Oh my, I'm surprised by the extent to which this spooked them, enough to issue that statement, as well as what the statement itself says about the character of their intended customers.
If you want a little bit more of a batteries-included experience, Zellij[1] is to tmux what Fish is to Bash and what Helix/Neovim is to Vim. If you like any of those, I highly recommend giving Zellij a try.
I gave it a try earlier this year but because of some bad incompatibility with Codex I had to go back to tmux, and then I just never picked Zellij back up. I thought about giving it a shot, but now I'm reading about herdr here which seems pretty cool too.
The anthropomorphization of artificial neural networks has been a problem for as long as they've been called "neural" networks. Not that I can complain about it, though. I got my first job as a machine learning engineer coming from academic neuroscience by putting "biological neural networks" on my resume.
Also, IANAL, but under U.S. copyright law, the weights of machine learning models are excluded from copyright as they are the product of an automated optimization process (e.g., stochastic gradient descent, expectation maximization, genetic algorithms) rather than human authorship. Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway, similar to how one might treat a ToS agreement. Nonetheless, this was partially tested last year in Thaler v. Perlmutter which affirmed that copyright requires human authorship, reading the Copyright Act's provisions on ownership, duration, and transferability as presupposing a human author[1].
If you want to assess the position of the U.S. Copyright Office for yourself, the relevant text can be found in the Compendium of U.S. Copyright Office Practices § 313.2, "Works That Lack Human Authorship"[2], which states:
> […] the Copyright Act protects “original works of authorship.” 17 U.S.C. § 102(a) (emphasis added). To qualify as a work of “authorship” a work must be created by a human being. See Burrow-Giles Lithographic Co., 111 U.S. at 58. Works that do not satisfy this requirement are not copyrightable.
> […] the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author. The crucial question is “whether the ‘work’ is basically one of human authorship, with the computer [or other device] merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.” U.S. COPYRIGHT OFFICE, REPORT TO THE LIBRARIAN OF CONGRESS BY THE REGISTER OF COPYRIGHTS 5 (1965).
Oh, and there's also a bit in the following Section 313.3, "Works That Do Not Constitute Copyrightable Subject Matter"[2], which explicitly excludes mathematical principles, formulas, algorithms, and equations, along with DNA sequences and other genetic or chemical compounds, regardless of whether they are produced by humans or by nature. If one takes the perspective that machine learning models are algorithms, the conclusions on copyrightability are pretty clear.
If the weights were produced by a purely mechanical process without any input, then I would be more inclined to believe the distinction there is significant. But as the optimization processes you've mentioned are merely tools that operate with a dataset, the provenance of that dataset becomes a factor towards copyright.
> Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway, similar to how one might treat a ToS agreement.
You may be conflating 2 things - the Muse model weights themselves, and what the models output when run. The former is prima facie the product of human authorship and hence has full protection of copyright law, the latter fits your characterization of likely not having copyright coverage. Meta can use the copyright on the model weights to dictate licensing terms. IANAL too.
Only in the way a math textbooks are facts. Model weights are bits on a medium,designed and derived from the combined efforts, judgement and expertise of many humans, before and after training.
Software bits have been copyrighted for so long, there are 5¼" floppies with "(c) 1977" printed on them.
Aren't model weights the output of human (?) authored software? The training software might have received creative input, but not the weights themselves. And if the training data itself counts, then the copyright would not be the labs, who stole the training data, but all the creatives who's works were used.
Aren't model weights the output of human (?) authored software? The training software might have received creative input, but not the weights themselves
How does this legal theory square with output of a compiler? Or pictures generated by photo-editing suites when humans tweak color levels?
Both your examples take human authored input and transform them in a pretty direct way.
I addressed this by saying if the training data counts as the human input, then the creative authorship of the weights are all the creatives that involuntarily supplied the training data -- not the labs training the models.
> the creative authorship of the weights are all the creatives that involuntarily supplied the training data -- not the labs training the models.
I agree with that copyright holders that provide training data were screwed over, and should be fairly compensated.
That said, a lot of work that goes into training a model is editorial: structure of the model, training regime, etc . Copyright applies not just to the literal elements, but to Sequence, Structure and Organization, which is how poetry Anthologies are under additional copyright to the editor, and not just divided among just the poets.
Yeah, for better or worse, writing style is practically uncorrelated with agentic performance, which is all the rage right now and the thing that most popular benchmarks currently prioritize.
And, in this case, the dollar-amount increase in GDP serves as a virtual quantitative proxy for the increase in mowed lawns (and the value thereof). In other words, the participants in this economy are collectively ~$200 richer with their mowed lawns than they were without them.
If everyone goes around mowing lawns for each other, the economy is richer in lawn mowing at the expense of all the other things that would have been funded had everyone mowed their own lawns and purchased different services instead.
I am confused with this, if "everyone mowed their own lawns" then the net result will be exactly the same, everyone will be busy the same and not poorer, just without money movement.
This is not the same. If everyone wants mowed lawns, and everyone is busy working on that, there is no opportunity cost, everyone is working on their top priorities. The broken window fallacy is a fallacy because the headline gdp figure doesn't account for the destruction of the window which cancels out the benefit. In the grass mowing analogy nothing has been destroyed, useful and priority work has been done all around.
If the pricing is fair and at arms' length. What's happening in reality is as if they are mowing each others' lawns at wink wink nudge nudge $1000. Not a good proxy for actual value created.
In the real world, you have to pay taxes. So people are incentivized to claim less value for the lawns mowed, or even just do it themselves, instead of benefiting from the division of labour.
Leverage doesn't work that way. (If it were so easy, it would load up my investment portfolio with a lot more leverage than I currently do. And I don't live in the US where regulation T would keep me to a puny 2x leverage.)
But also importantly the government of the residents' country is about 39% ($78) richer, if say the participants are honest in reporting this and the country is the UK and the participants are people like you and me in the tech industry who frequent HN and would think to do something like this.
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