25+ yr Java/JS dev
Linux novice - running Ubuntu (no windows/mac)

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Joined 1 year ago
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Cake day: June 14th, 2023

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  • I don’t think I would agree that just because something is public that it’s a public forum. I feel like the public has to own it as well. I looked it up and maybe it’s because I predate social media by rather a lot, but I think of it in the classical sense:

    Public forums are typically categorized into three types:

    1. Traditional Public Forums: Long-established spaces like parks or sidewalks, where people have historically exercised their rights to free speech and assembly.
    2. Designated Public Forums: Areas that the government intentionally opens up for public expression, such as town halls or school meeting rooms.
    3. Limited Public Forums: Spaces opened for specific types of discussions or activities but with certain restrictions on the subject matter or participants.

    The important factor being public ownership of the forum. I will concede that it has colloquially come to include public social media, but I think it’s important to distinguish that it’s not really the same thing at all as has been discussed through most of our history.

    Food for thought. I just think calling them public forums attaches too much importance to a profit seeking endeavor.



  • I asked ChatGPT for a tldr because same. The result reads like ad copy. Idk, man.


    The memory packaging market is evolving with advancements like flip-chip, wire-bond, and through-silicon via (TSV) technologies. These innovations enable smaller, more powerful, and faster devices, particularly in smartphones, where efficient space use is crucial for sleek designs. DRAM, while still used in PCs, faces declining adoption due to its complexity and the rise of alternatives like 3D TSV, which offer better functionality. The APAC region, especially China, is leading the growth in memory packaging, driven by investments in assembly infrastructure and rising demand for mobile applications using system-in-package (SiP) technologies.







  • You made a lot of points here. Many I agree with, some I don’t, but I specifically want to address this because it seems to be such a common misconception.

    It does and it doesn’t discard the original. It isn’t impossible to recreate the original (since all the data it gobbled up gets stored somewhere in some shape or form and can be truthfully recreated, at least judging by a few comments bellow and news reports). So AI can and does recreate (duplicate or distribute, perhaps) copyrighted works.

    AI stores original works like a dictionary does. All the words are there, but the order and meaning is completely gone. An original work is possible to recreate by randomly selecting words from the dictionary, but it’s unlikely.

    The thing that makes AI useful is that it understands the patterns words are typically used in. It orders words in the right way far more often than random chance. It knows “It was the best of” has a lot of likely options for the next word, but if it selects “times” as the next word, it’s far more likely to continue with, “it was the worst of times.” Because that sequence of words is so ubiquitous due to references to the classic story. But over the course of following these word patterns, it will quickly glom onto a different pattern and create a wholly new work from the original “prompt.”

    There are only two cases in which an original work should be duplicated: either the training data is far too small and the model is overtrained on that particular work, or the work is the most derivative text imaginable lacking any flair or originality.

    Adding more training data makes it less likely to recreate any original works.

    I am aware of examples where it was claimed an LLM reproduced entirely code functions including original comments. That is either a case of overtraining, or far too many people were already copying that code verbatim into their own, thus making that work very over represented in the training data (same thing, but it was infringing developers who poisoned the data, not researchers using bad training data).

    Bottom line: when created with enough data, no original works are stored in any way that allows faithful reproduction other than by chance so random that it’s similar to rolling dice over a dictionary.

    None of this means AI can do no wrong, I just don’t find the copyright claim compelling.