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Introducing Blue Dot, a short film by 3Lateral featuring renowned actor Radivoje Bukvić's monologue based on Mika Antic's poem. These nuanced results demonstrate the level of fidelity that artists and filmmakers can achieve when using #MetaHumanAnimator.

Unreal Engine

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Sanat Bilim & Teknoloji Müzik #MetaHumanAnimator

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New Paper on AI & Democracy Imagine two approaches to democracy. The one we have today, where citizens choose a professional politician to represent them and others. Or an augmented form of democracy, where each citizen controls a personalized AI that helps them participate in thousands of nuanced decisions. This second approach is the idea of Augmented Democracy I introduced six years ago at TED. In our latest paper we explore a simplified version of Augmented Democracy by combining off-the-shelf LLMs, such as ChatGPT, with data collected using a collaborative government program builder. This was an online game where people build a personalized government program using proposals extracted from the programs of the candidates of the 2022 presidential election in Brazil. So how accurate are these augmented forms of democracy? Imagine a user who gave us 40 answers. We can use the first 20 to fine-tune a model that we can test using the 20 answers the model didn’t see. We can then compare the accuracy of these predictions with the ones obtained by a “bundle” rule, which assumes that users that self-reported to be from the left or right always chose the proposals from the candidate that shares their political identity. This showed us that LLMs were more accurate at predicting policy preferences than the bundle rule, meaning that the preferences captured in the participation data were more nuanced than a left-right axis, and that the LLMs can capture some of that nuance. Also, the LLMs can choose among policies coming from the same candidate, which is something that we cannot do using a bundle rule. But can these LLMs help us complete the aggregate preferences of the population? Direct or unbundled forms of participation can result in incomplete data when people answer only a fraction of all questions. In our paper, we simulate this incompleteness by sampling the full dataset. We ask how close we can get to the full dataset by using a random sample, or a random sample augmented by predictions made by these LLMs. Overall, we find that LLM-augmented data gets much closer to the full dataset than a pure random sample. These results do not mean that augmented democracy technology is ready, but they means we are in a much better place to continue exploring this idea than six years ago. This paper was a collaborative effort with Jairo Gudino, PhD student at CCL at the University of Toulouse Capitole and Umberto Grandi from IRIT also at the University of Toulouse Capitole. We hope you find these results insightful!

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📽️ ELIZA'S SHADOW REALM Here is my new short film. It is made entirely with AI, and although it is not perfect, it still has some inconsistencies, it is a demonstration that with the tools available it is possible to create an animated short film that is almost professional and worthy of competing in a film festival. The script is made with ChatGPT, based on Coraline. The images in Midjourney, using the --sref and --cref parameters. Animations in Runway, music and sound effects in Pixabay, voiceover in , and editing in CapCut. 👉 I won't deny that it takes hours of work and requires dedication and artistic insight, but the important thing is that now anyone can create a short film from home. This short film also works very well as an audiobook. If you have children, I encourage you to show it to them. I think they will enjoy it 🙂 #aiartcommunity #aivideo #aicinema #AIart

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