Tag Archives: TensorFlow

Aligning thousands of Billie Eilish covers in an infinite music video experiment

Posted by Google Creative Lab

“Bad Guy” by Billie Eilish is one of the most-covered songs on YouTube, inspiring thousands of fans to upload their own versions. To celebrate all these covers, YouTube and Google Creative Lab built an AI experiment to combine all of them seamlessly in the world’s first infinite music video: Infinite Bad Guy. The experience aligns every cover to the same beat, no matter its genre, language, or instrumentation.

Finding all the covers

How do you find “Bad Guy” covers amidst all the billions of videos on YouTube? Just searching for “Bad Guy” would result in false positives, like videos of Billie being interviewed about the song, or miss covers that didn’t use the song name in their titles. YouTube’s ContentID system allows us to find videos that match the musical composition “Bad Guy” and also allows us to narrow our search to videos that appear to be performances or creative interpretations of the song. That way, we can also avoid videos where “Bad Guy” was just background music. We continue to run this search daily, collecting an ever-expanding list of potential covers to use in the experience.

Aligning all the covers to the same beat

A key part of the experience is being able to jump from cover to cover seamlessly. But fan covers of “Bad Guy” vary widely. Some might be similar to the original, like a dance video set to Billie’s track. Some might vary more in tempo and instrumentation, like a heavy metal cover. And others might diverge greatly from the original, like a clarinet version with no lyrics. How can you get all these covers on the same beat? After trying several approaches like dynamic time warping and chord recognition, we’ve found the most success with a recurrent neural network trained to recognize sections and beats of “Bad Guy.” We collaborated with our friends at IYOYO on cover alignment and they have a great writeup about the process.

Building the experience

Finding and aligning the covers is a fascinating research problem, but the crucial final step is making them explorable to everyone. We’ve tried to make it intuitive and fun to navigate all the infinite combinations, while keeping latency low so the song never drops a beat.

The experience centers around three YouTube players, a number we settled on after a lot of experimentation. Initially we thought more players would be more interesting, but the experience got chaotic and slow. Around the players we’ve added discoverable features like the hashtag drawer and stats page. Video game interfaces have been a big inspiration for us, as they combine multiple interactions in a single dashboard. We’ve also added an autoplay mode for users who want to just sit back and be taken through an ever-changing mix of covers.

We’re excited about how Infinite Bad Guy showcases the incredibly diverse talent of YouTube and the potential machine learning can have for music and creativity. Give it a try and see what beautiful, strange, and brilliant covers you can find.

Source: Google Developers Blog

Irem from Turkey shares her groundbreaking work in TensorFlow and advice for the community

Posted by Jennifer Kohl, Global Program Manager, Google Developer Groups

Irem presenting at a Google Developer Group event

We recently caught up with Irem Komurcu, a TensorFlow developer and researcher at Istanbul Technical University in Turkey. Irem has been a long-serving member of Google Developer Groups (GDG) Düzce and also serves as a Women Techmakers (WTM) ambassador. Her work with TensorFlow has received several accolades, including being named a Hamdi Ulukaya Girişimi fellow. As one one of twenty-four young entrepreneurs selected, she was flown to New York City last year to learn more about business and receive professional development.

With all this experience to share, we wanted you to hear how she approaches pursuing a career in tech, hones her TensorFlow skills with the GDG community, and thinks about how upcoming programmers can best position themselves for success. Check out the full interview below for more.

What inspired you to pursue a career in technology?

I first became interested in tech when I was in high school and went on to study computer engineering. At university, I had an eye-opening experience when I traveled from Turkey to the Google Developer Day event in India. It was here where I observed various code languages, products, and projects that were new to me.

In particular, I saw TensorFlow in action for the first time. Watching the powerful machine learning tool truly sparked my interest in deep learning and project development.

Can you describe your work with TensorFlow and Machine Learning?

I have studied many different aspects of Tensorflow and ML. My first work was on voice recognition and deep learning. However, I am now working as a computer vision researcher conducting various segmentation, object detection, and classification processes with Tensorflow. In my free time, I write various articles about best practices and strategies to leverage TensorFlow in ML.

What has been a useful learning resource you have used in your career?

I kicked off my studies on deep learning on tensorflow.org. It’s a basic first step, but a powerful one. There were so many blogs, codes, examples, and tutorials for me to dive into. Both the Google Developer Group and TensorFlow communities also offered chances to bounce questions and ideas off other developers as I learned.

Between these technical resources and the person-to-person support, I was lucky to start working with the GDG community while also taking the first steps of my career. There were so many opportunities to meet people and grow all around.

What is your favorite part of the Google Developer Group community?

I love being in a large community with technology-oriented people. GDG is a network of professionals who support each other, and that enables people to develop. I am continuously sharing my knowledge with other programmers as they simultaneously mentor me. The chance for us to collaborate together is truly fulfilling.

What is unique about being a developer in your country/region?

The number of women supported in science, technology, engineering, and mathematics (STEM) is low in Turkey. To address this, I partner with Women Techmakers (WTM) to give educational talks on TensorFlow and machine learning to women who want to learn how to code in my country. So many women are interested in ML, but just need a friendly, familiar face to help them get started. With WTM, I’ve already given over 30 talks to women in STEM.

What advice would you give to someone who is trying to grow their career as a developer?

Keep researching new things. Read everything you can get your eyes on. Technology has been developing rapidly, and it is necessary to make sure your mind can keep up with the pace. That’s why I recommend communities like GDG that help make sure you’re up to date on the newest trends and learnings.

Want to work with other developers like Irem? Then find the right Google Developer Developer Group for you, here.

Source: Google Developers Blog

Coral makes edge AI even more accessible in 2020

Posted by the Coral team

Coral Dev Board Mini and Accelerator Module feature Google's Edge TPU co-processor to accelerate AI at the edge.

Since we launched Coral back in March 2019, we’ve added a number of new product form factors to accommodate the many ways users are adding on-device ML to their products. We've also streamlined the ML workflow and added capabilities like model pipelining with multiple Edge TPUs for an easier and more robust developer experience. And from this, we’ve helped enable amazing use cases from smart water meters that prevent water loss with Olea Edge, to systems for improving harvest yield with Farmwave, to noise cancellation in meetings in Google’s own Series One meeting kits.

This week, we’ll begin shipping the Coral Accelerator Module, a multi-chip module that combines the Edge TPU and it’s power circuitry into a solderable package. The module exposes PCIe and USB2 interfaces, which make it even easier to integrate Coral into custom designs. Several companies are already taking advantage of the compact size and capabilities with their new products coming to market. Read more about how Gumstix, STD, Siana Systems and IEI are using our module.

And in December, we’ll begin shipping the Dev Board Mini, a smaller, more power-efficient, and value-oriented board that brings forward a more traditional, flattened single-board computer design. The Dev Board Mini pairs a Mediatek 8167 SoC with the Coral Accelerator Module over USB 2 and is a great way to evaluate the module as the center of a project or deployment.

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

We recently announced a new version of the Coral ML APIs and tools. This release brings the C++ API into parity with Python and makes it more modular, reusable and performant. At the same time it eliminates unnecessary abstractions and surfaces replacing them with native TensorFlow Lite APIs. This release also graduates the Model Pipelining API out of beta and introduces a new model partitioner that automatically partitions models based on profiling and up to 10x better performance.

We’ve added a pre-trained version of MobileDet — a state-of-the-art object detection model for mobile systems — into our models portfolio. We’re migrating our model-development workflow to TensorFlow 2, and we’re including a handful of updated or new models based on the TF2 Keras framework. For details, check out the full announcement on the TensorFlow blog.

We’re also excited to see great developer tools coming from our ecosystem partners. For example, PerceptiLabs offers a visual API for building TensorFlow models and recently published a new demo which trains a machine learning model to identify sign language optimized for the edge with Coral.

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

And SigFox released a radio transceiver board that stacks on either the Coral Dev Board or Dev Board Mini. This allows small data payloads to be transmitted across low power, long range radio networks for use cases like smart cities, fleet management, asset tracking, agriculture and energy. The PCB design will be offered as a free download on SigFox’s website. Google Cloud Solutions Architect Markku Lepisto will present the new design today, in the opening keynote at SigFox Connect.

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Just in time for harvest we wanted to share a story about how Farmwave is using Coral to improve the efficiency of farm equipment and reduce food waste. Traditional yield loss analysis involves hand-counting grains of corn left on the ground mid harvest. It’s a time and labor intensive task, and not feasible for farmers who measure the value of their half-million-dollar combines in minutes spent running them.

By leveraging Coral’s on-device AI capabilities, Farmwave was able to build a system that automates the count while the machine is running. Thus allowing farmers to make real-time adjustments to harvesting machines in response to conditions in the field, which can make a big difference in yield.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Kura Revolving Sushi Bar in Japan has always been committed to the highest standards of health and safety for its customers. Known for their tech forward approach, Kura has dabbled in sushi making robots, an automated prize machine called Bikkura-pon, and a patented dome-shaped dish cover, aptly dubbed Mr. Fresh. But most recently, Kura has used Coral to develop an AI powered system that not only facilitates efficiency for better customer experiences, but also enables better tracking to prevent foodborne illnesses.

Making AI more accessible

While this year has presented the world with many obstacles, we’ve been impressed by the new ideas and innovations coming forward through technology. By providing the necessary tools and technology for edge AI, we strive to empower society to create affordable, adaptable, and intelligent systems.

We are excited to share all that Coral has to offer as we evolve our platform. For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page.

Please visit Coral.ai to discover more about our edge ML platform and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

Source: Google Developers Blog

Coral makes edge AI even more accessible in 2020

Posted by the Coral team

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Making AI more accessible

We are excited to share all that Coral has to offer as we evolve our platform. For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page.

Please visit Coral.ai to discover more about our edge ML platform and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

Source: Google Developers Blog

Coral makes edge AI even more accessible in 2020

Posted by the Coral team

You can see the new Dev Board Mini and Accelerator Module in action in the latest episode of Level Up, where Markku Lepisto controls his studio lights with speech commands.

To get updates on when the board will be available for purchase and other Coral news, sign up for our newsletter.

Developing for the edge, now simplified

The MRQ design from SigFox enables prototyping at the edge for low bandwidth IoT solutions with Coral

Customers with a Coral edge

The tool, from Farmwave, includes custom-developed ML models, a harvester-mounted box with cameras, an in-cab display, and on- device AI acceleration from Coral.

Kura Sushi designed their intelligent QA system using a Raspberry Pi paired with the Coral USB Accelerator

Making AI more accessible

We are excited to share all that Coral has to offer as we evolve our platform. For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page.

Please visit Coral.ai to discover more about our edge ML platform and share your feedback at [email protected]. To receive future Coral updates directly in your inbox, sign up for our newsletter.

Source: Google Developers Blog

Mitigating Unfair Bias in ML Models with the MinDiff Framework

Posted by Flavien Prost, Senior Software Engineer, and Alex Beutel, Staff Research Scientist, Google Research

The responsible research and development of machine learning (ML) can play a pivotal role in helping to solve a wide variety of societal challenges. At Google, our research reflects our AI Principles, from helping to protect patients from medication errors and improving flood forecasting models, to presenting methods that tackle unfair bias in products, such as Google Translate, and providing resources for other researchers to do the same.

One broad category for applying ML responsibly is the task of classification — systems that sort data into labeled categories. At Google, such models are used throughout our products to enforce policies, ranging from the detection of hate speech to age-appropriate content filtering. While these classifiers serve vital functions, it is also essential that they are built in ways that minimize unfair biases for users.

Today, we are announcing the release of MinDiff, a new regularization technique available in the TF Model Remediation library for effectively and efficiently mitigating unfair biases when training ML models. In this post, we discuss the research behind this technique and explain how it addresses the practical constraints and requirements we’ve observed when incorporating it in Google’s products.

Unfair Biases in Classifiers
To illustrate how MinDiff can be used, consider an example of a product policy classifier that is tasked with identifying and removing text comments that could be considered toxic. One challenge is to make sure that the classifier is not unfairly biased against submissions from a particular group of users, which could result in incorrect removal of content from these groups.

The academic community has laid a solid theoretical foundation for ML fairness, offering a breadth of perspectives on what unfair bias means and on the tensions between different frameworks for evaluating fairness. One of the most common metrics is equality of opportunity, which, in our example, means measuring and seeking to minimize the difference in false positive rate (FPR) across groups. In the example above, this means that the classifier should not be more likely to incorrectly remove safe comments from one group than another. Similarly, the classifier’s false negative rate should be equal between groups. That is, the classifier should not miss toxic comments against one group more than it does for another.

When the end goal is to improve products, it’s important to be able to scale unfair bias mitigation to many models. However, this poses a number of challenges:

Sparse demographic data: The original work on equality of opportunity proposed a post-processing approach to the problem, which consisted of assigning each user group a different classifier threshold at serving time to offset biases of the model. However, in practice this is often not possible for many reasons, such as privacy policies. For example, demographics are often collected by users self-identifying and opting in, but while some users will choose to do this, others may choose to opt-out or delete data. Even for in-process solutions (i.e., methods that change how a model is trained) one needs to assume that most data will not have associated demographics, and thus needs to make efficient use of the few examples for which demographics are known.
Ease of Use: In order for any technique to be adopted broadly, it should be easy to incorporate into existing model architectures, and not be highly sensitive to hyperparameters. While an early approach to incorporating ML fairness principles into applications utilized adversarial learning, we found that it too frequently caused models to degenerate during training, which made it difficult for product teams to iterate and made new product teams wary.
Quality: The method for removing unfair biases should also reduce the overall classification performance (e.g., accuracy) as little as possible. Because any decrease in accuracy caused by the mitigation approach could result in the moderation model allowing more toxic comments, striking the right balance is crucial.

MinDiff Framework
We iteratively developed the MinDiff framework over the previous few years to meet these design requirements. Because demographic information is so rarely known, we utilize in-process approaches in which the model’s training objective is augmented with an objective specifically focused on removing biases. This new objective is then optimized over the small sample of data with known demographic information. To improve ease of use, we switched from adversarial training to a regularization framework, which penalizes statistical dependency between its predictions and demographic information for non-harmful examples. This encourages the model to equalize error rates across groups, e.g., classifying non-harmful examples as toxic.

There are several ways to encode this dependency between predictions and demographic information. Our initial MinDiff implementation minimized the correlation between the predictions and the demographic group, which essentially optimized for the average and variance of predictions to be equal across groups, even if the distributions still differ afterward. We have since improved MinDiff further by considering the maximum mean discrepancy (MMD) loss, which is closer to optimizing for the distribution of predictions to be independent of demographics. We have found that this approach is better able to both remove biases and maintain model accuracy.

MinDiff with MMD better closes the FPR gap with less decrease in accuracy
(on an academic benchmark dataset).

To date we have launched modeling improvements across several classifiers at Google that moderate content quality. We went through multiple iterations to develop a robust, responsible, and scalable approach, solving research challenges and enabling broad adoption.

Gaps in error rates of classifiers is an important set of unfair biases to address, but not the only one that arises in ML applications. For ML researchers and practitioners, we hope this work can further advance research toward addressing even broader classes of unfair biases and the development of approaches that can be used in practical applications. In addition, we hope that the release of the MinDiff library and the associated demos and documentation, along with the tools and experience shared here, can help practitioners improve their models and products.

Acknowledgements
This research effort on ML Fairness in classification was jointly led with Jilin Chen, Shuo Chen, Ed H. Chi, Tulsee Doshi, and Hai Qian. Further, this work was pursued in collaboration with Jonathan Bischof, Qiuwen Chen, Pierre Kreitmann, and Christine Luu. The MinDiff infrastructure was also developed in collaboration with Nick Blumm, James Chen, Thomas Greenspan, Christina Greer, Lichan Hong, Manasi Joshi, Maciej Kula, Summer Misherghi, Dan Nanas, Sean O'Keefe, Mahesh Sathiamoorthy, Catherina Xu, and Zhe Zhao. (All names are listed in alphabetical order of last names.)

Source: Google AI Blog

Peer Bonus Experiences: Building tiny models for the ML community with TensorFlow

Almost all the current state-of-the-art machine learning (ML) models take quite a lot of disk space. This makes them particularly inefficient in production situations. A bulky machine learning model can be exposed as a REST API and hosted on cloud services, but that same bulk may lead to hefty infrastructure costs. And some applications may need to operate in low-bandwidth environments, making cloud-hosted models less practical.

In a perfect world, your models would live alongside your application, saving data transfer costs and complying with any regulatory requirements restricting what data can be sent to the cloud. But storing multi-gigabyte models on today’s devices just isn’t practical. The field of on-device ML is dedicated to the development of tools and techniques to produce tiny—yet high performing!—ML models. Progress has been slow, but steady!

There has never been a better time to learn about on-device ML and successfully apply it in your own projects. With frameworks like TensorFlow Lite, you have an exceptional toolset to optimize your bulky models while retaining as much performance as possible. TensorFlow Lite also makes it very easy for mobile application developers to integrate ML models with tools like metadata and ML Model Binding, Android codegen, and others.

What is TensorFlow Lite?

“TensorFlow Lite is a production ready, cross-platform framework for deploying ML on mobile devices and embedded systems.” - TensorFlow Youtube

TensorFlow Lite provides first-class support for Native Android and iOS-based integrations (with many additional features, such as delegates). TensorFlow Lite also supports other tiny computing platforms, such as microcontrollers. TensorFlow Lite’s optimization APIs produce world-class, fast, and well-performing machine learning models.

Venturing into TensorFlow Lite

Last year, I started playing around with TensorFlow Lite while developing projects for Raspberry Pi for Computer Vision, using the official documentation and this course to fuel my initial learning. Following this interest, I decided to join a voluntary working group focused on creating sample applications, writing out tutorials, and creating tiny models. This working group consists of individuals from different backgrounds passionate about teaching on-device machine learning to others. The group is coordinated by Khanh LeViet (TensorFlow Lite team) and Hoi Lam (Android ML team). This is by far one of the most active working groups I have ever seen. And, back in our starting days, Khanh proposed a few different state-of-art machine learning models that were great fits for on-device machine learning:

DeepLabV3 segmentation models that classify each image pixels to a particular category like so -

Selfie2Anime model that can turn a selfie into an anime like so -

These ideas were enough for us to start spinning up Jupyter notebooks and VSCode. After months of work, we now have strong collaborations between machine learning GDEs and a bunch of different TensorFlow Lite models, sample applications, and tutorials for the community to learn from. Our collaborations have been fueled by the power of open source and all the tiny models that we have built together are available on TensorFlow Hub. There are numerous open source applications that we have built that demonstrate how to use these models.

The Cartoonizer model cartoonizes uploaded images

Margaret and I co-authored an end-to-end tutorial that was published from the official TensorFlow blog and published the TensorFlow Lite models on TensorFlow Hub. So far, the response we have received for this work has been truly mesmerizing. I’ve also shared my experiences with TensorFlow Lite in these blog posts and conference talks:

A Tale of Model Quantization in TF Lite
Plunging into Model Pruning in Deep Learning
A few good stuff in TF Lite
Doing more with TF Lite
Model Optimization 101

The power of collaboration

The working group is a tremendous opportunity for machine learning GDEs, Googlers, and passionate community individuals to collaborate and learn. We get to learn together, create together, and celebrate the joy of teaching others. I am immensely thankful, grateful, and humbled to be a part of this group. Lastly, I would like to wholeheartedly thank Khanh for being a pillar of support to us and for nominating me for the Google Open Source Peer Bonus Award.

By Sayak Paul, PyImageSearch—Guest Author

Source: Google Open Source Blog

Announcing DevFest 2020

Posted by Jennifer Kohl, Program Manager, Developer Community Programs

On October 16-18, thousands of developers from all over the world are coming together for DevFest 2020, the largest virtual weekend of community-led learning on Google technologies.

As people around the world continue to adapt to spending more time at home, developers yearn for community now more than ever. In years past, DevFest was a series of in-person events over a season. For 2020, the community is coming together in a whole new way – virtually – over one weekend to keep developers connected when they may want it the most.

The speakers

The magic of DevFest comes from the people who organize and speak at the events - developers with various backgrounds and skill levels, all with their own unique perspectives. In different parts of the world, you can find a DevFest session in many local languages. DevFest speakers are made up of various types of technologists, including kid developers , self-taught programmers from rural areas , and CEOs and CTOs of startups. DevFest also features a wide range of speakers from Google, Women Techmakers, Google Developer Experts, and more. Together, these friendly faces, with many different perspectives, create a unique and rich developer conference.

The sessions and their mission

Hosted by Google Developer Groups, this year’s sessions include technical talks and workshops from the community, and a keynote from Google Developers. Through these events, developers will learn how Google technologies help them develop, learn, and build together.

Sessions will cover multiple technologies, such as Android, Google Cloud Platform, Machine Learning with TensorFlow, Web.dev, Firebase, Google Assistant, and Flutter.

At our core, Google Developers believes community-led developer events like these are an integral part of the advancement of technology in the world.

For this reason, Google Developers supports the community-led efforts of Google Developer Groups and their annual tentpole event, DevFest. Google provides esteemed speakers from the company and custom technical content produced by developers at Google. The impact of DevFest is really driven by the grassroots, passionate GDG community organizers who volunteer their time. Google Developers is proud to support them.

The attendees

During DevFest 2019, 138,000+ developers participated across 500+ DevFests in 100 countries. While 2020 is a very different year for events around the world, GDG chapters are galvanizing their communities to come together virtually for this global moment. The excitement for DevFest continues as more people seek new opportunities to meet and collaborate with like-minded, community-oriented developers in our local towns and regions.

Join the conversation on social media with #DevFest.

Sign up for DevFest at goo.gle/devfest.

Still curious? Check out these popular talks from DevFest 2019 events around the world...

Scalable Vector Graphics (SVG)

How to add Flutter to your app

The Flutter journey: production app

Getting started with microcontrollers

Chet Haase and Romaine Guy: quality

Building the ultimate Beatles app

Source: Google Developers Blog

Doubling down on the edge with Coral’s new accelerator

Posted by The Coral Team

Moving into the fall, the Coral platform continues to grow with the release of the M.2 Accelerator with Dual Edge TPU. Its first application is in Google’s Series One room kits where it helps to remove interruptions and makes the audio clearer for better video meetings. To help even more folks build products with Coral intelligence, we’re dropping the prices on several of our products. And for those folks that are looking to level up their at home video production, we’re sharing a demo of a pose based AI director to make multi-camera video easier to make.

Coral M.2 Accelerator with Dual Edge TPU

The newest addition to our product family brings two Edge TPU co-processors to systems in an M.2 E-key form factor. While the design requires a dual bus PCIe M.2 slot, it brings enhanced ML performance (8 TOPS) to tasks such as running two models in parallel or pipelining one large model across both Edge TPUs.

The ability to scale across multiple edge accelerators isn’t limited to only two Edge TPUs. As edge computing expands to local data centers, cell towers, and gateways, multi-Edge TPU configurations will be required to help process increasingly sophisticated ML models. Coral allows the use of a single toolchain to create models for one or more Edge TPUs that can address many different future configurations.

A great example of how the Coral M.2 Accelerator with Dual Edge TPU is being used is in the Series One meeting room kits for Google Meet.

The new Series One room kits for Google Meet run smarter with Coral intelligence

Google’s new Series One room kits use our Coral M.2 Accelerator with Dual Edge TPU to bring enhanced audio clarity to video meetings. TrueVoice®, a multi-channel noise cancellation technology, minimizes distractions to ensure every voice is heard with up to 44 channels of echo and noise cancellation, making distracting sounds like snacking or typing on a keyboard a concern of the past.

Enabling the clearest possible communication in challenging environments was the target for the Google Meet hardware team. The consideration of what makes a challenging environment was not limited to unusually noisy environments, such as lunchrooms doubling as conference rooms. Any conference room can present challenging acoustics that make it difficult for all participants to be heard.

The secret to clarity without expensive and cumbersome equipment is to use virtual audio channels and AI driven sound isolation. Read more about how Coral was used to enhance and future-proof the innovative design.

Expanding the AI edge

Earlier this year, we reduced the prices of our prototyping devices and sensors. We are excited to share further price drops on more of our products. Our System-on-Module is now available for $99.99, and our Mini PCIe Accelerator, M.2 Accelerator A+E Key, and M.2 Accelerator B+M key are now available at $24.99. We hope this lower price will make our edge AI more accessible to more creative minds around the world. Later, this month our SoM offering will also expand to include 2 and 4GB RAM options.

Multi-cam with AI

As we expand our platform and product family, we continue to keep new edge AI use cases in mind. We are continually inspired by our developer community’s experimentation and implementations. When recently faced with the challenges of multicam video production from home, Markku Lepistö, Solutions Architect at Google Cloud, created this real-time pose-based multicam tool he so aptly dubbed, AI Director.

We love seeing such unique implementations of on-device ML and invite you to share your own projects and feedback at [email protected].

For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform.

Source: Google Developers Blog

Doubling down on the edge with Coral’s new accelerator

Posted by The Coral Team

Coral M.2 Accelerator with Dual Edge TPU

A great example of how the Coral M.2 Accelerator with Dual Edge TPU is being used is in the Series One meeting room kits for Google Meet.

The new Series One room kits for Google Meet run smarter with Coral intelligence

Expanding the AI edge

Multi-cam with AI

We love seeing such unique implementations of on-device ML and invite you to share your own projects and feedback at [email protected].

For a list of worldwide distributors, system integrators and partners, visit the Coral partnerships page. Please visit Coral.ai to discover more about our edge ML platform.