Google announced a massive shift last week in how it handles cookies, those pesky digital trackers that chase us around the internet and serve up targeted ads that are both creepy yet eerily precise reflections of our wants. The search giant, which just helped Alphabet Inc. surpass a $1 trillion valuation, said it will eventually stop supporting third-party cookies in its ubiquitous Chrome browser.

The move won’t end the Big Brother era of Big Tech, but Google is framing the decision as a significant step away from unbridled data mining. In a blog post, Google references privacy about a dozen times, an awkward pitch for a company that built a juggernaut of a business by tapping into cookies from its billions of users. Can Google, after pioneering and protecting an apparent invasion of privac , sell its browser to consumers as a privacy-first service?

Google is going to try. That’s because the other browser makers are embracing privacy as a competitive advantage. Apple Inc. added cookie restrictions to Safari several years ago. Microsoft Corp. has been building a raft of tracking-prevention mechanisms into its Edge browser. And Mozilla Corp. has made paid privacy tools a core selling point of its Firefox service, though they’ve failed to catch on so far, leading to job cuts last week.

When Google first introduced Chrome in 2008, it essentially marketed the new browser as an online operating system, one that would treat popular web services—email, messaging, video streaming—as full-blown applications, rather than clunky web pages. Chrome was a fast, refreshing alternative to Firefox and Internet Explorer. In the decade since, it has soared in popularity: Chrome today boasts 63% worldwide market share, according to StatCounter.

Chrome also became a huge source of data, facilitating an ecosystem of Google services that kept feeding its advertising engine with more user information. The browser’s search box defaulted, of course, to Google, while users could log into the platform via Gmail to seamlessly access its products such Drive, Docs, Maps and YouTube, enabling the company to fill up ever-more jars of cookies. The dominance of the browser raised privacy concerns. One test last year found a whopping 11,189 requests for cookies in a week of surfing on Chrome. But only recently has Google started comprehensively rethinking its privacy policies, partly due to regulatory pressure and changing consumer sentiments around data collection.

“Users are demanding greater privacy—including transparency, choice and control over how their data is used—and it’s clear the web ecosystem needs to evolve to meet these increasing demands,” Justin Schuh, director of Chrome engineering, wrote in the blog post last week.

Google deserves a measure of credit for adopting consumer protections that could undermine its relationship with marketers and publishers, and also raise further antitrust scrutiny. Still, such policies stop far short of ridding Google of ad-tracking altogether: They may simply end up increasing the value of so-called first-party cookies, which websites collect directly from their users, rather than through intermediaries. One company well-positioned to keep gobbling those up from its many devoted users? Google.

If you read one thing

Criticizing Facebook, U.S. presidential candidate Joe Biden called for the repeal of Section 230, the legal framework that protects internet companies from liability for user-generated content. The law provision has become a hot-button issue in the Democratic primary, at least among geeks. Some candidates are debating the merits of whether it promotes free expression online or gives technology companies an excuse for not adequately policing their platforms.

And here’s what you need to know in global technology news

Twitter co-founder Jack Dorsey sought help from Elon Musk on how to fix the social network. At a congressional antitrust hearing, Sonos accused Alphabet and Amazon of leveraging their market power to stifle competition. Best Buy’s Corie Barry faces an investigation into allegations of personal misconduct, just six months after taking over as the electronics retailer’s chief executive officer.

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There’s now a JavaScript library for executing neural networks inside a webpage, using the hardware-accelerated graphics API available in modern web browsers.

Developed by a team of MIT graduate students, TensorFire can run TensorFlow-style machine learning models on any GPU, without requiring the GPU-specific middleware typically needed by machine learning libraries such as Keras-js.

TensorFire is another step towards making machine learning available to the broadest possible audience, using hardware and software people are already likely to possess, and via advances in how accurate model predictions can be served with a fraction of the resources previously needed.

The machine learning power is in your browser

TensorFire works using the WebGL standard, a cross-platform system for rendering GPU-accelerated graphics in browsers. WebGL supports GLSL, a C-like language used to write shaders, which are short programs used to transform data directly on the GPU.

Shaders are typically used in the WebGL pipeline to transform how graphics are rendered—for example, to render shadows or other visual effects. But TensorFire uses shaders to run in parallel the computations needed to generate predictions from TensorFlow models. TensorFire also comes with a library for importing existing TensorFlow and Keras models.

With this framework, you can deploy a trained model directly into a web browser and serve predictions locally from the browser. The user doesn’t need to download, install, or compile anything; all the work is done directly in the browser. The data used to make the predictions is also processed entirely on the client. The brand of GPU doesn’t matter, either: Both AMD and Nvidia GPUs are supported.

One web-based example of TensorFire shows a style-transfer neural network, where the style of one piece of artwork can be mapped to another image. The slowest part of the demo is downloading the model and compiling the shader pipeline; the actual execution takes only a second or two.

TensorFire’s creators claim it’s faster than other solutions. Bouncing data between GPU and CPU is a common performance bottleneck, and so TensorFire avoids this by keeping as much data as possible on the GPU at a time.

Keep your data close and your predictions closer

The most prominent advantages of TensorFire’s approach are its portability and convenience. Modern web browsers run on most every operating system and hardware platform, and even low-end smartphones have generous amounts of GPU power to spare. Much of the work involved in getting useful results from machine learning models is setting up the machine learning pipeline, either to perform the training or to deliver the predictions. It is very useful to boil much of that process down to just opening up a web browser and clicking something, at least for certain classes of jobs.

Another advantage claimed by TensorFire’s creators is that it allows the deployment of predictions to be done entirely on the client. This won’t be as much of an advantage where both the trained model and the data are already deployed to the cloud. But it’s a good fit for applications where the deployed model is small, the data is client-side, and the user is uneasy about uploading anything.

A third advantage of TensorFire is that it theoretically loosens the restrictions on which brands of graphics cards can be used for machine learning, thanks to the high speed it gains from both Nvidia and AMD GPUs.

Historically, Nvidia’s CUDA standard has been the go-to for accelerating machine learning via GPUs, providing more performance than the more open-ended OpenCL standard, which supports a broad range of hardware. AMD has its own plans about how to work around OpenCL’s performance issues, but TensorFire lets users and developers sidestep the issue completely.

TensorFire also takes advantage of another growing phenomenon: making machine learning models more compact and efficient with a slight (typically undetectable) loss of accuracy. This “low-precision quantized tensor” approach means smaller models can be deployed to the client, and predictions can be made faster.

But TensorFire’s makers claim the “low-precision quantized tensor” approach allows the software to run on a broader range of GPUs and browsers, especially those that don’t support the full range of WebGL extensions.

Finally, the TensorFire team plans to release the library as an MIT-licensed open source project, so the acceleration work done in TensorFire could also be used in a broad range of applications—even those that don’t have anything to do with TensorFlow or machine learning. The framework’s creators note that the low-level GLSL API in TensorFire “can also be used to do arbitrary parallel general-purpose computation,” meaning that other frameworks for GPU-powered, in-browser, client-side computation could be built atop it.

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