AI Dominates 'Word of the Year' in Major Dictionaries!

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The article discusses how artificial intelligence (AI) has influenced the "word of the year" selections for various dictionaries in 2023. Cambridge chose "hallucinate," highlighting AI's tendency to generate plausible but invented information. This word reflects the issue with AI systems not knowing what they don't know. The article illustrates this with an example of AI fabricating information about a non-existent German surgeon.

Merriam-Webster selected "authentic," which gained new connotations in the age of AI, especially regarding the blurring lines between real and fake in the context of deepfakes and AI's impact on various fields. "Deepfake" also appeared on Merriam-Webster's longlist, indicating its evolution from a niche term to a more general one for AI-generated content.

Oxford's word of the year was "rizz," a slang for charisma, with "prompt" being a runner-up. "Prompt" has evolved in the context of AI, now used both as a noun and a verb related to initiating AI responses.

The article suggests that while AI terminology is increasingly entering the lexicon, terms like "latent space" are still not mainstream. The adoption of these words reflects the growing influence of AI and the evolving relationship between humans and technology, including the ethical and perceptual implications.

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UberEats seems to be employing artificial intelligence to create food images for its platform, a practice that has gone unnoticed by many of the featured restaurants. This use of AI technology came into the spotlight due to humorous errors in the generated images. Notably, there was an incident where the AI misinterpreted 'pizza pie' as a sweet pie dessert, rather than the traditional pizza. Another amusing mistake involved the creation of an imaginary brand of Ranch dressing, showcasing the AI's occasional missteps in understanding and visualizing culinary terms. These instances have sparked discussions about the reliability and accuracy of AI in representing food items on such widely used platforms.

The project proposes MagicAnimate, a diffusion-based human image animation framework that aims at enhancing temporal consistency, preserving reference images faithfully, and improving animation fidelity.

Given a reference image and the target DensePose motion sequence, MagicAnimate employs a video diffusion model and an appearance encoder for temporal modeling and identity preserving, respectively (left panel). To support long video animation, we devise a simple video fusion strategy that produces smooth video transitions during inference (right panel).

You can find the Demo on HuggingFace.

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