Why and How Baidu Cheated an Artificial Intelligence Test
The sport of training software to act intelligently just got its first cheating scandal. Last month Chinese search company Baidu announced that its image recognition software had inched ahead of Google’s on a standardized test of accuracy. On Tuesday the company admitted that it achieved those results by breaking the rules of that test. The academic experts who maintain that test say that makes Ba
Fujitsu Develops Deep Learning Acceleration Technology, Achieves World's Highest Speed - Fujitsu Global
Archived contentNOTE: this is an archived page and the content is likely to be out of date. Fujitsu Develops Deep Learning Acceleration Technology, Achieves World's Highest Speed Achieves training time of 75 seconds in ResNet-50 through highly-efficient distributed parallel processing Fujitsu Laboratories Ltd. Kawasaki, Japan, April 01, 2019 Fujitsu Laboratories Ltd. today announced that it has de
Integrate, deploy and scale award winning Machine Learning fast Machine learning Image recognition Facial recognition Custom models Deep learning The future Sentiment analysis Text analysis Fake news detection Machine intelligence Image classification Nudity detection Natural language processing Affordable AI NLP Textual entity extraction Video analytics Recommendations Personalization Multi-A/B t
この頃、バカンスシーズンなのか、ネタ切れなのか、画像向けTransformer論文が一息ついているので、ここでちょっと振り返ってみる。 2017年： そもそもの始まり Attention Is All You Need 自然言語向けに2017年に出たこのGoogle論文で、Attention構造が自然言語の方であっという間に広がる。 当然ながら、この流れで、計算量がかかるAttention部分がどんどんと違う手法で置き換えられた論文が増えてくる。 2019年： 画像認識にうっすらと浸透 画像認識でもConvolutionの代わりにAttentionが使われ始めたので、論文まとめ この記事で書いたように、ConvolutionをAttentionに変えようという論文が2019年からチラホラと出てくる。 この頃は、まだおっかなびっくりAttention構造に取り換えてもいけるぞ、とか、精度変わ
Why Google’s CEO Is Excited About Automating Artificial Intelligence
Why Google’s CEO Is Excited About Automating Artificial Intelligence AI software that can help make AI software could accelerate progress on making computers smarter. Sundar Pichai at the company’s annual developer conference in Mountain View, California. Machine-learning experts are in short supply as companies in many industries rush to take advantage of recent strides in the power of artificial
GitHub - mobimeo/node-yolo: Node bindings for YOLO/Darknet image recognition library
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Jetson Nanoにカメラを接続して、映像のリアルタイム画像類推を行う方法を記録しておこうと思います。 Jetson Nanoのjetson-inferenceビルド手順 Jetson NanoにDeveloper Kit SD Card Imageをインストールすると、JetPackが同時にインストールされるようです。 NVIDIA JETSON NANO 開発者キットを動かすのに必要なもの JetPackがインストールされた環境で、jetson-inferenceをビルドすることで、画像ファイル・カメラ映像等の類推(inference)、画像認識やオブジェクト検出等のサンプルプログラムを動かすことができるようです。 jetson-inference情報はこちらのサイトになります。 GitHub – dusty-nv-jetson-inference- Guide to deploy
先日3/7に行われた『TensorFlow Dev Summit 2019』で発表されたTensorFlow2.0 alpha今回はそのビギナー向けチュートリアルをGoogle colabでやってみたので、デモの一つ一つを解説したいと思います。 皆さんも、コーディングができなくても試せる機械学習を体験してみませんか？ 【参考】: https://www.tensorflow.org/alpha/tutorials/keras/basic_classification 【コード】: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/tutorials/quickstart/beginner.ipynb そもそもTensorFlowって？ TensorFlowとはGoogleがオープン
Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents.[1] RPA should not be confused with artificial intelligence as it is based on automotive technology following a predefined workflow.[2] It is sometimes referred to as software robotics (not to be confused with robot software). In traditional workf
This article is about machine learning. For the physics term, see M-theory. In machine learning and computer vision, M-theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-theory was later applied to other areas, such as speech recognition. On certain imag
But the success of Google's mobile search stems at least as much from a big improvement over the past year in Google’s speech recognition efforts. That's the result of research by legendary Google Fellow Jeff Dean and others in applying a fast-emerging branch of artificial intelligence called deep learning to recognizing speech in all its ambiguity and in noisy environments. Replacing part of Goog
Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural l
Build an Image Recognition API with Go and TensorFlow - Outcrawl
This tutorial shows how to build an image recognition service in Go using pre-trained TensorFlow Inception-V3 model. The service will run inside a Docker container, use TensorFlow Go package to process images and return labels that best describe them. Full source code is available on GitHub. Getting started Install Docker and Docker Compose. Configure container Inside project's root directory crea
Over the past few years the field of Machine Learning has entered the general parlance. From free massive open online courses to image recognition benchmarks being broken and decades of Atari games being mastered. During the same period developers have witnessed the release of several popular open source frameworks and libraries. The chart below shows different open source machine learning project
Welcoming Amazon Rekognition Video: Deep-Learning Based Video Recognition | Amazon Web Services
AWS News Blog Welcoming Amazon Rekognition Video: Deep-Learning Based Video Recognition It was this time last year during re:Invent 2016 that Jeff announced the Amazon Rekognition service launch. I was so excited about getting my hands dirty and start coding against the service to build image recognition solutions. As you may know by now, Amazon Rekognition Image is a cloud service that uses deep
Baidu fires researcher who told subordinates to break rules in image-recognition competition
Time's almost up! There's only one week left to request an invite to The AI Impact Tour on June 5th. Don't miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here. Chinese search giant Baidu has fired Ren Wu, the lead author of the Deep Image paper documenting the company’s latest image-recognition technology, for breaking the rul
Human Computation
Human Computation - 51:31 - Jul 26, 2006 Google engEDU - www.google.com () Rate: Google TechTalks July 26, 2006 Luis von Ahn is an assistant professor in the Computer Science Department at Carnegie Mellon University, ...all » Google TechTalks July 26, 2006 Luis von Ahn is an assistant professor in the Computer Science Department at Carnegie Mellon University, where he also received his Ph.D. in 2
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not nece
Computer Vision! Computer vision isn't just for PhD's and R&D folks anymore. Open source libraries like Tensorflow, Keras, and OpenCV are making it more accessible and easier to implement. When combined with advancements in algorithms like deep neural nets it just gets easier! In this post we'll walk you through building a deep neural net that can identify things contained within an image and show
PowerPoint 演示文稿
Squeeze-and-Excitation Networks Jie Hu1 , Li Shen2 , Gang Sun1 1 Momenta 2 University of Oxford Convolution A convolutional filer is expected to be an informative combination • Fusing channel-wise and spatial information • Within local receptive fields Exploration on Spatial Enhancement Multi-scale embedding Inception [9] Contextual embedding Inside-outside Network [13] Squeeze-and-Excitation (SE)
Reddit - Dive into anything
This subreddit is temporarily closed in protest of Reddit killing third party apps, see /r/ModCoord and /r/Save3rdPartyApps for more information. Dr. Andrew Ng is Chief Scientist at Baidu. He leads Baidu Research, which includes the Silicon Valley AI Lab, the Institute of Deep Learning and the Big Data Lab. The organization brings together global research talent to work on fundamental technologies
The Deep Mind of Demis Hassabis
In the race to recruit the best AI talent, Google scored a coup by getting the team led by a former video game guru and chess prodigy From the day in 2011 that Demis Hassabis co-founded DeepMind—with funding by the likes of Elon Musk—the UK-based artificial intelligence startup became the most coveted target of major tech companies. In June 2014, Hassabis and his co-founders, Shane Legg and Mustaf
ICLR is a relatively new conference that is primarily concerned with deep learning and learned representations. The conference is into its third year and had over 300 attendees, two of which were from Lyst. In this post we’ll discuss a few of the interesting papers and themes presented this year. Simplifying network topology One of the difficulties of employing deep convolutional networks is that
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performanc
Datasets for Data Mining
This page contains a list of datasets that were selected for the projects for Data Mining and Exploration. Students can choose one of these datasets to work on, or can propose data of their own choice. At the bottom of this page, you will find some examples of datasets which we judged as inappropriate for the projects. Particle physics data set Description: This data set was used in the KDD Cup 20
こんにちは、AI Lab の鈴木智之 (@tomoyukun) です。 最近、NLP分野で大きく成功した Transformer [1] が様々な研究領域で応用され始めており、最新論文をチェックしていく中で見ない日はないほどとなりました。今回はそんな中でも動画認識、特に動画分類において Transformer (及びそれを構成する計算機構) を応用した研究事例を紹介したいと思います。 動画分類は各動画に割り当てられたクラス (行動、イベント等) の分類問題です。動画認識というと行動検出・物体追跡・動画キャプション生成など動画を入力に定義される様々なタスクを含みますが、その中でも動画分類は汎用的な動画認識モデルのベンチマークタスクの一つとして位置付けられる、最も基本的なタスクです。 ※ この記事は Transformer 自体の解説は含みません。Transformer に関しては元論文 [1
人工言語処理入門
github.com 2017年は様々なGANの改良手法が開発されましたが，先月，Progressive GANという，中でもわかりやすいアイディアで高解像度な画像を生成できる手法が発表されたので，実験してみました． Progressive GAN まず，普通のGANについておさらいですが， この図のように， 1. Generatorがきれいな生成画像を作る 2. Discriminatorが生成画像とデータセット画像を見分ける という役割をそれぞれが果たし，Discriminatorを騙すGeneratorを鍛えることできれいな生成画像になっていくという過程です． この手法をベースに，より自然な，より多様な，画像生成が模索されているというのが現状です． さて，ここはポエムですが，大雑把に言って，自然な画像というのは，"細部が整っている", "全体が矛盾していない"の2つの観点が考えられる
まとめ資料 サーベイ The Deep Learning textbook by Ian Goodfellow and Yoshua Bengio and Aaron Courville Deep Learning in Neural Networks: An Overview 画像・動画 画像分類問題 AlexNet (ImageNet Classification with Deep Convolutional Neural Networks) GoogLeNet Visual Geometry Group Network 物体認識 R-CNN Fast R-CNN Faster R-CNN YOLO: Real-Time Object Detection Overfeat 特徴抽出・エンベッディング Caffe: Convolutional architecture for fast
December 2016 PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE National Science and Technology Council Artificial Intelligence, Automation, and the Economy Executive Office of the President Copyright Information This is a work of the U.S. Government a
December 2016 PREPARING FOR THE FUTURE OF ARTIFICIAL INTELLIGENCE National Science and Technology Council Artificial Intelligence, Automation, and the Economy Executive Office of the President Copyright Information This is a work of the U.S. Government and is in the public domain. It may be freely distributed, copied, and translated; acknowledgment of publication by the Executive Office of the Pr
The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. Long short-term memory (LSTM)[1] network is a recurrent neural network (RNN), aimed to deal with the vanishing gradient problem[2] present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods
Xbox One voice search: Powered by deep learning – Old GigaOm
It’s no secret that Microsoft(s msft) Research has been hard at work on deep learning techniques over the past few years, and the company showed off one of the reasons why on Tuesday: natural-language voice search on the new Xbox One console. From the blog post detailing the feature: “Over the past few years, we’ve focused on overcoming limitations of previous voice experiences. To achieve speed a
Self-Organising Textures
This article is part of the Differentiable Self-organizing Systems Thread, an experimental format collecting invited short articles delving into differentiable self-organizing systems, interspersed with critical commentary from several experts in adjacent fields. Self-classifying MNIST Digits Adversarial Reprogramming of Neural Cellular Automata Neural Cellular Automata (NCA We use NCA to refer to
Google says its speech recognition technology now has only an 8% word error rate
Google's Sundar Pichai talks about its advancements in deep learning at the 2015 Google I/O conference in San Francisco on May 28. Time's almost up! There's only one week left to request an invite to The AI Impact Tour on June 5th. Don't miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here. Google today announced its advancemen
We are using cookies to deliver you better experience. By using our website you're agreeing to this. Okay Team A company built by a great team with seamless vision for the future. Careers We believe in people and always looking for great minds with passion for AI and tech. Blog Learn from industry experts in machine learning and read insightful analysis. Research Publications Resource of scientifi
Typical speed camera traps have built-in OCR software that is used to recognize license plates. A clever hacker decided to see if he could defeat the system by using SQL Injection… The basic premise of this hack is that the hacker has created a simple SQL statement which will hopefully cause the database to delete any record of his license plate. Or so he (she?) hopes. Talk about getting off scot-
Tutorial: Large-Scale Distributed Systems for Training Neural Networks - Microsoft Research
Over the past few years, we have built large-scale computer systems for training neural networks, and then applied these systems to a wide variety of problems that have traditionally been very difficult for computers. We have made significant improvements in the state-of-the-art in many of these areas, and our software systems and algorithms have been used by dozens of different groups at Google t
TensolFlowのチュートリアルを全部やってまとめてみました - hiyoko9t’s blog
本記事では、機械学習ライブラリとして有名なTensorFlowの、公式ドキュメントのチュートリアルをまとめて紹介します。TensorFlowのチュートリアルは、それなりに量があるので、必要なものを参照しやすくするために本記事を書きました。 本題に入る前に、今回なぜライブラリとしてTensorFlowを選んだかを簡単にお話ししたいと思います。 Googleが開発したTensorFlow と言っても筆者は、機械学習ライブラリを触ったのは、この記事を投稿する一週間ほど前からになるので、他の有名なライブラリ(ChainerやTheano、kerasなど)との違いはほとんどわかりません。 ただ、TensorFlowのマニュアルを流し見していた時に、計算グラフという概念とその可視化が魅力的に映ったので、TensorFlowを選びました。また、世界的にTensorFlowがトップクラスに使用されているこ
Big companies v. startups
Looks like it takes six years to gross a U.S. career's worth of income. If you want to adjust for the increased tax burden from earning a lot in a few years, add an extra year. Maybe add one to two more years if you decide to live in the bay or in NYC. If you decide not to retire, lifetime earnings for a 40 year career comes in at almost $10M. One common, but false, objection to this is that your
The Revolutionary Technique That Quietly Changed Machine Vision Forever
The Revolutionary Technique That Quietly Changed Machine Vision Forever Machines are now almost as good as humans at object recognition, and the turning point occurred in 2012, say computer scientists. In space exploration, there is the Google Lunar X Prize for placing a rover on the lunar surface. In medicine, there is the Qualcomm Tricorder X Prize for developing a Star Trek-like device for diag
CRFasRNN
Conditional Random Fields as Recurrent Neural Networks Shuai Zheng*, Sadeep Jayasumana*, Bernardino Romera-Paredes, Vibhav Vineet^, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr. Torr Vision Group, University of Oxford, Stanford University, Baidu IDL * equal contribution. ^ Work conducted while authors at the University of Oxford. Online demo for semantic image segmentation. Pixel-level l
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Home · Machine Box · Machine learning in a box
画像認識向けTransformerを振り返る - Qiita
Jetson Nanoにカメラを接続してリアルタイム画像類推を行うには
非エンジニアがTensorFlow2.0 Alphaのビギナー向けチュートリアルをやってみた
Robotic process automation - Wikipedia
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M-theory (learning framework) - Wikipedia
Meet The Guy Who Helped Google Beat Apple's Siri
A Primer on Neural Network Models for Natural Language Processing
10 Years of Open Source Machine Learning
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Image Recognition in Python with Keras
ICLR 2015 | Lyst Engineering Blog
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
最近の Transformer × Video | | AI tech studio
画像処理系のDeep Learningの基本的な手法 - めも
Long short-term memory - Wikipedia
Image Tagging API, Image Auto-Tagging - Imagga
SQL Injection Fools Speed Traps And Clears Your Record