Last month, OpenAI announced its latest venture, Sora - a text-to-video generative AI model. It was the talk of the town and trending on X for days. Currently, everyone has one or the other opinion about its possibilities and its far-reaching implications.
What exactly does Sora do?
It creates videos matching the description of our text prompt. Sora can generate videos with a resolution of up to 1920 × 1080 pixels and in various aspect ratios of up to 60 seconds.
Let's see some examples to get a better idea
How does Sora work?
Sora uses a unique machine learning model known as the diffusive transformation model. This model is designed to generate high-quality images or videos through a process of gradual transformation. At the beginning of each frame, the model starts with static noise and then uses complex algorithms to transform the noise into something that resembles the description provided in the input prompt. As L&D Specialists, it’s not as important to understand how AI Technologies like Sora work. What’s more important is to understand how they can help us. But before we do that, let’s quickly discover how Sora is different from ChatGPT.
How is Sora similar to ChatGPT, and where do they differ?
Similarity
Both of them were made by OpenAI based on the GPT (Generative Pre-trained Transformer) architecture, which allows them to comprehend and generate human-like text.
Difference
Sora and ChatGPT differ in two ways, their input/output and their working process. Sora transforms video frames based on text prompts, while ChatGPT processes text for natural language tasks. Although, GPT4 can work with other multimedia formats as well.
Sora uses a diffusion transformation model, whereas ChatGPT uses a transformer model.
Sora specializes in video transformation, while ChatGPT does various text-based tasks like making conversation, answering questions, etc.
How is Sora different from Google’s Gemini 1.5 and Lumiere?
Gemini
Gemini 1.5 is an advanced language model developed by Google.
Capability: Gemini 1.5 can handle long contexts of information better than before, so it can handle and work with big sets of data more effectively. It can process up to one million tokens. Being multimodal, it can do comprehensive analysis and reasoning of vast amounts of information, from video and audio to code and text.
Model of working: It uses Mixture-Of-Experts (MOE) as the base architecture.
Where can It be used: It will be used in sectors where deep analysis of big data is required like scientific research, finance, healthcare administration, legal document analysis, etc.
Lumiere
Lumiere is an AI-driven video-generating tool made by Google.
Capability: Lumiere focuses on video generation, animation, and creating videos from image and text prompts. It can create videos with a maximum duration of 5 sec. It can animate certain parts of an image. Videos are not as realistic as Sora and video definition is limited to 512 × 512 pixels.
Model of working: Lumiere uses a diffusion model called Space-Time-U-Net (or STUNet) architecture.
Where can it be used: Most suitable for short-form content creation. In creating promotional videos for a marketing campaign, In L&D for short-form animations in learning programs, social media content creation, etc. Sora Uses AI for video generation and editing and is mostly catered to creative professionals like content creators and marketers.
The Rise of Sora in Learning and Development
Even though it is in its infancy, we can still predict and say it will be a game-changer for many sectors, especially for e-learning content creation.
Before understanding in which ways Sora can help L&D departments everywhere, let's go through the time-related difficulties involved in e-learning development.
In this article about how long it takes to create eLearning content, we can see several well-researched answers and insights from L&D veterans. To summarize, a 15-minute course took 30 hours to make, whereas 6 hours of learning content time took 800+ hours to develop. It can go longer depending on the complexity, graphics, and tech involved in it.
The most important takeaway is that 1 hour of content takes 180 hours to create. If it's multimedia content like videos, animation or graphics, it takes even longer. Until the use of Sora becomes more widespread, you can always manage your production time & costs with the help of our eLearning Course Development Calculator.
Imagine if there was a way we could create videos and animation with the exact vision of a team or company within seconds! Sora being a game-changer is not an overstatement at all.
Ways Sora can transform eLearning content creation
Save a lot more time (Duh): Sora allows us to create videos and animation with the exact vision the trainer/team has in mind within seconds. This time efficiency allows trainers to focus more on designing a new course and delivery without compromising on the visuals.
Cut down on production costs: Anyone in L&D knows the hefty cost of multimedia in e-learning production. By Leveraging AI, the cost of production can be greatly improved and eLearning Solution providers can also deliver multiple variations of the content. Sora could help by reducing the need for expensive stock images or graphic designers.
Creative freedom and customization: Sora opened doors for L&D professionals to be incredibly creative, as time and resources are no longer scarce. One can create unique visuals for the analogies and examples to assist in easy learning. Any complex visuals and process diagrams can turn into easy-to-understand visuals. Where L&D Specialists and service providers will shine the most is in their prompt engineering and curation methods.
Possible Risks and Challenges of Sora in E-learning
Just like how ChatGPT had its issues, Sora is not error-free either. Here, we will be focusing on the issues it could pose for e-learning content creation.
Copyright and Legality - Users need to make sure the visuals generated by Sora do not infringe on any copyrights or licensing agreements. This requires careful consideration and may involve additional research or legal guidance.
Limitations to Customization - Aligning the visuals to the company's branding could also be a challenge. While Sora gives you plenty of ways to personalize your experience, there are possible roadblocks that could stop you from perfectly matching your organization's branding rules, like when it comes to colour choices, fonts, or other branding details. Initial Learning Curve - As with any new technology, it will take a while to understand its full possibilities. There will be a lot of trial and error during the initial days. We might only be able to rely on it a little, till we get a better handle on it.
A possible way to mitigate these challenges would be to get guidance from e-learning Solution Providers with experience in the matter.
Conclusion
OpenAI made a fantastic breakthrough with Sora. As with any pioneering tech, it has its fair share of challenges. Its implications for e-learning development are massive, but we should move with caution.
In e-learning, Sora can help cut costs and production time; it will allow more freedom for creativity as time and money won't be a limitation any more.
Tech like Sora should be used as tools, not as shortcuts. It is important to not sacrifice creativity and integrity in any project or company.
Once the dust settles, we will be able to learn more about Sora's capabilities and how to navigate it through the lens of e-learning development. One thing that is for sure is we have a lot to learn in the upcoming days.