TLDR Index
TLDR AI

TLDR AI 2023-02-23

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01GENERAL

02HEADLINES & LAUNCHES

MACHINE LEARNING AND DATA LANDSCAPE - MATT TURCK

This 9th edition of the state of AI companies and growth gives great insight into the blistering pace of innovation. It is organized into four parts: Landscape (interactive), Financing, Data Infrastructure trends, ML/AI trends. The interactive landscape is a treat to go through to see various companies in different verticals.

mattturck.com8 min read

03RESEARCH & INNOVATION

3D OBJECT RECONSTRUCTION FROM A SINGLE IMAGE

This work uses off the shelf diffusion models to generate a prompt and “dream up” other 2D views for an object given a single image. They then fit a neural radiance field to generate a 3D representation of the object. This hierarchical chaining of models results in extremely compelling results. They also introduce some novel regularization for the final reconstruction step.

arxiv.org38 min read

ANIMATED AI

A cool collection of novel animations to help understand deep learning concepts like padding, stride, and normalization strategies! They are also permissively licensed if you want to use them for your teaching and presentations.

animatedai.github.io2 min read

LANGUAGE MODELS WITH LIMITED GPU MEMORY

The high computational and memory requirements of large language model (LLM) inference traditionally make it feasible only with multiple high-end accelerators. FlexGen aims to lower the resource requirements of LLM inference down to a single commodity GPU (e.g., T4, 3090) and allow flexible deployment for various hardware setups. The key technique behind FlexGen is to trade off between latency and throughput by developing techniques to increase the effective batch size.

github.com8 min read

04ENGINEERING & RESOURCES

05MISCELLANEOUS

THE FUTURE OF CHATBOTS IN SEARCH BY ANDREW NG

Microsoft and Google's attempts to integrate large language models (LLMs) into chatbots for search have faced criticism due to their tendency to make up facts and produce toxic output. However, this article remains optimistic about the future of chat-based search, emphasizing the potential of retrieval augmented generation to improve factual accuracy and the development of techniques to reduce toxic behaviors. The author believes that with continued research and innovation, chatbots will evolve to better meet user needs and expectations.

deeplearning.ai7 min read