[MUSIC PLAYING]
JESSICA EARLEY-CHA: I'm Jessica for "The Developer Show."
This is your weekly update on the coolest developer
news from Google.
The latest Crashlytics Flutter plugin
introduces two new fatal APIs--
a convenience API that plugs directly
into the FlutterError.on error, and a more generic API that
allows you to specify the error and set the trace.
Both APIs report exceptions as fatal events,
providing a way to distinguish between the priority
of the issue in the same intuitive way
like on native Android and iOS apps.
Instead of waiting for the user to quit and reopen the app,
the new fatal APIs--
they're on-demand.
On-demand means that the report is sent to Crashlytics
as soon as it's logged, providing you
with a real-time view into the issue within your Flutter apps
and eliminating the causes where your users don't relaunch
the apps for a long time or at all.
We release three big data flows to general availability
for real-time streaming and machine learning.
First is DataFlow Prime.
DataFlow Prime allows users to take advantage
of both horizontal autoscaling-- more machines--
and vertical autoscaling-- larger machines with more
memory, automatically for your streaming data processing
workloads, with batch coming in the near future.
Second, DataFlow Go.
DataFlow also provides native support
for Go for both batch and streaming data
processes workloads.
Finally, DataFlow ML.
DataFlow added out-of-the-box support for running PyTorch
and scikit-learn models directly within the pipeline.
The new run inference transform enables simplicity
by allowing models to be used in the production pipelines
with very little code.
Make sure to check out the Getting Started
guides for these.
We have added our 2022 Diversity Annual Report as a public data
set in BigQuery--
Google Cloud's powerful data warehousing tool.
The data includes demographical data
on workforce representation, hiring,
and attrition of employees at Google, including leadership.
You can see our hiring data by race, ethnicity, gender,
and intersectional hiring over time, by region, and more.
While Google's diversity data set
can help users compare their own data
sets to Google's current and historical trends,
DEI data is only useful when analyzed in the context
of other relevant data sets.
That's why Google also includes other public data
sets in BigQuery, such as related industries, DE&I data,
talent, and graduation pools.
By doing so, users can run a simple query
that then can compare Google's hiring and representation
to the related industries.
We explored how to build a generalist
agent to play many video games, simultaneously.
Our model trains an agent that can play 41 Atari
games simultaneously at close to human performance,
and can also be quickly adapted to new video
games via a fine tuning.
This approach significantly improves
upon the few existing alternatives
to learning multiple game agents,
such as temporal differences, learning,
or behavioral cloning.
Some key takeaways are from this--
first, don't optimize for return.
Just ask for optimality.
The idea is that training an agent on a range of experience,
from beginner to expert level, exposes the model
to a wide range of varieties and variations
in gameplay, which, in turn, helps it extract
useful rules of gameplay that allows it to become successful
under any circumstance.
Second, scaling up multiple game model
size to achieve better performance.
The performance increases predictability
with larger model size.
In particular, its performance appears
to have not yet hit a ceiling and, compared to other learning
system performance gains, are more significant with increase
in model size.
Last, pretrained multi-game decision transformers
are fast learners.
Multi-game decision transformers can
be considered pretrained models capable of being
fine-tuned rapidly on small new gameplay data.
We announced the first full version release
of the open source quantum programming framework,
Cirq 1.0.
Cirq is a Python framework for writing, running,
and analyzing the results of quantum computer programs.
It was designed for near-term quantum computers, those
with a few hundred qubits, and a few thousand quantum gates.
Several of our libraries were built on top of Cirq,
enabling different quantum-producing research
areas, like TensorFlow Quantum.
Also, numerous quantum-producing Cloud Services
from companies in the industry have also created with Cirq.
Here's a key takeaway from what we've learned.
As quantum producing hardware continues
to grow in scale and complexity, we
will be expecting that making software to support this growth
will be essential to continuing meaningful research
and progress.
Going forward, we will need an ever wider set of frameworks,
programming languages, and libraries
to achieve quantum computing's promise.
The Code Jam World Finals are coming up on August 5.
Code Jam is Google's longest running coding competition.
Participants have worked their way
through multiple rounds of algorithmic coding challenges
over the last few months.
And now, the top 25 competitors will face off at the World
Finals on August 5.
Tune in to the livestream during the round
to see the action unfold.
Hear from Code Jam engineers about the problems
the finalists are tackling.
And see who will be crowned the Code Jam 2022 World Champion,
winning the grand prize of $15,000.
Head over to the link to learn more, and keep up
with all the Code Jam World Finals action.
See you on the scoreboard.
To learn more about all of this week's stories,
make sure you check out the description
box below for all the links.
Please remember to like, subscribe, share,
and stay safe.
I'm Jessica for "The Developer Show."
Thanks for watching, and we'll see you next week.
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