It's a famous startup saying that the next big thing will start out looking like a toy. And there's no toy that VCs have been more excited about playing around with recently than DALL-E and other generative AI image tools. Put in a few key words into a tool like Midjourney, Stable Diffusion, or DALL-E and it's easy to see why the whimsical (and often wacky) images have captured investors' imagination. An AI-generated artwork even recently won an art competition at the Colorado State Fair, a result that didn't go over well among more traditional artists. It's become disruptive enough that this week Getty announced a ban of AI-generated images on its platform, following similar moves by some online art communities.

A sample DALL·E 2 generated image of "an astronaut riding a horse in a photorealistic style." A new method developed by researchers uses multiple models to create more complex images with better understanding. With the introduction of DALL-E, the internet had a collective feel-good moment. This artificial intelligence-based image generator is inspired by artist Salvador Dali and the lovable robot WALL-E and uses natural language to produce whatever mysterious and beautiful image your heart desires. Seeing typed-out inputs such as "smiling gopher holding an ice cream cone" instantly spring to life is a vivid AI-generated image clearly resonated with the world.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron, aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. Over the past few weeks, researchers at Google have demoed an AI system, PaLI, that can perform many tasks in over 100 languages. Elsewhere, a Berlin-based group launched a project called Source that's designed as a way of allowing artists, including visual artists, musicians and writers, to opt into -- and out of -- allowing their work being used as training data for AI. AI systems like OpenAI's GPT-3 can generate fairly sensical text, or summarize existing text from the web, ebooks and other sources of information.

Earlier this year, the technology company OpenAI released a program called DALL-E 2, which uses artificial intelligence to transform text into visual art. People enter prompts ("plasticine nerd working on a 1980s computer") and the software returns images that showcase humanlike vision and execution, veer into the bizarre, and might even tease creativity. The results were good enough for Cosmopolitan, which published the first-ever AI-generated magazine cover in June--an image of an astronaut swaggering over the surface of Mars--and they were good enough for the Colorado State Fair, which awarded an AI artwork first place in a fine-art competition. OpenAI gave more and more people access to its program, and those who remained locked out turned to alternatives like Craiyon and Midjourney. Soon, AI artwork seemed to be everywhere, and people started to worry about its impacts. Trained on hundreds of millions of image-text pairs, these programs' technical details are opaque to the general public--more black boxes in a tech ecosystem that's full of them.

On Wednesday, OpenAI released a new open source AI model called Whisper that recognizes and translates audio at a level that approaches human recognition ability. It can transcribe interviews, podcasts, conversations, and more. OpenAI trained Whisper on 680,000 hours of audio data and matching transcripts in 98 languages collected from the web. According to OpenAI, this open-collection approach has led to "improved robustness to accents, background noise, and technical language." It can also detect the spoken language and translate it to English.

We're entering the Age of Generative AI, a period in which computers amplify human creativity by generating content, code, insights, and actions from our prompts rather than simply scoring, identifying, or extrapolating from a dataset. Self-supervised learning algorithms are unlocking the hidden knowledge in large, unlabeled, unstructured datasets, allowing for the creation of surprisingly novel outcomes. Through multimodal learning, we can combine diverse data such as images, text, and various sequences. There seems to be no limits on where generative AI can be used--from generating artful pictures, video, and audio, to coding, interacting with software screens and making novel chemical and drug designs. For the past several years, A.I. has created artwork, but tools released this year have enabled AI to create art with increased realism and complexity.

Artificial intelligence (AI) is gaining traction in almost all industries, including the creative world. The emergence of AI-generated artwork has caught on as people seek to express their creativity through artwork that is unique, personalized, and reflects their identity. Tools like Google's Imagen, OpenAI DALLE-2, Midjourney, and Stable Diffusion are raising in popularity for generating artwork in seconds. While the process of creating art using AI is still largely accessible to the average person, there are challenges due to limited interaction and output that the average person has with tools like Imagen & DALLE-2 as well as concerns about how this technology may change the way we engage with art in the future. Stable Diffusion, on the other hand, is open-sourced and aims to make AI artwork and photo generation even more accessible and versatile compared to some of its counterparts.

The registration, effective September 15, applies to a comic book called Zarya of the Dawn. Kashtanova created the artwork for Zarya using Midjourney, a commercial image synthesis service. I was open how it was made and put Midjourney on the cover page. It wasn't altered in any other way. Just the way you saw it here.

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Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.