Nadella hung up in frustration. Microsoft owned nearly half of OpenAI’s for-profit arm—surely he should have been consulted on such a decision. What’s more, he knew that the firing would likely spark a civil war within OpenAI and, possibly, across the tech industry, which had been engaged in a pitched debate about whether the rapid advance of A.I. was to be celebrated or feared. Nadella called Mic

Azure AI Search: Outperforming vector search with hybrid retrieval and reranking Note: Since this blog's publication, the Azure AI Search team has released new learnings on RAG quality. Check out the latest report covering our evaluations on query pipeline performance, and the product improvements made based on these learnings. A common practice for implementing the retrieval step in retrieval-aug

Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases. Fine-tuning for GPT-3.5 Turbo is now available, with fine-tuning for GPT-4 coming this fall. This update gives developers the ability to customize models that perform better for their use cases and run these custom models at scale. Early tests have shown a fine-tuned version of GPT-3.5 Turbo can match, or even

This post is an exploration of the supply and demand of GPUs, particularly Nvidia H100s. We’re also releasing a song and music video on the same day as this post. This post went mega viral. It was on the frontpage of HN, techmeme, many email newsletters, got tweets from Andrej Karpathy and others, comments from Mustafa (who will have $1B of GPUs online soon) from Inflection and Emad from Stability

Unless you have been living under a rock, you have heard of this new ChatGPT assistant made by OpenAI. You might be aware of its capabilities for solving IQ tests, tackling leetcode problems or to helping people write LateX. It is an amazing resource for people to retrieve all kinds of information and solve tedious tasks, like copy-writing! Today, Frederic Besse told me that he managed to do somet

DEIM2023 第15回データ工学と情報マネジメントに関するフォーラム チュートリアル講演資料 Part1: NLP

Click lower right to download or find on Apple Podcasts, Spotify, Stitcher, etc. I've been working on a new series of posts about the most important century. The original series focused on why and how this could be the most important century for humanity. But it had relatively little to say about what we can do today to improve the odds of things going well. The new series will get much more speci

タイトル：Large Language Models are Zero-Shot Reasoners 著者：Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa 所属：東京大学、Google Research カンファ：NeurIPS 2022 論文URL：https://arxiv.org/abs/2205.11916 コード：https://github.com/kojima-takeshi188/zero_shot_cot ざっくりいうと 大規模言語モデル（LLM）で算術問題などを解くための連鎖的なプロンプト「Zero-shot-CoT」を提唱 GPT-3に「ステップバイステップで考えよう」を追加するだけで、MultiArithのゼロショット精度が17.7%→78.7%でSoTA L

by Yancey Strickler Most open and publicly available spaces on the web are overrun with bots, advertisers, trolls, data scrapers, clickbait, keyword-stuffing “content creators,” and algorithmically manipulated junk. It’s like a dark forest that seems eerily devoid of human life – all the living creatures are hidden beneath the ground or up in trees. If they reveal themselves, they risk being attac

Attention は "Attention is all you need" (Vaswani et al, 2017)で一躍有名になった手法ですが、実はこの論文の前からあった概念です。今回はこのAttentionの技術について、またこの論文について解説していきたいと思います。 1 Attentionの概念 Attentionとは、「注意」とあるように、画像や文章の特定の部分に注意を向けるよう、学習させていく方法です。人間の場合を考えてみましょう。私たちが何かを見るとき、全ての部分を同じように観察しているわけではありません。犬の絵があれば、「犬の顔」「犬の手足」「犬の尻尾」を認識して犬だと識別できるのであって、毛の一本一本や肋骨の形に注目している人は少ないでしょう。これと同じことを機械にさせることができれば、より人間に近い形で画像や文章を認識することができます。これがAttentionの

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We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems. Our alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent. We take an iterative, empirical approach: by attempting to