In this episode, we delve into three exciting news stories. First up, we explore the remarkable LIMA model, a 65 billion parameter model that performs almost as well as GPT4 and even outperforms Bard and Da Vinci! Find out how Meta's innovative approach is revolutionizing the open source AI realm. Next, we unravel the fascinating CoDi model, which utilizes compositional diffusion for generating multimodal outputs. Learn how this powerful model can transform text and audio inputs into stunning videos. Lastly, we uncover the mind-boggling Mind-to-Video technology that reconstructs videos based on brain activity. Join us as we discuss the possibilities and implications of mapping the human mind. We also discuss hilarious ChatGPT App reviews, which you won’t want to miss!
The Lima model is a large 65 billion parameter LLaMa model that was fine-tuned on a thousand carefully curated responses.
It performed almost as well as GPT4 and even better than models like Bard and Da Vinci.
Meta has released the Lima model as an open source model, showcasing their commitment to staying up-to-date in the AI field.
Lima's approach differs from other models by using supervised examples instead of human feedback, resulting in impressive responses.
This development is significant because it brings another open source model closer to competing with the massive models trained by OpenAI, providing an alternative approach to alignment.
CoDi is a model that specializes in any-to-any generation using compositional diffusion.
Unlike other models that primarily process text, CoDi is designed for multimodal tasks, allowing users to input combinations of text, audio, and video to generate corresponding outputs.
CoDi can generate videos based on text and audio inputs or produce new text outputs based on two different text inputs.
Understanding context is crucial for CoDi, as it needs to comprehend the relationships between different modalities to provide accurate and comprehensive results.
CoDi appears to be an open-source model, potentially an enhanced version of Meta's previously released ImageBART, although a direct comparison has not been made yet. The code for CoDi is likely available for use.
The team has developed a mind-meets-video model that reconstructs videos based on brain activity captured through fMRIs.
The training process involves pairing fMRI data with corresponding videos, allowing the model to learn the relationship between brain signals and video content.
The model aims to capture what a person is remembering or perceiving by analyzing their brain activity and finding similar videos from the training set.
Although it is not yet capable of mind reading, the model provides insights into how the brain processes and represents visual information.
The team's previous work focused on mind-to-image generation, and this mind-to-video model represents an impressive advancement, achieving a 45% increase in accuracy compared to previous methods.