LTS-102 is being updated in the LTS channel to 102.0.5005.189 (Platform Version: 14695.155.0) for most ChromeOS devices. Want to know more about Long Term Support? Click here.
The dev channel has been updated to 109.0.5414.25 for Windows,Mac and Linux.
- Admins: Ensure Google Meet can detect that it’s running inside a virtual machine (VM) by enabling the Enterprise Hardware Platform API policy in Chrome. Visit the API page and the Help Center to learn more about how to set Chrome policies for users or browsers and use VDIs with Google Meet.
- End users: There is no end user action required — Google Meet will automatically optimize your experience when using a VDI.
- Rapid Release and Scheduled Release domains: Gradual rollout (up to 15 days for feature visibility) starting on November 30, 2022
- Available to all Google Workspace customers, as well as legacy G Suite Basic and Business customers
Source: Google Workspace Updates
Introduction and background
The Serverless Migration Station mini-series helps App Engine developers modernize their apps to the latest language runtimes, such as from Python 2 to 3 or Java 8 to 17, or to sister serverless platforms Cloud Functions and Cloud Run. Another goal of this series is to demonstrate how to move away from App Engine's original APIs (now referred to as legacy bundled services) to Cloud standalone replacement services. Once no longer dependent on these proprietary services, apps become much more portable, making them flexible enough to:
- Run on 2nd generation App Engine runtimes
- Shift across to other serverless platforms, like Cloud Functions or Cloud Run (with or without Docker)
- Move to VM-based services like GKE or Compute Engine, or to other compute platforms
App Engine's Task Queue service provides infrastructure for executing tasks outside of the standard request-response workflow. Tasks may consist of workloads exceeding request timeouts or periodic tangential work. The Task Queue service provides two different queue types, push and pull, for developers to perform auxiliary work.
Push queues are covered in Migration Modules 7-9, demonstrating how to add use of push tasks to an existing baseline app followed by steps to migrate that functionality to Cloud Tasks, the standalone successor to the Task Queues push service. We turn to pull queues in today's video where Module 18 demonstrates how to add use of pull tasks to the same baseline sample app. Module 19 follows, showing how to migrate that usage to Cloud Pub/Sub.
Adding use of pull queues
In addition to registering page visits, the sample app needs to be modified to track visitors. Visits are comprised of a timestamp and visitor information such as the IP address and user agent. We'll modify the app to use the IP address and track how many visits come from each address seen. The home page is modified to show the top visitors in addition to the most recent visits:
|The sample app's updated home page tracking visits and visitors|
When visits are registered, pull tasks are created to track the visitors. The pull tasks sit patiently in the queue until they are processed in aggregate periodically. Until that happens, the top visitors table stays static. These tasks can be processed in a number of ways: periodically by a
cron or Cloud Scheduler job, a separate App Engine backend service, explicitly by a user (via browser or command-line HTTP request), event-triggered Cloud Function, etc. In the tutorial, we issue a
curl request to the app's endpoint to process the enqueued tasks. When all tasks have completed, the table then reflects any changes to the current top visitors and their visit counts:
|Processed pull tasks update the top visitors table|
Below is some pseudocode representing the core part of the app that was altered to add Task Queue pull task usage, namely a new data model class,
VisitorCount, to track visitor counts, enqueuing a (pull) task to update visitor counts when registering individual visits in
store_visit(), and most importantly, a new function
fetch_counts(), accessible via
/log, to process enqueued tasks and update overall visitor counts. The bolded lines represent the new or altered code.
|Adding App Engine Task Queue pull task usage to sample app|
This "migration" is comprised of adding Task Queue pull task usage to support tracking visitor counts to the Module 1 baseline app and arrives at the finish line with the Module 18 app. To get hands-on experience doing it yourself, do the codelab by hand and follow along with the video. Then you'll be ready to upgrade to Cloud Pub/Sub should you choose to do so.
In Fall 2021, the App Engine team extended support of many of the bundled services to 2nd generation runtimes (that have a 1st generation runtime), meaning you are no longer required to migrate pull tasks to Pub/Sub when porting your app to Python 3. You can continue using Task Queue in your Python 3 app so long as you retrofit the code to access bundled services from next-generation runtimes.
If you do want to move to Pub/Sub, see Module 19, including its codelab. All Serverless Migration Station content (codelabs, videos, and source code) are available at its open source repo. While we're initially focusing on Python users, the Cloud team is covering other runtimes soon, so stay tuned. Also check out other videos in the broader Serverless Expeditions series.
Source: Google Developers Blog
Source: The Official Google Blog
Wormholes — wrinkles in the fabric of spacetime that connect two disparate locations — may seem like the stuff of science fiction. But whether or not they exist in reality, studying these hypothetical objects could be the key to making concrete the tantalizing link between information and matter that has bedeviled physicists for decades.
Surprisingly, a quantum computer is an ideal platform to investigate this connection. The trick is to use a correspondence called AdS/CFT, which establishes an equivalence between a theory that describes gravity and spacetime (and wormholes) in a fictional world with a special geometry (AdS) to a quantum theory that does not contain gravity at all (CFT).
In “Traversable wormhole dynamics on a quantum processor”, published in Nature today, we report on a collaboration with researchers at Caltech, Harvard, MIT, and Fermilab to simulate the CFT on the Google Sycamore processor. By studying this quantum theory on the processor, we are able to leverage the AdS/CFT correspondence to probe the dynamics of a quantum system equivalent to a wormhole in a model of gravity. The Google Sycamore processor is among the first to have the fidelity needed to carry out this experiment.
Background: It from Qubit
The AdS/CFT correspondence was discovered at the end of a series of inquiries arising from the question: What’s the maximum amount of information that can fit in a single region of space? If one asked an engineer how much information could possibly be stored in a datacenter the answer would likely be that it depends on the number and type of memory chips inside it. But surprisingly, what is inside the data center is ultimately irrelevant. If one were to cram more and more memory chips with denser and denser electronics into the datacenter then it will eventually collapse into a black hole and disappear behind an event horizon.
When physicists such as Jacob Bekenstein and Stephen Hawking tried to compute the information content of a black hole, they found to their surprise that it is given by the area of the event horizon — not by the volume of the black hole. It looks as if the information inside the black hole was written on the event horizon. Specifically, a black hole with an event horizon that can be tiled with A tiny units of area (each unit, called a “Planck area,” is 2.6121×10−70 m2) has at most A/4 bits of information. This limit is known as the Bekenstein-Hawking bound.
This discovery that the maximum amount of information that could fit in a region was proportional not to its volume, but to the surface area of the region’s boundary hinted at an intriguing relationship between quantum information and the three-dimensional spatial world of our everyday experience. This relationship has been epitomized by the phrase “It from qubit,” describing how matter (“it”) emerges from quantum information (“qubit”).
While formalizing such a relationship is difficult for ordinary spacetime, recent research has led to remarkable progress with a hypothetical universe with hyperbolic geometry known as “anti-de Sitter space” in which the theory of quantum gravity is more naturally constructed. In anti-de Sitter space, the description of a volume of space with gravity acting in it can be thought of as encoded on the boundary enclosing the volume: every object inside the space has a corresponding description on the boundary and vice versa. This correspondence of information is called the holographic principle, which is a general principle inspired by Bekenstein and Hawking's observations.
|Schematic representation of anti-de Sitter space (interior of cylinder) and its dual representation as quantum information on the boundary (surface of cylinder).|
The AdS/CFT correspondence allows physicists to connect objects in space with specific ensembles of interacting qubits on the surface. That is, each region of the boundary encodes (in quantum information) the content of a region in spacetime such that matter at any given location can be "constructed" from the quantum information. This allows quantum processors to work directly with qubits while providing insights into spacetime physics. By carefully defining the parameters of the quantum computer to emulate a given model, we can look at black holes, or even go further and look at two black holes connected to each other — a configuration known as a wormhole, or an Einstein-Rosen bridge.
Experiment: Quantum Gravity in the Lab
Implementing these ideas on a Sycamore processor, we have constructed a quantum system that is dual to a traversable wormhole. Translated from the language of quantum information to spacetime physics via the holographic principle, the experiment let a particle fall into one side of a wormhole and observed it emerging on the other side.
Traversable wormholes were recently shown to be possible by Daniel Jafferis, Ping Gao and Aron Wall. While wormholes have long been a staple of science fiction, there are many possible spacetime geometries in which the formation of a wormhole is possible, but a naïvely constructed one would collapse on a particle traveling through it. The authors showed that a shockwave — i.e., a deformation of spacetime that propagates at the speed of light — of negative energy would solve this problem, propping open the wormhole long enough to enable traversability. The presence of negative energy in a traversable wormhole is similar to negative energy in the Casimir effect, where vacuum energy pushes together closely spaced plates. In both cases, quantum mechanics permits the energy density at a given location in space to be either positive or negative. On the other hand, if the wormhole experienced a shockwave of positive energy, no information would be allowed to pass through.
The simplest application of the holographic principle to create a wormhole requires many, many qubits — in fact, to approach the pencil-and-paper solutions given by theoretical physicists, one would need an arbitrarily large number of qubits. As the number of qubits is reduced, additional corrections are required that are still poorly understood today. New ideas were needed to build a traversable wormhole on a quantum computer with a limited number of qubits.
One of us (Zlokapa) adopted ideas from deep learning to design a small quantum system that preserved key aspects of gravitational physics. Neural networks are trained via backpropagation, a method that optimizes parameters by directly computing the gradient through the layers of the network. To improve the performance of a neural network and prevent it from overfitting to the training dataset, machine learning (ML) practitioners employ a host of techniques. One of these, sparsification, attempts to restrict the detail of information in the network by setting as many weights as possible to zero.
Similarly, to create the wormhole, we started with a large quantum system and treated it like a neural network. Backpropagation updated the parameters of the system in order to maintain gravitational properties while sparsification reduced the size of the system. We applied ML to learn a system that preserved only one key gravitational signature: the importance of using a negative energy shockwave. The training dataset compared dynamics of a particle traversing a wormhole propped open with negative energy and collapsed with positive energy. By ensuring the learned system preserved this asymmetry, we obtained a sparse model consistent with wormhole dynamics.
|Learning procedure to produce a sparse quantum system that captures gravitational dynamics. A single coupling consists of all six possible connections between a given group of four fermions.|
Working with Jafferis and a handful of collaborators from Caltech, Fermilab, and Harvard, we subjected the new quantum system to numerous tests to determine if it showed gravitational behavior beyond signatures induced by different energy shockwaves. For example, while quantum mechanical effects can transmit information across a quantum system in a diverse set of ways, information that travels in spacetime — including through a wormhole — must be causally consistent. This and other signatures were verified on classical computers, confirming that the dynamics of the quantum system were consistent with a gravitational interpretation as viewed through the dictionary of the holographic principle.
Implementing the traversable wormhole as an experiment on a quantum processor is an extraordinarily delicate process. The microscopic mechanism of information transfer across qubits is highly chaotic: imagine an ink drop swirling in water. As a particle falls into a wormhole, its information gets smeared over the entire quantum system in the holographic picture. For the negative energy shockwave to work, the scrambling of information must follow a particular pattern known as perfect size winding. After the particle hits the negative energy shockwave, the chaotic patterns effectively proceed in reverse: when the particle emerges from the wormhole, it is as if the ink drop has come back together by exactly undoing its original turbulent spread. If, at any point in time, a small error occurs, the chaotic dynamics will not undo themselves, and the particle will not make it through the wormhole.
On the Sycamore quantum processor, we measured how much quantum information passed from one side of the system to the other when applying a negative versus a positive energy shockwave. We observed a slight asymmetry between the two energies, showing the key signature of a traversable wormhole. Due to the protocol’s sensitivity to noise, the Sycamore processor’s low error rates were critical to measuring the signal; with even 1.5x the amount of noise, the signal would have been entirely obscured.
As quantum devices continue to improve, lower error rates and larger chips will allow deeper probes of gravitational phenomena. Unlike experiments such as LIGO that record data about gravity in the world around us, quantum computers provide a tool to explore theories of quantum gravity. We hope that quantum computers will help develop our understanding of future theories of quantum gravity beyond current models.
Gravity is only one example of the unique ability of quantum computers to probe complex physical theories: quantum processors can provide insight into time crystals, quantum chaos, and chemistry. Our work demonstrating wormhole dynamics represents a step towards discovering fundamental physics using quantum processors at Google Quantum AI.
You can also read more about this result here.
We would like to thank our Quantum Science Communicator Katherine McCormick for her help writing this blog post.
Source: Google AI Blog
Hi everyone! We've just released Chrome Dev 109 (109.0.5414.23) for Android. It's now available on Google Play.
If you find a new issue, please let us know by filing a bug.
Source: The Official Google Blog
Hi, everyone! We've just released Chrome 108 (108.0.5359.61) for Android: it'll become available on Google Play over the next few days.This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.
Android releases contain the same security fixes as their corresponding Desktop release (Windows: 108.0.5359.71/.72, Mac & Linux: 108.0.5359.71), unless otherwise noted.
The Chrome team is delighted to announce the promotion of Chrome 108 to the stable channel for Windows, Mac and Linux. This will roll out over the coming days/weeks.
Chrome 108.0.5359.71 ( Mac/linux) and 108.0.5359.71/72( Windows) contains a number of fixes and improvements -- a list of changes is available in the log. Watch out for upcoming Chrome and Chromium blog posts about new features and big efforts delivered in 108.
Security Fixes and Rewards
Note: Access to bug details and links may be kept restricted until a majority of users are updated with a fix. We will also retain restrictions if the bug exists in a third party library that other projects similarly depend on, but haven’t yet fixed.
This update includes 28 security fixes. Below, we highlight fixes that were contributed by external researchers. Please see the Chrome Security Page for more information.
[$15000] High CVE-2022-4174: Type Confusion in V8. Reported by Zhenghang Xiao (@Kipreyyy) on 2022-10-27
[$11000] High CVE-2022-4175: Use after free in Camera Capture. Reported by Leecraso and Guang Gong of 360 Alpha Lab on 2022-11-04
[$2000] High CVE-2022-4176: Out of bounds write in Lacros Graphics. Reported by @ginggilBesel on 2022-09-08
[$2000] High CVE-2022-4177: Use after free in Extensions. Reported by Chaoyuan Peng (@ret2happy) on 2022-10-28
[$NA] High CVE-2022-4178: Use after free in Mojo. Reported by Sergei Glazunov of Google Project Zero on 2022-10-18
[$NA] High CVE-2022-4179: Use after free in Audio. Reported by Sergei Glazunov of Google Project Zero on 2022-10-24
[$TBD] High CVE-2022-4180: Use after free in Mojo. Reported by Anonymous on 2022-10-26
[$TBD] High CVE-2022-4181: Use after free in Forms. Reported by Aviv A. on 2022-11-09
[$6000] Medium CVE-2022-4182: Inappropriate implementation in Fenced Frames. Reported by Peter Nemeth on 2022-09-28
[$5000] Medium CVE-2022-4183: Insufficient policy enforcement in Popup Blocker. Reported by David Sievers on 2021-09-22
[$5000] Medium CVE-2022-4184: Insufficient policy enforcement in Autofill. Reported by Ahmed ElMasry on 2022-09-01
[$5000] Medium CVE-2022-4185: Inappropriate implementation in Navigation. Reported by James Lee (@Windowsrcer) on 2022-10-10
[$5000] Medium CVE-2022-4186: Insufficient validation of untrusted input in Downloads. Reported by Luan Herrera (@lbherrera_) on 2022-10-21
[$5000] Medium CVE-2022-4187: Insufficient policy enforcement in DevTools. Reported by Axel Chong on 2022-11-04
[$3000] Medium CVE-2022-4188: Insufficient validation of untrusted input in CORS. Reported by Philipp Beer (TU Wien) on 2022-06-30
[$3000] Medium CVE-2022-4189: Insufficient policy enforcement in DevTools. Reported by NDevTK on 2022-07-15
[$3000] Medium CVE-2022-4190: Insufficient data validation in Directory. Reported by Axel Chong on 2022-10-27
[$2000] Medium CVE-2022-4191: Use after free in Sign-In. Reported by Jaehun Jeong(@n3sk) of Theori on 2022-10-12
[$1000] Medium CVE-2022-4192: Use after free in Live Caption. Reported by Samet Bekmezci @sametbekmezci on 2022-07-14
[$1000] Medium CVE-2022-4193: Insufficient policy enforcement in File System API. Reported by Axel Chong on 2022-08-19
[$TBD] Medium CVE-2022-4194: Use after free in Accessibility. Reported by Anonymous on 2022-10-03
[$NA] Medium CVE-2022-4195: Insufficient policy enforcement in Safe Browsing. Reported by Eric Lawrence of Microsoft on 2022-10-06
We would also like to thank all security researchers that worked with us during the development cycle to prevent security bugs from ever reaching the stable channel.
As usual, our ongoing internal security work was responsible for a wide range of fixes:
 Various fixes from internal audits, fuzzing and other initiatives
Many of our security bugs are detected using AddressSanitizer, MemorySanitizer, UndefinedBehaviorSanitizer, Control Flow Integrity, libFuzzer, or AFL.
Interested in switching release channels? Find out how here. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.