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Cake day: July 7th, 2023

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  • A major caveat I’ve noticed some people misunderstand: it’s corporate CLAs that are problematic. The Apache Foundation also requires contributors sign a CLA, but it’s to provide a legal fail safe and a way to update to say Apache 3.0 if need be one day. Apache’s non profit, open source mission aligns with respecting the rights of contributors and the community. Corporations, on the other hand, not so much.



  • CLAs can be abusive, but not necessarily. Apache Foundation contributors need to sign CLAs, which essentially codify in contract form the terms of the Apache 2.0 license. It’s a precaution, in case some jurisdiction doesn’t uphold the passive licensing scheme used otherwise. There’s also a relicensing clause, but that’s restricted to keeping in spirit, they can’t close the source.



  • After doing some Meta/Facebook VR development in my job the lack of popularity made increasingly more sense. In brief, they’re both incredibly incompetent and transparently greedy.

    I’m honestly baffled how they could spend so many tens of billions of dollars and have such bad software, it is completely bug ridden. You’ll hit a bug, research it, and find out it’s a major know bug for literal years they haven’t fixed. They care so little that they couldn’t bother to update the Oculus branding to Meta for over 3 years in various software tools and libraries.

    Their greed might be more salient aspect preventing adoption, though. They transparently wanted to be the gatekeepers to everything “metaverse” related, a business model that is now explicitly illegal in the EU after years of being merely very sketchy. They are straight up hostile to anyone else trying to implement enterprise or business features. Concrete example: fleet management software, aka MDM. There are third party tools that are cheaper and much more featured than Meta’s solution, but in the last year they’ve pushed hard to kick those third parties out of the ecosystem.

    I could go on, but in short nobody in their right mind would build a major business on their ecosystem. They’d rather let Meta burn billions in R&D and come back later. Besides, not even Meta is able to make money in the area now.




  • So this is probably another example of Google using too blunt of instruments for AI. LLMs are very suggestible and leading questions can severely bias responses. Most people using them without knowing a lot about the field will ask “bad” questions. So it likely has instructions to avoid “which is better” and instead provide pros and cons for the user to consider themselves.

    Edit: I don’t mean to excuse, just explain. If anything, the implication is that Google rushed it out after attempting to slap bandaids on serious problems. OpenAI and Anthropic, for example, have talked about how alignment training and human adjustment takes a majority of the development time. Since Google is in a self described emergency mode, cutting that process short seems a likely explanation.






  • Compression is actually a mathematical field that’s fairly well explored, and this isn’t compression. There are theoretical limits on how much you can compress data, so the data is always somewhere, either in the dictionary or the input. Trained models like these are gigantic, so even if it was perfect recall the ratio still wouldn’t be good. Lossy “compression” is another issue entirely, more of an engineering problem of determining how much data you can throw out while making acceptable compromises.


  • This is a classic problem for machine learning systems, sometimes called over fitting or memorization. By analogy, it’s the difference between knowing how to do multiplication vs just memorizing the times tables. With enough training data and large enough storage AI can feign higher “intelligence”, and that is demonstrably what’s going on here. It’s a spectrum as well. In theory, nearly identical recall is undesirable, and there are known ways of shifting away from that end of the spectrum. Literal AI 101 content.

    Edit: I don’t mean to say that machine learning as a technique has problems, I mean that implementations of machine learning can run into these problems. And no, I wouldn’t describe these as being intelligent any more than a chess algorithm is intelligent. They just have a much more broad problem space and the natural language processing leads us to anthropomorphize it.