Parse notes
Google’s language model Bard, when launched, refused to answer coding questions. That has changed now. It can help you code in a huge variety of languages and does a pretty good job! There hasn’t been any official validation released, so we don’t know how it compares to other program synthesis models, but it’s always nice to have options.
The Meta folks have been training some big models recently. Part of the pain of that is loss spikes. This is where training diverges and usually requires manual intervention to get things back on track. This paper outlines a hypothesis for these spikes. They claim that Adam is the culprit. Maybe some of the new optimizers like Lion or Shampoo can help?
This paper presents a novel method that distills and bakes Neural Radiance Fields (NeRFs) into efficient mesh-based neural representations, enabling real-time rendering and compatibility with parallel graphics pipelines. The approach uses a two-layer duplex mesh, screen-space convolutions, and a multiview distillation optimization strategy, resulting in superior performance and effectiveness demonstrated through experiments on standard datasets.
Transformers continue their march into all modalities, this time taking on audio with a cool voice cloning tool. There are some limitations, but with just a few seconds of audio, you can get a pretty great representation of your voice.
It seems like everywhere you look people are building retrieval augmentation into language models. Here is another tool to do that, using the almost-open sourced Llama models. This one has a nice recipe you can follow to teach the model about novel APIs and SDKs.
SpeechGPT is a web application that enables you to converse with ChatGPT.