344 items tagged “google”
2023
The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. Our evaluation results show that PaLM 2 models significantly outperform PaLM on a variety of tasks, including natural language generation, translation, and reasoning. These results suggest that model scaling is not the only way to improve performance. Instead, performance can be unlocked by meticulous data selection and efficient architecture/objectives. Moreover, a smaller but higher quality model significantly improves inference efficiency, reduces serving cost, and enables the model’s downstream application for more applications and users.
— PaLM 2 Technical Report, PDF
Leaked Google document: “We Have No Moat, And Neither Does OpenAI”
SemiAnalysis published something of a bombshell leaked document this morning: Google “We Have No Moat, And Neither Does OpenAI”.
[... 1,073 words]Bard now helps you code (via) Google have enabled Bard’s code generation abilities—these were previously only available through jailbreaking. It’s pretty good—I got it to write me code to download a CSV file and insert it into a SQLite database—though when I challenged it to protect against SQL injection it hallucinated a non-existent “cursor.prepare()” method. Generated code can be exported to a Colab notebook with a click.
If you ask Microsoft’s Bing chatbot if Google’s Bard chatbot has been shut down, it says yes, citing as evidence a news article that discusses a tweet in which a user asked Bard when it would be shut down and Bard said it already had, itself citing a comment from Hacker News in which someone joked about this happening, and someone else used ChatGPT to write fake news coverage about the event.
Don’t trust AI to talk accurately about itself: Bard wasn’t trained on Gmail
Earlier this month I wrote about how ChatGPT can’t access the internet, even though it really looks like it can. Consider this part two in the series. Here’s another common and non-intuitive mistake people make when interacting with large language model AI systems: asking them questions about themselves.
[... 1,950 words]Here are some absurdly expensive things you can do on a trip to Tokyo: Buy a golden toilet. There is a toilet in Tokyo that is made of gold and costs around 10 million yen. If you are looking for a truly absurd experience, you can buy this toilet and use it for your next bowel movement. [...]
Google Bard is now live. Google Bard launched today. There’s a waiting list, but I made it through within a few hours of signing up, as did other people I’ve talked to. It’s similar to ChatGPT and Bing—it’s the same chat interface, and it can clearly run searches under the hood (though unlike Bing it doesn’t tell you what it’s looking for).
Exploring MusicCaps, the evaluation data released to accompany Google’s MusicLM text-to-music model
Google Research just released MusicLM: Generating Music From Text. It’s a new generative AI model that takes a descriptive prompt and produces a “high-fidelity” music track. Here’s the paper (and a more readable version using arXiv Vanity).
[... 1,323 words]2022
Does Company ‘X’ have an Azure Active Directory Tenant? (via) Neat write-up from Shawn Tabrizi about looking up if a company has Active Directory single-sign-on configured (which is based on OpenID) by checking for an OpenID configuration endpoint. I particularly enjoyed this new-to-me trick: Google’s “I’m Feeling Lucky” search button redirects to the first result, which means it can double as an unofficial API endpoint for returning the URL of the first matching search result.
How Imagen Actually Works. Imagen is Google’s new text-to-image model, similar to (but possibly even more effective than) DALL-E. This article is the clearest explanation I’ve seen of how Imagen works: it uses Google’s existing T5 text encoder to convert the input sentence into an encoding that captures the semantic meaning of the sentence (including things like items being described as being on top of other items), then uses a trained diffusion model to generate a 64x64 image. That image is passed through two super-res models to increase the resolution to the final 1024x1024 output.
How to push tagged Docker releases to Google Artifact Registry with a GitHub Action. Ben Welsh’s writeup includes detailed step-by-step instructions for getting the mysterious “Workload Identity Federation” mechanism to work with GitHub Actions and Google Cloud. I’ve been dragging my heels on figuring this out for quite a while, so it’s great to see the steps described at this level of detail.
2021
Google Public DNS Flush Cache (via) Google Public DNS (8.8.8.8) have a flush cache page too.
Allo shows the ultimate failure of Google's Minimum Viable Product strategy. MVP works when you have almost no competition, or if you are taking a radically different approach to what's on the market, but it completely falls on its face when you are just straight-up cloning an established competitor. There's no reason to use a half-baked WhatsApp clone when regular WhatsApp exists.
google-cloud-4-words. This is really useful: every Google Cloud service (all 250 of them) with a four word description explaining what it does. I’d love to see the same thing for AWS. UPDATE: Turns out I had—I can’t link to other posts from blogmark descriptions yet, so search “aws explained” on this site to find it.
2020
Apple now receives an estimated $8 billion to $12 billion in annual payments — up from $1 billion a year in 2014 — in exchange for building Google’s search engine into its products. It is probably the single biggest payment that Google makes to anyone and accounts for 14 to 21 percent of Apple’s annual profits.
Design Docs at Google. Useful description of the format used for software design docs at Google—informal documents of between 3 and 20 pages that outline the proposed design of a new project, discuss trade-offs that were considered and solicit feedback before the code starts to be written.
The unofficial Google Cloud Run FAQ. This is really useful: a no-fluff, content rich explanation of Google Cloud Run hosted as a GitHub repo that actively accepts pull requests from the community. It’s maintained by Ahmet Alp Balkan, a Cloud Run engineer who states “Googlers: If you find this repo useful, you should recognize the work internally, as I actively fight for alternative forms of content like this”. One of the hardest parts of working with AWS and GCP is digging through the marketing materials to figure out what the product actually does, so the more alternative forms of documentation like this the better.
Why Google invested in providing Google Fonts for free. Fascinating comment from former Google Fonts team member Raph Levien. In short: text rendered as PNGs hurt Google Search, fonts were a delay in the transition from Flash, Google Docs needed them to better compete with Office and anything that helps create better ads is easy to find funding for.
Portable Cloud Functions with the Python Functions Framework (via) The new functions-framework library on PyPI lets you run Google Cloud Functions written in Python in other environments—on your local developer machine or bundled in a Docker container for example. I have real trouble trusting serverless platforms that lock you into a single provider (AWS Lambda makes me very uncomfortable) so this is a breath of fresh air.
2019
In general, reviewers should favor approving a CL [code review] once it is in a state where it definitely improves the overall code health of the system being worked on, even if the CL isn’t perfect.
Cloud Run Button: Click-to-deploy your git repos to Google Cloud (via) Google Cloud Run now has its own version of the Heroku deploy button: you can add a button to a GitHub repository which, when clicked, will provide an interface for deploying your repo to the user’s own Google Cloud account using Cloud Run.
Evolving “nofollow” – new ways to identify the nature of links (via) Slightly confusing announcement from Google: they’re introducing rel=ugc and rel=sponsored in addition to rel=nofollow, and will be treating all three values as “hints” for their indexing system. They’re very unclear as to what the concrete effects of these hints will be, presumably because they will become part of the secret sauce of their ranking algorithm.
Discussion about Altavista on Hacker News. Fascinating thread on Hacker News where Bryant Durrell, a former Director from Altavista provides some insider thoughts on how they lost against Google.
2018
The Friendship That Made Google Huge. The New Yorker profiles Jeff Dean and Sanjay Ghemawat, Google’s first and only level 11 Senior Fellows. This is some of the best writing on complex software engineering topics (map-reduce, Tensor Flow and the like) aimed at a general audience that I’ve ever seen. Also a very compelling case study in pair programming.
Tech Notes: TypeScript at Google (via) In which Evan Martin provides some fascinating colour on the state of JavaScript tooling within Google, which has some unique challenges given that Gmail is 14 years old now and Google have evolved their own internal JavaScript stack which differs widely from the rest of the industry (mainly because it predates most of the successful open source tools). “Which leads me to the middle path, which my little team has been pursuing: incrementally adopt some external tooling where it makes sense, by figuring out how to make it interoperate with our existing code base.”
Text Embedding Models Contain Bias. Here’s Why That Matters (via) Excellent discussion from the Google AI team of the enormous challenge of building machine learning models without accidentally encoding harmful bias in a way that cannot be easily detected.
Googlebot’s Javascript random() function is deterministic. random() as executed by Googlebot returns the same predicable sequence. More interestingly, Googlebot runs a much faster timer for setTimeout and setInterval—as Tom Anthony points out, “Why actually wait 5 seconds when you are a bot?”
2017
Cloud SQL for PostgreSQL adds high availability and replication. Google Cloud Platform now offers PostgreSQL with automatic asynchronous disk-level replication to a separate instance in a different availability zone, via their new “Regional Disks“ feature. Between this, Heroku, Citus and Amazon RDS the appeal of a self-maintained PostgreSQL instance continues to fall.
Oxford Deep NLP 2017 course (via) Slides, course description and links to lecture videos for the 2017 Deep Natural Language Processing course at the University of Oxford presented by a team from Google DeepMind.
The Xi Text Engine CRDT (via) Xi is “a modern editor with a backend written in Rust”—an open-source text editor project from Google built on some very interesting computer science (Conflict-free Replicated Data Types). It’s a native editor with server-backed synchronization as a first-class concept.