To understand how radically gaming is about to be transformed by Generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion Dreambooth, popular 2D Generative AI models, to generate images of potions for a hypothetical game. What's transformative about this work is not just that it saves time and money while also delivering quality – thus smashing the classic "you can only have two of cost, quality, or speed" triangle. Artists are now creating high-quality images in a matter of hours that would otherwise take weeks to generate by hand. What's truly transformative is that: There hasn't been a technology this revolutionary for gaming since real-time 3D.

Generative AI based art has proliferated in the past year, with increasingly impressive use cases from generating fake human faces to the creation of systems that can generate thousands of artistic images from text prompts - some of these images have even been "good" enough to win accolades from qualified judges. In this paper, we explore how Generative Models have impacted artistry, not only from a qualitative point of view, but also from an angle of exploitation of artists -- both via plagiarism, where models are trained on their artwork without permission, and via profit shifting, where profits in the art market have shifted from art creators to model owners. However, we posit that if deployed responsibly, AI generative models have the possibility of being a positive, new modality in art that does not displace or harm existing artists.

AI is everywhere right now. It's been slowly growing in popularity and importance, but recently it's burst into the mainstream with a number of high profile (and very cool) projections and applications. One of the most talked about has been the AI image generation technology such as Dall-E 2, Midjourney and Stable Diffusion. These programs use text prompts to create incredible pictures of scenes and characters which are only limited by the imagination. One guy from Colorado even won a prize for digital art at the State Fair, with an artwork that he created using Midjourney.

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We'll implement a naive semantic video search using OpenAI's CLIP model (ignoring audio) in Python. By the end of the post, we'll get results like this: Note that dog has the highest value, which is what we would hope for since the image is of a dog. But do the cat and misc values seem low enough compared to the dog value? Well, looking at the CLIP codebase we can see that softmax with a temperature parameter (i.e. So we can see that the model is pretty certain that "a photo of a dog" is the best of the options it was presented with to describe the image.

To understand how radically gaming is about to be transformed by Generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion Dreambooth, popular 2D Generative AI models, to generate images of potions for a hypothetical game. What's transformative about this work is not just that it saves time and money while also delivering quality – thus smashing the classic "you can only have two of cost, quality, or speed" triangle. Artists are now creating high-quality images in a matter of hours that would otherwise take weeks to generate by hand. What's truly transformative is that: There hasn't been a technology this revolutionary for gaming since real-time 3D. Spend any time at all talking to game creators, and the sense of excitement and wonder is palpable. So where is this technology going? And how will it transform gaming?

Blockchain enables creators to package and monetize their digital content in new ways. However, it's not the only tech stack that is doing so. Artificial intelligence (AI) is also redefining creativity in the digital space, and here's how. DALL-E, Stable Diffusion, and Midjourney are all generative AI models. They use AI algorithms to automatically generate digital content based on a simple prompt that would otherwise take a human a long time to complete.

Picture Lee Unkrich, one of Pixar's most distinguished animators, as a seventh grader. Some of the magic wears off, however, when Lee learns that the image had not appeared simply by asking for "a picture of a train." Instead, it had to be painstakingly coded and rendered--by hard-working humans. Now picture Lee 43 years later, stumbling onto DALL-E, an artificial intelligence that generates original works of art based on human-supplied prompts that can literally be as simple as "a picture of a train." As he types in words to create image after image, the wow is back.

The Governance Team's mission is to develop late-stage, credible AGI strategy and governance plans. Pursuing this mission involves developing, analyzing, and ultimately taking action toward concrete and realistic interventions that could contribute to ensuring that the development and deployment of increasingly powerful, general AI models go well for humanity. If you enjoy owning research projects end-to-end, thrive in roles where the ambitious entrepreneurial pursuit of open-ended goals is rewarded, and are strongly motivated to contribute to the roll-out of advanced general AI systems going well, you will find our work here uniquely challenging and rewarding. We are looking for experienced research scientists with a proven track record of delivering high-quality research autonomously and collaboratively. In this role, you will produce novel insights, ideas, and proposals that shift the needle on the clarity of our roadmap toward governing increasingly powerful, advanced AI models.

Proteins are the molecules that get work done in nature, and there's a whole industry emerging around successfully modifying and manufacturing them for various uses. But doing so is time consuming and haphazard; Cradle aims to change that with an AI-powered tool that tells scientists what new structures and sequences will make a protein do what they want it to. The company emerged from stealth today with a substantial seed round. AI and proteins have been in the news lately, but largely because of the efforts of research outfits like DeepMind and Baker Lab. Their machine learning models take in easily collected RNA sequence data and predict the structure a protein will take -- a step that used to take weeks and expensive special equipment.