Apollo-11 VS the-algorithm-ml

I often like to think about https://github.com/chrislgarry/Apollo-11 as an analogy. It's public domain with available source, in the assembly language in which it was written... so it fills all the definitions of OSS!

But the process by which that code arose, the ability to modify any line and understand its impact (heh) on a real execution environment, is dependent on a massive process that required billions of dollars and thousands of the smartest people on the planet. For all intents and purposes, without that environment, it is as reliably modifiable as an executable binary in any other context - or a set of weights, in this one!

But can a Transformer run on the Apollo Guidance Computer?

https://en.wikipedia.org/wiki/Apollo_Guidance_Computer

Frequency 2.048 MHz

Memory 15-bit wordlength + 1-bit parity

2048 words RAM (magnetic-core memory)

https://github.com/chrislgarry/Apollo-11/blob/master/Luminar...

You can look at the Colossus 2A code written by Margaret Hamilton and her very small team on Github.

"Burn Baby Burn" might be even more apt!

https://github.com/chrislgarry/Apollo-11/blob/master/Luminar...

My more positive take on this: our runtime environments are bloated because we have ways to enable trust, stability, and iteration speeds that people wouldn't have dreamed of in years past.

Your Notion desktop app and Google Chrome both support embedding & displaying multimedia content that's controlled by people that you may not trust, but they can draw on decades of engineering to sandbox that content. They can independently be updated without worrying about a centralized `flexbox.dll` that may or may not be the right version. They do not require building a new executable to make the vast majority of UI changes. And the cost is simply storage space and initial download bandwidth.

We can look with rose-colored glasses at an era of "every byte of assembly has been hand-crafted." I, too, look in awe at what was achieved with such things as https://github.com/chrislgarry/Apollo-11/tree/master/Luminar... . But that software, per https://en.wikipedia.org/wiki/Apollo_Guidance_Computer#Softw..., took 1400 person-years of work.

We have to compare apples to apples - the abstractions we have today would not prevent such a piece of software from being built, and indeed would allow us to build that exact software, even bit-for-bit the same, much more easily due to abstractions on our tooling itself. We have not departed a world where, given a nation-state budget, one could pay for 1400 person-years of work and create the AGC (though one might make arguments about the distraction levels of modern society, but that's a different thing entirely).

But we also exist in a world where I can build and ship a cross-platform video chat application in an afternoon (well, not counting app store approvals) and be reasonably confident that my app will be compatible with, and secure on, practically any computer or mobile device sold in the past half decade, regardless of how many other apps may have been installed on each device. I'd venture to say that Apollo engineers would, and do, find this aspect of our world fascinating, too.

Thankfully it was eventually migrated to GitHub

I said “parts of the recommender system code.”

This is the kind of highly emotional reaction that’s not helpful.

Yes, I am quite familiar with building ML models, both training and building my own for which I’ve been paid large sums of money, and I’m here to tell you that you don’t know what you’re taking about.

There’s so much more information about an ML system than just the trained model that is important for understanding the effects of the system on a society, and its legal, ethical, and social ramifications.

Just seeing the type of RS being used, the ranking approach, and the information on SimClusters is enough for RAI folks to start to understand the ecosystem effects and how that can show up downstream in social effects.

https://blog.twitter.com/engineering/en_us/topics/open-sourc...

Blog summarising the change: https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm

People who don't share your interests (or at least what Twitter thinks your interests are). This blog post explains it in detail.

Twitter's announcement | Main GitHub Repo | ML GitHub Repo | Engineering Blog Post

where? I searched through the repo and couldn't find it.