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Presenter from Learning Lab info graphic. Rule of thumb for both teachers and students

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Navigating AI: Educator's Rules of Thumb Infographic

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Create your own AI model.

Ai is created and developed by your own training dataset

Follows Google- No signing in, no PII don't use face

AI Literacy- In students literacy, branch of the digital citizenship.

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Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more - no expertise or coding required.

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Practice prompt engineering. Did you know that prompt engineering is writing? This is a great way to "trick" students into writing.

This is also a great introduction to understanding that not everything is as it seems... especially in the age of generative AI. Fact checking is a critical skill.

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Say What You See - Google Arts & Culture

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Learn the art of image prompting with the help of Google AI.

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Text to video creation tool

Adobe firefly

Deep fakes---need to research - ethical issues with these videos everything needs to be critically evaluated critical consumerism?

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Creating video from text

Sora is an AI model that can create realistic and imaginative scenes from text instructions.
Read technical report

All videos on this page were generated directly by Sora without modification.

We're teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction.

Introducing Sora, our text-to-video model. Sora can generate videos up to a minute long while maintaining visual quality and adherence to the user's prompt.

Safety

We'll be taking several important safety steps ahead of making Sora available in OpenAI's products. We are working with red teamers - domain experts in areas like misinformation, hateful content, and bias - who will be adversarially testing the model.

We're also building tools to help detect misleading content such as a detection classifier that can tell when a video was generated by Sora. We plan to include C2PA metadata in the future if we deploy the model in an OpenAI product.

In addition to us developing new techniques to prepare for deployment, we're leveraging the existing safety methods that we built for our products that use DALL·E 3, which are applicable to Sora as well.

For example, once in an OpenAI product, our text classifier will check and reject text input prompts that are in violation of our usage policies, like those that request extreme violence, sexual content, hateful imagery, celebrity likeness, or the IP of others. We've also developed robust image classifiers that are used to review the frames of every video generated to help ensure that it adheres to our usage policies, before it's shown to the user.

We'll be engaging policymakers, educators and artists around the world to understand their concerns and to identify positive use cases for this new technology. Despite extensive research and testing, we cannot predict all of the beneficial ways people will use our technology, nor all the ways people will abuse it. That's why we believe that learning from real-world use is a critical component of creating and releasing increasingly safe AI systems over time.

Research techniques

Sora is a diffusion model, which generates a video by starting off with one that looks like static noise and gradually transforms it by removing the noise over many steps.

Sora is capable of generating entire videos all at once or extending generated videos to make them longer. By giving the model foresight of many frames at a time, we've solved a challenging problem of making sure a subject stays the same even when it goes out of view temporarily.

Similar to GPT models, Sora uses a transformer architecture, unlocking superior scaling performance.

We represent videos and images as collections of smaller units of data called patches, each of which is akin to a token in GPT. By unifying how we represent data, we can train diffusion transformers on a wider range of visual data than was possible before, spanning different durations, resolutions and aspect ratios.

Sora builds on past research in DALL·E and GPT models. It uses the recaptioning technique from DALL·E 3, which involves generating highly descriptive captions for the visual training data. As a result, the model is able to follow the user's text instructions in the generated video more faithfully.

In addition to being able to generate a video solely from text instructions, the model is able to take an existing still image and generate a video from it, animating the image's contents with accuracy and attention to small detail. The model can also take an existing video and extend it or fill in missing frames. Learn more in our technical report.

Sora serves as a foundation for models that can understand and simulate the real world, a capability we believe will be an important milestone for achieving AGI.

Today, Sora is becoming available to red teamers to assess critical areas for harms or risks. We are also granting access to a number of visual artists, designers, and filmmakers to gain feedback on how to advance the model to be most helpful for creative professionals.

We're sharing our research progress early to start working with and getting feedback from people outside of OpenAI and to give the public a sense of what AI capabilities are on the horizon.

Sora is able to generate complex scenes with multiple characters, specific types of motion, and accurate details of the subject and background. The model understands not only what the user has asked for in the prompt, but also how those things exist in the physical world.

The model has a deep understanding of language, enabling it to accurately interpret prompts and generate compelling characters that express vibrant emotions. Sora can also create multiple shots within a single generated video that accurately persist characters and visual style.

The current model has weaknesses. It may struggle with accurately simulating the physics of a complex scene, and may not understand specific instances of cause and effect. For example, a person might take a bite out of a cookie, but afterward, the cookie may not have a bite mark.

The model may also confuse spatial details of a prompt, for example, mixing up left and right, and may struggle with precise descriptions of events that take place over time, like following a specific camera trajectory.

All videos on this page were generated directly by Sora without modification.

Research Leads Bill Peebles & Tim Brooks

Special Thanks Avi Nayak, Bob McGrew, Brad Lightcap, Chad Nelson, David Medina, Gabriel Goh, Greg Brockman, Hannah Wong, Ian Sohl, Jamie Kiros, James Betker, Jason Kwon, Jong Wook Kim, Mark Chen, Michelle Fradin, Mira Murati, Nick Turley, Peter Hoeschele, Prafulla Dhariwal, Rowan Zellers, Sam Altman, Sandhini Agarwal, Sarah Yoo, Spencer Papay, Srinivas Narayanan, Tifa Chen & Wesam Manassra

Elie Georges
Justin Wang
Kendra Rimbach
Niko Felix
Thomas Degry
Veit Moeller

Che Chang
Fred von Lohmann
Gideon Myles
Tom Stasi

External Engagement Alex Baker-Whitcomb, Allie Teague, Anna Makanju, Anna McKean, Becky Waite, Brittany Smith, Chan Park, Chris Lehane, David Duxin, David Robinson, James Hairston, Jonathan Lachman, Justin Oswald, Krithika Muthukumar, Lane Dilg, Leher Pathak, Olga Nowicka, Ryan Biddy, Sandro Gianella, Stephen Petersilge, Tom Rubin & Varun Shetty

Built by OpenAI in San Francisco, California
Published February 15, MMXXIV

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Sora is an AI model that can create realistic and imaginative scenes from text instructions. Read technical report All videos on this page were generated directly by Sora without modification. We're teaching AI to understand and simulate the physical world in motion, with the goal of training models that help people solve problems that require real-world interaction.

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Beyond the Band-Aid: Rethinking AI Detectors in Education

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Beyond the Band-Aid: Rethinking AI Detectors in Education

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LindyHoc | Beyond the Band-Aid: Rethinking AI Detectors in Education

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Explore why AI detectors fall short and discover effective alternatives that build trust and enhance teaching methods in the age of AI.

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TeachAI | AI Guidance for Schools Toolkit

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The AI Guidance for Schools Toolkit is designed to help education authorities, school leaders and teachers create their own thoughtful guidance to achieve potential benefits of incorporating artificial intelligence in primary and secondary K-12 education while mitigating risks.

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AI and the Future of Teaching & Learning 

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AI and the Future of Teaching & Learning 

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Additional Handout: https://www.ed.gov/media/document/ai-report-core-messagespdf-43860.pdf

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Navigating AI: Educator's Rules of Thumb Infographic

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Artificial Intelligence Guidelines - Tempe Union High School District

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Artificial Intelligence Guidelines - Tempe Union High School District

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https://lile.duke.edu/ai-and-teaching-at-duke-2/artificial-intelligence-policies-in-syllabi-guidelines-and-considerations/

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Duke Learning Innovation & Lifetime Education

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Generative AI and Teaching at Duke - Duke Learning Innovation & Lifetime Education

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Guidance for Instructors Last modified: August 12, 2024 Disclaimer: The field of generative AI and opinions about how to react to these tools in academia are evolving rapidly. As an…

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Generative AI is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts. The University supports responsible experimentation with generative AI tools, but there are important considerations to keep in mind when using these tools, including information security and data privacy, compliance, copyright, and academic integrity. These guidelines are updated periodically.

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Generative AI is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts.

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AI is always making predication based on the information within it's data base. Looking at live training data sets.

Understanding how it makes predications and datasets, you can discern the pros and cons of AI.

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Can a neural network learn to recognize doodles? See how well it does with your drawings and help teach it, just by playing.

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Guidance on the Future of Computer Science Education in an Age of AI

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Guidance on the Future of Computer Science Education in an Age of AI

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Guidance on the Future of Computer Science Education in an Age of AI