<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository curates significant AI and Data Science breakthroughs from 2023, targeting technical users. It offers a structured overview of advancements with video explanations, articles, and code links, facilitating rapid evaluation of new research.

How It Works

It presents a chronologically ordered list of AI papers, each accompanied by a summary, a link to a short video explanation, an in-depth article, and available code. This format enables quick comprehension of each breakthrough's core concepts and potential impact.

Quick Start & Requirements

This is a curated list, not an installable application. Users browse papers, videos, articles, and external code repositories. Specific requirements are dictated by the individual AI projects linked.

Highlighted Details

• Generative AI: Covers text-to-speech (VALL-E), music generation (MusicLM), image editing (InstructPix2Pix, Drag Your GAN), video synthesis (Stable Video Diffusion), and 3D generation (MVDream).
• Multimodality: Features models integrating diverse data, like embodied language models (PaLM-E) and visual instruction tuning (LLaVA).
• 3D Integration: Showcases efforts to bridge LLMs with 3D environments (3D-LLM).
• Efficiency & Control: Includes distilled models for faster transcription (Distil-Whisper) and enhanced image personalization (Key-Locked Rank One Editing).
• 3D Reconstruction: Highlights high-fidelity neural surface reconstruction (Neuralangelo).

Maintenance & Community

Maintained by louisfb01, active on YouTube and podcasting. Offers a weekly AI newsletter, community Discord, and sponsorship options. Users can suggest papers or connect via Twitter/LinkedIn.

Licensing & Compatibility

The repository lacks an explicit license. Users must consult the individual licenses of linked papers and code repositories for compatibility, particularly for commercial use.

Limitations & Caveats

As a curated list, it doesn't offer a unified setup. Each linked paper's code has its own dependencies and limitations. Some papers note specific challenges, such as Drag Your GAN's editing scope or Neuralangelo's issues with reflective scenes.