I think we can all agree that 2022 is the year of the AI generative art boom. If you've been keeping tabs on social media, you've likely seen some artificial intelligence-inspired memes floating around on your newsfeed. Some of the most widely shared images were developed by DALL-E Mini, now known as Craiyon, a publicly accessible image generation tool that debuted this year. Now, three additional applications, Midjourney, DALL-E 2, and Stable Diffusion have beta versions that are available to play with today! At a distance, it looks like the four tools operate with the same premise -- enter a text-based prompt and receive a series of relevant pictures through advanced machine learning that combs through and learns from millions of images on the internet.

"Not only do I refuse to speak in symbols--emoji, bitmoji, likes, reactions, whatever--I also judge people who do. With AI image generators like Dall-E Mini going mainstream, it'll only get easier to communicate in images. I'm afraid we're losing something essential, like actually having something to say." Your question assumes that there is a clear boundary between written languages and images, which, I'm sorry to point out, isn't true. Many writing systems, including cuneiform and Mandarin Chinese, originated with pictograms.

I was young when I first listened to the song Video Killed the Radio Star by the Buggles. I thought it was a fun and catchy song, almost like a jingle, even though I had no idea what the song was all about. But that was ok, I was just a young boy listening to the radio and watching videos on VCR machines. I didn't really pay much attention to the title or the lyrics. It was much later I realised what the lyrics actually meant. In my mind and in my car We can't rewind, we've gone too far Pictures came and broke your heart Put the blame on VCR The song was part of the Age of Plastic album, which had themes of nostalgia, anxiety of the effects of modern technology. While the album was released more than 40 years ago (it was released in 1980) these themes still ring true and clear.

Extremely realistic-looking images can now be created almost instantaneously by artificially intelligent internet bots. Should photographers, illustrators and graphic designers be threatened by this new industry development? In recent years, AI and smart programs have been advancing, especially in the fields of art, design and photography. Even Google is developing a new advanced AI system (opens in new tab) that can create hyper-realistic images from just a basic text prompt. You might be familiar with an AI program called the Dall-E Mini (opens in new tab), which set the internet abuzz a few months back with the ability to create any image you ask it for, and the results are completely original, too.

New research from Columbia university suggests that the safeguards that prevent image synthesis models such as DALL-E 2, Imagen and Parti from being able to output damaging or controversial imagery are susceptible to a kind of adversarial attack that involves'made up' words. The author has developed two approaches that can potentially override the content moderation measures in an image synthesis system, and has found that they are remarkably robust even across different architectures, indicating that the weakness is more than just systemic, and may key on some of the most fundamental principle of text-to-image synthesis. The first, and the stronger of the two, is called macaronic prompting. The term'macaronic' originally refers to a mixture of multiple languages, as found in Esperanto or Unwinese. Perhaps the most culturally-diffused example would be Urdu-English, a type of'code mixing' common in Pakistan, which quite freely mixes English nouns and Urdu suffixes.

Text-guided image generation models have made impressive strides in recent years. State-of-the-art models, like DALL-E 2 [1], Imagen [2], and Parti [3], can generate coherent images matching a remarkably wide variety of prompts in virtually any visual domain and style. While the ability to generate high-quality images of any subject is an exciting development for content creation, it also raises ethical questions about potential misuse of this technology. In particular, text-guided image generation models may be used to produce fake imagery of existing individuals for misinformation (so-called "deepfakes" [4]), or produce visual content deemed offensive or harmful. These concerns have been used to justify the decision to limit access to large text-guided image generation models, as well as moderate their use according to content policies implemented in prompt filters.

It only took Matt Laming, a 19-year-old from the United Kingdom, about a month to hit a million followers on Twitter. And all it required was sharing a steady stream of the most outlandish computer-generated images that he and a bunch of internet strangers could think up. In recent weeks, the digital marketing apprentice, better known online as @weirddalle, has shared images depicting things like people vacuuming in the forest, the Demogorgon from Netflix's "Stranger Things" holding a basketball, and a Beanie Baby that looks a lot like Danny DeVito. These and other pictures, which range from ridiculous to disturbing, were created with a freely available artificial intelligence system called Craiyon. To use it, you just type what you'd like it to envision -- "A rainbow lion eating a slice of pizza" -- and it will spit out pictures in response.

You type in words – however nonsensical or disjointed – and the algorithm creates a unique image based on your search. This is Dall-E 2, a startlingly advanced, image-generating AI trained on 250 million images, named after the surrealist artist Salvador Dalí and Pixar's Wall-E. While use of Dall-E 2 is currently limited to a narrow pool of people, Dall-E mini (or Craiyon) is a free, unrelated version that is open to the public. Drawing on 15m images, Dall-E mini's algorithm offers a smorgasbord of surreal images, complete with absurd compositions and blurred human forms. Already, trends have emerged: nuclear explosions, dumpster fires, toilets and giant eyeballs abound. On a dedicated Reddit thread, people delight in the images generated by the free, low-resolution version, which range from amusing (Kim Jong-un lego) to dark (The Last Supper by Salvador Dali), hellish (synchronized swimming in lava) and deeply disturbing (Steve Jobs introducing a guillotine). Like other machine-learning networks, this AI model seems biased in its images of people – who appear, perhaps unsurprisingly, overwhelmingly white and mostly male.

An image of babies doing parkour generated by DALL-E mini. An image of babies doing parkour generated by DALL-E mini. DALL-E mini is the AI bringing to life all of the goofy "what if" questions you never asked: What if Voldemort was a member of Green Day? What if there was a McDonald's in Mordor? What if scientists sent a Roomba to the bottom of the Mariana Trench?

Since humans invented art, sometime in the Paleolithic era, they've produced lots of pictures--"The Starry Night," some memes, that photo of Donald Trump staring at the eclipse. What does it all add up to? A few years ago, a company called OpenAI fed a good deal of those images, along with text descriptions, into the neural network of an artificial intelligence named DALL-E. DALL-E was being trained to create original art of its own, in any style, depicting in uncanny detail almost anything desired, based on written prompts. But a mastery of the entire universe of human imagery makes for difficult choices.