On Thursday, OpenAI announced the introduction of an API for its DALL-E image synthesis model that will allow developers to easily integrate its AI image generation technology into their apps. DALL-E, currently available as a standalone commercial service, allows people to generate novel 1024 1024 images from text descriptions called "prompts." The generated images can vary in style from photorealistic to abstract--and many styles in between. OpenAI's new API allows software developers to feed an image prompt and other parameters into OpenAI's DALL-E engine over the Internet. OpenAI then generates the image on its servers and returns the image for display in the requesting app.

On Thursday, OpenAI announced the introduction of an API for its DALL-E image synthesis model that will allow developers to easily integrate its AI image generation technology into their apps. DALL-E, currently available as a standalone commercial service, allows people to generate novel 1024x1024 images from text descriptions called "prompts." The generated images can vary in style from photorealistic to abstract--and many styles in between. OpenAI's new API allows software developers to feed an image prompt and other parameters into OpenAI's DALL-E engine over the Internet. OpenAI then generates the image on its servers and returns the image for display in the requesting app.

Can AI tools like Dall-E be used for business? Within the realm of blockchain and gamification, artificial intelligence (AI) is a key component to the Web3 movement. AI isn't a new thing as the earliest successful AI program was written in 1951, but has evolved quite a bit since then. Trending recently is a unique AI program called Dall-E. Though it appears to be something for only entertainment, this can also be utilized with brands as a useful tool.

At long last, DALL-E 2, OpenAI's image-generating AI system, is available as an API, meaning developers can build the system into their apps, websites and services. In a blog post today, OpenAI announced that any developer can start tapping the power of DALL-E 2 -- which more than three million people are now using to produce over four million images a day -- once they create an OpenAI API account as part of the public beta. Pricing for the DALL-E 2 API varies by resolution. For 1024 1024 images, the cost is $0.02 per image; 512 512 images are $0.018 per image; and 256 256 images are $0.016 per image. Volume discounts are available to companies working with OpenAI's enterprise team.

It's called Imagen and it's Google's answer to the wave of text-to-image models whose creations have flooded the internet this year. But unlike those released by Meta, Microsoft, and OpenAI, the internet giant is taking a cautious approach by offering users a limited taste inside its AI Test Kitchen. As with DALL-E, Make-A-Scene, and Stable Diffusion, Imagen uses A.I. imaging technologies to turn short text descriptions into unique photorealistic images. Google, however, ranks its model as best-in-class, stating in a paper released in May that "human raters strongly prefer Imagen over other methods." The version available inside AI Test Kitchen, an app Google released in August to receive reactions to its A.I. projects, will have two functions for users to play with, City Dreamer and Wobble.

PROteolysis TArgeting Chimeras (PROTACs) are an emerging therapeutic modality for degrading a protein of interest (POI) by marking it for degradation by the proteasome. Recent developments in artificial intelligence (AI) suggest that deep generative models can assist with the de novo design of molecules with desired properties, and their application to PROTAC design remains largely unexplored. We show that a graph-based generative model can be used to propose novel PROTAC-like structures from empty graphs. Our model can be guided towards the generation of large molecules (30--140 heavy atoms) predicted to degrade a POI through policy-gradient reinforcement learning (RL). Rewards during RL are applied using a boosted tree surrogate model that predicts a molecule's degradation potential for each POI. Using this approach, we steer the generative model towards compounds with higher likelihoods of predicted degradation activity. Despite being trained on sparse public data, the generative model proposes molecules with substructures found in known degraders. After fine-tuning, predicted activity against a challenging POI increases from 50% to >80% with near-perfect chemical validity for sampled compounds, suggesting this is a promising approach for the optimization of large, PROTAC-like molecules for targeted protein degradation.

A lot has transpired within a span of just a few months – that's the speed of AI for you. From tech that seemed gimmicky at first, generative AI tools have made AI infrastructure more accessible. What seemed far-flung from mainstream society is now cheap, fast and easy to build for developers across the spectrum. Several startups have either cropped up or pivoted to building platforms anchored around these open-source and closed-source models from companies like OpenAI. Important LLMs like OpenAI's GPT-3 and other foundational models like Stable Diffusion have been made commercially available via API across applications.

True, when we write academic articles nowadays, nobody expects you to provide the trail of references all the way down to Aristotle. But few people would say that taking someone's recent NeurIPS paper and republishing it would be ok. Yes, it is a continuum, but it's still real. What exactly is common knowledge and what deserves a reference at a given point in time varies by person, depending on their domain knowledge and principles. Still, everybody has a fairly clear idea of what their own boundaries are. Would you personally be comfortable with changing some variable names in a StackOverflow snippet and passing it as your own work? Would you tell your child it's ok to copy-paste essay passages from public domain sources - after all, it's not illegal? How about if you hear an apt metaphor in someone's keynote that you haven't heard anywhere else - would you say that it's "just English" and use it as your own? Whatever your answers are to these questions - you have these answers, which means that you have your own attribution norms.

In recent months, many people have begun to explore a new pastime: generating their own images using several widely-distributed programs such as DALL-E, Midjourney, and Stable Diffusion. These programs offer a straightforward interface wherein nontechnical users can input a descriptive phrase and receive corresponding pictures, or at least amusingly bad approximations of the results they intended. For most users, such artificial intelligence1 (AI)-generated art is harmless fun that requires no computer graphics skills to produce and is suitable for social media posts (see Figure 1). However, AI algorithms combine aspects of existing data to generate their outputs. DALL-E, Stable Diffusion, and other popular programs pull images directly from the internet to train their algorithms. Though these images might be easily obtainable--from the huge Google Images database, for example--the creators have not always licensed their art for reuse or use in the production of derivative works.

Has AI made Creativity a Thing of the Past? Recently, there has been an avalanche of interest in generative AI: programs that can produce text, speech, images, designs, music, and even computer code in an uncanny resemblance to human creations. AI systems, such as GPT-3, which mostly produces text, but can also produce images and computer code, or Dall-E, Stable Diffusion, or Midjourney, which produce images, are the tip of the iceberg in an expanding field that is attracting millions of users and billions of dollars in investments. The outputs of these systems can rival the quality of human products and work faster and cheaper than human writers, artists, and composers. Industries such as animated images for television and film are choosing AI artists over human ones to save time and money.