Parse notes
While diffusion methods are great for creating images and videos, they have a hard time editing videos without changing how objects look over time. StableVideo uses a special technique to make sure edited objects look consistent throughout a video.
Snap is launching Dreams, a way to place pictures of you in fantastical places and scenarios using selfies and AI.
This current era of LLMs, Transformers, and diffusion models is completely different from any prior AI period.
Another cool games paper from Deepmind. The researchers try to improve their AlphaZero chess playing agent to reduce hallucination and improve broad creativity. They find that the types of moves it plays are more diverse and can solve really challenging positional puzzles. Interesting to see how it works out long term.
Chatbots that act like fictional characters are gaining popularity, but current methods need refining for better imitation. This study presents a new algorithm using improved prompts and character memories from scripts.
RLIPv1 is a method that helps computers connect what they see in images to descriptive words, but it has some problems, especially with being slow and lacking data. This new paper introduces RLIPv2, a faster version that uses a new tool (ALIF) to better merge images and words. It also has a new way to get more descriptive data from images.
OpenPipe is a flexible playground for comparing and optimizing LLM prompts. It lets you quickly generate, test, and compare candidate prompts, and can automatically translate those prompts between models.
HuggingFace released a neat little ML framework in Rust that is lightning fast and supports a number of powerful models.
A fun blog about creating game assets using a classic AI technique. There are lots of good tidbits here around dataset curation, hyperparameter optimization, and human based evaluation. Could be cool to expand this with SDXL pixel LoRAs and truly synthesize maps from scratch.
In this article, Max Woolf breaks down how he made the newest version of Stable Diffusion better by training it on bad AI-generated images.
There are parallels between human fetish formation and AI alignment challenges, suggesting that understanding how misinterpretations of evolutionary cues lead to fetishes might offer insights into AI generalization problems. The article explores the potential for this analogy to inform AI training and interpretability, acknowledging the differences between biological evolution and AI programming.
According to a new survey from IBM, 40% of the workforce will have to reskill as a result of implementing AI and automation over the next three years.
Powerful software to enable deployment of language models in production systems.