For researchers, Facebook is something of a black box. It’s hard to know what its 2.8 billion active users across the globe are seeing at any given time because the social media giant keeps most of its data to itself. If some users are seeing ads aimed at “Jew haters,” or Russian-generated memes comparing Hillary Clinton to Satan, well, so be it. Mark Zuckerberg has his strategy down cold: apologize when exposed, then move on to the next appalling scheme.
Some data scientists, though, have managed to pierce the darkness. Among them are Laura Edelson and Damon McCoy of New York University’s Center for Cybersecurity. With a tool called Ad Observer, which volunteers add to their browsers, they were able to track ads that Facebook users were being exposed to and draw some conclusions. For instance, they learned that users are more likely to engage with extreme falsehoods than with truthful material, and that more than 100,000 political ads are missing from an archive Facebook set up for researchers.
As you would expect, Facebook executives took these findings seriously. So what did they do? Did they change the algorithm to make it more likely that users would see reliable information in their news feed? Did they restore the missing ads and take steps to make sure such omissions wouldn’t happen again?
They did not. Instead, they cut off access to Edelson’s and McCoy’s accounts, making it harder for them to dig up such embarrassing facts in the future.
“There is still a lot of important research we want to do,” they wrote in a recent New York Times op-ed. “When Facebook shut down our accounts, we had just begun studies intended to determine whether the platform is contributing to vaccine hesitancy and sowing distrust in elections. We were also trying to figure out what role the platform may have played leading up to the Capitol assault on Jan. 6.”
In other words, they want to find out how responsible Zuckerberg, Sheryl Sandberg and the rest are for spreading a deadly illness and encouraging an armed insurrection. No wonder Facebook looked at what the researchers were doing and told them, gee, you know, we’d love to help, but you’re violating our privacy rules.
But that’s not even a real concern. Writing at the Columbia Journalism Review, Mathew Ingram points out that the privacy rules Facebook agreed to following the Cambridge Analytica scandal apply to Facebook itself, not to users who voluntarily agree to provide information to researchers.
Ingram quotes Princeton professor Jonathan Mayer, an adviser to Vice President Kamala Harris when she was a senator, who tweeted: “Facebook’s legal argument is bogus. The order “restricts how *Facebook* shares user information. It doesn’t preclude *users* from volunteering information about their experiences on the platform, including through a browser extension.”
The way Ingram describes it, as well as Edelson and McCoy themselves, Facebook’s actions didn’t stop their work altogether, but it has slowed it down and made it more difficult. Needless to say, the company should be doing everything it can to help with such research. Then again, Zuckerberg has never shown much regard for such mundane matters as public health and the future of democracy, especially when there’s money to be made.
By contrast, Facebook’s social media competitor Twitter has actually been much more open about making its data available to researchers. My Northeastern colleague John Wihbey, who co-authored an important study several years ago about how journalists use Twitter, says the difference explains why there have been more studies published about Twitter than Facebook. “This is unfortunate,” he says, “as it is a smaller network and less representative of the general public.”
It’s like the old saw about looking for your car keys under a street light because that’s where the light is. Trouble is, with fewer than 400 million active users, Twitter is little more than a rounding error in Facebook’s universe.
Earlier this year, MIT’s Technology Review published a remarkable story documenting how Facebook shied away from cracking down on extremist content, focusing instead on placating Donald Trump and other figures on the political right before the 2020 election. Needless to say, the NYU researchers represent an especially potent threat to the Zuckerborg since they plan to focus on the role that Facebook played in amplifying the disinformation that led to the insurrection, whose aftermath continues to befoul our body politic.
When the history of this ugly era is written, the two media giants that will stand out for their malignity are Fox News, for knowingly poisoning tens of millions of people with toxic falsehoods, and Facebook, for allowing its platform be used to amplify those falsehoods. Eventually, the truth will be told — no matter what steps Zuckerberg takes to slow it down. There should be hell to pay.
GBH News contributor Dan Kennedy’s blog, Media Nation, is online at dankennedy.net.
Meet the Developers – Linux Kernel Team (David Vernet)
Credit: Larry Ewing (email@example.com) and The GIMP for the original design of Tux the penguin.
For today’s interview, we have David Vernet, a core systems engineer on the Kernel team at Meta. He works on the BPF (Berkeley Packet Filter) and the Linux kernel scheduler. This series highlights Meta Software Engineers who contribute to the Linux kernel. The Meta Linux Kernel team works with the broader Linux community to add new features to the kernel and makes sure that the kernel works well in Meta production data centers. Engineers on the team work with peers in the industry to make the kernel better for Meta’s workloads and to make Linux better for everyone.
Tell us about yourself.
I’m a systems engineer who’s spent a good chunk of his career in the kernel space, and some time in the user-space as well working on a microkernel. Right now, I’m focusing most of my time on BPF and the Linux kernel scheduler.
I started my career as a web developer after getting a degree in math. After going to grad school, I realized that I was happiest when hacking on low-level systems and figuring out how computers work.
As a kernel developer at Meta, what does your typical day look like?
I’m not a maintainer of any subsystems in the kernel, so my typical day is filled with almost exclusively coding and engineering. That being said, participating in the upstream Linux kernel community is one of the coolest parts of being on the kernel team, so I still spend some time reading over upstream discussions. A typical day goes something like this:
Hack on the project that I’m working on. Lately, that’s adding a user-space ringbuffer map type to BPF.
Work on drafting an article for lwn.net.
What have you been excited about or incredibly proud of lately?
I recently submitted a patch-set to enable a new map type in BPF. This allows user-space to publish messages to BPF programs in the kernel over the ringbuffer. This map type is exciting because it sets the stage to enable frameworks for user-space to drive logic in BPF programs in a performant way.
Is there something especially exciting about being a kernel developer at a company like Meta?
The Meta kernel team has a strong upstream-first culture. Bug fixes that we find in our Meta kernel, and features that we’d like to add, are almost always first submitted to the upstream kernel, and then they are backported to our internal kernel.
Do you have a favorite part of the kernel dev life cycle?
I enjoy architecting and designing APIs. Kernel code can never crash and needs to be able to run forever. I find it gratifying to architect systems in the kernel that make it easy to reason about correctness and robustness and provide intuitive APIs that make it easy for other parts of the kernel to use your code.
I also enjoy iterating with the upstream community. It’s great that your patches have a whole community of people looking at them to help you find bugs in your code and suggest improvements that you may never have considered on your own. A lot of people find this process to be cumbersome, but I find that it’s a small price to pay for what you get out of it.
Tell us a bit about the topic you presented at the Linux Plumbers Conference this year.
We presented the live patch feature in the Linux kernel, describing how we have utilized it at Meta and how our hyper-scale has shown some unique challenges with the feature.
What are some of the misconceptions about kernel or open source software development that you have encountered in your career?
The biggest misconception is that it’s an exclusive, invite-only club to contribute to the Linux kernel. You certainly must understand operating systems to be an effective contributor and be ready to receive constructive criticism when there is scope for improvement in your code. Still, the community always welcomes people who come in with an open mind and want to contribute.
What resources are helpful in getting started in kernel development?
There is a lot of information out there that people have written on how to get integrated into the Linux kernel community. I wrote a blog post on how to get plugged into Linux kernel upstream mailing list discussions, and another on how to submit your first patch. There is also a video on writing and submitting your first Linux kernel patch from Greg Kroah-Hartman.
In terms of resources to learn about the kernel itself, there are many resources and books, such as:
- Linux Weekly News
- Linux Kernel Programming (parts 1 and 2)
- Linux Kernel in a Nutshell: A Desktop Quick Reference
- Our public blog posts
Where can people find you and follow your work?
I have a blog where I talk about my experiences as a systems engineer: https://www.bytelab.codes/. I publish articles that range from topics that are totally newcomer friendly to more advanced topics that discuss kernel code in more detail. Feel free to check it out and let me know if there’s anything you’d like me to discuss.
First seen at developers.facebook.com
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What Postman means for your WhatsApp projects
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The WhatsApp team is able to offer, via Postman, an API collection that pre-fills environment variables and walks you through your initial test requests – helping developers dive right in to using the Cloud API. Our product managers show you how easy it is to get started with Postman in this session from Conversations:
The public Postman workspace fosters collaboration – allowing environments, collections, and documentation augmentation to happen in one place.
Postman’s API documentation tools augment our own documentation and allows developers to contribute directly to the community’s shared knowledge, building a strong reference library for all developers and encouraging new, innovative use cases.
Working with Postman from the beginning helps create a developer-friendly experience for the WhatsApp Business Platform – allowing you to get started quickly, build community, and share knowledge.
Want to know more about our partnership with Postman? Check out their case study, follow along with the video above, or dive right into the Postman Workspace for the WhatsApp Business Platform.
First seen at developers.facebook.com
Summer of open source: building more efficient AI with PyTorch
Note: Special thanks to Less Wright, Partner Engineer, Meta AI, for review of and additional insights into the post.
This post on creating efficient artificial intelligence (AI) is the second in the “Summer of open source” series. This series aims to provide a handful of useful resources and learning content in areas where open source projects are creating impact across Meta and beyond. Follow along as we explore other areas where Meta Open Source is moving the industry forward by sharing innovative, scalable tools.
PyTorch: from foundational technology to foundation
Since its initial release in 2016, PyTorch has been widely used in the deep learning community, and its roots in research are now consistently expanding for use in production scenarios. In an exciting time for machine learning (ML) and artificial intelligence (AI), where novel methods and use cases for AI models continue to expand, PyTorch has reached the next chapter in its history as it moves to the newly established, independent PyTorch Foundation under the Linux Foundation umbrella. The foundation is made up of a diverse governing board including representatives from AMD, Amazon Web Services, Google Cloud, Microsoft Azure and Nvidia, and the board is intended to expand over time. The mission includes driving adoption of AI tooling through vendor-neutral projects and making open source tools, libraries and other components accessible to everyone. The move to the foundation will also enable PyTorch and its open source community to continue to accelerate the path from prototyping to production for AI and ML.
Streamlining AI processes with Meta open source
PyTorch is a great example of the power of open source. As one of the early open source deep learning frameworks, PyTorch has allowed people from across disciplines to experiment with deep learning and apply their work in wide-ranging fields. PyTorch supports everything from experiments in search applications to autonomous vehicle development to ground-penetrating radar, and these are only a few of its more recent applications. Pairing a versatile library of AI tools with the open source community unlocks the ability to quickly iterate on and adapt technology at scale for many different uses.
As AI is being implemented more broadly, models are trending up in size to tackle more complex problems, but this also means that the resources needed to train these models have increased substantially. Fortunately, many folks in the developer community have recognized the need for models to use fewer resources—both from a practical and environmental standpoint. This post will explore why quantization and other types of model compression can be a catalyst for efficient AI.
Establishing a baseline for using PyTorch
Most of this post explores some intermediate and advanced features of PyTorch. If you are a beginner that is looking to get started, or an expert that is currently using another library, it’s easiest to get started with some basics. Check out the beginner’s guide to PyTorch, which includes an introduction to a complete ML workflow using the Fashion MNIST dataset.
Here are some other resources that you might check out if you’re new to PyTorch:
- PyTorch Community Stories: Learn how PyTorch is making an impact across different industries like agriculture, education, travel and others
- PyTorch Beginner Series: Explore a video playlist of fundamental techniques including getting started with tensors, building models, training and inference in PyTorch.
Quantization: Applying time-tested techniques to AI
There are many pathways to making AI more efficient. Codesigning hardware and software to optimize for AI can be highly effective, but bespoke hardware-software solutions take considerable time and resources to develop. Creating faster and smaller architectures is another path to efficiency, but many of these architectures suffer from accuracy loss when compared to larger models, at least for the time being. A simpler approach is to find ways of reducing the resources that are needed to train and serve existing models. In PyTorch, one way to do that is through model compression using quantization.
Quantization is a mathematical technique that has been used to create lossy digital music files and convert analog signals to digital ones. By executing mathematical calculations with reduced precision, quantization allows for significantly higher performance on many hardware platforms. So why use quantization to make AI more efficient? Results show that in certain cases, using this relatively simple technique can result in dramatic speedups (2-4 times) for model inference.
The parameters that make up a deep learning model are typically decimal numbers in floating point (FP) precision; each parameter requires either 16 bits or 32 bits of memory. When using quantization, numbers are often converted to INT4 or INT8, which occupy only 4 or 8 bits. This reduces how much memory models require. Additionally, chip manufacturers include special arithmetic that makes operations using integers faster than using decimals.
There are 3 methods of quantization that can be used for training models: dynamic, static and quantize-aware training (QAT). A brief overview of the benefits and weaknesses is described in the table below. To learn how to implement each of these in your AI workflows, read the Practical Quantization in PyTorch blog post.
Additional overhead in every forward pass
May need regular recalibration for distribution drift
Quantize-Aware Training (QAT)
High computational cost
Additional features for speeding up your AI workflow
Quantization isn’t the only way to make PyTorch-powered AI more efficient. Features are updated regularly, and below are a few other ways that PyTorch can improve AI workflows:
Inference mode: This mode can be used for writing PyTorch code if you’re only using the code for running inference. Inference mode changes some of the assumptions when working with tensors to speed up inference. By telling PyTorch that you won’t use tensors for certain applications later (in this case, autograd), it adjusts to make code run faster in these specific scenarios.
Low precision: Quantization works only at inference time, that is, after you have trained your model. For the training process itself, PyTorch uses AMP, or automatic mixed precision training, to find the best format based on which tensors are used (FP16, FP32 or BF16). Low-precision deep learning in PyTorch has several advantages. It can help lower the size of a model, reduce the memory that is required to train models and decrease the power that is needed to run models. To learn more, check out this tutorial for using AMP with CUDA-capable GPUs.
Channels last: When it comes to vision models, NHWC, otherwise known as channels-last, is a faster tensor memory format in PyTorch. Having data stored in the channels-last format accelerates operations in PyTorch. Formatting input tensors as channels-last reduces the overhead that is needed for conversion between different format types, resulting in faster inference.
Optimize for inference: This TorchScript prototype implements some generic optimizations that should speed up models in all environments, and it can also prepare models for inference with build-specific settings. Primary use cases include vision models on CPUs (and GPUs) at this point. Since this is a prototype, it’s possible that you may run into issues. Raise an issue that occurs on the PyTorch GitHub repository.
Unlocking new potential in PyTorch
Novel methods for accelerating AI workflows are regularly explored on the PyTorch blog. It’s a great place to keep up with techniques like the recent BetterTransformer, which increases speedup and throughput in Transformer models by up to 2 times for common execution scenarios. If you’re interested in learning how to implement specific features in PyTorch, the recipes page allows you to search by categories like model optimization, distributed training and interpretability. This post is only a sampling of how tools like PyTorch are moving open source and AI forward.
To stay up to date with the latest in Meta Open Source for artificial intelligence and machine learning, visit our open source site, subscribe to our YouTube channel, or follow us on Facebook, Twitter and LinkedIn.
First seen at developers.facebook.com
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