Google Research Blog

From the text:

> I can't name a single thing from Google X or Amazon 126, Apple or Microsoft Research departments.

This is a very narrow way of looking at who's making things of value.

Let's take a single example out of those companies mentioned: Apple's iPhone and the demand it generated for smartphones, pushed many industries to invest in research, development and mass production of high resolution, low cost, low power screens, low power (and now even faster than desktop workstations) compute in small form factor SoC, ever so small low noise and high resolution camera sensors and lenses, faster, higher bandwidth, cheaper and more widely accessible cell networks, and so on and on and on...

Take any single one of the above components/infrastructure and one can list 100 products of value stemming from its cheap and widespread availability. Apple didn't just make a phone, it changed computing forever (something Jobs would probably say on stage) in span of a few years.

Also, did you forget https://research.google/ or just ignored it? There are mind numbing number of research papers *with* corresponding global scale real products used by millions/billions of people, that based on those research.

I am sick of people glorifying the past when today, right under their noses, exists orders of magnitude greater research and innovation. Their title may not be sweet and straightforward like "invention of the telephone" but that's how progress works — the more you move forward, the…

Here are other tips for video:

- https://twitter.com/jugurthahadjar/status/1276670098602954754

- https://twitter.com/jugurthahadjar/status/1276671264116158464

Reproduced here:

>If a talk doesn't provide a link to slides but shows them on the screen, I watch it muted at 16x with "Video Speed Controller". 50 minutes become ~3 minutes and it's easier to decide if the talk is worth the time and/or has high information density in the topic of interest.

>If there are no slides linked nor shown, I use "Youtube Captions Search" to search for expressions of interest in the video, and go straight to where they're mentioned. If I like the treatment of the subject, I make a note to invest in that video and watch it.

There are many articles when you search for "google key moments algorithm" which relay a "recent Google announcement" without a source, or a link.

However, here's a link to a thread:
https://support.google.com/youtube/thread/18138167/youtube-test-features-and-experiments

>[November 23 2020] Testing automatic video chapters: We want to make it easier for people to navigate videos with video chapters, so we are experimenting with automatically adding video chapters (so creators don't have to manually add timestamps). We’ll use machine learning to recognize text in order to auto generate video chapters. We’re testing this out with a small group of videos. If your videos are impacted, you can opt out of this experiment and/or share feedback here. More on video chapters…

Encoding speed is not really a concern for web uses, where the image is decoded potentially millions or even billions of times more than it is encoded.

I agree, PSNR is a terrible measure of quality. The study "Benchmarking JPEG XL lossy/lossless image compression" (https://research.google/pubs/benchmrking-jpeg-xl-lossylossless-image-compression) which you are an author on included a controlled subjective evaluation done by EPFL using an ITU recommend methodology. The subjective results concluded:

"HEVC with SCC generally results in better rate/distortion performance when compared to JPEG XL at low bitrates (< 0.75), better preserving sharp lines and smooth areas"

It is known that AVIF performs better than HEVC. Can you say why it was not included in your 2020 subjective evaluation? It would be nice to not need to speculate on the relative quality of AVIF v JPEG XL at web bitrates.

Their defensiveness makes sense in the current context where in the last few weeks organisations like the White House [1] and Google [2] are explicitly calling out the importance and imminent need of moving away from memory unsafe languages like C and C++. If everyone focussed on this one issue, it is possible that we might actually start moving away from C and C++ in the next 5-10 years.

Sutter pointing out that memory safety isn't the only vector for system vulnerabilities would have the effect of spreading cybersecurity efforts and budgets across all of them. In that case memory safety isn't the foremost problem it's being portrayed as, and it isn't worth migrating away from C and C++.

[1] - https://www.whitehouse.gov/wp-content/uploads/2024/02/Final-ONCD-Technical-Report.pdf

[2] - https://research.google/pubs/secure-by-design-googles-perspective-on-memory-safety/

This reminds me about Google's paper titled "Machine Learning: The High Interest Credit Card of Technical Debt"

https://research.google/pubs/pub43146/

Abstract: Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.

Don't want to get to pedantic on that response. The model can contain complex information. There is already evidence it can form a model of the world. So why not something like steps to get from A to B.

And, it is clear that LLMs can follow steps. One didn't place in the Math Olympiad without some ability to follow steps.

https://research.google/blog/teaching-language-models-to-reason-algorithmically/

And, Anyway, when I asked it, it said it could

"Yes, an LLM model can contain the steps of an algorithm, especially when prompted to "think step-by-step" or use a "chain-of-thought" approach, which allows it to break down a complex problem into smaller, more manageable steps and generate a solution by outlining each stage of the process in a logical sequence; essentially mimicking how a human would approach an algorithm. "