Mark Zuckerberg invests in CAPTCHA‑crushing AI which “thinks like a human” | WeLiveSecurity
Mark Zuckerberg, Paypal founder Elon Musk and Ashton Kutcher have invested $40 million in an artificial-intelligence start-up, Vicarious, which can already ‘read’ CAPTCHA codes – and aims to mimic functions of the human brain. Mark Zuckerberg, Paypal founder Elon Musk and Ashton Kutcher have invested $40 million in an artificial-intelligence start-up, Vicarious, which has already sent ripples thro
I'm learning datamining, machine learning, image processing etc by myself now but will start uni next year probably doing the same.I tried Octave briefly and wasn't that impressed. Ok some neat functionality and easy matrix manipulation but pretty ugly and language isn't as nice as Python. Not sure about interoperability with other tools. I already knew Python so I naturally tried numpy+scipy+matp
October 2016 PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE National Science and Technology Council PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE Executive Office of the President National Science and Technology Council Committee on Technology
October 2016 PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE National Science and Technology Council PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE Executive Office of the President National Science and Technology Council Committee on Technology About the National Science and Technology Council The National Science and Technology Council (NSTC) is the principal means by which the Executi
No Silver Bullet Essence and Accidents of Software Engineering
No Silver Bullet Essence and Accidents of Software Engineering Computer Magazine; April 1987 by Frederick P. Brooks, Jr., University of North Carolina at Chapel Hill This article was First Published In Information Processing 1986, ISBN No. 0444-7077-3, H. J. Kugler, Ed., Elsevia Science Publishers B.V. (North-holland) IFIP 1986. Scanned from a poor-quality faxed copy by Brad Cox which is
ヤフー株式会社は、2023年10月1日にLINEヤフー株式会社になりました。LINEヤフー株式会社の新しいブログはこちらです。LINEヤフー Tech Blog こんにちは。ヤフーで画像処理エンジニアをしている吉橋です。先日2023年7月25日から28日まで、浜松にて国内最大級の画像分野の学会、画像の認識・理解シンポジウム（MIRU）2023が開催されました。 ヤフーもスポンサーとして協賛し、企業ブースの設営や研究発表・聴講のために総勢10名で参加しました。興味があっても参加できなかった・または来年以降の参加を検討しているみなさんのために、この記事では会場の様子や、画像生成AIに関するヤフーの研究発表内容を紹介します。 MIRUとは？ 国内の画像分野では言わずと知れた学会でもあるMIRUは画像処理や、人工知能（AI）の視覚機能を研究する分野「コンピュータビジョン」など、情報学における画像分
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce Kosmos-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot). Specifically, we train Kosmos-1 from scratch on web-scale multimodal co
Confirmed: Google's Motorola Mobility Acquires Image And Gesture Recognition Company Viewdle | TechCrunch
Earlier this week, we heard rumors that Google was in the process of acquiring the augmented reality and image recognition firm Viewdle. Turns out those rumors were true. Google’s Motorola Mobility unit just announced that it has indeed acquired the company. Here is the statement we just received from a Motorola spokesperson: “Motorola Mobility today announced that it has acquired Viewdle, a leadi
It would appear that Wolfram, the company behind the Siri search engine, is bringing its original product, Mathematica, to the iPad. In response to a comment on Reddit, when asked if there was an iPad version of Wolfram for iPad in the works, Stephen Wolfram said, “stay tuned.” What Is Mathematica? Almost any workflow involves computing results, and that’s what Mathematica does—from building a hed
Last week a Google engineer told us “The next big thing for image search would be the ability to search based on visual concepts, such as a picture of a house on a mountain with a river in front of it.” And now, Google Images allows you to restrict your search to a specific category – albeit in an “unofficial” mode only – and one of these categories may well be powered by actual image recognition
A Comparison of Distributed Machine Learning Platforms
On distributed systems broadly defined and other curiosities. The opinions on this site are my own. This paper surveys the design approaches used in distributed machine learning (ML) platforms and proposes future research directions. This is joint work with my students Kuo Zhang and Salem Alqahtani. We wrote this paper in Fall 2016, and I will be going to ICCCN'17 (Vancouver) to present this paper
Hideki Nakayama's Home Page Japanese/English 経歴 2002年4月 東京大学理科一類入学 2006年3月 東京大学工学部機械情報工学科卒業 2006年4月 東京大学大学院情報理工学系研究科知能機械情報学専攻修士課程進学 2008年4月 同博士課程進学 2008年4月 日本学術振興会特別研究員（DC1） 2011年3月 東京大学大学院情報理工学系研究科知能機械情報学専攻博士課程修了 所属 東京大学大学院情報理工学系研究科 知能機械情報学専攻 國吉・原田研究室 (2011年3月まで) E-mail: New (2011/3) My Ph.D. thesis is now available. (2011/3) All recognition results of ILSVRC2010 test images are avail
DeepHear - Composing and harmonizing music with neural networks Introduction and tl;dr I trained a network to generate random bars of music, based on Scott Joplin's ragtime music. It is a fully connected Deep Belief Network, set up to perform an auto-encoding task. The results sound something like this . (Warning: sound, obviously. Each snippet lasts around 17 seconds.) Or this . If the music do
Opinion | Artificial Intelligence Is Stuck. Here’s How to Move It Forward. (Published 2017)
Artificial Intelligence is colossally hyped these days, but the dirty little secret is that it still has a long, long way to go. Sure, A.I. systems have mastered an array of games, from chess and Go to “Jeopardy” and poker, but the technology continues to struggle in the real world. Robots fall over while opening doors, prototype driverless cars frequently need human intervention, and nobody has y
February 22, 2017 Volume 15, issue 1 PDF Making Money Using Math Modern applications are increasingly using probabilistic machine-learned models. Erik Meijer "If Google were created from scratch today, much of it would be learned, not coded." —Jeff Dean, Google Senior Fellow, Systems and Infrastructure Group Machine learning, or ML, is all the rage today, and there are good reasons for that. Model
MIRU2014
会場：岡山コンベンションセンター（岡山市北区） 日程：2014年7月28日(月)～31日(木) Call For Paper (PDF) (3.0MB) Call For Participation(PDF) (1.9MB) 今回で17回目を迎える画像の認識・理解シンポジウム（MIRU）は，画像の認識と理解技術に関する国内最大規模の会議であり，基礎から応用まで最新の研究発表と討論の場として，また，研究者，技術者，そして次世代を担う学生の交流の場として，毎回大変多くの方々にご参加いただいています．昨年のMIRU2013からは議論の更なる活発化と国内研究コミュニティーの国際化を目指し，発表方針に関して改革を行いました．MIRU2014もその方針を踏襲しております． The 17th Meeting on Image Recognition and Understanding (MIRU2014
Mark Zuckerberg, wearing his trademark gray shirt, stood on a small stage at the front of a large room, a set of six gold-colored helium balloons hovering in the air behind him. The balloons spelled out “HACK 50,” and were perhaps the most ostentatious evidence that today, here at Building 20, the Frank Gehry-designed Facebook headquarters in Silicon Valley’s Menlo Park, is the company’s 50th hack
What is MLOps?
Machine learning operations, MLOps, are best practices for businesses to run AI successfully with help from an expanding smorgasbord of software products and cloud services. Note: This article was updated in March 2023 with the latest information on MLOps software and service providers. MLOps may sound like the name of a shaggy, one-eyed monster, but it’s actually an acronym that spells success in
進化が早すぎるDeep Learning技術を追うために，毎日1本論文読みチャレンジをはじめましたホエイです． Image Recognition，Semantic Segmentationを極めるべく，画像認識タスクで圧倒的な成功を収めているResNet周辺の論文を読んでいたのでここにまとめます．また，随時追加していきます． 以下が読んだ論文のジャンル別一覧です． ## ResNetとResNet亜種 - (ResNet)Deep Residual Learning for Image Recognition - (ResNeXt)Aggregated Residual Transformations for Deep Neural Networks - (Wide-ResNet)Wide Residual Networks - (PyramidNet)Deep Pyramidal Re
9 Applications of Deep Learning for Computer Vision - MachineLearningMastery.com
The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on some specific problems. It is not just the performance of deep learning models on benchmark problems that is most interesting; it is the fact th
Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From an Explainable AI Competition
AbstractIn 2018, a landmark challenge in artificial intelligence (AI) took place, namely, the Explainable Machine Learning Challenge. The goal of the competition was to create a complicated black box model for the dataset and explain how it worked. One team did not follow the rules. Instead of sending in a black box, they created a model that was fully interpretable. This leads to the question of
Intel FPGAs Break Record for Deep Learning Facial Recognition - High-Performance Computing News Analysis | insideHPC
Intel FPGAs Break Record for Deep Learning Facial Recognition Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN). Developed with ZTE, a leading technology telecommunications equipment and systems company, the image recognition technology is what many companies in Internet search and AI are trying to advance. Perception, such as recogniz
https://swest.toppers.jp/SWEST20/program/pdfs/s4a_proceeding.pdf
農業における深層学習の活用 〜Raspberry Piで実装するキュウリ選別システム〜 #2018/08/31 SWEST20 ハウス栽培で年間を通してキュウリを栽培・出荷 ○ ほ場面積0.4ha ○ 家族経営 まだまだ手作業が多い！特に果菜類 機械化されたと言われる近代農業だが・・・ 農業の労働時間 農林水産省：品目別経営統計（2007年）より ・ピーマン ・きゅうり ・トマト ・ミニトマト 果菜類は手間がかか る！ 機械化できていない →大規模化もできな い 10aあたりの労働時間（ h） ● 品目別１０Aあたりの労働時間 特に細かい作業が多い果菜類は労働時間が多い傾向がある きゅうり栽培の労働時間 ● きゅうり栽培における作業別の労働時間の割合 農林水産省：品目別経営統計（2007年）より 収穫 39.8% 管理 19.2% 出荷（選別など） ２２．１％ きゅうり農家の仕事の約1/5
Databases or Datasets for Computer Vision Applications and Testing Generally, to avoid confusion, in this bibliography, the word database is used for database systems or research and would apply to image database query techniques rather than a database containing images for use in specific applications. I have chosen to use dataset to describe collections of images used by researchers in some doma
Object Detection（物体認識）モデルに関する指標である mAP (mean Average Precision) について考える。正しいとは限らない。正しいと良いなぁ。 概要 Object Detection モデルの評価指標として使われる mAP について、定義とか実装を考えたり調べたりしたよ 実際に実装してみたよ mAP に似た指標についても考えてみたよ 一般的な Average Precision と Object Detection における Average Precision は全く別物なので、混同してはいけないよ Object Detection とは Object Detection は、１枚の画像の中から目的となる対象物の位置を Bounding Box（四角の枠）として予測するものである。一般的な Classification, Regression などの
The HCIL has conducted a broad range of research over the years. Below is a SAMPLE of current or recent projects organized by topics. Included as well are links to specific labs working on those topics. For older projects see the Research Project Archive Trace Center The Trace Research & Development Center is a pioneer in the field of technology and disability, and is known for high-impact researc
シンガポールのAIスタートアップViSenzeが、楽天ベンチャーズらのリードによるシリーズBラウンドで1,050万米ドルを調達 - BRIDGE（ブリッジ）テクノロジー＆スタートアップ情報
e コマースおよびデジタルビジネス向けビジュアルテクノロジーを開発する人工知能（AI）企業 ViSenze は、楽天ベンチャーズのリードによるシリーズ B ラウンドを1,050万米ドルの調達でクローズした。ともにラウンドをリードしたのはクロスボーダー投資会社の WI Harper Group と、アメリカやアジアでアーリーステージテクノロジースタートアップに投資する VC の Enspire Capital だ。 Singapore Press Holdings の投資部門 SPH Media Fund、Alibaba（阿里巴巴）の元 CTO である John Wu （吴炯）氏が設立したアジアの代替資産管理会社 FengHe Fund Management（風和投資管理）、Raffles Venture Partners、Phillip Private Equity と UOB Ventu
A Berkeley View of Systems Challenges for AI Ion Stoica Dawn Song Raluca Ada Popa David A. Patterson Michael W. Mahoney Randy H. Katz Anthony D. Joseph Michael Jordan Joseph M. Hellerstein Joseph Gonzalez Ken Goldberg Ali Ghodsi David E. Culler Pieter Abbeel Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2017-159 http://www2.eecs.ber
Want an open-source deep learning framework? Take your pick
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Earlier this week, Google made a splash when it released its TensorFlow artificial intelligence software on GitHub under an open-source license. Google has a sizable stable of AI talent, and AI is working behind the scenes in popular products, including Gmail and Google se
TensorFlowのInception-v3で画像を分類してみた(Python API編) | SoraLab / ソララボ
TensorFlowのチュートリアルの画像認識(Python API編)に従って、Inception-v3による画像の分類にチャレンジしてみました。 『インセプション』と言うと、今年のアカデミー主演男優賞を受賞したレオナルド・ディカプリオの昔の映画を思い出してしまいますが、Inception-v3は、映画の名前ではなく、GoogleのImageNet画像認識モデルの名前です。 (2016/4/24追記) Googleフォトでは、22のレイヤーで構成されるニューラルネットワークを採用したマシンラーニングモデル「Inception」（同名の映画から命名）を使用している。 と言う記事を見つけました。映画『インセプション』を思い出したのは、偶然ではなかったようです。 チュートリアルの画像認識 TensorFlowのチュートリアルの画像認識(Python API編)(https://www.tens
英語で論文執筆する人を助けるかもしれない英語表現集
内容はv0.0.3くらいのイメージ． Abstract 査読付きの国際会議やジャーナルに論文を投稿する際にネイティブではない人々が皆乗り越えねばならないのが言語の壁である． 昨今は英語での執筆をサポートしてくれるツールが多く存在しており[ DeepL, Grammarly, ChatGPT, Thesaurus.com ]，英語に苦しむ人々を取り巻く状況は改善していると言える． しかしこういったツールを用いるにしても，そもそも英語表現の存在を知っているのと知らないのとでは大きく違うだろう．欲を言えば実際にトップ会議などに採択されているような論文で使われている表現から欲しいものをピックアップできれば嬉しい． そこで本記事では，8割自分のため，残りの2割で多少の人々が救われると良いなという動機で，項目ごとに使えそうな表現と実際の用例そのものをAI系の論文(トップ会議，ジャーナルの査読を通ったも
GitHub - hermanhermitage/videocoreiv: Tools and information for the Broadcom VideoCore IV (RaspberryPi)
Disclaimer: This is a independent documentation project based on a combination of static analysis and trial and error on real hardware. This work is 100% independent from and not sanctioned by or connected with Broadcom or its agents. No Broadcom documents or materials were used beyond those publically available (see Referenced Materials). This work was undertaken and the information provided for
Image Recognition API & General Purpose Computer Vision and Captioning - CloudSight AI
With high quality image recognition, the CloudSight API recognizes, captions, and classifies the details of an image within seconds. Try it for free today.
This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should I use?” The answer to the question varies depending on many fac
What’s in an image: fast, accurate image segmentation with Cloud TPUs | Google Cloud Blog
What’s in an image: fast, accurate image segmentation with Cloud TPUs Google designed Cloud TPUs from the ground up to accelerate cutting-edge machine learning (ML) applications, from image recognition, to language modeling, to reinforcement learning. And now, we’ve made it even easier for you to use Cloud TPUs for image segmentation—the process of identifying and labeling regions of an image base
K-nearest neighbors (k-NN) is a Machine Learning algorithm for supervised machine learning type. It is used for both regression and classification tasks. As we already know, a supervised machine learning algorithm depends on labeled input data, which the algorithm learns to produce accurate outputs when input unlabeled data. k-NN aims to predict the test data set by calculating the distance betwee
1. 残差接続 (residual connection)とは [概要] 残差接続 (residual connection)とは，CNNの1種である ResNet [He et al., 2016a], [He et al., 2016b] の構成部品である残差ブロックにおいて，毎ブロックに配置される「スキップ接続 + そのあとの2経路の出力の足し算」の部品のことである． 要は 「残差接続 ≒ スキップ接続」ではあるが，スキップ接続のうち，ResNetの場合の残差ブロックを形成する形を，特に残差接続と呼ぶ．ResNetで，提案された「残差ブロックの多層化」の文脈では，スキップ接続を「残差接続」と別途呼び分けたほうが「残差ブロックを反復して構成しているネットワーク構造である」ことが伝わりやすくなる． 関連記事：ResNetの，従来のCNNと最も異なる点は？ 【Q and A記事】 この記事
![残差接続 (residual connection) [ResNet] | CVMLエキスパートガイド](https://cdn-ak-scissors.b.st-hatena.com/image/square/3b7f340c1903359b276f828da8b76d3a56f23e12/height=288;version=1;width=512/https%3A%2F%2Fcvml-expertguide.net%2Fwp-content%2Fuploads%2F2021%2F07%2FResNet_residual_block-1.png)
Editor’s Note: While Andrew Ng, chief scientist at Baidu was delivering his ISC keynote this morning on how HPC is supercharging AI, his colleague Greg Diamos, research scientist at Baidu’s Silicon Valley AI Lab, was preparing to present a paper on GPU-based deep learning at the 33rd International Conference on Machine Learning in New York. Greg Diamos, senior researcher, Silicon Valley AI Lab, Ba
The success of deep neural networks is mostly due their ability to learn meaningful features from the data. Features learned in the hidden layers of deep neural networks trained in computer vision tasks have been shown to be similar to mid-level vision features. We leverage this fact in this work and propose the visualization regularizer for image tasks. The proposed regularization technique enfor
3つの要点 ✔️ 深層学習を用いたData AugmentationにはGANやスタイル変換などを用いたものがある ✔️ 深層学習を用いたDAの利点/欠点をまとめた ✔️ 基本的なDAと組み合わせることでさらに高い精度を達成できる A survey on Image Data Augmentation for Deep Learning written by Connor Shorten, Taghi M. Khoshgoftaar (Submitted on 06 July 2019) Comments: Published by Journal of Big Data Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processi
Amazon EC2でのDeep Learningのためのダイナミックトレーニングの紹介 | Amazon Web Services
Amazon Web Services ブログ Amazon EC2でのDeep Learningのためのダイナミックトレーニングの紹介 本日（2018/11/27）、Deep Learningモデルのためのダイナミックトレーニング（Dynamic Training: DT）を発表することに興奮しています。DTを使用すると、Deep Learningの実務者は、クラウドの弾力性と規模の経済性を活用して、モデルトレーニングのコストと時間を削減できます。DTの最初のリファレンス実装は、Apache MXNetに基づいており、オープンソースで Dynamic Training with Apache MXNet に公開されています。このブログ記事は、DTの概念、実現したトレーニングの結果やトレーニングへの活用方法を紹介します。 Deep Learningモデルの分散トレーニング ニューラルネット
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Python+Scipy+Matplotlib vs Matlab? | Hacker News
ヤフーの画像分野の研究内容紹介（MIRU2023 レポート）
Language Is Not All You Need: Aligning Perception with Language Models
Wolfram Mathematica coming to the iPad - 9to5Mac
New: Google Image Search Categories
Hideki Nakayama's Page
http://web.mit.edu/felixsun/www/neural-music.html
Making Money Using Math - ACM Queue
Exclusive: Inside Facebook’s AI Hackathon
ResNetまわりの論文まとめ | ALIS
Dataset Detailed References
mAP (mean Average Precision) について考える - Qiita
Research Areas - Human-Computer Interaction Lab
A Berkeley View of Systems Challenges for AI
Which machine learning algorithm should I use?
k-NN (k-Nearest Neighbors) in Supervised Machine Learning
残差接続 (residual connection) [ResNet] | CVMLエキスパートガイド
Baidu Researcher Pushes GPU Scalability for Deep Learning
Visualization Regularizers for Neural Network based Image Recognition
【応用編】深層学習を用いた画像Data Augmentationを一挙にまとめてみた！