AI Index Report 2024 – Artificial Intelligence Index
Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surroundin
Stanford CRFM
Alpaca: A Strong, Replicable Instruction-Following Model Authors: Rohan Taori* and Ishaan Gulrajani* and Tianyi Zhang* and Yann Dubois* and Xuechen Li* and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto We introduce Alpaca 7B, a model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. On our preliminary evaluation of single-turn instruction following, Alpac
Visual Relationship Detection with Language Priors
Visual relationships capture a wide variety of interactions between pairs of objects in images (e.g. "man riding bicycle" and "man pushing bicycle"). Consequently, the set of possible relationships is extremely large and it is difficult to obtain sufficient training examples for all possible relationships. Because of this limitation, previous work on visual relationship detection has concentrated
Stanford CS 224N | Natural Language Processing with Deep Learning
Logistics Lectures: are on Tuesday/Thursday 4:30 PM - 5:50 PM Pacific Time in NVIDIA Auditorium. The lectures will also be livestreamed on Canvas via Panopto. Lecture videos for enrolled students: are posted on Canvas (requires login) shortly after each lecture ends. Unfortunately, it is not possible to make these videos viewable by non-enrolled students. Publicly available lecture videos and vers
CS 329S | Syllabus
Schedule & syllabus The lecture slides, notes, tutorials, and assignments will be posted online here as the course progresses. Lecture times are 3:15 - 4:45pm PST. All deadlines are at 11:59pm PST. This schedule is subject to change according to the pace of the class. See Past course for the last year's lectures. Date Description Materials Events
デザイン思考研究所 - 【無料教材配布】デザイン思考に関する教材/動画を公開。研修/トレーニング/ワークショップ、コンサルティングも実施
実践を前提としたワークショップが中心。「デザイン思考」の考え方がわかりやすく学べ、身につきやすいのが特徴です。フレームワークを使った問題解決への進め方、アウトプットの仕方などを体験しながら、つまづきやすいポイントも確認でき、参加者が現場に戻った際に実践ですぐ役立つプログラムになっています。
CS224U: Natural Language Understanding - Spring 2023
CS224u can be taken entirely online and asynchronously. Our class meetings will be recorded, and the core content will also be delivered via slides, videos, and Python notebooks. MW 3:00–4:20 pm, Gates B1 Discussion Canvas Gradescope Github Staff email: Cloud credits and computing support for student projects generously provided by AWS Educate
Stanford大学流科学技術論文の書き方 | Y.Yamamoto's Website
研究論文の書き方は重要にも関わらず研究室で体系的に教えられることはほとんどない（と予想される）．従って個々人で情報を集める，書籍を購入するなどして論文執筆能力を向上させるしかない． 論文の書き方に関するウェブページ，書籍などは探せば色々見つかる．以下に僕が実際に参考にしているものをピックアップしておく： Writing for Computer Science 情報検索の分野で有名な研究者Justin Zoble氏の著作 コンピュータサイエンスの英語文書の書き方 “Writing for Computer Science”の訳書 Tips for Writing Technical Papers Stanford大学InfoLabのJeniffer Widom氏が公開しているウェブページ 以下は，Stanford大学InfoLabのJennifer Widom氏が公開している”Tips f
Tatsunori Hashimoto | Home
Assistant Professor, Stanford thashim [AT] stanford.edu Bio I am currently an assistant professor at the computer science department in Stanford university. My research uses tools from statistics to make machine learning systems more robust and trustworthy — especially in complex systems such as large language models. The goal of my research is to use robustness and worst-case performance as a len
Our Journey towards Data-Centric AI: A Retrospective
This article provides a brief, biased retrospective of our road to data-centric AI. Our hope is to provide an entry point for people interested in this area, which has been scattered to the nooks and crannies of AI—even as it drives some of our favorite products, advancements, and benchmark improvements. We’re collecting pointers to these resources on GitHub, and plan to write a few more articles
Value of Data and AI
Course overview Many of the most valuable companies in the world and the most innovative startups have business models based on data and AI, but our understanding about the economic value of data, networks and algorithmic assets remains at an early stage. For example, what is the value of a new dataset or an improved algorithm? How should practitioners understand and exploit the AI service market
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Executive Education - Value of Data and AI Application
Andrew Ng: Opportunities in AI - 2023
https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf
Stanford Spin-Out Snorkel AI Solves a Major Data Problem
Labeling and Crowdsourcing - Data-centric AI Resource Hub
http://vldb.org/pvldb/vol14/p2341-kang.pdf
Accelerating Queries over Unstructured Data with ML, Part 4 (Accelerating Aggregation Queries with Expensive Predicates) · Stanford DAWN
Christopher Manning, Stanford NLP
Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021
