Posted by Jeanine Banks – VP/GM, Developer X and Developer Relations, and Burak Gokturk – VP/GM, Cloud AI and Industry Solutions
Learn more about how to integrate Gemini Pro into your app or business at ai.google.dev
This article is also published on the Keyword blog.
Last week, we announced Gemini, our largest and most capable AI model and the next step in our journey to make AI more helpful for everyone. It comes in three sizes: Ultra, Pro and Nano. We've already started rolling out Gemini in our products: Gemini Nano is in Android, starting with Pixel 8 Pro, and a specifically tuned version of Gemini Pro is in Bard.
Today, we’re making Gemini Pro available for developers and enterprises to build for your own use cases, and we’ll be further fine-tuning it in the weeks and months ahead as we listen and learn from your feedback.
Gemini Pro is available today
The first version of Gemini Pro is now accessible via the Gemini API and here’s more about it:
Gemini Pro outperforms other similarly-sized models on research benchmarks.
Today’s version comes with a 32K context window for text, and future versions will have a larger context window.
In today’s release, Gemini Pro accepts text as input and generates text as output. We’ve also made a dedicated Gemini Pro Vision multimodal endpoint available today that accepts text and imagery as input, with text output.
Gemini Pro has SDKs that help you build apps that run anywhere.
Google AI Studio: The fastest way to build with Gemini
Google AI Studio is a free, web-based developer tool that enables you to quickly develop prompts and then get an API key to use in your app development. You can sign into Google AI Studio with your Google account and take advantage of the free quota, which allows 60 requests per minute — 20x more than other free offerings. When you’re ready, you can simply click on “Get code” to transfer your work to your IDE of choice, or use one of the quickstart templates available in Android Studio, Colab or Project IDX. To help us improve product quality, when you use the free quota, your API and Google AI Studio input and output may be accessible to trained reviewers. This data is de-identified from your Google account and API key.
Google AI Studio is a free, web-based developer tool that enables you to quickly develop prompts and then get an API key to use in your app development.
Build with Vertex AI on Google Cloud
When it's time for a fully-managed AI platform, you can easily transition from Google AI Studio to Vertex AI, which allows for customization of Gemini with full data control and benefits from additional Google Cloud features for enterprise security, safety, privacy and data governance and compliance.
With Vertex AI, you will have access to the same Gemini models, and will be able to:
Tune and distill Gemini with your own company’s data, and augment it with grounding to include up-to-minute information and extensions to take real-world actions.
Build Gemini-powered search and conversational agents in a low code / no code environment, including support for retrieval-augmented generation (RAG), blended search, embeddings, conversation playbooks and more.
Deploy with confidence. We never train our models on inputs or outputs from Google Cloud customers. Your data and IP are always your data and IP.
To read more about our new Vertex AI capabilities, visit the Google Cloud blog.
Gemini Pro pricing
Right now, developers have free access to Gemini Pro and Gemini Pro Vision through Google AI Studio, with up to 60 requests per minute, making it suitable for most app development needs. Vertex AI developers can try the same models, with the same rate limits, at no cost until general availability early next year, after which there will be a charge per 1,000 characters or per image across Google AI Studio and Vertex AI.
Big impact, small price: Because of our investments in TPUs, Gemini Pro can be served more efficiently.
Looking ahead
We’re excited that Gemini is now available to developers and enterprises. As we continue to fine-tune it, your feedback will help us improve. You can learn more and start building with Gemini on ai.google.dev, or use Vertex AI’s robust capabilities on your own data with enterprise-grade controls.
Early next year, we’ll launch Gemini Ultra, our largest and most capable model for highly complex tasks, after further fine-tuning, safety testing and gathering valuable feedback from partners. We’ll also bring Gemini to more of our developer platforms like Chrome and Firebase.
Posted by Karina Govindji Senior Director – LEAD - Global Workforce Diversity, and Noa Havazelet – Head of Google's accelerator programs across Europe and Israel
Applications are also open for underrepresented founders in North America
Artificial intelligence (AI) stands at the forefront of transformative technologies, reshaping industries and redefining the way we live and work. Yet, a closer look at the AI startup ecosystem reveals a stark gender disparity. Women, despite their profound capabilities and innovative prowess, often find themselves navigating a maze of obstacles in their entrepreneurial journey. Despite investment in AI software is booming globally, the venture capital funding problem for women is even more marked. Women-founded startups accounted for only 2.1% of VC deals involving AI startups1. This is a reality that demands attention and action. Globally in 2023, all-women founding teams raised just 3% of all dollars invested in the year, with mixed gender founding teams taking 15%, leaving 82% of dollars to flow to founding teams that are all men2.
The Google for Startups Accelerator: Women Founders program (Europe & Israel) provides a comprehensive mix of mentorship, technical support, and workshops, establishing a robust foundation for participants. Beyond Google's expert guidance, the accelerator cultivates a collaborative network among women founders, propelling innovation within the tech startup space. By empowering women founders, the Google for Startups Accelerator: Women Founders program (Europe & Israel) proactively contributes to creating a more inclusive and equitable tech community.
Applications for the Google for Startups Accelerator: Women Founders Europe & Israel program are open until January 19th, 2024. You can learn more and apply here.
In a similar vein, in North America, two other Google for Startups Accelerator programs for underrepresented founders have opened applications for the fifth Women Founders and Black Founders programs. These 10 week equity- free programs are best suited for Seed to Series A, high potential revenue generative women-led and black-led startups with growing teams (5+ employees). Applications for both programs close on February 1st, 2024.
To further explore these opportunities and why you should apply - listen to what past participants of the North American Women Founder and Black Founder programs have to say here.
Join us virtually from 9:30 am to 1:00 pm PT for an immersive and insightful set of deep dives for every level of Machine Learning experience.
The Women in ML Symposium is an inclusive event for anyone passionate about the transformative fields of Machine Learning (ML) and Artificial Intelligence (AI). Meet this year’s women in ML as they uncover practical applications across multiple industries and discuss the latest advancements in frameworks, generative AI, and more.
Joana Carrasqueira, presenter for “Enabling Anyone to Build with Google AI”
Joana is a Developer Relations Lead for AI/ML at Google and her mission is to empower individuals and organizations to harness the power of AI to address real-world challenges.
She is a business leader with a track record of bringing strategic vision and global cross-functional programs to life. She’s also the creator of Google’s Women in ML program and flagship symposium, a pioneering initiative that has equipped thousands of developers with knowledge and skills in AI/ML.
Prior to Google, she worked at the Silicon Valley Innovation Center on innovation consulting for Forbes top500, startups and Venture Capital firms. Served as Education Manager at the International Pharmaceutical Federation, working closely with WHO, UNESCO, the United Nations and started her career at the Portuguese Pharmaceutical Society.
Joana holds an MBA from IE Business School, a Master in Pharmaceutical Sciences and a Leadership Certificate from U.C. Berkeley in California.
Sharbani Roy, presenter for “What’s New in Machine Learning?”
Sharbani is Sr. Director in Google’s Core Machine Learning group.
Before joining Google, Sharbani led engineering and product teams in Amazon Alexa, focused on media streaming, real-time communication, and applied ML (e.g., NLU, CV, and AR) for 1P/3P developers and end consumers.
Sharbani holds degrees in physics and mathematics from the University of Chicago and an MBA from Stanford University, and lives in Seattle with her husband and three children.
Eve Phillips, presenter for “Future of Frameworks: Navigate the OSS Landscape"
Eve is a Director of Product Management at Google.
Currently, Eve leads the ML Frameworks product team, which includes responsibility for TensorFlow, JAX and Keras. Previously, she led product teams within Google for Clinicians and ChromeOS. Prior to Google, she served as CEO of Empower Interactive, delivering tech-enabled behavioral health.
Earlier, she held roles in leading technology companies and investors including Trilogy, Microsoft, and Greylock.
Eve earned a BS and M.Eng in EECS from MIT and an MBA from Stanford.
Meenu Gaba, presenter for “Data-Centric AI: A New Paradigm"
Meenu leads the Machine Learning infrastructure team at Google, with a mission to power AI innovation with world-class ML infrastructure and services.
She is a technology leader with years of experience launching new products and growing small teams into mature scalable, multi-tiered organizations that are poised to deliver high quality products. Meenu enjoys fast-paced, dynamic, highly iterative/innovative environments and has lots of experience in balancing these disciplines while fostering a people-first culture and forming solid grounds for cross-functional relationships.
Meenu holds a Master's degree in Computer Science. In her free time, she enjoys hiking, solving crosswords, and watching movies.
Kelly Shaefer, presenter for “Maximize Your Data Exploration”
Kelly leads product teams at Google Labs, building both entirely new AI products and AI-enabled features into Google's largest existing products.
In the past, she led the Growth team for Google Workspace, including Gmail, Drive, Docs, and many more.
Outside of Google, she led the Enterprise product team at Stripe and was the P&L owner for Stripe's multi-billion dollar Payments area.
Kelly has an undergraduate degree from Wharton at UPenn, and an MBA from Harvard Business School.
Divyashree Sreepathihalli, presenter for “Keras: Shortcut to AI Mastery”
Divya is a talented machine learning software engineer who is currently a part of the Keras team at Google.
In this role, she specializes in developing Keras core modeling APIs and KerasCV to improve the functionality of the software.
Prior to joining Google, Divya worked as a Deep Learning Scientist for Zazu Sensor, a startup group in Intel's Emerging Growth Incubation (EGI) group. Her work there focused on computer vision and deep learning algorithm development for object detection and tracking, resulting in significant advancements for the startup.
Divya completed her Masters in Computer Engineering from Texas A&M University where she focused on Artificial intelligence in 2017.
Na Li, presenter for “Prototype ML with Visual Blocks”
Na Li is a software engineer manager from Google CoreML.
She leads a team to build developer tools to support ML development journey, from prototyping to model visualization and benchmarking.
Prior to Google, she was a research scientist at Harvard, working in HCI domain.
Throughout her career, Na strives to make ML accessible for everyone.
Zoe Wang, presenter for “Deploying ML Models to Mobile Devices”
Zoe is a technical program manager at Google.
Her career has been focused on Machine Learning (ML) productionization.
Currently she works with her team bringing ML models to mobile devices that power some of AI features for Pixel and other edge devices.
Prior to Google, Zoe worked at Meta on ML Platforms for end-to-end ML lifecycles.
Yvonne Li, presenter for “New GenAI Products and Solutions on Google Cloud”
Yvonne Li is a software engineer on the Duet Platform team at Google, where she focuses on improving the quality of generative AI models.
As a machine learning engineer and developer advocate at IBM, she designed and developed language models and curated open source datasets.
She has over 3 years of experience in the big tech industry, and is passionate about using machine learning to solve real-world problems.
Yvonne is the author of two Coursera courses: Data Analysis with R, and, Data Visualization with R.
Nithya Natesan, presenter for “AI-powered Infrastructure: Cloud TPUs”
Nithya Natesan is a Group Product Manager in the Cloud ML Accelerators team focussing on GPU / TPU offerings for Google Cloud.
Prior to Google, she was head of product management at NVIDIA, launching several products like DGX Cloud, Base Command Platform.
She has ~14 years of experience in hyper convergence Data Center software products, with recent focus on ML / AI Infra and Platform products. She is passionate about building rock solid PM teams, and shipping high quality usable ML / AI products.
Andrada Vulpe, presenter for “Community Matters: 8 Reasons Why You Should Be Involved with Kaggle”
Andrada is a Data Scientist at Endava, a Notebooks Grandmaster on Kaggle, a Dev Expert at Weights and Biases and a proud Z by HP Data Science Global Ambassador.
She is highly passionate about Python, R, Machine and Deep Learning, powerful visualizations and everything in between.
Andrada finished her MSc in Data Science and Analytics in the UK and won 2 Kaggle Analytics competitions.
Jeehae Lee, presenter for “From Recovering Pro Golfer to AI Entrepreneur”
Jeehae Lee is a golf industry executive who has worked to create and build transformational sports technology businesses.
As the Co-Founder & CEO of Sportsbox AI, Jeehae is currently developing products using AI-enabled 3D motion analysis technology that will help participants of various sports and fitness activities learn and improve their skills.
Before founding Sportsbox, she spent five years between 2015 and 2020 at Topgolf Entertainment Group, leading strategy and new business development for various divisions including Toptracer. Between 2012 and 2013, she was at global sports and entertainment marketing agency, IMG, representing professional golfer icon Michelle Wie West. Prior to her career in sports business, she played professional golf at the highest level in the sport, competing on the LPGA tour for three years between 2009 and 2011.
Jeehae is a proud graduate of Phillips Academy in Andover, MA, and has a BA in Economics from Yale and an MBA from The Wharton School at University of Pennsylvania.
Jingwan (Cynthia) Lu, panelist for “The Impact of Generative AI in Different Industries”
Cynthia is a senior director from Adobe leading an applied research organization focusing on developing the Adobe Firefly family of GenAI models built from the ground up.
Her team started training Adobe’s first large-scale foundational model and helped rally together the rest of the company to roll out a new web-based product called Firefly featuring the image generation model as the first step in early 2023.
The same technology and its extension power Adobe Photoshop’s Generative Fill and Generative Expand features giving users intelligent image inpainting and outpainting experience. Time recognizes Adobe Photoshop Generative Fill and Generative Expand as best inventions of 2023 in the AI category.
Before Firefly, Jingwan was a computer vision research scientist and team lead who pioneered and led a large group effort to explore early generative models such as GANs within Adobe.
Wei Xiao, panelist for “The Impact of Generative AI in Different Industries”
Wei is the Director of Developer Relations at NVIDIA for the Middle East, Africa, and emerging regions. Her primary focus is to drive AI and accelerated computing integration within the ecosystem.
Before assuming her current role, Wei Xiao headed Ecosystem Engineering and Evangelism teams at both ARM and Samsung Semiconductor.
In addition to her professional endeavors, Wei dedicates her free time to teaching AI courses at the Graduate School of Computer Science at Santa Clara University.
Priya Mathur, panelist for “The Impact of Generative AI in Different Industries”
Priya is a Staff Data Science Manager at Google and she is the founder of Sparkle – GenAI Data Analyst.
At Google, she leads Data Science for Home Platform Monetization and GenAI efforts for DSPA.
Previously at Groupon, she led Data Science for App Push Notifications and TV Ads.
Katherine Chou, panelist for “The Impact of Generative AI in Different Industries”
Katherine is the Senior Director of Research and Innovations at Google with a specific focus on nurturing scientific and technical breakthroughs that can lead to global impact for science, health, climate, and advancement of platform technologies for our developers and researchers.
Katherine is focused on improving the availability and accuracy of healthcare using machine learning. She is a serial intrapreneur, particularly interested in removing health inequities and improving health and well-being outcomes across all populations.
She previously developed products within Google[x] Labs for Life Sciences (now Verily) and co-founded Medical Brain (now “Health AI'') at Google. She also headed up global teams to develop partner solutions and establish developer ecosystems for Mobile Payments, Mobile Search, GeoCommerce, YouTube, and Android.
Outside of Google, she is a Board member and Program Chair of Lewa Wildlife Conservancy, a Scientific Advisor to the ARCS Foundation, a fellow of the Zoological Society of London, and collaborates with other wildlife NGOs and the Cambridge Business Sustainability Programme in applying the Silicon Valley innovation mindset to new areas.
Katherine holds a double major in Computer Science and Economics at Stanford University and an M.S. in CS specialized in graphics.
Jaimie Hwang, presenter for “Take Action, Learn More, Start Building with Google AI”
Jaimie Hwang is a global product marketing leader with over a decade of experience, specifically in AI/ML.
She has built and led global product marketing teams at a number of AI companies, including an award-winning computer vision startup and tech giant Amazon.
She specializes in executive thought leadership, product storytelling, and integrated GTM strategy. She is passionate about promoting AI technology that is built responsibly and solves real-world problems in a human-centric way.
Jaimie holds a BS in Journalism and Integrated Marketing and Communications from Northwestern University. She lives in Seattle, Washington.
Save your spot at WiML Symposium 2023
The Women in ML Symposium offers sessions for all expertise levels, from beginners to advanced practitioners. RSVP today to secure your spot and explore our comprehensive agenda. We can’t wait to see you there!
Join us virtually from 9:30 am to 1:00 pm PT for an immersive and insightful set of deep dives for every level of Machine Learning experience.
The Women in ML Symposium is an inclusive event for anyone passionate about the transformative fields of Machine Learning (ML) and Artificial Intelligence (AI). Meet this year’s women in ML as they uncover practical applications across multiple industries and discuss the latest advancements in frameworks, generative AI, and more.
Joana Carrasqueira, presenter for “Enabling Anyone to Build with Google AI”
Joana is a Developer Relations Lead for AI/ML at Google and her mission is to empower individuals and organizations to harness the power of AI to address real-world challenges.
She is a business leader with a track record of bringing strategic vision and global cross-functional programs to life. She’s also the creator of Google’s Women in ML program and flagship symposium, a pioneering initiative that has equipped thousands of developers with knowledge and skills in AI/ML.
Prior to Google, she worked at the Silicon Valley Innovation Center on innovation consulting for Forbes top500, startups and Venture Capital firms. Served as Education Manager at the International Pharmaceutical Federation, working closely with WHO, UNESCO, the United Nations and started her career at the Portuguese Pharmaceutical Society.
Joana holds an MBA from IE Business School, a Master in Pharmaceutical Sciences and a Leadership Certificate from U.C. Berkeley in California.
Sharbani Roy, presenter for “What’s New in Machine Learning?”
Sharbani is Sr. Director in Google’s Core Machine Learning group.
Before joining Google, Sharbani led engineering and product teams in Amazon Alexa, focused on media streaming, real-time communication, and applied ML (e.g., NLU, CV, and AR) for 1P/3P developers and end consumers.
Sharbani holds degrees in physics and mathematics from the University of Chicago and an MBA from Stanford University, and lives in Seattle with her husband and three children.
Eve Phillips, presenter for “Future of Frameworks: Navigate the OSS Landscape"
Eve is a Director of Product Management at Google.
Currently, Eve leads the ML Frameworks product team, which includes responsibility for TensorFlow, JAX and Keras. Previously, she led product teams within Google for Clinicians and ChromeOS. Prior to Google, she served as CEO of Empower Interactive, delivering tech-enabled behavioral health.
Earlier, she held roles in leading technology companies and investors including Trilogy, Microsoft, and Greylock.
Eve earned a BS and M.Eng in EECS from MIT and an MBA from Stanford.
Meenu Gaba, presenter for “Data-Centric AI: A New Paradigm"
Meenu leads the Machine Learning infrastructure team at Google, with a mission to power AI innovation with world-class ML infrastructure and services.
She is a technology leader with years of experience launching new products and growing small teams into mature scalable, multi-tiered organizations that are poised to deliver high quality products. Meenu enjoys fast-paced, dynamic, highly iterative/innovative environments and has lots of experience in balancing these disciplines while fostering a people-first culture and forming solid grounds for cross-functional relationships.
Meenu holds a Master's degree in Computer Science. In her free time, she enjoys hiking, solving crosswords, and watching movies.
Kelly Shaefer, presenter for “Maximize Your Data Exploration”
Kelly leads product teams at Google Labs, building both entirely new AI products and AI-enabled features into Google's largest existing products.
In the past, she led the Growth team for Google Workspace, including Gmail, Drive, Docs, and many more.
Outside of Google, she led the Enterprise product team at Stripe and was the P&L owner for Stripe's multi-billion dollar Payments area.
Kelly has an undergraduate degree from Wharton at UPenn, and an MBA from Harvard Business School.
Divyashree Sreepathihalli, presenter for “Keras: Shortcut to AI Mastery”
Divya is a talented machine learning software engineer who is currently a part of the Keras team at Google.
In this role, she specializes in developing Keras core modeling APIs and KerasCV to improve the functionality of the software.
Prior to joining Google, Divya worked as a Deep Learning Scientist for Zazu Sensor, a startup group in Intel's Emerging Growth Incubation (EGI) group. Her work there focused on computer vision and deep learning algorithm development for object detection and tracking, resulting in significant advancements for the startup.
Divya completed her Masters in Computer Engineering from Texas A&M University where she focused on Artificial intelligence in 2017.
Na Li, presenter for “Prototype ML with Visual Blocks”
Na Li is a software engineer manager from Google CoreML.
She leads a team to build developer tools to support ML development journey, from prototyping to model visualization and benchmarking.
Prior to Google, she was a research scientist at Harvard, working in HCI domain.
Throughout her career, Na strives to make ML accessible for everyone.
Zoe Wang, presenter for “Deploying ML Models to Mobile Devices”
Zoe is a technical program manager at Google.
Her career has been focused on Machine Learning (ML) productionization.
Currently she works with her team bringing ML models to mobile devices that power some of AI features for Pixel and other edge devices.
Prior to Google, Zoe worked at Meta on ML Platforms for end-to-end ML lifecycles.
Yvonne Li, presenter for “New GenAI Products and Solutions on Google Cloud”
Yvonne Li is a software engineer on the Duet Platform team at Google, where she focuses on improving the quality of generative AI models.
As a machine learning engineer and developer advocate at IBM, she designed and developed language models and curated open source datasets.
She has over 3 years of experience in the big tech industry, and is passionate about using machine learning to solve real-world problems.
Yvonne is the author of two Coursera courses: Data Analysis with R, and, Data Visualization with R.
Nithya Natesan, presenter for “AI-powered Infrastructure: Cloud TPUs”
Nithya Natesan is a Group Product Manager in the Cloud ML Accelerators team focussing on GPU / TPU offerings for Google Cloud.
Prior to Google, she was head of product management at NVIDIA, launching several products like DGX Cloud, Base Command Platform.
She has ~14 years of experience in hyper convergence Data Center software products, with recent focus on ML / AI Infra and Platform products. She is passionate about building rock solid PM teams, and shipping high quality usable ML / AI products.
Andrada Vulpe, presenter for “Community Matters: 8 Reasons Why You Should Be Involved with Kaggle”
Andrada is a Data Scientist at Endava, a Notebooks Grandmaster on Kaggle, a Dev Expert at Weights and Biases and a proud Z by HP Data Science Global Ambassador.
She is highly passionate about Python, R, Machine and Deep Learning, powerful visualizations and everything in between.
Andrada finished her MSc in Data Science and Analytics in the UK and won 2 Kaggle Analytics competitions.
Jeehae Lee, presenter for “From Recovering Pro Golfer to AI Entrepreneur”
Jeehae Lee is a golf industry executive who has worked to create and build transformational sports technology businesses.
As the Co-Founder & CEO of Sportsbox AI, Jeehae is currently developing products using AI-enabled 3D motion analysis technology that will help participants of various sports and fitness activities learn and improve their skills.
Before founding Sportsbox, she spent five years between 2015 and 2020 at Topgolf Entertainment Group, leading strategy and new business development for various divisions including Toptracer. Between 2012 and 2013, she was at global sports and entertainment marketing agency, IMG, representing professional golfer icon Michelle Wie West. Prior to her career in sports business, she played professional golf at the highest level in the sport, competing on the LPGA tour for three years between 2009 and 2011.
Jeehae is a proud graduate of Phillips Academy in Andover, MA, and has a BA in Economics from Yale and an MBA from The Wharton School at University of Pennsylvania.
Jingwan (Cynthia) Lu, panelist for “The Impact of Generative AI in Different Industries”
Cynthia is a senior director from Adobe leading an applied research organization focusing on developing the Adobe Firefly family of GenAI models built from the ground up.
Her team started training Adobe’s first large-scale foundational model and helped rally together the rest of the company to roll out a new web-based product called Firefly featuring the image generation model as the first step in early 2023.
The same technology and its extension power Adobe Photoshop’s Generative Fill and Generative Expand features giving users intelligent image inpainting and outpainting experience. Time recognizes Adobe Photoshop Generative Fill and Generative Expand as best inventions of 2023 in the AI category.
Before Firefly, Jingwan was a computer vision research scientist and team lead who pioneered and led a large group effort to explore early generative models such as GANs within Adobe.
Wei Xiao, panelist for “The Impact of Generative AI in Different Industries”
Wei is the Director of Developer Relations at NVIDIA for the Middle East, Africa, and emerging regions. Her primary focus is to drive AI and accelerated computing integration within the ecosystem.
Before assuming her current role, Wei Xiao headed Ecosystem Engineering and Evangelism teams at both ARM and Samsung Semiconductor.
In addition to her professional endeavors, Wei dedicates her free time to teaching AI courses at the Graduate School of Computer Science at Santa Clara University.
Priya Mathur, panelist for “The Impact of Generative AI in Different Industries”
Priya is a Staff Data Science Manager at Google and she is the founder of Sparkle – GenAI Data Analyst.
At Google, she leads Data Science for Home Platform Monetization and GenAI efforts for DSPA.
Previously at Groupon, she led Data Science for App Push Notifications and TV Ads.
Katherine Chou, panelist for “The Impact of Generative AI in Different Industries”
Katherine is the Senior Director of Research and Innovations at Google with a specific focus on nurturing scientific and technical breakthroughs that can lead to global impact for science, health, climate, and advancement of platform technologies for our developers and researchers.
Katherine is focused on improving the availability and accuracy of healthcare using machine learning. She is a serial intrapreneur, particularly interested in removing health inequities and improving health and well-being outcomes across all populations.
She previously developed products within Google[x] Labs for Life Sciences (now Verily) and co-founded Medical Brain (now “Health AI'') at Google. She also headed up global teams to develop partner solutions and establish developer ecosystems for Mobile Payments, Mobile Search, GeoCommerce, YouTube, and Android.
Outside of Google, she is a Board member and Program Chair of Lewa Wildlife Conservancy, a Scientific Advisor to the ARCS Foundation, a fellow of the Zoological Society of London, and collaborates with other wildlife NGOs and the Cambridge Business Sustainability Programme in applying the Silicon Valley innovation mindset to new areas.
Katherine holds a double major in Computer Science and Economics at Stanford University and an M.S. in CS specialized in graphics.
Jaimie Hwang, presenter for “Take Action, Learn More, Start Building with Google AI”
Jaimie Hwang is a global product marketing leader with over a decade of experience, specifically in AI/ML.
She has built and led global product marketing teams at a number of AI companies, including an award-winning computer vision startup and tech giant Amazon.
She specializes in executive thought leadership, product storytelling, and integrated GTM strategy. She is passionate about promoting AI technology that is built responsibly and solves real-world problems in a human-centric way.
Jaimie holds a BS in Journalism and Integrated Marketing and Communications from Northwestern University. She lives in Seattle, Washington.
Save your spot at WiML Symposium 2023
The Women in ML Symposium offers sessions for all expertise levels, from beginners to advanced practitioners. RSVP today to secure your spot and explore our comprehensive agenda. We can’t wait to see you there!
As large screens become increasingly important within the Android app ecosystem, we are committed to enhance tools to help Android developers adapt their apps for these large screen form factors. In doing so, we strive to ensure that we can bring impactful tools to enhance the overall experience for building for all large screens such as foldables, tablets, and Chromebooks.
Over the last year, the team has worked on bringing Android 13 to the Desktop AVD, along with some additional enhancements to input support within the emulator. The Android 13 release of the Desktop AVD is now available within Android Studio. To test using this emulator, create a new virtual device.
What is the Desktop AVD?
Android Studio comes bundled with various virtual devices that run on different API levels and architectures. These emulators help developers test Android apps across a variety of devices, allowing for testing across different screen sizes, form factors, and APIs.
When an Android app runs on a Chromebook, it uses functionality that mirrors desktop behaviors, such as minimizing, maximizing, or resizing to a user-specified size. The Desktop Android Virtual Device (AVD) is an emulator that allows testing in a freeform windowing mode, similar to a Chromebook, to support this functionality.
What enhancements come with the Android 13 desktop AVD?
Most laptops use a keyboard—and it’s a common input device for increased productivity with tablets and foldables. Prior to Android 13, the Desktop AVD relied solely on uncustomizable input mapping built into Android Studio, which can cause friction points for users who rely on physical devices for mapped input and shortcuts. The Android 13 release of the Desktop AVD adds support for common keyboard interactions with Android apps. You can now test shortcuts, support keys, and mouse support to help you adhere to the large screen app quality guidelines.
Keyboard Shortcuts
The majority of apps within Google Play are designed for mobile usage and as such do not always support keyboard interactions. In Android 13, the Desktop AVD adds support for commonly used shortcuts, such as Ctrl+C (Copy) and Ctrl+V (Paste). These shortcuts can be used when copying text from a TextView/Text composable or pasting text into an EditText/TextField. These shortcuts are intercepted by the system and automatically applied.
Custom shortcuts (which are not intercepted by the system) are also included in this release. An example of this type of shortcut: a media player app that uses the Spacebar to play or pause media. You must use the new Hardware Input feature within Android Studio Hedgehog to use custom shortcuts. This will allow Android Studio to pass custom shortcuts directly to the emulator. If this is not enabled, Android Studio may consume the key combination.
Support keys
Android 13 supports additional keymappings for support keys. These keys are mapped to controls that are similar to experiences for keyboard shortcuts on a desktop. Some examples of these support keys include:
Esc: Dismisses pop-ups and notifications.
Delete / Backspace: Deletes text within an EditText or TextField
Arrow Keys: Provides in-app navigation (Arrow Up/Down to scroll).
Mouse support
In addition to enhanced keyboard support, there are additional mouse controls integrated into the Desktop AVD. Using the scroll wheel sends a mouse scroll event to the app that has input focus. Right-clicking the mouse sends a right-click event—which can be used to show context menus if the app supports it.
Where can you start?
Large screen app quality provides guidance around creating high quality large screen apps across all form factors, outlining a comprehensive set of quality requirements for most types of Android apps. Not all requirements need to be met, but it’s best practice for you to adhere to the requirements that make sense for your apps.
Create a desktop emulator today in Android Studio Hedgehog to see how your Android app responds to keyboard and mouse inputs and freeform window resizing.
Posted by Dominik Mengelt – Developer Relations Engineer
During the last months we've been working hard to align the Google Pay user experience across Web and Android. We are committed to advancing all Google Pay surfaces progressively, and creating a more cohesive experience for your users. In addition, the Google Pay sheets for Android and Chrome on Android now use the latest Material 3 design system with Web to follow in early 2024.
UX improvements on Android
Aligning the bottom sheets on Android and Chrome for Android (Mobile Web) led to a ~2.5% increase in conversion rate and a ~39% reduction in errors for users using Google Pay with Chrome on Android[1].
Figure 1: The identical Google Pay bottom sheets for Android (left) and Chrome on Android (right)
A completely revamped Google Pay sheet on the Web
On the web we aligned the user experience to be the same as on Android. Additionally we gave the Payment Handler window a more minimalistic look. With these changes we are seeing a conversion rate increase of ~9%.[1]
Figure 2: Google Pay displayed inside the new minimalistic Payment Handler window
No changes required!
Whether you are a merchant integrating Google Pay on your own or through a PSP, you don’t need to make any changes. We've already rolled out these changes to most of our users. This means that your users are likely already benefiting from the new experience or will very soon. For certain features, for example dynamic price updates, Google Pay will temporarily show the previous user experience. We are actively working on migrating all features to benefit from the new updated design.
Getting started with Google Pay
Not yet using Google Pay? Take a look at the documentation to start integrating Google Pay today. Learn more about the integration by taking a look at our sample application for Android on GitHub or use one of our button components for your web integration. When you are ready, head over to the Google Pay & Wallet console and submit your integration for production access.
Follow @GooglePayDevs on X (formerly Twitter) for future updates. If you have questions, tag @GooglePayDevs and include #AskGooglePayDevs in your tweets.
Passkeys are a safer and simpler alternative to passwords that works on all modern browsers and platforms. They enable signing into online accounts by using a device screen lock–with a fingerprint, facial recognition, PIN or a pattern.
More and more online services are adding passkey support every day. On 10 October, 2023, Google accounts made passkeys the default sign in method for all devices that support it.
To accelerate our way into a passwordless future, from 23-27 October we are hosting Passkeys Week–an online event where you can learn everything you need to know to successfully implement passkeys. Use #PasskeysWeek to participate in the conversation and spread the word about your products that support passkeys.
Keep an eye on @ChromiumDev and @AndroidDev, where we'll share new learning materials, including blog posts, case studies and pathways to earn passkeys badges on your Google Developer Profile.
On 25 October at 10 AM PDT, we’ll host a live Q&A session on Google for Developers YouTube channel where you can get all your questions about passkeys answered by passkeys engineers from Google. Bookmark this link or click "Notify me" to get alerted when the livestream is about to start:
The recording will also be available on the channel after the event — we hope you will tune in.
Passkeys are a safer and simpler alternative to passwords that works on all modern browsers and platforms. They enable signing into online accounts by using a device screen lock–with a fingerprint, facial recognition, PIN or a pattern.
More and more online services are adding passkey support every day. On 10 October, 2023, Google accounts made passkeys the default sign in method for all devices that support it.
To accelerate our way into a passwordless future, from 23-27 October we are hosting Passkeys Week–an online event where you can learn everything you need to know to successfully implement passkeys. Use #PasskeysWeek to participate in the conversation and spread the word about your products that support passkeys.
Keep an eye on @ChromiumDev and @AndroidDev, where we'll share new learning materials, including blog posts, case studies and pathways to earn passkeys badges on your Google Developer Profile.
On 25 October at 10 AM PDT, we’ll host a live Q&A session on Google for Developers YouTube channel where you can get all your questions about passkeys answered by passkeys engineers from Google. Bookmark this link or click "Notify me" to get alerted when the livestream is about to start:
The recording will also be available on the channel after the event — we hope you will tune in.
This article was originally posted on the Firebase blog.
For the past six years, we have shared the latest and greatest updates to Firebase, Google’s app development platform, at our annual Firebase Summit – this year, we wanted to do something a little different for our community of developers. So, in addition to the Flutter Firebase festival that just wrapped up, and meeting you all over the world at DevFests, we’re thrilled to announce our very first Firebase Demo Day, happening on November 8, 2023!
What is Demo Day?
Demo Day will be a virtual experience where we'll unveil short demos (i.e. pre-recorded videos) that showcase what's new, what's possible, and how you can solve your biggest app development challenges with Firebase. You’ll hear directly from our team about what they’ve been working on in a format that will feel both refreshing but also familiar.
What will you learn?
You’ll learn how Firebase can help you build and run fullstack apps faster, harness the power of AI to build smart experiences, and use Google technology and tools together to be more productive. We’ve been working closely with our friends from Flutter, Google Cloud, and Project IDX to ensure the demos cover a variety of topics and feature integrated solutions from your favorite Google products.
How can you participate?
Since Demo Day is not your typical physical or virtual event, you don’t need to worry about registering, securing a ticket, or even traveling. This is one of the easiest ways to peek at the exciting future of Firebase! Simply bookmark the website (and add the event to your calendar), then check back on Wednesday, November 8, 2023 at 1:00 pm EST to watch the videos at your own pace and be inspired to make your app the best it can be for users and your business.
In the meantime, we encourage you to follow us on X (formerly Twitter) and LinkedIn and join the conversation using #FirebaseDemoDay. We’ll be sharing teasers and behind-the-scenes footage throughout October as we count down to Demo Day, so stay tuned!
There are three primary ways that you can use the new MediaPipe Image Generator task:
Text-to-image generation based on text prompts using standard diffusion models.
Controllable text-to-image generation based on text prompts and conditioning images using diffusion plugins.
Customized text-to-image generation based on text prompts using Low-Rank Adaptation (LoRA) weights that allow you to create images of specific concepts that you pre-define for your unique use-cases.
Models
Before we get into all of the fun and exciting parts of this new MediaPipe task, it’s important to know that our Image Generation API supports any models that exactly match the Stable Diffusion v1.5 architecture. You can use a pretrained model or your fine-tuned models by converting it to a model format supported by MediaPipe Image Generator using our conversion script.
You can also customize a foundation model via MediaPipe Diffusion LoRA fine-tuning on Vertex AI, injecting new concepts into a foundation model without having to fine-tune the whole model. You can find more information about this process in our official documentation.
If you want to try this task out today without any customization, we also provide links to a few verified working models in that same documentation.
Image Generation through Diffusion Models
The most straightforward way to try the Image Generator task is to give it a text prompt, and then receive a result image using a diffusion model.
Like MediaPipe’s other tasks, you will start by creating an options object. In this case you will only need to define the path to your foundation model files on the device. Once you have that options object, you can create the ImageGenerator.
After creating your new ImageGenerator, you can create a new image by passing in the prompt, the number of iterations the generator should go through for generating, and a seed value. This will run a blocking operation to create a new image, so you will want to run it in a background thread before returning your new Bitmap result object.
val result= imageGenerator.generate(prompt_string, iterations, seed)
val bitmap= BitmapExtractor.extract(result?.generatedImage())
In addition to this simple input in/result out format, we also support a way for you to step through each iteration manually through the execute() function, receiving the intermediate result images back at different stages to show the generative progress. While getting intermediate results back isn’t recommended for most apps due to performance and complexity, it is a nice way to demonstrate what’s happening under the hood. This is a little more of an in-depth process, but you can find this demo, as well as the other examples shown in this post, in our official example app on GitHub.
Image Generation with Plugins
While being able to create new images from only a prompt on a device is already a huge step, we’ve taken it a little further by implementing a new plugin system which enables the diffusion model to accept a condition image along with a text prompt as its inputs.
We currently support three different ways that you can provide a foundation for your generations: facial structures, edge detection, and depth awareness. The plugins give you the ability to provide an image, extract specific structures from it, and then create new images using those structures.
LoRA Weights
The third major feature we’re rolling out today is the ability to customize the Image Generator task with LoRA to teach a foundation model about a new concept, such as specific objects, people, or styles presented during training. With the new LoRA weights, the Image Generator becomes a specialized generator that is able to inject specific concepts into generated images.
LoRA weights are useful for cases where you may want every image to be in the style of an oil painting, or a particular teapot to appear in any created setting. You can find more information about LoRA weights on Vertex AI in the MediaPipe Stable Diffusion LoRA model card, and create them using this notebook. Once generated, you can deploy the LoRA weights on-device using the MediaPipe Tasks Image Generator API, or for optimized server inference through Vertex AI’s one-click deployment.
In the example below, we created LoRA weights using several images of a teapot from the Dreambooth teapot training image set. Then we use the weights to generate a new image of the teapot in different settings.
Image generation with the LoRA weights
Next Steps
This is just the beginning of what we plan to support with on-device image generation. We’re looking forward to seeing all of the great things the developer community builds, so be sure to post them on X (formally Twitter) with the hashtag #MediaPipeImageGen and tag @GoogleDevs. You can check out the official sample on GitHub demonstrating everything you’ve just learned about, read through our official documentation for even more details, and keep an eye on the Google for Developers YouTube channel for updates and tutorials as they’re released by the MediaPipe team.
Acknowledgements
We’d like to thank all team members who contributed to this work: Lu Wang, Yi-Chun Kuo, Sebastian Schmidt, Kris Tonthat, Jiuqiang Tang, Khanh LeViet, Paul Ruiz, Qifei Wang, Yang Zhao, Yuqi Li, Lawrence Chan, Tingbo Hou, Joe Zou, Raman Sarokin, Juhyun Lee, Geng Yan, Ekaterina Ignasheva, Shanthal Vasanth, Glenn Cameron, Mark Sherwood, Andrei Kulik, Chuo-Ling Chang, and Matthias Grundmann from the Core ML team, as well as Changyu Zhu, Genquan Duan, Bo Wu, Ting Yu, and Shengyang Dai from Google Cloud.
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