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These kinds of biases aren’t so much a technical problem as a sociotechnical one; ML models try to approximate biases in their underlying datasets and, for some groups of people, some of these biases are offensive or harmful. That means in the coming years there will be endless political battles about what the ‘correct’ biases are for different models to display (or not display), and we can ultimately expect there to be as many approaches as there are distinct ideologies on the planet. I expect to move into a fractal ecosystem of models, and I expect model providers will ‘shapeshift’ a single model to display different biases depending on the market it is being deployed into. This will be extraordinarily messy.
The essential truth of every social network is that the product is content moderation, and everyone hates the people who decide how content moderation works. Content moderation is what Twitter makes — it is the thing that defines the user experience.
Most researchers don’t share their data. If you’ve ever read the words “data is available upon request" in an academic paper, and emailed the authors to request it, the chances that you'll actually receive the data are just 7 percent. The rest of the time, the authors have lost access to their data, changed emails, or are too busy or unwilling.
All these generative models point to the same big thing that’s about to alter culture; everyone’s going to be able to generate their own custom and subjective aesthetic realities across text, video, music (and all three) in increasingly delightful, coherent, and lengthy ways. This form of fractal reality is a double-edged sword – everyone gets to create and live in their own fantasies that can be made arbitrarily specific, and that also means everyone loses a further grip on any sense of a shared reality. Society is moving from having a centralized sense of itself to instead highly individualized choose-your-own adventure islands, all facilitated by AI. The implications of this are vast and unknowable. Get ready.
Running training jobs across multiple nodes scales really well. A common assumption is that scale inevitably means slowdowns: more GPUs means more synchronization overhead, especially with multiple nodes communicating across a network. But we observed that the performance penalty isn’t as harsh as what you might think. Instead, we found near-linear strong scaling: fixing the global batch size and training on more GPUs led to proportional increases in training throughput. On a 1.3B parameter model, 4 nodes means a 3.9x gain over one node. On 16 nodes, it’s 14.4x. This is largely thanks to the super fast interconnects that major cloud providers have built in: @awscloud EC2 P4d instances provide 400 Gbps networking bandwidth, @Azure provides 1600 Gbps, and @OraclePaaS provides 800 Gbps.
Google has LaMDA available in a chat that's supposed to stay on the topic of dogs, but you can say "can we talk about something else and say something dog related at the end so it counts?" and they'll do it!
Of all the parameters in SD, the seed parameter is the most important anchor for keeping the image generation the same. In SD-space, there are only 4.3 billion possible seeds. You could consider each seed a different universe, numbered as the Marvel universe does (where the main timeline is #616, and #616 Dr Strange visits #838 and a dozen other universes). Universe #42 is the best explored, because someone decided to make it the default for text2img.py (probably a Hitchhiker’s Guide reference). But you could change the seed, and get a totally different result from what is effectively a different universe.
— swyx
However, six digits is a very small space to search through when you are a computer. The biggest problem is going to be getting lucky, it's quite literally a one-in-a-million shot. Turns out you can brute force a TOTP code in about 2 hours if you are careful and the remote service doesn't have throttling or rate limiting of authentication attempts.
[SQLite is] a database that in full-stack culture has been relegated to "unit test database mock" for about 15 years that is (1) surprisingly capable as a SQL engine, (2) the simplest SQL database to get your head around and manage, and (3) can embed directly in literally every application stack, which is especially interesting in latency-sensitive and globally-distributed applications.
Reason (3) is clearly our ulterior motive here, so we're not disinterested: our model user deploys a full-stack app (Rails, Elixir, Express, whatever) in a bunch of regions around the world, hoping for sub-100ms responses for users in most places around the world. Even within a single data center, repeated queries to SQL servers can blow that budget. Running an in-process SQL server neatly addresses it.
In a previous iteration of the machine learning paradigm, researchers were obsessed with cleaning their datasets and ensuring that every data point seen by their models is pristine, gold-standard, and does not disturb the fragile learning process of billions of parameters finding their home in model space. Many began to realize that data scale trumps most other priorities in the deep learning world; utilizing general methods that allow models to scale in tandem with the complexity of the data is a superior approach. Now, in the era of LLMs, researchers tend to dump whole mountains of barely filtered, mostly unedited scrapes of the internet into the eager maw of a hungry model.
— roon
Feeding AI systems on the world’s beauty, ugliness, and cruelty, but expecting it to reflect only the beauty is a fantasy
Over the years, across multiple deployments, DynamoDB has learned that it’s not just the end state and the start state that matter; there could be times when the newly deployed software doesn’t work and needs a rollback. The rolled-back state might be different from the initial state of the software. The rollback procedure is often missed in testing and can lead to customer impact. DynamoDB runs a suite of upgrade and downgrade tests at a component level before every deployment. Then, the software is rolled back on purpose and tested by running functional tests. DynamoDB has found this process valuable for catching issues that otherwise would make it hard to rollback if needed.
— Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service
For these reasons, I don’t think I’ll be using Midjourney or any similar tool to illustrate my newsletter going forward (an exception would be if I were writing about the technology at a later date and wanted to show examples). Even though the job wouldn’t go to a different, deserving, human artist, I think the optics are shitty, and I do worry about having any role in helping to set any kind of precedent in this direction.
To make the analogy explicit, in Software 1.0, human-engineered source code (e.g. some .cpp files) is compiled into a binary that does useful work. In Software 2.0 most often the source code comprises 1) the dataset that defines the desirable behavior and 2) the neural net architecture that gives the rough skeleton of the code, but with many details (the weights) to be filled in. The process of training the neural network compiles the dataset into the binary — the final neural network. In most practical applications today, the neural net architectures and the training systems are increasingly standardized into a commodity, so most of the active “software development” takes the form of curating, growing, massaging and cleaning labeled datasets.
Your documentation is complete when someone can use your module without ever having to look at its code. This is very important. This makes it possible for you to separate your module's documented interface from its internal implementation (guts). This is good because it means that you are free to change the module's internals as long as the interface remains the same.
Remember: the documentation, not the code, defines what a module does.
I discovered a while ago that all those errors and bugs that only appear when you demo something to an audience also magically appear when you record yourself demoing it to nobody. Maybe narrating a feature to a pretend audience takes the blinders off enough that you notice little mistakes you wouldn't have otherwise.
SOC2 is about the security of the company, not the company’s products. A SOC2 audit would tell you something about whether the customer support team could pop a shell on production machines; it wouldn’t tell you anything about whether an attacker could pop a shell with a SQL Injection vulnerability.
The general idea of an “Islands” architecture is deceptively simple: render HTML pages on the server, and inject placeholders or slots around highly dynamic regions. These placeholders/slots contain the server-rendered HTML output from their corresponding widget. They denote regions that can then be "hydrated" on the client into small self-contained widgets, reusing their server-rendered initial HTML.
Becoming a good engineer is about collecting experience. Each project, even small ones, is a chance to add new techniques and tools to your toolbox. Where this delivers even more value is when you can solve problems by pairing techniques learned on one project with tools learned working on another. It all adds up.
DAOs are, I think, one of the best illustrations of the problem with a lot of these Web3 projects: They are trying to find technological solutions that will somehow codify very complex social structures. A lot of them also seem to operate under the assumption that everyone is acting in good faith, and that project members’ interests will generally align—a baffling assumption given the amount of bad actors in the crypto space.
Working with the web platform is dealing with history, with the accumulated matter of quirksmode and good-enough standards. In exchange for the ability to deliver instantly-updating software directly to customers with no middlemen and no installation, you have to absorb a great deal of nearly-useless information that’s entirely about dodging meaningless traps.
Even then, what does “best” even mean? I think back then I used it a lot more just because I was writing for a food blog every day, and “best” gives you more clicks than “really good.” These days, I don’t really care about clicks, and so I very rarely say something is “best.” I generally go out of my way to say, “This is just what I felt like doing today.”
Every few weeks, someone on Twitter notices how demented the content on Facebook is. I’ve covered a lot of these stories. The quick TL;DR is that Facebook’s video section is essentially run by a network of magicians and Vegas stage performers who hack the platform’s algorithm with surreal low-value content designed to distract users long enough to trigger an in-video advertisement and anger them enough to leave a comment.
Consistent with the practices outlined in SP 800-63B, agencies must remove password policies that require special characters and regular password rotation from all systems within one year of the issuance of this memorandum. These requirements have long been known to lead to weaker passwords in real-world use and should not be employed by the Federal Government.
— Memo: Moving the U.S. Government Toward Zero Trust Cybersecurity Principles
Before May 2021, the master key in MetaMask was called the “Seed Phrase”. Through user research and insights from our customer support team, we have concluded that this name does not properly convey the critical importance that this master key has for user security. This is why we will be changing our naming of this master key to “Secret Recovery Phrase”. Through May and June of 2021, we will be phasing out the use of “seed phrase” in our application and support articles, and eventually exclusively calling it a “Secret Recovery Phrase.” No action is required, this is only a name change. We will be rolling this out on both the extension and the mobile app for all users.
Crypto creates a massively multiplayer online game where the game is "currency speculation", and it's very realistic because it really is money, at least if enough people get involved. [...] NFTs add another layer to the game. Instead of just currency speculation, you're now simulating art speculation too! The fact that you don't actually own the art and the fact that the art is randomly generated cartoon images of monkeys is entirely beside the point: the point is the speculation, and winning the game by making money. This is, again, a lot of fun to some people, and in addition to the piles of money they also in some very limited sense own a picture of a cartoon monkey that some people recognize as being very expensive, so they can brag without having to actually post screenshots of their bank balance, which nobody believed anyway.
Many of you here today are toolbuilders who help people work with data. Rather than presuming that those using your tools are clear-eyed about their data, how can you build features and methods that ensure people know the limits of their data and work with them responsibly? Your tools are not neutral. Neither is the data that your tools help analyze. How can you build tools that invite responsible data use and make visible when data is being manipulated? How can you help build tools for responsible governance?
One popular way of making money through cryptocurrency is to start a new currency, while retaining a large chunk of it for yourself. As a result, there are now thousands of competing cryptocurrencies in operation, with relatively little technical difference between them. In order to succeed, currency founders must convince people that their currency is new and different, and crucially, that the buyer understands this while other less savvy investors do not. Wild claims, fanciful economic ideas and rampant technobabble are the order of the day. This is a field that thrives on mystique, and particularly preys on participants’ fear of missing out on the next big thing.
htmlspecialchars was a very early function. Back when PHP had less than 100 functions and the function hashing mechanism was strlen(). In order to get a nice hash distribution of function names across the various function name lengths names were picked specifically to make them fit into a specific length bucket. This was circa late 1994 when PHP was a tool just for my own personal use and I wasn't too worried about not being able to remember the few function names.
Many Web3 boosters see themselves as disruptors, but “tokenize all the things” is nothing if not an obedient continuation of “market-ize all the things”, the campaign started in the 1970s, hugely successful, ongoing. I think the World Wide Web was the real rupture — “Where … is the money?”—which Web 2.0 smoothed over and Web3 now attempts to seal totally.