TLDR AI 2023-02-22

RIVAL TRAINING

BAIN AND OPENAI PARTNERSHIP

DOUBT THE WORTH OF YOUR WORK

This article discusses how the prevalence of impressive AI projects on social media platforms like Instagram can make other developers feel inadequate about their own work. The author encourages AI developers to judge their projects based on their own standards and not to be swayed by what's currently trendy. The field of AI is constantly evolving, with new technologies emerging all the time, and it's important to keep learning and stay up to date. The article reminds developers that the emergence of a new hot technology doesn't mean their current project is any less valuable.

TALKING ABOUT LARGE LANGUAGE MODELS

Technology and Philosophy have found a fun intersection with Large Language Models (LLMs). We often use anthropomorphising words such as “knows”, “understands”, or “believes” when referring to these systems. This paper advocates the community to take care when talking about these extremely capable systems and use more fitting language (e.g., encodes, stores, contains).

ENCODING IMAGES IN TEXT, UNSUPERVISED TEXT-IMAGE ALIGNMENT

This paper introduces LQAE which uses a pre-trained language model and encodes images as a sequence of text tokens, then they train a decoder with the BERT masking objective which learns to represent similar images with similar clusters of text tokens. They train the decoder to reconstruct the original image from the predicted text token embeddings. This serves to align the two modalities in an unsupervised manner. A fun side effect is the ability to compress images as text (just a few kb) and reconstruct a lossy version via a single forward pass.

PIX2PIX3D: 3D-AWARE CONDITIONAL IMAGE SYNTHESIS

This work proposes pix2pix3D, a 3D-aware conditional generative model for controllable photorealistic image synthesis. Given a 2D label map, such as a segmentation or edge map, the model learns to synthesize a corresponding image from different viewpoints. To enable explicit 3D user control, the authors extend conditional generative models with neural radiance fields. Given widely-available monocular images and label map pairs, the model learns to assign a label to every 3D point in addition to color and density, which enables it to render the image and pixel-aligned label map simultaneously.

TURN YOUR DATAFRAME INTO A UI FOR VISUAL ANALYSIS

PyGWalker (pronounced Pig Walker) is a graphical tool for exploratory analysis of your data. It allows you to quickly inspect relationships, correlations, and distributions. Examples and screenshots are available in the repository readme.

UNIVERSAL AUDIO SYNTHESIS MODEL

Universal vocoder for generating high fidelity audio with the ability to condition on specific features. This is the first large scale GAN model of its kind. Models, code, and samples are available.

ADDING EXTRA CONDITIONS

Researchers have introduced a neural network structure called ControlNet, which can control pretrained large diffusion models to support additional input conditions. This new method allows for task-specific learning in an end-to-end fashion and is capable of learning with a small training dataset. The training is as fast as fine-tuning a diffusion model and can be done on a personal device, or it can scale to large amounts of data using powerful computation clusters. The researchers demonstrate that ControlNets can enable conditional inputs such as edge maps, segmentation maps, and key points for large diffusion models, which can benefit various related applications.

BIOGPT

This GitHub repository contains the implementation of BioGPT, a generative pre-trained transformer for biomedical text generation and mining. It outperforms GPT-3 with just a fraction of the parameters.

“THOUGHT”

MAGAZINE PAUSES SUBMISSIONS DUE TO SURGE OF AI-GENERATED CONTENT

GENERATIVE AI IS COMING FOR THE LAWYERS

OUR SOCIETY IS NOT PREPARED FOR THIS AI AWESOMENESS