TLDR AI 2023-03-08

SALESFORCE $250MM GENERATIVE AI FUND

STABILITY AI ACQUIRES CLIPDROP

STATE OF COMPETITIVE ML 2022

PALM-E

The term “positive transfer” is somewhat of a holy grail for large scale models. It means that when you train on two modalities, you get better performance than you would have training on just one or the other. This paper from Google shows that training on robot controls, video, and text shows positive transfer between domains. This is hypothesized to be a function of model scale, and this model is an impressive 562 billion parameters.

PRISMER: A VISION-LANGUAGE MODEL WITH MULTI-MODAL EXPERTS

The article discusses the high computational and data requirements of large pre-trained models used in vision-language learning, which require massive amounts of image-text data to be trained. The proposed alternative approach involves using separate sub-networks or "experts" for specific tasks, allowing for the use of domain-specific data and architectures that would not be feasible with a single large network. The proposed model, Prismer, is a visually conditioned autoregressive text generation model that utilizes powerful vision-only and language-only models along with modality-specific vision experts for open-ended vision-language reasoning tasks. The expert models are pre-trained and frozen, and connected through lightweight trainable components, comprising only 20% of total network parameters, leading to improved training efficiency and scalability.

DAVINCI FUNCTIONS

There are common things you want to do with large language models such as text-divinci-003 such as write a list, answer a question, or verify a statement. This repo provides some simple tools for doing those tasks. It is a cool step towards building an abstraction layer on top of these models.

REPRESENTATION FROM 2D SUPERVISION

The paper introduces "Nerflets," a local neural radiance field that represents a 3D scene efficiently and with structure awareness. Nerflets are composed of multiple neural radiance fields that maintain their own spatial position, orientation, and extent to contribute to panoptic, density, and radiance reconstructions. By using only photometric and inferred panoptic image supervision, Nerflets allow the extraction of panoptic and photometric renderings from arbitrary views, and enable tasks rare for NeRFs, such as 3D panoptic segmentation and interactive editing. The experiments show that Nerflets fit and approximate the scene more efficiently than traditional global NeRFs.

SGD IN SQL

Looking to reduce MLOps complexity? Just write everything in your database! This fun little post takes this idea to the limit and writes gradient calculations in SQL.

SCALE AI: WHY DATA WILL POWER THE AI REVOLUTION

CONSIDERATIONS FOR AI-NATIVE STARTUPS

DON’T FEAR AN AI-INDUCED JOBS APOCALYPSE JUST YET

This article makes the case that because the west suffers from too little automation, not too much, it is unlikely that AI causes mass unemployment.

VCS SKEPTICAL OVER AI

CATEGORY THEORY MACHINE LEARNING

STABLEDIFFUSION-PLUGIN