# Best AI Papers (Yearly Review)

A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.

Repository: https://github.com/louisfb01/best_AI_papers_2022
Canonical: https://ross.abutalabs.com/products/best-ai-papers-yearly-review
Homepage: https://www.louisbouchard.ai
License: MIT
License Family: permissive
Topics: python, computer-vision, computer-science, deep-learning, machine-learning, artificial-intelligence, ai, paper, technology, innovation, machinelearning, papers, neural-network, sota, state-of-the-art, state-of-art, 2022
Last push: 2023-10-18T11:46:39+00:00
Link (homepage): https://www.louisbouchard.ai
Link (site_page): https://www.louisbouchard.ai/about
Link (site_page): https://www.louisbouchard.ai/faq

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1692, "days_push": 1050, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3183, forks 195 (observed 2026-08-28T04:07:47.922669+00:00)

## What it is
A yearly curated list of the most significant AI and data science research papers, organized by release date with video explanations, in-depth article links, and code. It serves as a learning resource summarizing annual AI breakthroughs rather than a software tool.

## Use cases
- find the most important AI papers published in a given year
- catch up on AI research breakthroughs with video summaries
- discover state-of-the-art papers with links to code implementations
- learn what happened in AI and data science in 2022
- build a reading list of influential machine learning research
- stay updated on AI innovations like diffusion models and neural rendering

## When to choose
- you want a curated, human-selected overview of a year's AI research instead of searching arXiv yourself
- you prefer papers paired with plain-language video explanations and articles
- you want direct links to code implementations of research papers

## When to avoid
- you need a searchable, up-to-date paper database rather than a fixed yearly snapshot
- you need runnable software or a library - this is a reading list, not a tool
- you need coverage of years after 2022, which are in separate repositories

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, nlp, computer-vision, machine-learning, deep-learning
- domain: artificial-intelligence, machine-learning, computer-vision, tutorials, education
- platform: -
- tags: curated-list, ai-papers, awesome-list, yearly-review, research-papers, video-explanations, natural-language-processing, web-server

## Member repositories
- louisfb01/best_AI_papers_2022 (main) score 32
- louisfb01/best_AI_papers_2021 (main) score 32
- louisfb01/Best_AI_paper_2020 (mirror) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:47.922669+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:25:01.608062+00:00, confidence not recorded.
  - readme: https://github.com/louisfb01/best_AI_papers_2022 (fetched 2026-08-28T04:07:47.922669+00:00, sha 2b2af25df217)
  - homepage: https://www.louisbouchard.ai (fetched 2026-08-29T09:40:05.279074+00:00, sha 2a39d59cf079)
  - site_page: https://www.louisbouchard.ai/about (fetched 2026-08-29T09:40:05.282792+00:00, sha ceadedd4d6c8)
  - site_page: https://www.louisbouchard.ai/faq (fetched 2026-08-29T09:40:05.284675+00:00, sha 435b053cfaa1)
- Data as of 2026-08-30T08:39:29.467469+00:00.