Artificial intelligence has advanced tremendously in recent years, with models capable of generating convincing text, images, videos, and more. AI models can broadly be categorized into text models and image models, with each type suited for different tasks.
Text models are trained on large volumes of text data to understand and generate human-like language. They power applications like chatbots, summarization tools, and even creative writing aids.
Some popular text models include GPT-3, created by OpenAI, and Google’s BERT. GPT-3 is noted for its ability to generate remarkably coherent text given just a prompt, while BERT specializes in question answering and language understanding. Newer models like Anthropic’s Claude build upon previous designs to be more helpful, harmless, and honest.
On the other hand, image models are trained on huge datasets of images, learning to generate and modify pictures. They empower applications like photo editing tools and computer vision systems.
DALL-E 3 by OpenAI and Stable Diffusion are two image models that have impressed many with their photographic manipulation abilities. They can create entirely new scenes and objects simply based on text descriptions, opening up new creative possibilities.
Text-to-Video and Image-to-Video
Leveraging both text and image models, AI can now also generate videos. For text-to-video, language models create a text description of the desired video, which is passed to an image model that renders sequential images. Those images are then combined into a video, complete with voices and sound effects.
Similarly, for image-to-video, an initial image is provided along with descriptive text of how the image should move and transform over time. The same rendering and combination process ensues, resulting in an artificial video matching the prompts.
Models like Anthropic’s Constitutional AI are designed to enable such applications while avoiding potential harms through self-supervision techniques. With the rapid pace of AI advancement, technologies like text-to-video generation are likely to become increasingly mainstream in the near future.
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