calny

Joined 7 years ago

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Member since

Aug 1

building AI systems for law, insurance, and creative firms tech & insurance lawyer twitter @joe_wilbert jwilbert dot com

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Comment · 1 month ago

I was irritated by this exact same misleading ad a few weeks ago and tweeted about it into the void. Kudos for the author for trying to do something about it.

Comment · 3 months ago

well it's good they didn't train on the test!

Comment · 4 months ago

I’m also curious! Are you keeping a secret because you don’t want people to try to find work arounds, kind of like prompting LLMs not to say “delve” or “tapestry” in order to make your AI writing sound less like AI? Or is it something else?

Comment · 4 months ago

I'm curious about they will apply the part saying "AI-generated music will not be monetizable." What does AI-generated music mean, exactly? What if you make an AI generated bassline but produce the rest of a track by hand? How about an AI vocal? Or a mix of AI stems and your own recordings?

Tidal's terms and conditions (https://tidal.com/terms) say that:

> “AI-Generated Content” means any audio content, inclusive of musical works and sound recordings, that is wholly or substantially generated by generative artificial intelligence, with limited or no direct human creative input beyond an initial text prompt or similar instruction. ... You acknowledge that AI detection technology may produce false positives or false negatives.

And:

> If you use TIDAL Upload, your Tracks may be scanned for the purpose of identifying whether the content is AI-Generated Content, and to label such content accordingly on the Tidal platform. You acknowledge that such scanning and labeling is performed on a best-efforts basis and that Tidal shall not be liable for any inaccuracies in AI detection or labeling. AI-Generated Content uploaded to Tidal is not eligible for monetization. If you believe your Tracks were erroneously tagged as AI-Generated, you can reach out to [email protected].

Comment · 4 months ago

Not to great effect, AFAIK. Laurie Voss (creator of npm) had a good presentation a few months back on all the different agent interaction protocols, and was skeptical whether they (including A2A) added much value. https://youtu.be/kqB_xML1SfA?si=lxehX1-_z_dBoZtQ

Comment · 7 months ago

I was reading up on the author and saw this interesting bit[0]:

> An algorave (from an algorithm and rave) is an event where people dance to music generated from algorithms, often using live coding techniques. Alex McLean of Slub and Nick Collins coined the word "algorave" in 2011, and the first event under such a name was organised in London, England. It has since become a movement, with algoraves taking place around the world.

[0] https://en.wikipedia.org/wiki/Algorave

Comment · 8 months ago

Thanks! Appreciate the response and will look into this

Comment · 8 months ago

Thanks! I missed that. The attribution by training data source category (arxiv vs wikipedia vs nemotron etc.) is an interesting approach.

Comment · 8 months ago

You're right, I've followed the litigation closely. I've advocated for years that "training is fair use" and I'm generally an anti-IP hawk who DEFENDS copyright/trademark cases. Only recently have I started to concede the issue might have more nuance than "all training is fair use, hard stop." And I still think Judge Alsup got it right.

That said, even if model training is fair use, model output can still be infringing. There would be a strong case, for example, if the end user guides the LLM to create works in a way that copies another work or mimics an author or artist's style. This case clearly isn't that. On the similarity at issue here, I haven't personally compared. I hope you're right.

Comment · 8 months ago

The maintainer's response: https://github.com/chardet/chardet/issues/327#issuecomment-4...

The second part here is problematic, but fascinating: "I then started in an empty repository with no access to the old source tree, and explicitly instructed Claude not to base anything on LGPL/GPL-licensed code." Problem - Claude almost certainly was trained on the LGPL/GPL original code. It knows that is how to solve the problem. It's dubious whether Claude can ignore whatever imprints that original code made on its weights. If it COULD do that, that would be a pretty cool innovation in explainable AI. But AFAIK LLMs can't even reliably trace what data influenced the output for a query, see https://iftenney.github.io/projects/tda/, or even fully unlearn a piece of training data.

Is anyone working on this? I'd be very interested to discuss.

Some background - I'm a developer & IP lawyer - my undergrad thesis was "Copyright in the Digital Age" and discussed copyleft & FOSS. Been litigating in federal court since 2010 and training AI models since 2019, and am working on an AI for litigation platform. These are evolving issues in US courts.

BTW if you're on enterprise or a paid API plan, Anthropic indemnifies you if its outputs violate copyright. But if you're on free/pro/max, the terms state that YOU agree to indemnify THEM for copyright violation claims.[0]

[0] https://www.anthropic.com/legal/consumer-terms - see para. 11 ("YOU AGREE TO INDEMNIFY AND HOLD HARMLESS THE ANTHROPIC PARTIES FROM AND AGAINST ANY AND ALL LIABILITIES, CLAIMS, DAMAGES, EXPENSES (INCLUDING REASONABLE ATTORNEYS’ FEES AND COSTS), AND OTHER LOSSES ARISING OUT OF … YOUR ACCESS TO, USE OF, OR ALLEGED USE OF THE SERVICES ….")

Comment · 8 months ago

I get it, I just meant the fish is poorly done, when I’d have guessed it would be relatively simple part. Maybe the black dot eye is misplaced idk.

Comment · 8 months ago

Great pelican but what’s up with that fish in the basket?

Comment · 9 months ago

I didn't catch that on first read, but I see why you'd say that. LLMs are ridiculous in the constant usage "it's not X it's Y" -- It's in almost every response from Opus 4.5. "It's not X it's Y" is ruined for regular writing.

I'm also skeptical of anything that claims to reliably detect AI writing. FWIW, I plugged the comment into Pangram Labs, which claims to be the most reliably and seems to have worked well before. It categorized the comment as 100% human written with medium confidence.

Comment · 9 months ago

Stated more cynically, many platforms have an interest in attention hijacking. Done well, agents' 'laser focused attention' could help users avoid wasting time (wandering attention) and money (impulse buys). This is a good thing, even if it dings revenue of some existing platforms. If a company's business model is impulse buying and ad revenue (this isn't eBay IMO), then good riddance.