Koala bears dunking basketballs? Meet the new AI tech creating realistic images from any description

By Alma Fabiani

Published Apr 14, 2022 at 11:58 AM

Reading time: 2 minutes

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Images courtesy of OpenAI

AI research laboratory OpenAI is currently making headlines again for its latest invention DALL·E 2—or should we say, for its update to the success that was its predecessor, DALL·E. To put it simply, DALL·E 2 is a machine learning model that can generate stunning images from text descriptions in less time than ever before.

As many of you probably know, AI systems being able to create images and art from text is nothing new. We’ve previously seen how the technology could help transform your favourite celebrities into Disney characters or even how it could reimagine your childhood cartoon characters as humans. But DALL·E 2, when compared to other machine learning models out there, can generate more realistic and accurate images with four times greater resolution thanks to OpenAI’s advanced deep learning techniques. Not to mention the images are scarily realistic.

Announced on 6 April 2022 on the company’s Twitter account, the reveal of DALL·E 2 was accompanied by a thread of wonderful photos which quickly grabbed the attention of the platform’s users.

A short video that successfully harnessed the power of the technology—just letting you know, you’re about to see a Panda bear ice skating and a Koala bear dunk a basketball—and what it can do followed the thread:

Like every milestone OpenAI announcement, DALL·E 2 comes with a detailed paper as well as a unique landing page for enthusiasts to read more about how the machine learning model works. A video providing more of an overview of what the technology is capable of doing and what its limitations are has also been shared online.

If you’re not sure you’re up for reading the full paper on DALL·E 2, we’ve got you covered. To put it as simply as possible, the system is a generative model, which is a special branch of machine learning, that creates complex output instead of performing prediction or classification tasks on input data. Basically, you provide DALL·E 2 with a text description and it generates an image that fits the description.

Generative models are a hot area of research that received much attention with the introduction of generative adversarial networks (GAN) in 2014. You probably remember websites such as ThisPersonDoesNotExist or ThisStartUpDoesNotExist, right? Well, these were early examples of what GANs could help us do.

Since then, however, the field has seen tremendous improvements, and generative models have been used for a vast variety of tasks including creating artificial faces, deepfakes, synthesised voices, and more. What sets DALL·E 2 apart from other generative models is its capability to maintain semantic consistency in the images it creates. This consistency is clearly shown in OpenAI’s shared examples.

Let’s say you enter the description “Teddy bears shopping for groceries in the style of ukiyo-e” into the AI system. DALL·E 2 will offer you those images (among many others).