Artificial intelligence now powers all of Facebook’s translation

Artificial intelligence now powers all of Facebook’s translation

  • On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
  • Back in May, the company’s artificial intelligence division, called Facebook AI Research, announced that they had developed a kind of neural network called a CNN (that stands for convolutional neural network, not the news organization where Wolf Blitzer works) that was a fast, accurate translator.
  • Now, Facebook says that they have incorporated that CNN tech into their translation system, as well as another type of neural network, called an RNN (the R is for recurrent).
  • Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a “phrase-based machine translation” technique that wasn’t powered by neural networks.
  • As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it.

On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
Continue reading “Artificial intelligence now powers all of Facebook’s translation”

Artificial intelligence now powers all of Facebook’s translation

Artificial intelligence now powers all of Facebook’s translation

  • On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
  • Back in May, the company’s artificial intelligence division, called Facebook AI Research, announced that they had developed a kind of neural network called a CNN (that stands for convolutional neural network, not the news organization where Wolf Blitzer works) that was a fast, accurate translator.
  • Now, Facebook says that they have incorporated that CNN tech into their translation system, as well as another type of neural network, called an RNN (the R is for recurrent).
  • Facebook says that the new AI-powered translation is 11 percent more accurate than the old-school approach, which is what they call a “phrase-based machine translation” technique that wasn’t powered by neural networks.
  • As an example of the difference between the two translation systems, Facebook demonstrated how the old approach would have translated a sentence from Turkish into English, and then showed how the new AI-powered system would do it.

On Thursday, Facebook announced that all of its user translation services—those little magic tricks that happen when you click “see translation” beneath a post or comment—are now powered by neural networks, which are a form of artificial intelligence.
Continue reading “Artificial intelligence now powers all of Facebook’s translation”

Mark Cuban on artificial intelligence and machine learning

CUBAN: We are about to enter a period of artificial intelligence and machine learning

  • He used that claim as a launch point to discuss the swiftly evolving nature of jobs due to automation, using Trump’s work with US factories to underscore his point.
  • “Pay attention to the number of jobs in those companies two and three years out,” Cuban said.
  • Trump has been touting his role in US job creation, using announcements like General Motors’ pledge to invest $1 billion into US factories, as examples of places his pressure has helped American workers.
  • Cuban, however, counters that companies that are building factories will actually have a lower net employment once the factory is complete, since the factories will have robots and other automated processes that will take jobs away from humans.
  • “He thinks he’s creating more jobs, when in essence that’s not happening,” Cuban said in interview in February with Bloomberg’s Cory Johnson.

Billionaire entrepreneur Mark Cuban’s prediction for the future of the workforce includes more robots and less human workers.
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[1610.09204] Judging a Book By its Cover

Judging a Book By its Cover  #machinelearning

  • Title: Judging a Book By its Cover
  • Determining the genre of a book is a difficult task because covers can be ambiguous and genres can be overarching.
  • We propose a method of using a Convolutional Neural Network (CNN) to predict the genre of a book based on the visual clues provided by its cover.
  • The purpose is to investigate whether relationships between books and their covers can be learned.
  • Despite this, we show that a CNN can extract features and learn underlying design rules set by the designer to define a genre.

Continue reading “[1610.09204] Judging a Book By its Cover”

Automotive Technology Solutions Overview

  • DRIVER’S ED FOR SELF-DRIVING CARS: HOW OUR DEEP LEARNING TECH TAUGHT A CAR TO DRIVE
  • Now we can successfully train a CNN to operate on NVIDIA DRIVE PX and understand the contextual rules-of-engagement between a vehicle and the road.
  • Subscribe and get the latest news on NVIDIA automotive solutions.
  • Giving Cars the Power to See, Think, and Learn
  • Driverless cars in the first global autonomous motorsports competition, Roborace Championship, will be powered by the NVIDIA DRIVE PX 2 AI supercomputers.

Paving the way for autonomous cars, NVIDIA DRIVE car computers helps cars see, think, and learn.
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[1608.06197] CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting  #deeplearning

  • We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image.
  • As most crowd datasets have limited training samples (<100 images) and deep learning based approaches require large amounts of training data, we perform multi-scale data augmentation.
  • Abstract: Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds.
  • Augmenting the training samples in such a manner helps in guiding the CNN to learn scale invariant representations.
  • Our method is tested on the challenging UCF_CC_50 dataset, and shown to outperform the state of the art methods.

Read the full article, click here.


@Deep_Hub: “CrowdNet: A Deep Convolutional Network for Dense Crowd Counting #deeplearning”



[1608.06197] CrowdNet: A Deep Convolutional Network for Dense Crowd Counting