Tag Archives: machine learning

AI in the newsroom: What’s happening and what’s next?

Bringing people together to discuss the forces shaping journalism is central to our mission at the Google News Lab. Earlier this month, we invited Nick Rockwell, the Chief Technology Officer from the New York Times, and Luca D’Aniello, the Chief Technology Officer at the Associated Press, to Google’s New York office to talk about the future of artificial intelligence in journalism and the challenges and opportunities it presents for newsrooms.

The event opened with an overview of the AP's recent report, "The Future of Augmented Journalism: a guide for newsrooms in the age of smart machines,” which was based on interviews with dozens of journalists, technologists, and academics (and compiled with the help of a robot, of course). As early adopters of this technology, the AP highlighted a number of their earlier experiments:

Boxing match image captured by one of AP’s AI-powered cameras
This image of a boxing match was captured by one of AP’s AI-powered cameras.
  • Deploying more than a dozen AI-powered robotic cameras at the 2016 Summer Olympics to capture angles not easily available to journalists
  • Using Google’s Cloud Vision API to classify and tag photos automatically throughout the report
  • Increasing news coverage of quarterly earnings reports from 400 to 4,000 companies using automation

The report also addressed key concerns, including risks associated with unchecked algorithms, potential for workflow disruption, and the growing gap in skill sets.

Here are three themes that emerged from the conversation with Rockwell and D’Aniello:

1. AI will increase a news organization's ability to focus on content creation

D’Aniello noted that journalists, often “pressed for resources,” are forced to “spend most of their time creating multiple versions of the same content for different outlets.” AI can reduce monotonous tasks like these and allow journalists to to spend more of their time on their core expertise: reporting.

For Rockwell, AI could also be leveraged to power new reporting, helping journalists analyze massive data sets to surface untold stories. Rockwell noted that “the big stories will be found in data, and whether we can find them or not will depend on our sophistication using large datasets.”

2. AI can help improve the quality of dialogue online and help organizations better understand their readers' needs.

Given the increasing abuse and harassment found in online conversations, many publishers are backing away from allowing comments on articles. For the Times, the Perspective API tool developed by Jigsaw (part of Google’s parent company Alphabet), is creating an opportunity to encourage constructive discussions online by using machine learning to increase the efficiency of comment moderation. Previously, the Times could only moderate comments on 10 percent of articles. Now, the technology has allowed them to allow commenting on all articles.

The Times is also thinking about using AI to increase the relevance of what they deliver to readers. As Rockwell notes, “Our readers have always looked to us to filter the world, but to do that only through editorial curation is a one-size-fits-all approach. There is a lot we can do to better serve them.”

3. Applying journalistic standards is essential to AI’s successful implementation in newsrooms

Both panelists agreed that the editorial standards that go into creating quality journalism should be applied to AI-fueled journalism. As Francesco Marconi, the author of the AP report, remarked, “Humans make mistakes. Algorithms make mistakes. All the editorial standards should be applied to the technology.”

Here are a few approaches we’ve seen for how those standards can be applied to the technology:

  • Pairing up journalists with the tech. At the AP, business journalists trained software to understand how to write an earnings report.
  • Serving as editorial gatekeepers. News editors should play a role in synthesizing and framing the information AI produces.
  • Ensuring more inclusive reporting. In 2016, Google.org, USC and the Geena Davis Foundation used machine learning to create a tool that collects data on gender portrayals in media.

What’s ahead

What will it take for AI to be a positive force in journalism? The conversation showed that while the path wasn’t certain, getting to the right answers would require close collaboration between the technology industry, news organizations, and journalists.

“There is a lot of work to do, but it’s about the mindset,” D’Aniello said. “Technology was seen as a disruptor of the newsroom, and it was difficult to introduce things. I don’t think this is the case anymore. The urgency and the need is perceived at the editorial level.”

We look forward to continuing to host more conversations on important topics like this one. Learn more about the Google News Lab on our website.

Header image of robotic camera courtesy of Associated Press.

AI in the newsroom: What’s happening and what’s next?

Bringing people together to discuss the forces shaping journalism is central to our mission at the Google News Lab. Earlier this month, we invited Nick Rockwell, the Chief Technology Officer from the New York Times, and Luca D’Aniello, the Chief Technology Officer at the Associated Press, to Google’s New York office to talk about the future of artificial intelligence in journalism and the challenges and opportunities it presents for newsrooms.

The event opened with an overview of the AP's recent report, "The Future of Augmented Journalism: a guide for newsrooms in the age of smart machines,” which was based on interviews with dozens of journalists, technologists, and academics (and compiled with the help of a robot, of course). As early adopters of this technology, the AP highlighted a number of their earlier experiments:

Boxing match image captured by one of AP’s AI-powered cameras
This image of a boxing match was captured by one of AP’s AI-powered cameras.
  • Deploying more than a dozen AI-powered robotic cameras at the 2016 Summer Olympics to capture angles not easily available to journalists
  • Using Google’s Cloud Vision API to classify and tag photos automatically throughout the report
  • Increasing news coverage of quarterly earnings reports from 400 to 4,000 companies using automation

The report also addressed key concerns, including risks associated with unchecked algorithms, potential for workflow disruption, and the growing gap in skill sets.

Here are three themes that emerged from the conversation with Rockwell and D’Aniello:

1. AI will increase a news organization's ability to focus on content creation

D’Aniello noted that journalists, often “pressed for resources,” are forced to “spend most of their time creating multiple versions of the same content for different outlets.” AI can reduce monotonous tasks like these and allow journalists to to spend more of their time on their core expertise: reporting.

For Rockwell, AI could also be leveraged to power new reporting, helping journalists analyze massive data sets to surface untold stories. Rockwell noted that “the big stories will be found in data, and whether we can find them or not will depend on our sophistication using large datasets.”

2. AI can help improve the quality of dialogue online and help organizations better understand their readers' needs.

Given the increasing abuse and harassment found in online conversations, many publishers are backing away from allowing comments on articles. For the Times, the Perspective API tool developed by Jigsaw (part of Google’s parent company Alphabet), is creating an opportunity to encourage constructive discussions online by using machine learning to increase the efficiency of comment moderation. Previously, the Times could only moderate comments on 10 percent of articles. Now, the technology has allowed them to allow commenting on all articles.

The Times is also thinking about using AI to increase the relevance of what they deliver to readers. As Rockwell notes, “Our readers have always looked to us to filter the world, but to do that only through editorial curation is a one-size-fits-all approach. There is a lot we can do to better serve them.”

3. Applying journalistic standards is essential to AI’s successful implementation in newsrooms

Both panelists agreed that the editorial standards that go into creating quality journalism should be applied to AI-fueled journalism. As Francesco Marconi, the author of the AP report, remarked, “Humans make mistakes. Algorithms make mistakes. All the editorial standards should be applied to the technology.”

Here are a few approaches we’ve seen for how those standards can be applied to the technology:

  • Pairing up journalists with the tech. At the AP, business journalists trained software to understand how to write an earnings report.
  • Serving as editorial gatekeepers. News editors should play a role in synthesizing and framing the information AI produces.
  • Ensuring more inclusive reporting. In 2016, Google.org, USC and the Geena Davis Foundation used machine learning to create a tool that collects data on gender portrayals in media.

What’s ahead

What will it take for AI to be a positive force in journalism? The conversation showed that while the path wasn’t certain, getting to the right answers would require close collaboration between the technology industry, news organizations, and journalists.

“There is a lot of work to do, but it’s about the mindset,” D’Aniello said. “Technology was seen as a disruptor of the newsroom, and it was difficult to introduce things. I don’t think this is the case anymore. The urgency and the need is perceived at the editorial level.”

We look forward to continuing to host more conversations on important topics like this one. Learn more about the Google News Lab on our website.

Header image of robotic camera courtesy of Associated Press.

Source: Google Cloud


Introducing the TensorFlow Research Cloud

Posted by Zak Stone, Product Manager for TensorFlow
Researchers require enormous computational resources to train the machine learning (ML) models that have delivered recent breakthroughs in medical imaging, neural machine translation, game playing, and many other domains. We believe that significantly larger amounts of computation will make it possible for researchers to invent new types of ML models that will be even more accurate and useful.
To accelerate the pace of open machine-learning research, we are introducing the TensorFlow Research Cloud (TFRC), a cluster of 1,000 Cloud TPUs that will be made available free of charge to support a broad range of computationally-intensive research projects that might not be possible otherwise.
The TensorFlow Research Cloud offers researchers the following benefits:
  • Access to Google's all-new Cloud TPUs that accelerate both training and inference
  • Up to 180 teraflops of floating-point performance per Cloud TPU
  • 64 GB of ultra-high-bandwidth memory per Cloud TPU
  • Familiar TensorFlow programming interfaces
You can sign up here to request to be notified when the TensorFlow Research Cloud application process opens, and you can optionally share more information about your computational needs. We plan to evaluate applications on a rolling basis in search of the most creative and ambitious proposals.
The TensorFlow Research Cloud program is not limited to academia — we recognize that people with a wide range of affiliations, roles, and expertise are making major machine learning research contributions, and we especially encourage those with non-traditional backgrounds to apply. Access will be granted to selected individuals for limited amounts of compute time, and researchers are welcome to apply multiple times with multiple projects.
Since the main goal of the TensorFlow Research Cloud is to benefit the open machine learning research community as a whole, successful applicants will be expected to do the following:
  • Share their TFRC-supported research with the world through peer-reviewed publications, open-source code, blog posts, or other open media
  • Share concrete, constructive feedback with Google to help us improve the TFRC program and the underlying Cloud TPU platform over time
  • Imagine a future in which ML acceleration is abundant and develop new kinds of machine learning models in anticipation of that future
For businesses interested in using Cloud TPUs for proprietary research and development, we will offer a parallel Cloud TPU Alpha program. You can sign up here to learn more about this program. We recommend participating in the Cloud TPU Alpha program if you are interested in any of the following:
  • Accelerating training of proprietary ML models; models that take weeks to train on other hardware can be trained in days or even hours on Cloud TPUs
  • Accelerating batch processing of industrial-scale datasets: images, videos, audio, unstructured text, structured data, etc.
  • Processing live requests in production using larger and more complex ML models than ever before
We hope the TensorFlow Research Cloud will allow as many researchers as possible to explore the frontier of machine learning research and extend it with new discoveries! We encourage you to sign up today to be among the first to know as more information becomes available.

Introducing the TensorFlow Research Cloud



Researchers require enormous computational resources to train the machine learning (ML) models that have delivered recent breakthroughs in medical imaging, neural machine translation, game playing, and many other domains. We believe that significantly larger amounts of computation will make it possible for researchers to invent new types of ML models that will be even more accurate and useful.

To accelerate the pace of open machine-learning research, we are introducing the TensorFlow Research Cloud (TFRC), a cluster of 1,000 Cloud TPUs that will be made available free of charge to support a broad range of computationally-intensive research projects that might not be possible otherwise.
The TensorFlow Research Cloud offers researchers the following benefits:
  • Access to Google’s all-new Cloud TPUs that accelerate both training and inference
  • Up to 180 teraflops of floating-point performance per Cloud TPU
  • 64 GB of ultra-high-bandwidth memory per Cloud TPU
  • Familiar TensorFlow programming interfaces
You can sign up here to request to be notified when the TensorFlow Research Cloud application process opens, and you can optionally share more information about your computational needs. We plan to evaluate applications on a rolling basis in search of the most creative and ambitious proposals.

The TensorFlow Research Cloud program is not limited to academia — we recognize that people with a wide range of affiliations, roles, and expertise are making major machine learning research contributions, and we especially encourage those with non-traditional backgrounds to apply. Access will be granted to selected individuals for limited amounts of compute time, and researchers are welcome to apply multiple times with multiple projects.
Since the main goal of the TensorFlow Research Cloud is to benefit the open machine learning research community as a whole, successful applicants will be expected to do the following:
  • Share their TFRC-supported research with the world through peer-reviewed publications, open-source code, blog posts, or other open media
  • Share concrete, constructive feedback with Google to help us improve the TFRC program and the underlying Cloud TPU platform over time
  • Imagine a future in which ML acceleration is abundant and develop new kinds of machine learning models in anticipation of that future
For businesses interested in using Cloud TPUs for proprietary research and development, we will offer a parallel Cloud TPU Alpha program. You can sign up here to learn more about this program. We recommend participating in the Cloud TPU Alpha program if you are interested in any of the following:
  • Accelerating training of proprietary ML models; models that take weeks to train on other hardware can be trained in days or even hours on Cloud TPUs
  • Accelerating batch processing of industrial-scale datasets: images, videos, audio, unstructured text, structured data, etc.
  • Processing live requests in production using larger and more complex ML models than ever before
We hope the TensorFlow Research Cloud will allow as many researchers as possible to explore the frontier of machine learning research and extend it with new discoveries! We encourage you to sign up today to be among the first to know as more information becomes available.

Build and train machine learning models on our new Google Cloud TPUs

We’re excited to announce that our second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference. We call them Cloud TPUs, and they will initially be available via Google Compute Engine.

We’ve witnessed extraordinary advances in machine learning (ML) over the past few years. Neural networks have dramatically improved the quality of Google Translate, played a key role in ranking Google Search results and made it more convenient to find the photos you want with Google Photos. Machine learning allowed DeepMind’s AlphaGo program to defeat Lee Sedol, one of the world’s top Go players, and also made it possible for software to generate natural-looking sketches.

These breakthroughs required enormous amounts of computation, both to train the underlying machine learning models and to run those models once they’re trained (this is called “inference”). We’ve designed, built and deployed a family of Tensor Processing Units, or TPUs, to allow us to support larger and larger amounts of machine learning computation, first internally and now externally.

While our first TPU was designed to run machine learning models quickly and efficiently—to translate a set of sentences or choose the next move in Go—those models still had to be trained separately. Training a machine learning model is even more difficult than running it, and days or weeks of computation on the best available CPUs and GPUs are commonly required to reach state-of-the-art levels of accuracy.

Research and engineering teams at Google and elsewhere have made great progress scaling machine learning training using readily-available hardware. However, this wasn’t enough to meet our machine learning needs, so we designed an entirely new machine learning system to eliminate bottlenecks and maximize overall performance. At the heart of this system is the second-generation TPU we're announcing today, which can both train and run machine learning models.

tpu-v2-hero
Our new Cloud TPU delivers up to 180 teraflops to train and run machine learning models.

Each of these new TPU devices delivers up to 180 teraflops of floating-point performance. As powerful as these TPUs are on their own, though, we designed them to work even better together. Each TPU includes a custom high-speed network that allows us to build machine learning supercomputers we call “TPU pods.” A TPU pod contains 64 second-generation TPUs and provides up to 11.5 petaflops to accelerate the training of a single large machine learning model. That’s a lot of computation!

Using these TPU pods, we've already seen dramatic improvements in training times. One of our new large-scale translation models used to take a full day to train on 32 of the best commercially-available GPUs—now it trains to the same accuracy in an afternoon using just one eighth of a TPU pod.

tpu-v2-1
A “TPU pod” built with 64 second-generation TPUs delivers up to 11.5 petaflops of machine learning acceleration.

Introducing Cloud TPUs

We’re bringing our new TPUs to Google Compute Engine as Cloud TPUs, where you can connect them to virtual machines of all shapes and sizes and mix and match them with other types of hardware, including Skylake CPUs and NVIDIA GPUs. You can program these TPUs with TensorFlow, the most popular open-source machine learning framework on GitHub, and we’re introducing high-level APIs, which will make it easier to train machine learning models on CPUs, GPUs or Cloud TPUs with only minimal code changes.

With Cloud TPUs, you have the opportunity to integrate state-of-the-art ML accelerators directly into your production infrastructure and benefit from on-demand, accelerated computing power without any up-front capital expenses. Since fast ML accelerators place extraordinary demands on surrounding storage systems and networks, we’re making optimizations throughout our Cloud infrastructure to help ensure that you can train powerful ML models quickly using real production data.

Our goal is to help you build the best possible machine learning systems from top to bottom. While Cloud TPUs will benefit many ML applications, we remain committed to offering a wide range of hardware on Google Cloud so you can choose the accelerators that best fit your particular use case at any given time. For example, Shazam recently announced that they successfully migrated major portions of their music recognition workloads to NVIDIA GPUs on Google Cloud and saved money while gaining flexibility.

Introducing the TensorFlow Research Cloud

Much of the recent progress in machine learning has been driven by unprecedentedly open collaboration among researchers around the world across both industry and academia. However, many top researchers don’t have access to anywhere near as much compute power as they need. To help as many researchers as we can and further accelerate the pace of open machine learning research, we'll make 1,000 Cloud TPUs available at no cost to ML researchers via the TensorFlow Research Cloud.

Sign up to learn more

If you’re interested in accelerating training of machine learning models, accelerating batch processing of gigantic datasets, or processing live requests in production using more powerful ML models than ever before, please sign up today to learn more about our upcoming Cloud TPU Alpha program. If you’re a researcher expanding the frontier of machine learning and willing to share your findings with the world, please sign up to learn more about the TensorFlow Research Cloud program. And if you’re interested in accessing whole TPU pods via Google Cloud, please let us know more about your needs.

Source: Google Cloud


Build and train machine learning models on our new Google Cloud TPUs

We’re excited to announce that our second-generation Tensor Processing Units (TPUs) are coming to Google Cloud to accelerate a wide range of machine learning workloads, including both training and inference. We call them Cloud TPUs, and they will initially be available via Google Compute Engine.

We’ve witnessed extraordinary advances in machine learning (ML) over the past few years. Neural networks have dramatically improved the quality of Google Translate, played a key role in ranking Google Search results and made it more convenient to find the photos you want with Google Photos. Machine learning allowed DeepMind’s AlphaGo program to defeat Lee Sedol, one of the world’s top Go players, and also made it possible for software to generate natural-looking sketches.

These breakthroughs required enormous amounts of computation, both to train the underlying machine learning models and to run those models once they’re trained (this is called “inference”). We’ve designed, built and deployed a family of Tensor Processing Units, or TPUs, to allow us to support larger and larger amounts of machine learning computation, first internally and now externally.

While our first TPU was designed to run machine learning models quickly and efficiently—to translate a set of sentences or choose the next move in Go—those models still had to be trained separately. Training a machine learning model is even more difficult than running it, and days or weeks of computation on the best available CPUs and GPUs are commonly required to reach state-of-the-art levels of accuracy.

Research and engineering teams at Google and elsewhere have made great progress scaling machine learning training using readily-available hardware. However, this wasn’t enough to meet our machine learning needs, so we designed an entirely new machine learning system to eliminate bottlenecks and maximize overall performance. At the heart of this system is the second-generation TPU we're announcing today, which can both train and run machine learning models.

tpu-v2-hero
Our new Cloud TPU delivers up to 180 teraflops to train and run machine learning models.

Each of these new TPU devices delivers up to 180 teraflops of floating-point performance. As powerful as these TPUs are on their own, though, we designed them to work even better together. Each TPU includes a custom high-speed network that allows us to build machine learning supercomputers we call “TPU pods.” A TPU pod contains 64 second-generation TPUs and provides up to 11.5 petaflops to accelerate the training of a single large machine learning model. That’s a lot of computation!

Using these TPU pods, we've already seen dramatic improvements in training times. One of our new large-scale translation models used to take a full day to train on 32 of the best commercially-available GPUs—now it trains to the same accuracy in an afternoon using just one eighth of a TPU pod.

tpu-v2-1
A “TPU pod” built with 64 second-generation TPUs delivers up to 11.5 petaflops of machine learning acceleration.

Introducing Cloud TPUs

We’re bringing our new TPUs to Google Compute Engine as Cloud TPUs, where you can connect them to virtual machines of all shapes and sizes and mix and match them with other types of hardware, including Skylake CPUs and NVIDIA GPUs. You can program these TPUs with TensorFlow, the most popular open-source machine learning framework on GitHub, and we’re introducing high-level APIs, which will make it easier to train machine learning models on CPUs, GPUs or Cloud TPUs with only minimal code changes.

With Cloud TPUs, you have the opportunity to integrate state-of-the-art ML accelerators directly into your production infrastructure and benefit from on-demand, accelerated computing power without any up-front capital expenses. Since fast ML accelerators place extraordinary demands on surrounding storage systems and networks, we’re making optimizations throughout our Cloud infrastructure to help ensure that you can train powerful ML models quickly using real production data.

Our goal is to help you build the best possible machine learning systems from top to bottom. While Cloud TPUs will benefit many ML applications, we remain committed to offering a wide range of hardware on Google Cloud so you can choose the accelerators that best fit your particular use case at any given time. For example, Shazam recently announced that they successfully migrated major portions of their music recognition workloads to NVIDIA GPUs on Google Cloud and saved money while gaining flexibility.

Introducing the TensorFlow Research Cloud

Much of the recent progress in machine learning has been driven by unprecedentedly open collaboration among researchers around the world across both industry and academia. However, many top researchers don’t have access to anywhere near as much compute power as they need. To help as many researchers as we can and further accelerate the pace of open machine learning research, we'll make 1,000 Cloud TPUs available at no cost to ML researchers via the TensorFlow Research Cloud.

Sign up to learn more

If you’re interested in accelerating training of machine learning models, accelerating batch processing of gigantic datasets, or processing live requests in production using more powerful ML models than ever before, please sign up today to learn more about our upcoming Cloud TPU Alpha program. If you’re a researcher expanding the frontier of machine learning and willing to share your findings with the world, please sign up to learn more about the TensorFlow Research Cloud program. And if you’re interested in accessing whole TPU pods via Google Cloud, please let us know more about your needs.

Source: Google Cloud


Using Machine Learning to Explore Neural Network Architecture



At Google, we have successfully applied deep learning models to many applications, from image recognition to speech recognition to machine translation. Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large — a typical 10-layer network can have ~1010 candidate networks! For this reason, the process of designing networks often takes a significant amount of time and experimentation by those with significant machine learning expertise.
Our GoogleNet architecture. Design of this network required many years of careful experimentation and refinement from initial versions of convolutional architectures.
To make this process of designing machine learning models much more accessible, we’ve been exploring ways to automate the design of machine learning models. Among many algorithms we’ve studied, evolutionary algorithms [1] and reinforcement learning algorithms [2] have shown great promise. But in this blog post, we’ll focus on our reinforcement learning approach and the early results we’ve gotten so far.

In our approach (which we call "AutoML"), a controller neural net can propose a “child” model architecture, which can then be trained and evaluated for quality on a particular task. That feedback is then used to inform the controller how to improve its proposals for the next round. We repeat this process thousands of times — generating new architectures, testing them, and giving that feedback to the controller to learn from. Eventually the controller learns to assign high probability to areas of architecture space that achieve better accuracy on a held-out validation dataset, and low probability to areas of architecture space that score poorly. Here’s what the process looks like:
We’ve applied this approach to two heavily benchmarked datasets in deep learning: image recognition with CIFAR-10 and language modeling with Penn Treebank. On both datasets, our approach can design models that achieve accuracies on par with state-of-art models designed by machine learning experts (including some on our own team!).

So, what kind of neural nets does it produce? Let’s take one example: a recurrent architecture that’s trained to predict the next word on the Penn Treebank dataset. On the left here is a neural net designed by human experts. On the right is a recurrent architecture created by our method:

The machine-chosen architecture does share some common features with the human design, such as using addition to combine input and previous hidden states. However, there are some notable new elements — for example, the machine-chosen architecture incorporates a multiplicative combination (the left-most blue node on the right diagram labeled “elem_mult”). This type of combination is not common for recurrent networks, perhaps because researchers see no obvious benefit for having it. Interestingly, a simpler form of this approach was recently suggested by human designers, who also argued that this multiplicative combination can actually alleviate gradient vanishing/exploding issues, suggesting that the machine-chosen architecture was able to discover a useful new neural net architecture.

This approach may also teach us something about why certain types of neural nets work so well. The architecture on the right here has many channels so that the gradient can flow backwards, which may help explain why LSTM RNNs work better than standard RNNs.

Going forward, we’ll work on careful analysis and testing of these machine-generated architectures to help refine our understanding of them. If we succeed, we think this can inspire new types of neural nets and make it possible for non-experts to create neural nets tailored to their particular needs, allowing machine learning to have a greater impact to everyone.

References

[1] Large-Scale Evolution of Image Classifiers, Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Quoc Le, Alex Kurakin. International Conference on Machine Learning, 2017.

[2] Neural Architecture Search with Reinforcement Learning, Barret Zoph, Quoc V. Le. International Conference on Learning Representations, 2017.

Coarse Discourse: A Dataset for Understanding Online Discussions



Every day, participants of online communities form and share their opinions, experiences, advice and social support, most of which is expressed freely and without much constraint. These online discussions are often a key resource of information for many important topics, such as parenting, fitness, travel and more. However, these discussions also are intermixed with a clutter of disagreements, humor, flame wars and trolling, requiring readers to filter the content before getting the information they are looking for. And while the field of Information Retrieval actively explores ways to allow users to more efficiently find, navigate and consume this content, there is a lack of shared datasets on forum discussions to aid in understanding these discussions a bit better.

To aid researchers in this space, we are releasing the Coarse Discourse dataset, the largest dataset of annotated online discussions to date. The Coarse Discourse contains over half a million human annotations of publicly available online discussions on a random sample of over 9,000 threads from 130 communities from reddit.com.

To create this dataset, we developed the Coarse Discourse taxonomy of forum comments by going through a small set of forum threads, reading every comment, and deciding what role the comments played in the discussion. We then repeated and revised this exercise with crowdsourced human editors to validate the reproducibility of the taxonomy's discourse types, which include: announcement, question, answer, agreement, disagreement, appreciation, negative reaction, elaboration, and humor. From this data, over 100,000 comments were independently annotated by the crowdsourced editors for discourse type and relation. Along with the raw annotations from crowdsourced editors, we also provide the Coarse Discourse annotation task guidelines used by the editors to help with collecting data for other forums and refining the task further.
An example thread annotated with discourse types and relations. Early findings suggest that question answering is a prominent use case in most communities, while some communities are more converationally focused, with back-and-forth interactions.
For machine learning and natural language processing researchers trying to characterize the nature of online discussions, we hope that this dataset is a useful resource. Visit our GitHub repository to download the data. For more details, check out our ICWSM paper, “Characterizing Online Discussion Using Coarse Discourse Sequences.”

Acknowledgments
This work was done by Amy Zhang during her internship at Google. We would also like to thank Bryan Culbertson, Olivia Rhinehart, Eric Altendorf, David Huynh, Nancy Chang, Chris Welty and our crowdsourced editors.

TensorFlow Benchmarks and a New High-Performance Guide

Posted by Josh Gordon on behalf of the TensorFlow team

We recently published a collection of performance benchmarks that highlight TensorFlow's speed and scalability when training image classification models, like InceptionV3 and ResNet, on a variety of hardware and configurations.

To help you build highly scalable models, we've also added a new High-Performance Models guide to the performance site on tensorflow.org. Together with the guide, we hope these benchmarks and associated scripts will serve as a reference point as you tune your code, and help you get the most performance from your new and existing hardware.

When running benchmarks, we tested using both real and synthetic data. We feel this is important to show, as it exercises both the compute and input pipelines, and is more representative of real-world performance numbers than testing with synthetic data alone. For transparency, we've also shared our scripts and methodology.

Collected below are highlights of TensorFlow's performance when training with an NVIDIA® DGX-1™, as well as with 64 NVIDIA® Tesla® K80 GPUs running in a distributed configuration. In-depth results, including details like batch-size and configurations used for the various platforms we tested, are available on the benchmarks site.

Training with NVIDIA® DGX-1™ (8 NVIDIA® Tesla® P100s)

Our benchmarks show that TensorFlow has nearly linear scaling on an NVIDIA® DGX-1™for training image classification models with synthetic data. With 8 NVIDIA Tesla P100s, we report a speedup of 7.99x (99% efficiency) for InceptionV3 and 7.91x (98% efficiency) for ResNet-50, compared to using a single GPU.
The following are results comparing training with synthetic and real data. The benchmark results show a small difference between training data placed statically on the GPU (synthetic) and executing the full input pipeline with data from ImageNet. One strength of TensorFlow is the ability of its input pipeline to saturate state-of-the-art compute units with large inputs.




Training with NVIDIA® Tesla® K80 (Single server, 8 GPUs)

With 8 NVIDIA® Tesla® K80s in a single-server configuration, TensorFlow has a 7.4x speedup on Inception v3 (93% efficiency) and a 7.4x speedup on ResNet-50, compared to a single GPU. For this benchmark, we used Google Compute Engine instances.

Distributed Training with NVIDIA® Tesla® K80 (up to 64 GPUs)

With 64 Tesla K80s running on Amazon EC2 instancesin a distributed configuration, TensorFlow has a 59x speedup (92% efficiency) for InceptionV3 and a 52x speedup (82% efficiency) for ResNet-50 using synthetic data.

Discussion

During our testing of the DGX-1 and other platforms, we explored a variety of configurations using NCCL, a collective communications library, part of the NVIDIA Deep Learning SDK. Our hypothesis before testing began was that replicating the variables across GPUs and syncing them with NCCL would be the optimal approach. The results were not always as expected. Optimal configurations varied depending not only on the GPU, but also on the platform and model tested. On the DGX-1, for example, VGG16 and AlexNet performed best when replicating the variables on each of the GPUs and updating them using NCCL, while InceptionV3 and ResNet performed best when placing the shared variable on the CPU. These intricacies highlight the need for comprehensive benchmarking. Models have to be tuned for each platform, and a one size fits all approach is likely to result in suboptimal performance in many cases.

To get peak performance, it is necessary to benchmark with a mix of settings to determine which ones are likely to perform best on each platform. The script that accompanies the article on creating High-Performance Models was created not only to illustrate how to achieve the highest performance, but also as a tool to benchmark a platform with a variety of settings. The benchmarks page lists the configurations that we found which provided optimal performance for the platforms tested.

As many people have pointed out in response to various benchmarks that have been performed on other platforms, increases to samples per second does not necessarily correlate to faster convergence, and as batch sizes increase it can be more difficult to converge to the highest accuracy levels.

As a team, we hope to do future tests that focus on time to convergence to high levels of accuracy. We hope these numbers and the guide will prove useful to you as you tune your code for performance.

We'd like to thank NVIDIA for sharing a DGX-1 for benchmark testing and for their technical assistance. We're looking forward to NVIDIA's upcoming Voltaarchitecture, and to working closely with them to optimize TensorFlow's performance there, and to expand support for FP16.

Thanks for reading, and as always, we look forward to working with you on forums like GitHub issues, Stack Overflow, the discuss@tensorflow.orglist, and @TensorFlow.

Bringing down the language barriers – making the internet more inclusive

There are currently over 400* million Internet users in India, but with only 20% of the population fluent in English, most Internet users have significant language barriers to getting the full value of the Internet. A speaker of Indian languages like Hindi or Tamil still has trouble finding content to read and or use services that they can use in their own languages.

To build rich and empowering experiences for everyone means first and foremost making things work in the languages people speak. Today, we’re taking a huge step forward by launching new set of products and features that will empower the Internet ecosystem to create more language content and better serve the needs of a billion Indians who’re coming online rapidly.

Neural Machine Translation: The world’s content, in your language
Starting today, when you use Google Translate, you might notice that the translation is more accurate and easier to understand, especially when translating full sentences. That’s because we’ve brought our new Neural Machine Translation technology to translations between English and nine widely used Indian languages — Hindi, Bengali, Marathi, Tamil, Telugu, Gujarati, Punjabi, Malayalam and Kannada.

Neural translation is a lot better than our old phrase-based system, translating full sentences at a time, instead of pieces of a sentence. It uses this broader context to help it figure out the most relevant translation, which it then rearranges and adjusts to be more like a human speaking with proper grammar. This new technique improves the quality of translation more in a single jump than we’ve seen in the last ten years combined.

Just like it’s easier to learn a language when you already know a related language, we’ve discovered that our neural technology speaks each language better when it learns several at a time. For example, we have a whole lot more sample data for Hindi than its relatives Marathi and Bengali, but when we train them all together, the translations for all improve more than if we’d trained each individually.

Screen Shot 2017-04-24 at 12.07.04 PM.png
Phrase based Translation      Neural Machine Translation

You can try, these out on iOS and Android Google Translate apps, at translate.google.co.in and through Google Search.

But how does this make the whole web better for everyone — Chrome has it covered!
That’s where Chrome’s built-in Translate functionality comes into play. Every day, more than 150 million web pages are translated by Chrome users through the magic of machine translations with one click or tap.  The Chrome team and the Google Translate team have worked together to bring the power of Neural Machine Translation to web content, making full-page translations more accurate and easier to read.

Today, we’re extending Neural Machine Translation built into Chrome to and from English for the same nine Indian languages (Bengali, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Tamil Telugu and Hindi). This means higher quality translations of everything from song lyrics to news articles to cricket discussions.

            
Screen Shot 2017-04-24 at 5.10.07 PM.png

Gboard in 22 Indian Languages and more
Being able to type in your language of choice is as important as understanding content on the web. Today, we are ramping up support to include 11 new languages to the list of 11 existing Indian languages —with transliteration support—including Hindi, Bengali, Telugu, Marathi, Tamil, Urdu, and Gujarati.

Gboard has all the things you love about your Google Keyboard — speed and accuracy, Glide Typing and voice typing — plus Google Search built in. It also allows you to search and use Google Translate right in your keyboard (just tap the “G” button to get started). And—as a reminder—Gboard already has a Hinglish language option for those of you who often switch back and forth between Hindi and English.

With today’s update, we’ve also dropped in a new text editing tool that makes it easier to select, copy and paste, plus new options for resizing and repositioning the keyboard so it fits to your hand and texting style. And to top it all off, this Gboard update comes with some under-the-hood improvements including better accuracy and predictions while you type

Like Google Indic Keyboard, Gboard has auto-correction and prediction in these new languages, plus two layouts for each—one in the native language script and one with the QWERTY layout for transliteration, which lets you spell words phonetically using the QWERTY alphabet and get text output in your native language script. For example, type “aapko holi ki hardik shubhkamnay” and get “आपको होली की हार्दिक शुभकामनायें ”.

hindi_translit_fixed.gif                                           

This is available today on Google Play Store, so make sure you’re running the latest version of the app.

Auto-translated local reviews in Maps
The local information across Google Search and Maps helps millions of people, every day, to discover and share great places. Our goal is to build a map tailored to each user and their likes and preferences and make it work for everyone in their local languages. Starting today, we’ll automatically add translations to local reviews on Google Maps, both on mobile and desktop. With this update, millions of these reviews – from restaurants to cafes or hotels – will appear in your own language.

All you need to do is launch Google Maps, open reviews, and they’ll appear in both the original language as well as the language you set on your device. So for instance if you speak Tamil and travel to Kolkata, and you want to see reviews of the popular restaurants in Kolkata, you can now automatically see reviews both in your own language and the original language of the review.

Hindi Dictionary in Search
When you search for the meaning of a word in English, for instance “meaning of nostalgic”, you’ll get a dictionary straight in Google Search. Today, in collaboration with the Oxford University Press, we’re bringing the Rajpal & Sons Hindi dictionary online. This new experience supports transliteration so you don’t even need to switch to a Hindi keyboard. So  the next time when you’d like to know more about a word, say Nirdeshak, you can just type in Nirdeshak ka matlab in Search, and you’ll instantly get to see word meanings and dictionary definitions on the search results page, including English translations.

pasted image 0 (11).png

While all these new products and improvements takes us closer to make the web more useful for Indian Language users.  We realise that we can’t do this alone, we need India’s internet ecosystem to come together to build apps and more content to make India’s Internet that serve its users need. And one way to effectively get the Internet Industry together to solve for local language users is to really understand the users, understand their needs to shape India’s Internet landscape. We have worked with KPMG India to compile an industry report titled “Indian Languages - Defining India’s Internet” - which provides rich insights on what we need to do together as an Industry to bring the Internet alive for every Indian.

Source: *Indian Languages - Defining India’s Internet”  Report

Posted By Barak Turovsky, Group Product Manager, Google Translate