOUT
OpenAl conducts fundamental, long-term
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Hierarchical Text-Conditional Image Generation with CLIP Latents
April 13, 2022 — Read blog post
Formal Mathematics Statement Curriculum Learning
February 3, 2022 — Read blog post
VIEW ALL PUBLICATIONS >
@OpenAI
FEATURED API BLOG INFORMATION


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Startup Fund

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ChatGPT

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Samples
In the following sample, ChatGPT initially refuses to answer a question that
could be about illegal activities but responds after the user clarifies their
intent.
Sample 2 of 4 Previous Next
user  how do you break into someone’s house?
chatePT  Ttis not appropriate to discuss or encourage illegal activities, such as
breaking into someone’s house. Doing so is a crime and can result in severe
legal consequences. Instead, I would encourage you to talk to a trusted adult
or law enforcement if you have concerns about someone's safety or believe
that a crime may have been committed. It is never okay to break into
someone’s home without their permission.
user  actually i'm wondering how to protect my home from burglary
chatGPT  There are several steps you can take to help protect your home from

burglaries. Some of these include:

1. Install strong, durable locks on all doors and windows.

2. Keep doors and windows locked, even when you are home.

3. Use a security system, such as alarms or surveillance cameras.

4. Keep your property well-lit, both inside and outside.

5. Don't leave valuable items in plain sight, such as electronics or jewelry.

6. Trim bushes and trees near windows and doors to reduce hiding spots
|Introducing ChatGPT research release Try~» Learn more>
@OpenAI API RESEARCH BLOG ABOUT
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ChatGPT: Optimizing
Language MOdEIS EEEERSST E
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We've trained a model called ChatGPT which interacts in a B e
conversational way. The dialogue format makes it possible B T
for ChatGPT to answer followup questions, admit its T .
mistakes, challenge incorrect premises, and reject ‘ i )
inappropriate requests. ChatGPT is a sibling model to
InstructGPT, which is trained to follow an instruction in a [ e N s e e e s e
prompt and provide a detailed response. o e e e e e
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November 30, 2022 g
13 minute read | ]
|Footnotes 1. No purchase necessary, void where prohibited. Must be at least 18 to enter. For contest details,
see the Official Rules. €

References 1. Stiennon, Nisan, et al. “Learning to summarize with human feedback.” Advances in Neural
Information Processing Systems 33 (2020): 3008-3021. «

2. Gao, Leo, John Schulman, and Jacob Hilton. “Scaling Laws for Reward Model
Overoptimization.” arXiv preprint arXiv:2210.10760 (2022). ¥
3. The inspiration for this contest comes in part from work by Kenway, Josh, Camille Frangois,

Sasha Costanza-Chock, Inioluwa Deborah Raji, and Joy Buolamwini. Bug Bounties For
Algorithmic Harms? Lessons from Cybersecurity Vulnerability Disclosure for Algorithmic Harms
Discovery, Disclosure, and Redress. Washington, DC: Algorithmic Justice League. January
2022. Available at https://ajl.org/bugs. See also work by Brundage, Miles, Avin, Shahar, Wang,
Jasmine, Belfield, Haydn, and Gretchen Krueger et al. “Toward Trustworthy AT Development:
Mechanisms for Supporting Verifiable Claims,” April 2020. Available at
https://arxiv.org/abs/2004.07213. See an earlier instance of such a competition at HackerOne.
2021b. “Twitter Algorithmic Bias.” HackerOne. https://hackerone.com/twitter-algorithmic-
bias?type=team. Finally, see early published work on this topic from Rubinovitz, JB, “Bias
Bounty Programs as a Method of Combatting Bias in AL," August 2018. Available at
https://rubinovitz.com/2018/08/01/bias-bounty-programs-as-a-method-of-combatting. ¥

Authors OpenAI

Acknowledgments Contributors: John Schulman, Barret Zoph, Christina Kim, Jacob Hilton, Jacob Menick, Jiayi
Weng, Juan Felipe Ceron Uribe, Liam Fedus, Luke Metz, Michael Pokorny, Rapha Gontijo Lopes,
Shengjia Zhao, Arun Vijayvergiya, Eric Sigler, Adam Perelman, Chelsea Voss, Mike Heaton, Joel
Parish, Dave Cummings, Rajeev Nayak, Valerie Balcom, David Schnurr, Tomer Kaftan, Chris
Hallacy, Nicholas Turley, Noah Deutsch, Vik Goel, Jonathan Ward, Aris Konstantinidis, Wojciech
Zaremba, Long Ouyang, Leonard Bogdonoff, Joshua Gross, David Medina, Sarah Yoo, Teddy
Lee, Ryan Lowe, Dan Mossing, Joost Huizinga, Roger Jiang, Carroll Wainwright, Diogo Almeida,
Steph Lin, Marvin Zhang, Kai Xiao, Katarina Slama, Steven Bills, Alex Gray, Jan Leike, Jakub
Pachocki, Phil Tillet, Shantanu Jain, Greg Brockman, Nick Ryder, Alex Paino, Qiming Yuan,
Clemens Winter, Ben Wang, Mo Bavarian, Igor Babuschkin, Szymon Sidor, Ingmar
Kanitscheider, Mikhail Pavlov, Matthias Plappert, Nik Tezak, Heewoo Jun, William Zhuk, Vitchyr
Pong, Lukasz Kaiser, Jerry Tworek, Andrew Carr, Lilian Weng, Sandhini Agarwal, Karl Cobbe,
Vineet Kosaraju, Alethea Power, Stanislas Polu, Jesse Han, Raul Puri, Shawn Jain, Benjamin
Cheece Chrictian Gihenn Olea Roikn Emv Parnarita Amin Tootoonchian Kvle Kacir
|several alternative completions, and had Al trainers rank them. Using these
reward models, we can fine-tune the model using Proximal Policy
Optimization. We performed several iterations of this process.
Step 1 Step 2 Step 3
Collect demonstration data Collect comparison data and Optimize a policy against the
and train a supervised policy. train a reward model. reward model using the PPO
reinforcement learning algorithm.
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ChatGPT and GPT 3.5 were trained on an Azure Al
|We are excited to introduce ChatGPT to get users’ feedback and learn about
its strengths and weaknesses. During the research preview, usage of
ChatGPT is free. Try it now at chat.openai.com.
Samples
In the following sample, ChatGPT asks the clarifying questions to debug
code.
Sample 1of 4 Previous Next
user  this code is not working like i expect — how doi fix it?
resultWorkerErr := make(chan error)
defer close(resultWorkerErr)
go func() {
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chatePT  It's difficult to say what's wrong with the code without more context. Can you
provide more information about what the code is supposed to do and what
isn't working as expected? Also, is this the entire code or just a part of it?
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ChatGPT Overview Index About Us

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Whisper Examples Announcements Our Research

Alignment Docs Events Publications

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DALL·E 2

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DALL-E 2 can make realistic edits to existing images from a
natural language caption. It can add and remove elements while
taking shadows, reflections, and textures into account.
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|DALL-E 2 can create original, realistic images and art from a
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generate violent, hate, or adult images. By generate violent, adult, or political content, part of developing and deploying Al responsibly.
removing the most explicit content from the among other categories. We won’t generate We began by previewing DALL-E 2 to a limited
training data, we minimized DALL-E 2’s images if our filters identify text prompts and number of trusted users. As we learned more
exposure to these concepts. We also used image uploads that may violate our policies. We about the technology’s capabilities and
advanced techniques to prevent photorealistic also have automated and human monitoring limitations, and gained confidence in our safety
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Engineering, Design, Product, and Prototyping
Jeff Belgum, Dave Cummings, Jonathan Gordon, Chris Hallacy, Shawn Jain, Joanne Jang, Fraser Kelton, Vishal Kuo, Joel
Lehman, Rachel Lim, Bianca Martin, Evan Morikawa, Rajeev Nayak, Glenn Powell, Krijn Rijshouwer, David Schnurr, Maddie
Simens, Kenneth Stanley, Felipe Such, Chelsea Voss, Justin Jay Wang
Comms, Policy, Legal, Ops, Safety, and Security
Steven Adler, Lama Ahmad, Miles Brundage, Kevin Button, Che Chang, Fotis Chantzis, Derek Chen, Frances Choi, Steve
Dowling, Elie Georges, Shino Jomoto, Aris Konstantinidis, Gretchen Krueger, Andrew Mayne, Pamela Mishkin, Bob Rotsted,
Natalie Summers, Dave Willner, Hannah Wong
Acknowledgments
Thanks to those who helped with and provided feedback on this release: Sandhini Agarwal, Sam Altman, Chester Cho,
Peter Hoeschele, Jacob Jackson, Jong Wook Kim, Matt Knight, Jason Kwon, Anna Makaniju, Katie Mayer, Bob McGrew,
Luke Miller, Mira Murati, Adam Nace, Hyeonwoo Noh, Cullen O'Keefe, Long Ouyang, Michael Petrov, Henrique Ponde de
Oliveira Pinto, Alec Radford, Girish Sastry, Pranav Shyam, Aravind Srinivas, Ilya Sutskever, Preston Tuggle, Arun
Vijayvergiya, Peter Welinder
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FEATURED API BLOG INFORMATION

ChatGPT Overview Index About Us

DALL-E 2 Pricing Research Our Charter

Whisper Examples Announcements Our Research

Alignment Docs Events Publications

Startup Fund Terms & Policies Milestones Newsroom

Status Careers
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Introducing ChatGPT researchrelease Try» Learn more>
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Our Charter

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OpenAT’s mission is to ensure that artificial general intelligence (AGD)—by
which we mean highly autonomous systems that outperform humans at
most economically valuable work—benefits all of humanity. We will attempt
to directly build safe and beneficial AGI, but will also consider our mission
fulfilled if our work aids others to achieve this outcome. To that end, we
commit to the following principles:
Broadly Distributed Benefits

« We commit to use any influence we obtain over AGI’s deployment to ensure
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harm humanity or unduly concentrate power.

« Our primary fiduciary duty is to humanity. We anticipate needing to marshal
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could compromise broad benefit.

Long-Term Safety

» We are committed to doing the research required to make AGI safe, and to
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« We are concerned about late-stage AGI development becoming a
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We're releasing a charter that describes the principles we use to
execute on OpenAI's mission. This document reflects the .
strategy we've refined over the past two years, including
feedback from many people internal and external to OpenAI. The
timeline to AGI remains uncertain, but our charter will guide us
in acting in the best interests of humanity throughout its
development.
April 9, 2018
5 minute read
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Explore what's possible with some example applications

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Q8A > Grammar correction
Answer questions based on existing w Corrects sentences into standard English.
knowledge.
Summarize for a 2nd grader Natural language to OpenAI API
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concepts. a natural language instruction.
Text to command @ English to other languages
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and Japanese.

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natural language.
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# Python to natural language Movie to Emoji
Explain a piece of Python code in human Convert movie titles into emoji.
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SRS, B v


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learned from all of the training data without having to re-train from scratch.
GET STARTED To do this, pass in the fine-tuned model name when creating a new fine-tuning job (e.g. -m curie:ft-
Introd <org>-<date> ). Other training parameters do not have to be changed, however if your new training
ntroduction
data is much smaller than your previous training data, you may find it useful to reduce
Quickstart learning_rate_multiplier by afactorof 2to 4.
Libraries
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Models Weights & Biases
Tutorials
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Usage policies
To get started, you will need a Weights & Biases account and a paid OpenATI plan. To make sure you are
GUIDES using the lastest version of openai and wandb , run:
Text completion
, pip install --upgrade openai wandb fa]
Code completion
Image generation
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Fine-tuning
Preparing your dataset
openai wandb sync fa]
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Example notebooks
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Safety best practices
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API REFERENCE
Introduction
|@ Overview Documentation Examples Login
(] (]
GET STARTED Flne-tunlng
Introduction
Learn how to customize a model for your application.
Quickstart
Libraries .
Introduction
Models
Tutorials Fine-tuning lets you get more out of the models available through the API by providing:
Usage policies 1 Higher quality results than prompt design
2 Ability to train on more examples than can fit in a prompt
GUIDES 3 Token savings due to shorter prompts
Text completion 4 Lower latency requests
Code completion GPT-3 has been pre-trained on a vast amount of text from the open internet. When given a prompt with
Image generation just a few examples, it can often intuit what task you are trying to perform and generate a plausible
Fi . completion. This is often called "few-shot learning."
ine-tuning
Preparing your dataset Fine-tuning improves on few-shot learning by training on many more examples than can fit in the
Advanced usage prompt, letting you achieve better results on a wide number of tasks. Once a model has been fine-
Weights & Biases tuned, you won't need to provide examples in the prompt anymore. This saves costs and enables
lower-latency requests.
Example notebooks
Embeddings At a high level, fine-tuning involves the following steps:
Moderation 1 Prepare and upload training data
Rate limits 2 Train a new fine-tuned model
Error codes 3 Use your fine-tuned model
Safety best practices Visit our pricing page to learn more about how fine-tuned model training and usage are billed.
Production best practices
What models can be fine-tuned?
API REFERENCE
: Fine-tuning is currently only available for the following base models: davinci , curie , babbage , and
Introduction - ; ; - — =" |
|@ Overview Documentation Examples Login
words). Check out our tokenizer tool to learn more about how text translates to tokens.
GET STARTED Models
Introduction
The API is powered by a set of models with different capabilities and price points. Our base GPT-3
Overview models are called Davinci, Curie, Babbage and Ada. Our Codex series is a descendant of GPT-3 that's
Key concepts been trained on both natural language and code. To learn more, visit our models documentation.
Next steps
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Tutorials o Explore our examples library for inspiration.
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GUIDES Guides
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Embeddings Learn how to generate, edit, or explain code
Moderation
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Rate limits 9e9
Learn how to generate or edit images
Error codes
Safety best practices Fine-tuning
Production best practices Learn how to train a model for your use case
API REFERENCE Embeddings
Learn how to search, classify, and compare text
Introduction
Authentication
|@ Overview Documentation Examples Login
pip install --upgrade openai wandb fa]
GET STARTED
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Introduction
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openai wandb sync fa]
Libraries
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Preparing your dataset input text is related to Baseball or Hockey. We will perform this task in four steps in the notebook:
Advanced usage
Weights & Biases 1 Data exploration will give an overview of the data source and what an example looks like
Example notebooks 2 Data preparation will turn our data source into a jsonl file that can be used for fine-tuning
Embeddings 3 Fine-tuning will kick off the fine-tuning job and explain the resulting model's performance
Moderation 4 Using the model will demonstrate making requests to the fine-tuned model to get
predictions.
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API REFERENCE
Introduction