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DALL-E is an artificial intelligence (AI) system created by OpenAI that can produce realistic images from text prompts. The name DALL-E is a blend of Salvador Dali, the famous artist, and Pixar’s WALL-E movie.
OpenAI officially announced DALL-E in January 2021. The system uses a combination of two previous models to produce realistic images – GPT-3 and Generative Adversarial Networks (GANs).
Following DALL-E’s initial success, OpenAI announced its successor, DALL-E 2, in April 2022. DALL-E 2 builds on the original system’s capabilities by being able to create more realistic images and incorporate different styles that were previously impossible.
Techopedia Explains DALL-E
DALL-E was the first AI-powered system to highlight the possibilities of text-to-image functionality. Users can provide short phrases that DALL-E will understand and create images representing the prompt. DALL-E also incorporates an evaluation mechanism to determine whether the final picture is accurate.
DALL-E’s mechanism combines natural language processing, machine learning, and computer vision elements. This means that the images DALL-E is able to produce can be abstract and impossible in the real world. For example, a user could prompt DALL-E to create a picture of a fox with three hands reading a Harry Potter book – and it would quickly oblige.
Given the incredible possibilities offered by DALL-E, the system has quickly gained attention from the mainstream media and social media. This attention has been both positive and negative due to its disruptive capacity within industries like advertising, art, and entertainment.
How Does DALL-E’s Technology Work?
The mechanics behind DALL-E’s system are highly complex and challenging to understand for non-specialists. However, DALL-E follows four important steps when producing images:
- Preprocessing: DALL-E takes the text prompts provided by users and converts them into vectors. It then uses a language model (e.g. GPT-3) to understand what the user wants to achieve.
- Encoding: The vectors created in the preprocessing stage are used to create an image that accurately matches the text prompt provided by the user.
- Decoding: DALL-E will refine the image multiple times to ensure realism during the decoding phase. Following this, DALL-E will ‘evaluate’ the final result through the discriminator network – if more changes are needed, the system will facilitate additional refinements.
- Output: Once all refinements are complete, the finalized image is presented to the user as an output.
With DALL-E 2, this process has been improved so that outputs more accurately match inputs. Moreover, DALL-E 2 can provide a much higher image quality than the original system could.
Potential Applications of DALL-E
The potential applications of DALL-E are endless and will apply to many fields. Here are some of the more common examples:
- Advertising: Advertisers can use DALL-E to create realistic images of the products they want to sell. This will significantly reduce business costs, as photography and editing requirements will be drastically reduced.
- Entertainment: DALL-E could completely reshape the entertainment industry, whether that be movies, TV shows, or video games. The developers of media franchises could use DALL-E to conceptualize characters, levels, backgrounds, or any other element of the design process – removing the need to pay for experts in that area.
- Art: DALL-E’s outputs could theoretically create a whole new area of the art world – AI artwork. This would likely provide countless ways that users could monetize the created artwork.
- Schools: Teachers could use DALL-E to provide visual aids to boost their students’ learning capabilities. This could be particularly useful if the teacher isn’t skilled in drawing/painting yet still wishes to use these visual aids in the classroom.
Although the possible benefits of DALL-E are limitless, many ethical concerns have been brought to light regarding this technology. The most prominent concern relates to ‘deepfakes‘ – images or videos created by AI systems with no basis in the real world.
The rise of deepfakes is a genuine concern globally, as they could have far-reaching ramifications. For example, someone could use an AI system like DALL-E to create a photorealistic image of a politician in a compromising situation. Media outlets could then share this image, damaging the politician’s reputation.
There are also concerns over ownership rights regarding DALL-E’s outputs. Who owns these images – is it the user who provides the text prompt, or is it DALL-E (OpenAI)? There is no clear answer to this right now, which is raising questions regarding copyright issues and intellectual property rights.
