“Revolutionary: Google AI Creates Lengthy HD Video from Text and Writes Its Own Robotic Code!”

Breakthrough in AI Technology Allows for HD Video Storytelling from Text

In recent years, artificial intelligence (AI) technology has been advancing at an astonishing pace, enabling machines to perform tasks that were previously impossible. One of the latest breakthroughs in AI technology is the development of long-form HD video storytelling from text. This innovative technology works by combining Google’s image and AI model with the Phenaki large language model. The result is a series of short videos, generated from crisp individual images, that are time-coherent and of high resolution.

The Phenaki large language model generates tokens over time, which the AI uses to create a long-form coherent story. The quality of the videos generated by this AI technology is above HD resolution and provides a new way of storytelling, allowing filmmakers, YouTubers, and other video storytellers to augment their work in ways that were previously impossible. The technology is versatile and can be used to generate specific scenes in a variety of settings as well as start with a single image and turn it into a video based on one or more text prompts.

Image and video sets are large, and both the image and video and Phenaki have been trained using both video and images to contribute to the quality and diversity of the output. Google trained image and video with a publicly available LAION 400-M image Text data set, containing 60 million image text matches plus an additional 14 million image text matches.

Phenaki can turn sequences of text of a certain length into a video of any length, including videos of up to several minutes in total length. However, the quality of the images is not as good as image and video since the data used to train image and video may contain some inappropriate content. Therefore, Google does not intend to release or open-source the AI model at this time.

Imogen video is based on Google’s image and text of image artificial intelligence model and uses cascaded diffusion models to generate high-resolution videos after embedding user-entered text through a natural language processing pre-training model. A basic video diffusion model generates a 16-frame image with a resolution of 24×48 at a rate of 3 frames per second. Then, multiple temporal super resolution and spatial super resolution machine learning models generate the final images. The machine learning model generates a 5.3-second video at 24 frames per second with a total length of 128 frames and a resolution of 1280×768.

Phenaki is a new causal model for learning video representation that compresses video to a small representation of discrete tokens to generate video tokens from text. It uses a bi-directional mass Transformer neural network conditioned on pre-computed text tokens. The generated video tokens are subsequently de-tokenized to make the actual video.

Google AI can now write its own robotics code using large language models like Palm, which have already been trained using millions of lines of code with natural language instruction. Large language models are proficient in writing code to control robotics if provided with a set of example instructions in the form of comments coupled with some corresponding code.

Cap, which is a robot-centric formulation of language model-generated programs executed on physical systems, is used to write robotics code with few-shot prompting. Outputting code provided improved generalization and task performance over directly learning robot tasks and outputting natural language actions. Caplet single systems perform many complex and varied robotics tasks without task-specific training.

In order to generate codes for a new task given natural language instruction, cap uses a code-writing language model that, when prompted with hints and examples, can write new code to implement new instructions. Hierarchical code generation is central to the approach, which triggers language models to recursively define new functions, build their own libraries over time, and create an evolving code base.

Pythonic language models can make use of logic structures, like sequences, selections, and loops, to create new functions at runtime. They can also make use of third-party libraries to interpolate points, generate and analyze shapes, and solve spatial geometric problems. These models do not just adapt to new directions, but they also translate precise numbers into ambiguous descriptions according to context to trigger behavioral common sense.

In conclusion, the breakthrough in AI technology allowing for long-form HD video storytelling from text provides a new way of telling stories, enabling filmmakers, YouTubers, and other video storytellers to augment their work in ways that were previously impossible. The technology is versatile and can be used to generate specific scenes in a variety of settings or turn a single image into a video. Furthermore, Google’s AI technology can now write its own robotics code, reducing the need for tedious and specialized expertise in programming robots.

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