Tag Archives: problem solving

Get ready for Google I/O: Program lineup revealed

Posted by Timothy Jordan – Director, Developer Relations and Open Source

Developers, get ready! Google I/O is just around the corner, kicking off live from Mountain View with the Google keynote on Tuesday, May 14 at 10 am PT, followed by the Developer keynote at 1:30 pm PT.

But the learning doesn’t stop there. Mark your calendars for May 16 at 8 am PT when we’ll be releasing over 150 technical deep dives, demos, codelabs, and more on-demand. If you register online, you can start building your 'My I/O' agenda today.

Here's a sneak peek at some of the exciting highlights from the I/O program preview:

Unlocking the power of AI: The Gemini era unlocks a new frontier for developers. We'll showcase the newest features in the Gemini API, Google AI Studio, and Gemma. Discover cutting-edge pre-trained models from Kaggle, and delve into Google's open-source libraries like Keras and JAX.

Android: A developer's playground: Get the latest updates on everything Android! We'll cover groundbreaking advancements in generative AI, the highly anticipated Android 15, innovative form factors, and the latest tools and libraries in the Jetpack and Compose ecosystem. Plus, discover how to optimize performance and streamline your development workflow.

Building beautiful and functional web experiences: We’ll cover Baseline updates, a revolutionary tool that empowers developers with a clear understanding of web features and API interoperability. With Baseline, you'll have access to real-time information on popular developer resource sites like MDN, Can I Use, and web.dev.

The future of ChromeOS: Get a glimpse into the exciting future of ChromeOS. We'll discuss the developer-centric investments we're making in distribution, app capabilities, and operating system integrations. Discover how our partners are shaping the future of Chromebooks and delivering world-class user experiences.

This is just a taste of what's in store at Google I/O. Stay tuned for more updates, and get ready to be a part of the future.

Don't forget to mark your calendars and register for Google I/O today!

Get ready for Google I/O: Program lineup revealed


Developers, get ready! Google I/O is just around the corner, kicking off live from Mountain View with the Google keynote on Tuesday, May 14 at 10 am PT, followed by the Developer keynote at 1:30 pm PT.

But the learning doesn’t stop there. Mark your calendars for May 16 at 8 am PT when we’ll be releasing over 150 technical deep dives, demos, codelabs, and more on-demand. If you register online, you can start building your 'My I/O' agenda today.

Here's a sneak peek at some of the exciting highlights from the I/O program preview:

Unlocking the power of AI: The Gemini era unlocks a new frontier for developers. We'll showcase the newest features in the Gemini API, Google AI Studio, and Gemma. Discover cutting-edge pre-trained models from Kaggle, and delve into Google's open-source libraries like Keras and JAX.

Android: A developer's playground: Get the latest updates on everything Android! We'll cover groundbreaking advancements in generative AI, the highly anticipated Android 15, innovative form factors, and the latest tools and libraries in the Jetpack and Compose ecosystem. Plus, discover how to optimize performance and streamline your development workflow.

Building beautiful and functional web experiences: We’ll cover Baseline updates, a revolutionary tool that empowers developers with a clear understanding of web features and API interoperability. With Baseline, you'll have access to real-time information on popular developer resource sites like MDN, Can I Use, and web.dev.

The future of ChromeOS: Get a glimpse into the exciting future of ChromeOS. We'll discuss the developer-centric investments we're making in distribution, app capabilities, and operating system integrations. Discover how our partners are shaping the future of Chromebooks and delivering world-class user experiences.

This is just a taste of what's in store at Google I/O. Stay tuned for more updates, and get ready to be a part of the future.

Don't forget to mark your calendars and register for Google I/O today!

Posted by Timothy Jordan – Director, Developer Relations and Open Source

How to effectively A/B test power consumption for your Android app’s features

Posted by Mayank Jain - Product Manager, and Yasser Dbeis - Software Engineer; Android Studio

Android developers have been telling us they're looking for tools to help optimize power consumption for different devices on Android.

The new Power Profiler in Android Studio helps Android developers by showing power consumption happening on devices as the app is being used. Understanding power consumption across Android devices can help Android developers identify and fix power consumption issues in their apps. They can run A/B tests to compare the power consumption of different algorithms, features or even different versions of their app.

The new Power Profiler in Android Studio
The new Power Profiler in Android Studio

Apps which are optimized for lower power consumption lead to an improved battery and thermal performance of the device, which means an improved user experience on Android.

This power consumption data is made available through the On Device Power Monitor (ODPM) on Pixel 6+ devices, segmented by each sub-system called “Power Rails”. See Profileable power rails for a list of supported sub-systems.

The Power Profiler can help app developers detect problems in several areas:

    • Detecting unoptimized code that is using more power than necessary.
    • Finding background tasks that are causing unnecessary CPU usage.
    • Identifying wakelocks that are keeping the device awake when they are not needed.

Once a power consumption issue has been identified, the Power Profiler can be used when testing different hypotheses to understand why the app could be consuming excessive power. For example, if the issue is caused by background tasks, the developer can try to stop the tasks from running unnecessarily or for longer periods. And if the issue is caused by wakelocks, the developer can try to release the wakelocks when the resource is not in use or use them more judiciously. Then compare the power consumption before/after the change using the Power Profiler.

In this blog post, we showcase a technique which uses A/B testing to understand how your app’s power consumption characteristics might change with different versions of the same feature - and how you can effectively measure them.

A real-life example of how the Power Profiler can be used to improve the battery life of an app.

Let’s assume you have an app through which users can purchase their favorite movies.

Sample app to demonstrate A/B testing for measure power consumption
Sample app to demonstrate A/B testing for measure power consumption 
Video (c) copyright Blender Foundation | www.bigbuckbunny.org

As your app becomes popular and is used by more users, you realize that a high quality 4K video takes very long to load every time the app is started. Because of its large size, you want to understand its impact on power consumption on the device.

Originally, this video was in 4K quality in the best of intentions, so as to showcase the best possible movie highlights to your customers.

This makes you think…

    • Do you really need a 4K video banner on the home screen?
    • Does it make sense to load a 4K video over the network every time your app is run?
    • How will the power consumption characteristics of your app change if you replace the 4K video with something of lower quality (while still preserving the vivid look & feel of the video)?

This is a perfect scenario to perform an A/B test for power consumption

With an A/B test, you can test two slightly different variations of the video banner feature and choose the one with the better power consumption characteristics.

Scenario A : Run the app with 4K video banner on screen & measure power consumption

Scenario B : Run the app with lower resolution video banner on screen & measure power consumption

A/B Test setup

Let's take a moment and set up our Android Studio profiler to run this A/B test. We need to start the app and attach the CPU profiler to it and trigger a system trace (where the Power Profiler will be shown).

Step 1

Create a custom “Run configuration” by clicking the 3 dot menu > Edit

Custom run configuration
Custom run configuration

Step 2

Then select the “Profiling” tab and ensure that “Start this recording on startup” and CPU Activity > System Trace is selected. Then click “Apply”.

Edit configuration settings
Edit configuration settings

Now simply run the “Profile app startup profiling with low overhead” whenever you want to run this app from start and attach the CPU profiler to it.

Note on precision

The following example scenarios use the entire app startup for estimating the power consumption for this blog’s purpose. However you can use more advanced techniques to have even higher precision in getting power readings. Some techniques to try are:

    • Isolate and measure power consumption for video playback only after a tap event on the video player
    • Use the trace markers API to mark the start and stop time for power measurement timeline - and then only measure power consumption within that marked window

Scenario A

In this scenario, we run the app with 4K video playing and measure power consumption for the first 30 seconds. We can optionally also run the scenario A multiple times and average out the readings. Once the System trace is shown in Android Studio, select the 0-30 second time range from the timeline selection panel and record as a screenshot for comparing against scenario B

Power consumption in scenario A - playing a 4k video
Power consumption in scenario A - playing a 4k video

As you can see, the average power consumed by WLAN, CPU cores & Memory combined is about 1,352 mW (milliwatts)

Now let's compare and contrast how this power consumption changes in Scenario B

Scenario B

In this scenario, we run the app with low quality video playing and measure power consumption for the first 30 seconds. As before, we can also optionally run scenario B multiple times and average out the power consumption readings. Again, once the System trace is shown in Android Studio, select the 0-30 second time range from the timeline selection panel.

Power consumption in scenario B - playing a lower quality video
Power consumption in scenario B - playing a lower quality video

The total power consumed by WLAN, CPU Little, CPU Big and CPU Mid & Memory is about 741 mW (milliwatts)

Conclusion

All else being equal, Scenario B (with lower quality video) consumed 741 mW power as compared to Scenario A (with 4K video) which required 1,352 mW power.

Scenario B (lower quality video) took 45% less power than Scenario A (4K) - while the lower quality video provides little to no visual difference in perceived quality of the app’s screen.

As a result of this A/B test for power consumption, you conclude that replacing the 4K video with a lower quality video on our app’s home screen not only reduces power consumption by 45%, also reduces the required network bandwidth and can potentially also improve the thermal performance of the devices.

If your app’s business logic still requires the 4K video to be shown on the app’s screen, you can explore strategies like:

    • Caching the 4K video across subsequent runs of the app.
    • Loading video on a user tap.
    • Loading an image initially and only load the video after the screen has fully rendered (delayed loading).

The overall power consumption numbers presented in the above A/B test scenario might seem small, but it shows the techniques that app developers can use to effectively A/B test power consumption for their app’s features using the Power Profiler in Android Studio.

Next Steps

The new Power Profiler is available in Android Studio Hedgehog onwards. To know more, please head over to the official documentation.

Build with Google AI video series, Season 2: more AI patterns

Posted by Joe Fernandez – Google AI Developer Relations

We are off to another exciting year in Artificial Intelligence (AI) and it's time to build more applications with Google AI technology! The Build with Google AI video series is for developers looking to build helpful and practical applications with AI. We focus on useful code projects you can implement and extend in an afternoon to bring the power of artificial intelligence into your workflow or organization. Our first season received over 100,000 views in six weeks! We are glad to see that so many of you liked the series, and we are excited to bring you even more Google AI application projects.

Today, we are launching Season 2 of the Build with Google AI series, featuring projects built with Google's Gemini API technology. The launch of Gemini and the Gemini API has brought developers even more advanced AI capabilities, including advanced reasoning, content generation, information synthesis, and image interpretation. Our goal with this season is to help you put those capabilities to work for you and your organizations.


AI app patterns

The Build with Google AI series features practical application code projects created for you to use and customize. However, we know that you are the best judge of what you or your organization needs to solve day-to-day problems and get work done. That's why each application we feature in this series is also meant to be used as an AI pattern. You can extend the applications immediately to solve problems and provide value for your business, and these applications show you a general coding pattern for getting value out of AI technology.

For this second season of this series, we show how you can leverage Google's Gemini AI model capabilities for applications. Here's what's coming up:

  • AI Slides Reviewer with Google Workspace (3/20) - Image interpretation is one of the Gemini model's biggest new features. We show you how to make practical use of it with a presentation review app for Google Slides that you can customize with your organization's guidelines and recommendations. 
  • AI Flutter Code Agent with Gemini API (3/27) - Code generation was the most popular episode from last season, so we are digging deeper into this topic. Build a code generation extension to write Flutter code and explore user interface designs and looks with just a few words of description.
  • AI Data Agent with Google Cloud (4/3) - Why write code to extract data when you can just ask for it? Build a web application that uses Gemini API's Function Calling feature to translate questions into code calls and data into plain language answers.

Season 1 upgraded to Gemini API: We've upgraded Season 1 tutorials and code projects to use the Gemini API so you can take advantage of the latest in generative AI technology from Google. Check them out!


Learn from the developers

Just like last season, we'll go back to the studio to talk with coders who built these projects so they can share what they learned along the way. How do you make the Gemini model review an entire presentation? What's the most effective way to generate code with AI? How do you get a database to answer questions with the Gemini API? Get insights into coding with AI to jump start your own development project.


New home for AI developer content

Developers interested in Google's AI offerings now have a new home at ai.google.dev. There you'll find a wealth of resources for building with AI from Google, including the Build with Google AI tutorials. Stay tuned for much more content through the rest of the year.

We are excited to bring you the second season of Build with Google AIcheck out Season 2 right now! Use those video comments to let us know what you think and tell us what you'd like to see in future episodes.

Keep learning! Keep building!

YouTube Ads Creative Analysis

Posted by Brian Craft, Satish Shreenivasa, Huikun Zhang, Manisha Arora and Paul Cubre – gTech Data Science Team


Introduction


Why analyze YouTube ads?

YouTube has billions of monthly logged-in users and every day people watch billions of hours of video and generate billions of views. Businesses can connect with YouTube users using YouTube ads, which are promotional videos that appear on YouTube's website and app, with a variety of video ad formats and goals.

Image of a sample YouTube in-stream skippable video ad
A sample YouTube in-stream skippable video ad

The Challenge

An effective video ad focuses on the ABCDs.

  • Attention: Capturing the viewer's attention till the end.
  • Branding: Helping them hear or visualize the brand.
  • Connection: Making them feel something about the brand.
  • Direction: Encouraging them to take action.

But each YouTube ad has a varying number of components, for instance, objects, background music or a logo. Each of these components affect the view through rate (which is referred to as VTR for the remainder of the post) of the video ad. Therefore, analyzing video ads through the lens of the components in the ad helps businesses understand what about the ad improves VTR. The insights from these analyses can be used to inform the creation of new creatives and to optimize existing creatives to improve VTR.


The Proposal

We propose a machine learning based approach for analyzing a company’s YouTube ads to assess which components affect VTR, for the purpose of optimizing a video ad’s performance. We illustrate how to:

  • Use Google Cloud Video Intelligence API to extract the components of each video ad, using the underlying video files.
  • Transform that extracted data to engineered features that map to actionable business questions.
  • Use a machine learning model to isolate the effect on VTR of each engineered feature.
  • Interpret and action on those insights to improve video ad performance, for instance altering existing creatives or create new creatives to be used in an AB test.

Approach


The Process

The proposed analysis has 5 steps, discussed below.

1. Define Business Questions
Align on a list of business questions that are actionable, for instance “does having a logo in the opening shot affect VTR?” We suggest taking feasibility into account ahead of time, for instance if a product disclaimer is necessary to have for legal reasons, there is no reason to assess the impact a disclaimer has on VTR.

2. Raw Component Extraction
Use Google Cloud technologies, such as the Google Cloud Video Intelligence API, and underlying video files to extract raw components from each video ad. For instance, but not limited to, objects appearing in the video at a particular timestamp, presence of text and its location on the screen, or the presence of specific sounds.

3. Feature Engineering
Using the raw components extracted in step 2, engineer features that align to the business questions defined in step 1. For example, if the business question is “does having a logo in the opening shot affect VTR”, create a feature that labels each video as either 1, having a logo in the opening shot or 0, not having a logo in the opening shot. Repeat this for each feature.

4. Modeling
Create an ML model using the engineered features from step 3, using VTR as the target in the model.

5. Interpretation
Extract statistically significant features from the ML model and interpret their effect on VTR. For example, “there is an xx% observed uplift in VTR when there is a logo in the opening shot.”


Feature Engineering


Data Extraction

Consider 2 different YouTube Video Ads for a web browser, each highlighting a different product feature. Ad A has text that says “Built In Virus Protection'', while Ad B has text that says “Automatic Password Saving”.

The raw text can be extracted from each video ad and allow for the creation of tabular datasets, such as the below. For brevity and simplicity, the example carried forward will deal with text features only and forgo the timestamp dimension.

 Ad

 Detected Raw Text

 Ad A

 Built In Virus Protection

 Ad B

 Automatic Password Saving


Preprocessing

After extracting the raw components in each ad, preprocessing may need to be applied, such as removing case sensitivity and punctuation.

 Ad

 Detected Raw Text

 Processed Text

 Ad A

 Built IVirus Protection

 built ivirus protection

 Ad B

 Automatic Password Saving

 automatic password saving


Manual Feature Engineering

Consider a scenario where the goal is to answer the business question, “does having a textual reference to a product feature affect VTR?”

This feature could be built manually by exploring all the text in all the videos in the sample and creating a list of tokens or phrases that indicate a textual reference to a product feature. However, this approach can be time consuming and limits scaling.

Image of pseudo code for manual feature engineering
Pseudo code for manual feature engineering

AI Based Feature Engineering

Instead of manual feature engineering as described above, the text detected in each video ad creative can be passed to an LLM along with a prompt that performs the feature engineering automatically.

For example, if the goal is to explore the value of highlighting a product feature in a video ad, ask an LLM if the text “‘built in virus protection’ is a feature callout”, followed by asking the LLM if the text “‘automatic password saving’ is a feature callout”.

The answers can be extracted and transformed to a 0 or 1, to later be passed to a machine learning model.

 Ad

 Raw Text

 Processed Text

 Has Textual Reference to Feature

 Ad A

 Built IVirus Protection

 built ivirus protection

 Yes

 Ad B

 Automatic Password Saving

 automatic password saving

 Yes



Modeling


Training Data

The result of the feature engineering step is a dataframe with columns that align to the initial business questions, which can be joined to a dataframe that has the VTR for each video ad in the sample.

 Ad

 Has Textual Reference to Feature

 VTR*

 Ad A

 Yes

 10%

 Ad B

 Yes

 50%


*Values are random and not to be interpreted in any way.

Modeling is done using fixed effects, bootstrapping and ElasticNet. More information can be found here in the post Introducing Discovery Ad Performance Analysis, written by Manisha Arora and Nithya Mahadevan.

Interpretation

The model output can be used to extract significant features, coefficient values, and standard deviation.

Coefficient Value (+/- X%)
Represents the absolute percentage uplift in VTR. Positive value indicates positive impact on VTR and a negative value indicates a negative impact on VTR.

Significant Value (True/False)
Represents whether the feature has a statistically significant impact on VTR.

 Feature

 Coefficient*

 Standard Deviation*

 Significant?*

 Has Textual Reference to Feature

0.0222

0.000033

True


*Values are random and not to be interpreted in any way.

In the above hypothetical example, the feature “Has Feature Callout” has a statistically significant, positive impact of VTR. This can be interpreted as “there is an observed 2.22% absolute uplift in VTR when an ad has a textual reference to a product feature.”

Challenges

Challenges of the above approach are:

  • Interactions among the individual features input into the model are not considered. For example, if “has logo” and “has logo in the lower left” are individual features in the model, their interaction will not be assessed. However, a third feature can be engineered combining the above as “has large logo + has logo in the lower left”.
  • Inferences are based on historical data and not necessarily representative of future ad creative performance. There is no guarantee that insights will improve VTR.
  • Dimensionality can be a concern as given the number of components in a video ad.

Activation Strategies


Ads Creative Studio

Ads Creative Studio is an effective tool for businesses to create multiple versions of a video by quickly combining text, images, video clips or audio. Use this tool to create new videos quickly by adding/removing features in accordance with model output.

Image of sample video creation features in Ads creative studio
Sample video creation features in Ads creative studio

Video Experiments

Design a new creative, varying a component based on the insights from the analysis, and run an AB test. For example, change the size of the logo and set up an experiment using Video Experiments.


Summary


Identifying which components of a YouTube Ad affect VTR is difficult, due to the number of components contained in the ad, but there is an incentive for advertisers to optimize their creatives to improve VTR. Google Cloud technologies, GenAI models and ML can be used to answer creative centric business questions in a scalable and actionable way. The resulting insights can be used to optimize YouTube ads and achieve business outcomes.


Acknowledgements

We would like to thank our collaborators at Google, specifically Luyang Yu, Vijai Kasthuri Rangan, Ahmad Emad, Chuyi Wang, Kun Chang, Mike Anderson, Yan Sun, Nithya Mahadevan, Tommy Mulc, David Letts, Tony Coconate, Akash Roy Choudhury, Alex Pronin, Toby Yang, Felix Abreu and Anthony Lui.