Tag Archives: machine learning

Supercharge your Computer Vision models with the TensorFlow Object Detection API



(Cross-posted on the Google Open Source Blog)

At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems.
Detected objects in a sample image (from the COCO dataset) made by one of our models. Image credit: Michael Miley, original image.
Last October, our in-house object detection system achieved new state-of-the-art results, and placed first in the COCO detection challenge. Since then, this system has generated results for a number of research publications1,2,3,4,5,6,7 and has been put to work in Google products such as NestCam, the similar items and style ideas feature in Image Search and street number and name detection in Street View.

Today we are happy to make this system available to the broader research community via the TensorFlow Object Detection API. This codebase is an open-source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. Our goals in designing this system was to support state-of-the-art models while allowing for rapid exploration and research. Our first release contains the following:
The SSD models that use MobileNet are lightweight, so that they can be comfortably run in real time on mobile devices. Our winning COCO submission in 2016 used an ensemble of the Faster RCNN models, which are are more computationally intensive but significantly more accurate. For more details on the performance of these models, see our CVPR 2017 paper.

Are you ready to get started?
We’ve certainly found this code to be useful for our computer vision needs, and we hope that you will as well. Contributions to the codebase are welcome and please stay tuned for our own further updates to the framework. To get started, download the code here and try detecting objects in some of your own images using the Jupyter notebook, or training your own pet detector on Cloud ML engine!

Acknowledgements
The release of the Tensorflow Object Detection API and the pre-trained model zoo has been the result of widespread collaboration among Google researchers with feedback and testing from product groups. In particular we want to highlight the contributions of the following individuals:

Core Contributors: Derek Chow, Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings, Viacheslav Kovalevskyi, Kevin Murphy

Also special thanks to: Andrew Howard, Rahul Sukthankar, Vittorio Ferrari, Tom Duerig, Chuck Rosenberg, Hartwig Adam, Jing Jing Long, Victor Gomes, George Papandreou, Tyler Zhu

References
  1. Speed/accuracy trade-offs for modern convolutional object detectors, Huang et al., CVPR 2017 (paper describing this framework)
  2. Towards Accurate Multi-person Pose Estimation in the Wild, Papandreou et al., CVPR 2017
  3. YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video, Real et al., CVPR 2017 (see also our blog post)
  4. Beyond Skip Connections: Top-Down Modulation for Object Detection, Shrivastava et al., arXiv preprint arXiv:1612.06851, 2016
  5. Spatially Adaptive Computation Time for Residual Networks, Figurnov et al., CVPR 2017
  6. AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions, Gu et al., arXiv preprint arXiv:1705.08421, 2017
  7. MobileNets: Efficient convolutional neural networks for mobile vision applications, Howard et al., arXiv preprint arXiv:1704.04861, 2017

Supercharge your Computer Vision models with the TensorFlow Object Detection API

Crossposted on the Google Research Blog

At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems.
Detected objects in a sample image (from the COCO dataset) made by one of our models.
Image credit: Michael Miley, original image
Last October, our in-house object detection system achieved new state-of-the-art results, and placed first in the COCO detection challenge. Since then, this system has generated results for a number of research publications1,2,3,4,5,6,7 and has been put to work in Google products such as NestCam, the similar items and style ideas feature in Image Search and street number and name detection in Street View.

Today we are happy to make this system available to the broader research community via the TensorFlow Object Detection API. This codebase is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models.  Our goals in designing this system was to support state-of-the-art models while allowing for rapid exploration and research.  Our first release contains the following:
The SSD models that use MobileNet are lightweight, so that they can be comfortably run in real time on mobile devices. Our winning COCO submission in 2016 used an ensemble of the Faster RCNN models, which are are more computationally intensive but significantly more accurate.  For more details on the performance of these models, see our CVPR 2017 paper.

Are you ready to get started?
We’ve certainly found this code to be useful for our computer vision needs, and we hope that you will as well.  Contributions to the codebase are welcome and please stay tuned for our own further updates to the framework. To get started, download the code here and try detecting objects in some of your own images using the Jupyter notebook, or training your own pet detector on Cloud ML engine!

By Jonathan Huang, Research Scientist and Vivek Rathod, Software Engineer

Acknowledgements
The release of the Tensorflow Object Detection API and the pre-trained model zoo has been the result of widespread collaboration among Google researchers with feedback and testing from product groups. In particular we want to highlight the contributions of the following individuals:

Core Contributors: Derek Chow, Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings, Viacheslav Kovalevskyi, Kevin Murphy

Also special thanks to: Andrew Howard, Rahul Sukthankar, Vittorio Ferrari, Tom Duerig, Chuck Rosenberg, Hartwig Adam, Jing Jing Long, Victor Gomes, George Papandreou, Tyler Zhu

References
  1. Speed/accuracy trade-offs for modern convolutional object detectors, Huang et al., CVPR 2017 (paper describing this framework)
  2. Towards Accurate Multi-person Pose Estimation in the Wild, Papandreou et al., CVPR 2017
  3. YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video, Real et al., CVPR 2017 (see also our blog post)
  4. Beyond Skip Connections: Top-Down Modulation for Object Detection, Shrivastava et al., arXiv preprint arXiv:1612.06851, 2016
  5. Spatially Adaptive Computation Time for Residual Networks, Figurnov et al., CVPR 2017
  6. AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions, Gu et al., arXiv preprint arXiv:1705.08421, 2017
  7. MobileNets: Efficient convolutional neural networks for mobile vision applications, Howard et al., arXiv preprint arXiv:1704.04861, 2017

How Google Cloud is transforming Japanese businesses

This week, we welcomed 13,000 executives, developers, IT managers and partners to our largest Asia-Pacific Cloud event, Google Cloud Next Tokyo. During this event, we celebrated the many ways that Japanese companies such as Kewpie, Sony (and even cucumber farmers) have transformed and scaled their businesses using Google Cloud. 

Since the launch of the Google Cloud Tokyo region last November, roughly 40 percent of Google Compute Engine core hour usage in Tokyo is from customers new to Google Cloud Platform (GCP). The number of new customers using Compute Engine has increased by an average of 21 percent monthly over the last three months, and the total number of paid customers in Japan has increased by 70 percent over the last year.

By supplying compliance statements and documents for FISC — an important Japanese compliance standard — for both GCP and G Suite, we’re making it easier to do business with Google Cloud in Japan.

Here are a few of the exciting announcements that came out of Next Tokyo:

Retailers embracing enterprise innovation  

One of the biggest retailers in Japan, FamilyMart, will work with Google’s Professional Services Organization to transform the way it works, reform its store operations, and build a retail model for the next generation. FamilyMart is using G Suite to facilitate a collaborative culture and transform its business to embrace an ever-changing landscape. Furthermore, it plans to use big data analysis and machine learning to develop new ways of managing store operations. The project, — dubbed “Famima 10x” — kicks off by introducing G Suite to facilitate a more flexible work style and encourage a more collaborative, innovative culture. 

Modernizing food production with cloud computing, data analytics and machine learning

Kewpie, a major food manufacturer in Japan famous for their mayonnaise, takes high standards of food production seriously. For its baby food, it used to depend on human eyes to evaluate 4 - 5 tons of food materials daily, per factory, to root out bad potato cubes — a labor-intensive task that required intense focus on the production line. But over the course of six months, Kewpie has tested Cloud Machine Learning Engine and TensorFlow to help identify the bad cubes. The results of the tests were so successful that Kewpie adopted the technology.

Empowering employees to conduct effective data analysis

Sony Network Communications Inc. is a division of Sony Group that develops and operates cloud services and applications for Sony group companies. It converted from Hive/Hadoop to BigQuery and established a data analysis platform based on BigQuery, called Private Data Management Platform. This not only reduces data preparation and maintenance costs, but also allows a wide range of employees — from data scientists to those who are only familiar with SQL — to conduct effective data analysis, which in turn made its data-driven business more productive than before.

Collaborating with partners

During Next Tokyo, we announced five new Japanese partners that will help Google Cloud better serve customers.

  • NTT Communications Corporation is a respected Japanese cloud solution provider and new Google Cloud partner that helps enterprises worldwide optimize their information and communications technology environments. GCP will connect with NTT Communications’ Enterprise Cloud, and NTT Communications plans to develop new services utilizing Google Cloud’s big data analysis and machine intelligence solutions. NTT Communications will use both G Suite and GCP to run its own business and will use its experiences to help both Japanese and international enterprises.

  • KDDI is already a key partner for G Suite and Chrome devices and will offer GCP to the Japanese market this summer, in addition to an expanded networking partnership.

  • Softbank has been a G Suite partner since 2011 and will expand the collaboration with Google Cloud to include solutions utilizing GCP in its offerings. As part of the collaboration, Softbank plans to link GCP with its own “White Cloud” service in addition to promoting next-generation workplaces with G Suite.

  • SORACOM, which uses cellular and LoRaWAN networks to provide connectivity for IoT devices, announced two new integrations with GCP. SORACOM Beam, its data transfer support service, now supports Google Cloud IoT Core, and SORACOM Funnel, its cloud resource adapter service, enables constrained devices to send messages to Google Cloud Pub/Sub. This means that a small, battery-powered sensor can keep sending data to GCP by LoRaWAN for months, for example.

Create Cloud Spanner instances in Tokyo

Cloud Spanner is the world’s first horizontally-scalable and strongly-consistent relational database service. It became generally available in May, delivering long-term value for our customers with mission-critical applications in the cloud, including customer authentication systems, business-transaction and inventory-management systems, and high-volume media systems that require low latency and high throughput. Starting today, customers can store data and create Spanner instances directly in our Tokyo region.

Jamboard coming to Japan in 2018

At Next Tokyo, businesses discussed how they can use technology to improve productivity, and make it easier for employees to work together. Jamboard, a digital whiteboard designed specifically for the cloud, allows employees to sketch their ideas whiteboard-style on a brilliant 4k display, and drop images, add notes and pull things directly from the web while they collaborate with team members from anywhere. This week, we announced that Jamboard will be generally available in Japan in 2018.

Why Japanese companies are choosing Google Cloud

For Kewpie, Sony and FamilyMart, Google’s track record building secure infrastructure all over the world was an important consideration for their move to Google Cloud. From energy-efficient data centers to custom servers to custom networking gear to a software-defined global backbone to specialized ASICs for machine learning, Google has been living cloud at scale for more than 15 years—and we bring all of it to bear in Google Cloud.

We hope to see many of you as we go on the road to meet with customers and partners, and encourage you to learn more about upcoming Google Cloud events.

How Google Cloud is transforming Japanese businesses

This week, we welcomed 13,000 executives, developers, IT managers and partners to our largest Asia-Pacific Cloud event, Google Cloud Next Tokyo. During this event, we celebrated the many ways that Japanese companies such as Kewpie, Sony (and even cucumber farmers) have transformed and scaled their businesses using Google Cloud. 

Since the launch of the Google Cloud Tokyo region last November, roughly 40 percent of Google Compute Engine core hour usage in Tokyo is from customers new to Google Cloud Platform (GCP). The number of new customers using Compute Engine has increased by an average of 21 percent monthly over the last three months, and the total number of paid customers in Japan has increased by 70 percent over the last year.

By supplying compliance statements and documents for FISC — an important Japanese compliance standard — for both GCP and G Suite, we’re making it easier to do business with Google Cloud in Japan.

Here are a few of the exciting announcements that came out of Next Tokyo:

Retailers embracing enterprise innovation  

One of the biggest retailers in Japan, FamilyMart, will work with Google’s Professional Services Organization to transform the way it works, reform its store operations, and build a retail model for the next generation. FamilyMart is using G Suite to facilitate a collaborative culture and transform its business to embrace an ever-changing landscape. Furthermore, it plans to use big data analysis and machine learning to develop new ways of managing store operations. The project, — dubbed “Famima 10x” — kicks off by introducing G Suite to facilitate a more flexible work style and encourage a more collaborative, innovative culture. 

Modernizing food production with cloud computing, data analytics and machine learning

Kewpie, a major food manufacturer in Japan famous for their mayonnaise, takes high standards of food production seriously. For its baby food, it used to depend on human eyes to evaluate 4 - 5 tons of food materials daily, per factory, to root out bad potato cubes — a labor-intensive task that required intense focus on the production line. But over the course of six months, Kewpie has tested Cloud Machine Learning Engine and TensorFlow to help identify the bad cubes. The results of the tests were so successful that Kewpie adopted the technology.

Empowering employees to conduct effective data analysis

Sony Network Communications Inc. is a division of Sony Group that develops and operates cloud services and applications for Sony group companies. It converted from Hive/Hadoop to BigQuery and established a data analysis platform based on BigQuery, called Private Data Management Platform. This not only reduces data preparation and maintenance costs, but also allows a wide range of employees — from data scientists to those who are only familiar with SQL — to conduct effective data analysis, which in turn made its data-driven business more productive than before.

Collaborating with partners

During Next Tokyo, we announced five new Japanese partners that will help Google Cloud better serve customers.

  • NTT Communications Corporation is a respected Japanese cloud solution provider and new Google Cloud partner that helps enterprises worldwide optimize their information and communications technology environments. GCP will connect with NTT Communications’ Enterprise Cloud, and NTT Communications plans to develop new services utilizing Google Cloud’s big data analysis and machine intelligence solutions. NTT Communications will use both G Suite and GCP to run its own business and will use its experiences to help both Japanese and international enterprises.

  • KDDI is already a key partner for G Suite and Chrome devices and will offer GCP to the Japanese market this summer, in addition to an expanded networking partnership.

  • Softbank has been a G Suite partner since 2011 and will expand the collaboration with Google Cloud to include solutions utilizing GCP in its offerings. As part of the collaboration, Softbank plans to link GCP with its own “White Cloud” service in addition to promoting next-generation workplaces with G Suite.

  • SORACOM, which uses cellular and LoRaWAN networks to provide connectivity for IoT devices, announced two new integrations with GCP. SORACOM Beam, its data transfer support service, now supports Google Cloud IoT Core, and SORACOM Funnel, its cloud resource adapter service, enables constrained devices to send messages to Google Cloud Pub/Sub. This means that a small, battery-powered sensor can keep sending data to GCP by LoRaWAN for months, for example.

Create Cloud Spanner instances in Tokyo

Cloud Spanner is the world’s first horizontally-scalable and strongly-consistent relational database service. It became generally available in May, delivering long-term value for our customers with mission-critical applications in the cloud, including customer authentication systems, business-transaction and inventory-management systems, and high-volume media systems that require low latency and high throughput. Starting today, customers can store data and create Spanner instances directly in our Tokyo region.

Jamboard coming to Japan in 2018

At Next Tokyo, businesses discussed how they can use technology to improve productivity, and make it easier for employees to work together. Jamboard, a digital whiteboard designed specifically for the cloud, allows employees to sketch their ideas whiteboard-style on a brilliant 4k display, and drop images, add notes and pull things directly from the web while they collaborate with team members from anywhere. This week, we announced that Jamboard will be generally available in Japan in 2018.

Why Japanese companies are choosing Google Cloud

For Kewpie, Sony and FamilyMart, Google’s track record building secure infrastructure all over the world was an important consideration for their move to Google Cloud. From energy-efficient data centers to custom servers to custom networking gear to a software-defined global backbone to specialized ASICs for machine learning, Google has been living cloud at scale for more than 15 years—and we bring all of it to bear in Google Cloud.

We hope to see many of you as we go on the road to meet with customers and partners, and encourage you to learn more about upcoming Google Cloud events.

MobileNets: Open Source Models for Efficient On-Device Vision

Crossposted on the Google Research Blog

Deep learning has fueled tremendous progress in the field of computer vision in recent years, with neural networks repeatedly pushing the frontier of visual recognition technology. While many of those technologies such as object, landmark, logo and text recognition are provided for internet-connected devices through the Cloud Vision API, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of our users, anytime, anywhere, regardless of internet connection. However, visual recognition for on device and embedded applications poses many challenges — models must run quickly with high accuracy in a resource-constrained environment making use of limited computation, power and space.

Today we are pleased to announce the release of MobileNets, a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.
Example use cases include detection, fine-grain classification, attributes and geo-localization.
This release contains the model definition for MobileNets in TensorFlow using TF-Slim, as well as 16 pre-trained ImageNet classification checkpoints for use in mobile projects of all sizes. The models can be run efficiently on mobile devices with TensorFlow Mobile.
Model Checkpoint
Million MACs
Million Parameters
Top-1 Accuracy
Top-5 Accuracy
569
4.24
70.7
89.5
418
4.24
69.3
88.9
291
4.24
67.2
87.5
186
4.24
64.1
85.3
317
2.59
68.4
88.2
233
2.59
67.4
87.3
162
2.59
65.2
86.1
104
2.59
61.8
83.6
150
1.34
64.0
85.4
110
1.34
62.1
84.0
77
1.34
59.9
82.5
49
1.34
56.2
79.6
41
0.47
50.6
75.0
34
0.47
49.0
73.6
21
0.47
46.0
70.7
14
0.47
41.3
66.2
Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. Top-1 and Top-5 accuracies are measured on the ILSVRC dataset.
We are excited to share MobileNets with the open source community. Information for getting started can be found at the TensorFlow-Slim Image Classification Library. To learn how to run models on-device please go to TensorFlow Mobile. You can read more about the technical details of MobileNets in our paper, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

By Andrew G. Howard, Senior Software Engineer and Menglong Zhu, Software Engineer

Acknowledgements
MobileNets were made possible with the hard work of many engineers and researchers throughout Google. Specifically we would like to thank:

Core Contributors: Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam

Special thanks to: Benoit Jacob, Skirmantas Kligys, George Papandreou, Liang-Chieh Chen, Derek Chow, Sergio Guadarrama, Jonathan Huang, Andre Hentz, Pete Warden

Four signs you’re wasting time at work and how G Suite can help

We all waste time at work, whether it’s on purpose (brushing up on Wonder Woman's history) or on accident (really should have budgeted more time for internal reviews). Luckily, G Suite can help you accomplish more at work, quicker. Here are four tell-tale signs you’re spending time on the wrong things, and tips on how to avoid these time-sinks.

1. You’ve spent more time emailing co-workers than you have actually working 

The average worker spends an estimated 13 hours per week writing emails—nearly two full work days. Luckily, you can cut back on time spent replying to emails with Smart Reply in Gmail. Smart Reply uses machine learning to generate quick, natural language responses for you.

2. You’ve spent the past hour formatting slides for a presentation

Is an image centered? Should you use “Times New Roman” or “Calibri?” Formatting presentations monopolizes too much of our time and takes away from what’s really valuable: sharing insights.

But you can save time polishing your presentations by using Explore in Slides, powered by machine learning. Explore generates design suggestions for your presentation so you don’t have to worry about cropping, resizing or reformatting. You can also use Explore in Docs, which makes it easy to research right within your documents. Explore will recommend related topics to help you learn more or even suggest photos or more content you can add to your document. Check out how to use Explore in Slides and Docs in this episode of the G Suite Show:

Explore feature for Docs and Slides | The G Suite Show

3. You can’t find a file you know you saved in your drive

Where is that pesky file? According to a McKinsey report, employees spend almost two hours every day searching and gathering information. That’s a lot of time.

Curb time wasted with Quick Access in Drive, which uses machine intelligence to predict and suggest files you need when you need them. Natural Language Processing (NLP) also makes it possible for you to search the way you speak. Say you’re trying to find an important file from 2016. Simply search “spreadsheets I created in 2016” and voilà! 

Another way to avoid losing files is by using Team Drives, a central location in Drive that houses shared files. In Team Drives, all team members can access files (or manage individual share permissions), so you don’t have to worry about tracking down a file after someone leaves or granting access to every doc that you create.

4. You’ve fussed with a spreadsheet formula over and over again

According to internal Google data, less than 30 percent of enterprise users feel comfortable manipulating formulas within spreadsheets. “=SUM(a+b)” is easy, but more sophisticated calculations can be challenging.

Bypass remembering formulas and time-consuming analysis and dive straight into finding insights with Explore in Sheets, which uses machine learning to crunch numbers for you. Type in questions (in words, not formulas) in Explore in Sheets on web, Android or iOS to learn more about your data instantly. And now, you can use the same powerful technology to create charts for you within Sheets. Instead of manually building graphs, ask Explore to do it for you by typing the request in words.

GIF

Stop wasting time on menial tasks and focus more on important, strategic work. To learn more about other G Suite apps that can help you save time, visit https://gsuite.google.com/.

Four signs you’re wasting time at work and how G Suite can help

We all waste time at work, whether it’s on purpose (brushing up on Wonder Woman's history) or on accident (really should have budgeted more time for internal reviews). Luckily, G Suite can help you accomplish more at work, quicker. Here are four tell-tale signs you’re spending time on the wrong things, and tips on how to avoid these time-sinks.

1. You’ve spent more time emailing co-workers than you have actually working 

The average worker spends an estimated 13 hours per week writing emails—nearly two full work days. Luckily, you can cut back on time spent replying to emails with Smart Reply in Gmail. Smart Reply uses machine learning to generate quick, natural language responses for you.

2. You’ve spent the past hour formatting slides for a presentation

Is an image centered? Should you use “Times New Roman” or “Calibri?” Formatting presentations monopolizes too much of our time and takes away from what’s really valuable: sharing insights.

But you can save time polishing your presentations by using Explore in Slides, powered by machine learning. Explore generates design suggestions for your presentation so you don’t have to worry about cropping, resizing or reformatting. You can also use Explore in Docs, which makes it easy to research right within your documents. Explore will recommend related topics to help you learn more or even suggest photos or more content you can add to your document. Check out how to use Explore in Slides and Docs in this episode of the G Suite Show:

Explore feature for Docs and Slides | The G Suite Show

3. You can’t find a file you know you saved in your drive

Where is that pesky file? According to a McKinsey report, employees spend almost two hours every day searching and gathering information. That’s a lot of time.

Curb time wasted with Quick Access in Drive, which uses machine intelligence to predict and suggest files you need when you need them. Natural Language Processing (NLP) also makes it possible for you to search the way you speak. Say you’re trying to find an important file from 2016. Simply search “spreadsheets I created in 2016” and voilà! 

Another way to avoid losing files is by using Team Drives, a central location in Drive that houses shared files. In Team Drives, all team members can access files (or manage individual share permissions), so you don’t have to worry about tracking down a file after someone leaves or granting access to every doc that you create.

4. You’ve fussed with a spreadsheet formula over and over again

According to internal Google data, less than 30 percent of enterprise users feel comfortable manipulating formulas within spreadsheets. “=SUM(a+b)” is easy, but more sophisticated calculations can be challenging.

Bypass remembering formulas and time-consuming analysis and dive straight into finding insights with Explore in Sheets, which uses machine learning to crunch numbers for you. Type in questions (in words, not formulas) in Explore in Sheets on web, Android or iOS to learn more about your data instantly. And now, you can use the same powerful technology to create charts for you within Sheets. Instead of manually building graphs, ask Explore to do it for you by typing the request in words.

GIF

Stop wasting time on menial tasks and focus more on important, strategic work. To learn more about other G Suite apps that can help you save time, visit https://gsuite.google.com/.

Source: Drive


Four signs you’re wasting time at work and how G Suite can help

We all waste time at work, whether it’s on purpose (brushing up on Wonder Woman's history) or on accident (really should have budgeted more time for internal reviews). Luckily, G Suite can help you accomplish more at work, quicker. Here are four tell-tale signs you’re spending time on the wrong things, and tips on how to avoid these time-sinks.

1. You’ve spent more time emailing co-workers than you have actually working 

The average worker spends an estimated 13 hours per week writing emails—nearly two full work days. Luckily, you can cut back on time spent replying to emails with Smart Reply in Gmail. Smart Reply uses machine learning to generate quick, natural language responses for you.

2. You’ve spent the past hour formatting slides for a presentation

Is an image centered? Should you use “Times New Roman” or “Calibri?” Formatting presentations monopolizes too much of our time and takes away from what’s really valuable: sharing insights.

But you can save time polishing your presentations by using Explore in Slides, powered by machine learning. Explore generates design suggestions for your presentation so you don’t have to worry about cropping, resizing or reformatting. You can also use Explore in Docs, which makes it easy to research right within your documents. Explore will recommend related topics to help you learn more or even suggest photos or more content you can add to your document. Check out how to use Explore in Slides and Docs in this episode of the G Suite Show:

Explore feature for Docs and Slides | The G Suite Show

3. You can’t find a file you know you saved in your drive

Where is that pesky file? According to a McKinsey report, employees spend almost two hours every day searching and gathering information. That’s a lot of time.

Curb time wasted with Quick Access in Drive, which uses machine intelligence to predict and suggest files you need when you need them. Natural Language Processing (NLP) also makes it possible for you to search the way you speak. Say you’re trying to find an important file from 2016. Simply search “spreadsheets I created in 2016” and voilà! 

Another way to avoid losing files is by using Team Drives, a central location in Drive that houses shared files. In Team Drives, all team members can access files (or manage individual share permissions), so you don’t have to worry about tracking down a file after someone leaves or granting access to every doc that you create.

4. You’ve fussed with a spreadsheet formula over and over again

According to internal Google data, less than 30 percent of enterprise users feel comfortable manipulating formulas within spreadsheets. “=SUM(A1, B1)" or "=SUM(1, 2)" is easy, but more sophisticated calculations can be challenging.

Bypass remembering formulas and time-consuming analysis and dive straight into finding insights with Explore in Sheets, which uses machine learning to crunch numbers for you. Type in questions (in words, not formulas) in Explore in Sheets on the web to learn more about your data instantly. And now, you can use the same powerful technology to create charts for you within Sheets. Instead of manually building graphs, ask Explore to do it for you by typing the request in words.

GIF

Stop wasting time on menial tasks and focus more on important, strategic work. To learn more about other G Suite apps that can help you save time, visit https://gsuite.google.com/.

Visualize data instantly with machine learning in Google Sheets

Sorting through rows and rows of data in a spreadsheet can be overwhelming. That’s why today, we’re rolling out new features in Sheets that make it even easier for you to visualize and share your data, and find insights your teams can act on.

Ask and you shall receive → Sheets can build charts for you

Explore in Sheets, powered by machine learning, helps teams gain insights from data, instantly. Simply ask questions—in words, not formulas—to quickly analyze your data. For example, you can ask “what is the distribution of products sold?” or “what are average sales on Sundays?” and Explore will help you find the answers.  

Now, we’re using the same powerful technology in Explore to make visualizing data even more effortless. If you don’t see the chart you need, just ask. Instead of manually building charts, ask Explore to do it by typing in “histogram of 2017 customer ratings” or “bar chart for ice cream sales.” Less time spent building charts means more time acting on new insights.

Sheets GIF

Instantly sync your data from Sheets → Docs or Slides

Whether you’re preparing a client presentation or sharing sales forecasts, keeping up-to-date data is critical to success, but it can also be time-consuming if you need to update charts or tables in multiple sources. This is why we made it easier to programmatically update charts in Docs and Slides last year.   

Now, we’re making it simple to keep tables updated, too. Just copy and paste data from Sheets to Docs or Slides and tap the “update” button to sync your data.

Sheets bundle - still

Even more Sheets updates

We’re constantly looking for ways to improve our customers’ experience in Sheets. Based on your feedback, we’re rolling out more updates today to help teams get work done faster:

  • Keyboard shortcuts: Change default shortcuts in your browser to the same spreadsheet shortcuts you’re already used to. For example, delete a row quickly by using “Ctrl+-.”  
  • Upgraded printing experience: Preview Sheet data in today’s new print interface. Adjust margins, select scale and alignment options or repeat frozen rows and columns before you print your work.
  • Powerful new chart editing experience: Create and edit charts in a new, improved sidebar. Choose from custom colors in charts or add additional trendlines to model data. You can also create more chart types, like 3D charts. This is now also available for iPhones and iPads
  • More spreadsheet functions: We added new functions to help you find insights, bringing the total function count in Sheets to more than 400. Try “SORTN,” a function unique to Sheets, which can show you the top three orders or best-performing months in a sales record spreadsheet. Sheets also support statistical functions like “GAMMADIST,” “F.TEST” and “CHISQ.INV.RT.”

These new features in Sheets are rolling out starting today. Learn how Sheets can help you find valuable insights.

Source: Google Cloud


Visualize data instantly with machine learning in Google Sheets

Sorting through rows and rows of data in a spreadsheet can be overwhelming. That’s why today, we’re rolling out new features in Sheets that make it even easier for you to visualize and share your data, and find insights your teams can act on.

Ask and you shall receive → Sheets can build charts for you

Explore in Sheets, powered by machine learning, helps teams gain insights from data, instantly. Simply ask questions—in words, not formulas—to quickly analyze your data. For example, you can ask “what is the distribution of products sold?” or “what are average sales on Sundays?” and Explore will help you find the answers.  

Now, we’re using the same powerful technology in Explore to make visualizing data even more effortless. If you don’t see the chart you need, just ask. Instead of manually building charts, ask Explore to do it by typing in “histogram of 2017 customer ratings” or “bar chart for ice cream sales.” Less time spent building charts means more time acting on new insights.

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Instantly sync your data from Sheets → Docs or Slides

Whether you’re preparing a client presentation or sharing sales forecasts, keeping up-to-date data is critical to success, but it can also be time-consuming if you need to update charts or tables in multiple sources. This is why we made it easier to programmatically update charts in Docs and Slides last year.   

Now, we’re making it simple to keep tables updated, too. Just copy and paste data from Sheets to Docs or Slides and tap the “update” button to sync your data.

Sheets bundle - still

Even more Sheets updates

We’re constantly looking for ways to improve our customers’ experience in Sheets. Based on your feedback, we’re rolling out more updates today to help teams get work done faster:

  • Keyboard shortcuts: Change default shortcuts in your browser to the same spreadsheet shortcuts you’re already used to. For example, delete a row quickly by using “Ctrl+-.”  
  • Upgraded printing experience: Preview Sheet data in today’s new print interface. Adjust margins, select scale and alignment options or repeat frozen rows and columns before you print your work.
  • Powerful new chart editing experience: Create and edit charts in a new, improved sidebar. Choose from custom colors in charts or add additional trendlines to model data. You can also create more chart types, like 3D charts. This is now also available for iPhones and iPads
  • More spreadsheet functions: We added new functions to help you find insights, bringing the total function count in Sheets to more than 400. Try “SORTN,” a function unique to Sheets, which can show you the top three orders or best-performing months in a sales record spreadsheet. Sheets also support statistical functions like “GAMMADIST,” “F.TEST” and “CHISQ.INV.RT.”

These new features in Sheets are rolling out starting today. Learn how Sheets can help you find valuable insights.